From 86bd16d496ecdd7d0d487d1949a137df624e5147 Mon Sep 17 00:00:00 2001
From: Wei Kang <wkang.pku@gmail.com>
Date: Wed, 2 Apr 2025 22:10:06 +0800
Subject: [PATCH 01/22] [KWS]Remove graph compiler (#1905)

---
 egs/wenetspeech/KWS/run.sh                |  6 +--
 egs/wenetspeech/KWS/zipformer/decode.py   |  1 -
 egs/wenetspeech/KWS/zipformer/finetune.py | 39 +++++---------
 egs/wenetspeech/KWS/zipformer/train.py    | 65 ++++++++++-------------
 4 files changed, 43 insertions(+), 68 deletions(-)

diff --git a/egs/wenetspeech/KWS/run.sh b/egs/wenetspeech/KWS/run.sh
index 8472b8531..0af7c1595 100755
--- a/egs/wenetspeech/KWS/run.sh
+++ b/egs/wenetspeech/KWS/run.sh
@@ -108,7 +108,7 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
 fi
 
 if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
-  log "Stage 2: Finetune the model"
+  log "Stage 3: Finetune the model"
 
   # The following configuration of lr schedule should work well
   # You may also tune the following parameters to adjust learning rate schedule
@@ -143,7 +143,7 @@ if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
 fi
 
 if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
-  log "Stage 1: Decode the finetuned model."
+  log "Stage 4: Decode the finetuned model."
   export CUDA_VISIBLE_DEVICES="0"
   for t in small large; do
     python ./zipformer/decode.py \
@@ -170,7 +170,7 @@ if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
 fi
 
 if [ $stage -le 5 ] && [ $stop_stage -ge 5 ]; then
-  log "Stage 2: Export the finetuned model."
+  log "Stage 5: Export the finetuned model."
 
   python ./zipformer/export.py \
       --epoch 10 \
diff --git a/egs/wenetspeech/KWS/zipformer/decode.py b/egs/wenetspeech/KWS/zipformer/decode.py
index 340a41231..a628c7e58 100755
--- a/egs/wenetspeech/KWS/zipformer/decode.py
+++ b/egs/wenetspeech/KWS/zipformer/decode.py
@@ -35,7 +35,6 @@ from lhotse.cut import Cut
 from train import add_model_arguments, get_model, get_params
 
 from icefall import ContextGraph
-from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
 from icefall.checkpoint import (
     average_checkpoints,
     average_checkpoints_with_averaged_model,
diff --git a/egs/wenetspeech/KWS/zipformer/finetune.py b/egs/wenetspeech/KWS/zipformer/finetune.py
index d19172b38..cd437da4c 100755
--- a/egs/wenetspeech/KWS/zipformer/finetune.py
+++ b/egs/wenetspeech/KWS/zipformer/finetune.py
@@ -90,6 +90,7 @@ from train import (
     add_training_arguments,
     compute_validation_loss,
     display_and_save_batch,
+    encode_text,
     get_adjusted_batch_count,
     get_model,
     get_params,
@@ -100,7 +101,6 @@ from train import (
 )
 
 from icefall import diagnostics
-from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
 from icefall.checkpoint import remove_checkpoints
 from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
 from icefall.checkpoint import (
@@ -110,11 +110,11 @@ from icefall.checkpoint import (
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
-from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
     get_parameter_groups_with_lrs,
+    num_tokens,
     setup_logger,
     str2bool,
     text_to_pinyin,
@@ -254,7 +254,6 @@ def load_model_params(
 def compute_loss(
     params: AttributeDict,
     model: Union[nn.Module, DDP],
-    graph_compiler: CharCtcTrainingGraphCompiler,
     batch: dict,
     is_training: bool,
 ) -> Tuple[Tensor, MetricsTracker]:
@@ -289,7 +288,7 @@ def compute_loss(
     warm_step = params.warm_step
 
     texts = batch["supervisions"]["text"]
-    y = graph_compiler.texts_to_ids(texts, sep="/")
+    y = [c.supervisions[0].tokens for c in supervisions["cut"]]
     y = k2.RaggedTensor(y)
 
     with torch.set_grad_enabled(is_training):
@@ -347,7 +346,6 @@ def train_one_epoch(
     model: Union[nn.Module, DDP],
     optimizer: torch.optim.Optimizer,
     scheduler: LRSchedulerType,
-    graph_compiler: CharCtcTrainingGraphCompiler,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
     scaler: GradScaler,
@@ -418,7 +416,6 @@ def train_one_epoch(
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
-                    graph_compiler=graph_compiler,
                     batch=batch,
                     is_training=True,
                 )
@@ -436,7 +433,7 @@ def train_one_epoch(
             optimizer.zero_grad()
         except:  # noqa
             save_bad_model()
-            display_and_save_batch(batch, params=params, graph_compiler=graph_compiler)
+            display_and_save_batch(batch, params=params)
             raise
 
         if params.print_diagnostics and batch_idx == 5:
@@ -523,7 +520,6 @@ def train_one_epoch(
             valid_info = compute_validation_loss(
                 params=params,
                 model=model,
-                graph_compiler=graph_compiler,
                 valid_dl=valid_dl,
                 world_size=world_size,
             )
@@ -576,14 +572,10 @@ def run(rank, world_size, args):
         device = torch.device("cuda", rank)
     logging.info(f"Device: {device}")
 
-    lexicon = Lexicon(params.lang_dir)
-    graph_compiler = CharCtcTrainingGraphCompiler(
-        lexicon=lexicon,
-        device=device,
-    )
+    token_table = k2.SymbolTable.from_file(params.lang_dir / "tokens.txt")
 
-    params.blank_id = lexicon.token_table["<blk>"]
-    params.vocab_size = max(lexicon.tokens) + 1
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 1
 
     if not params.use_transducer:
         params.ctc_loss_scale = 1.0
@@ -666,17 +658,10 @@ def run(rank, world_size, args):
     else:
         train_cuts = wenetspeech.nihaowenwen_train_cuts()
 
-    def encode_text(c: Cut):
-        # Text normalize for each sample
-        text = c.supervisions[0].text
-        text = "/".join(
-            text_to_pinyin(text, mode=params.pinyin_type, errors=params.pinyin_errors)
-        )
-        c.supervisions[0].text = text
-        return c
+    _encode_text = partial(encode_text, token_table=token_table, params=params)
 
     train_cuts = train_cuts.filter(remove_short_utt)
-    train_cuts = train_cuts.map(encode_text)
+    train_cuts = train_cuts.map(_encode_text)
 
     if params.start_batch > 0 and checkpoints and "sampler" in checkpoints:
         # We only load the sampler's state dict when it loads a checkpoint
@@ -691,7 +676,7 @@ def run(rank, world_size, args):
 
     valid_cuts = wenetspeech.nihaowenwen_dev_cuts()
     valid_cuts = valid_cuts.filter(remove_short_utt)
-    valid_cuts = valid_cuts.map(encode_text)
+    valid_cuts = valid_cuts.map(_encode_text)
     valid_dl = wenetspeech.valid_dataloaders(valid_cuts)
 
     if not params.print_diagnostics and params.scan_for_oom_batches:
@@ -699,7 +684,6 @@ def run(rank, world_size, args):
             model=model,
             train_dl=train_dl,
             optimizer=optimizer,
-            graph_compiler=graph_compiler,
             params=params,
         )
 
@@ -724,7 +708,6 @@ def run(rank, world_size, args):
             model_avg=model_avg,
             optimizer=optimizer,
             scheduler=scheduler,
-            graph_compiler=graph_compiler,
             train_dl=train_dl,
             valid_dl=valid_dl,
             scaler=scaler,
@@ -760,6 +743,8 @@ def main():
     WenetSpeechAsrDataModule.add_arguments(parser)
     args = parser.parse_args()
     args.exp_dir = Path(args.exp_dir)
+    args.lang_dir = Path(args.lang_dir)
+    args.return_cuts = True
 
     world_size = args.world_size
     assert world_size >= 1
diff --git a/egs/wenetspeech/KWS/zipformer/train.py b/egs/wenetspeech/KWS/zipformer/train.py
index 40960c2ae..5d9d8de36 100755
--- a/egs/wenetspeech/KWS/zipformer/train.py
+++ b/egs/wenetspeech/KWS/zipformer/train.py
@@ -53,6 +53,7 @@ import argparse
 import copy
 import logging
 import warnings
+from functools import partial
 from pathlib import Path
 from shutil import copyfile
 from typing import Any, Dict, Optional, Tuple, Union
@@ -79,7 +80,6 @@ from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer2
 
 from icefall import diagnostics
-from icefall.char_graph_compiler import CharCtcTrainingGraphCompiler
 from icefall.checkpoint import load_checkpoint, remove_checkpoints
 from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
 from icefall.checkpoint import (
@@ -90,11 +90,11 @@ from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
-from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
     get_parameter_groups_with_lrs,
+    num_tokens,
     setup_logger,
     str2bool,
     text_to_pinyin,
@@ -776,7 +776,6 @@ def save_checkpoint(
 def compute_loss(
     params: AttributeDict,
     model: Union[nn.Module, DDP],
-    graph_compiler: CharCtcTrainingGraphCompiler,
     batch: dict,
     is_training: bool,
 ) -> Tuple[Tensor, MetricsTracker]:
@@ -811,7 +810,7 @@ def compute_loss(
     warm_step = params.warm_step
 
     texts = batch["supervisions"]["text"]
-    y = graph_compiler.texts_to_ids(texts, sep="/")
+    y = [c.supervisions[0].tokens for c in supervisions["cut"]]
     y = k2.RaggedTensor(y).to(device)
 
     with torch.set_grad_enabled(is_training):
@@ -859,7 +858,6 @@ def compute_loss(
 def compute_validation_loss(
     params: AttributeDict,
     model: Union[nn.Module, DDP],
-    graph_compiler: CharCtcTrainingGraphCompiler,
     valid_dl: torch.utils.data.DataLoader,
     world_size: int = 1,
 ) -> MetricsTracker:
@@ -872,7 +870,6 @@ def compute_validation_loss(
         loss, loss_info = compute_loss(
             params=params,
             model=model,
-            graph_compiler=graph_compiler,
             batch=batch,
             is_training=False,
         )
@@ -895,7 +892,6 @@ def train_one_epoch(
     model: Union[nn.Module, DDP],
     optimizer: torch.optim.Optimizer,
     scheduler: LRSchedulerType,
-    graph_compiler: CharCtcTrainingGraphCompiler,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
     scaler: GradScaler,
@@ -971,7 +967,6 @@ def train_one_epoch(
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
-                    graph_compiler=graph_compiler,
                     batch=batch,
                     is_training=True,
                 )
@@ -988,7 +983,7 @@ def train_one_epoch(
             optimizer.zero_grad()
         except:  # noqa
             save_bad_model()
-            display_and_save_batch(batch, params=params, graph_compiler=graph_compiler)
+            display_and_save_batch(batch, params=params)
             raise
 
         if params.print_diagnostics and batch_idx == 5:
@@ -1077,7 +1072,6 @@ def train_one_epoch(
             valid_info = compute_validation_loss(
                 params=params,
                 model=model,
-                graph_compiler=graph_compiler,
                 valid_dl=valid_dl,
                 world_size=world_size,
             )
@@ -1098,6 +1092,20 @@ def train_one_epoch(
         params.best_train_loss = params.train_loss
 
 
+def encode_text(c: Cut, token_table: k2.SymbolTable, params: AttributeDict):
+    text = c.supervisions[0].text
+    tokens = text_to_pinyin(text, mode=params.pinyin_type, errors=params.pinyin_errors)
+    ids = []
+    for t in tokens:
+        if t in token_table:
+            ids.append(token_table[t])
+        else:
+            logging.warning(f"Text : {text} has OOV token : {t} , encode to <unk>")
+            ids.append(token_table["<unk>"])
+    c.supervisions[0].tokens = ids
+    return c
+
+
 def run(rank, world_size, args):
     """
     Args:
@@ -1130,14 +1138,10 @@ def run(rank, world_size, args):
         device = torch.device("cuda", rank)
     logging.info(f"Device: {device}")
 
-    lexicon = Lexicon(params.lang_dir)
-    graph_compiler = CharCtcTrainingGraphCompiler(
-        lexicon=lexicon,
-        device=device,
-    )
+    token_table = k2.SymbolTable.from_file(params.lang_dir / "tokens.txt")
 
-    params.blank_id = lexicon.token_table["<blk>"]
-    params.vocab_size = max(lexicon.tokens) + 1
+    params.blank_id = token_table["<blk>"]
+    params.vocab_size = num_tokens(token_table) + 1
 
     if not params.use_transducer:
         params.ctc_loss_scale = 1.0
@@ -1216,17 +1220,10 @@ def run(rank, world_size, args):
 
         return True
 
-    def encode_text(c: Cut):
-        # Text normalize for each sample
-        text = c.supervisions[0].text
-        text = "/".join(
-            text_to_pinyin(text, mode=params.pinyin_type, errors=params.pinyin_errors)
-        )
-        c.supervisions[0].text = text
-        return c
+    _encode_text = partial(encode_text, token_table=token_table, params=params)
 
     train_cuts = train_cuts.filter(remove_short_and_long_utt)
-    train_cuts = train_cuts.map(encode_text)
+    train_cuts = train_cuts.map(_encode_text)
 
     if params.start_batch > 0 and checkpoints and "sampler" in checkpoints:
         # We only load the sampler's state dict when it loads a checkpoint
@@ -1240,7 +1237,7 @@ def run(rank, world_size, args):
     )
 
     valid_cuts = wenetspeech.valid_cuts()
-    valid_cuts = valid_cuts.map(encode_text)
+    valid_cuts = valid_cuts.map(_encode_text)
     valid_dl = wenetspeech.valid_dataloaders(valid_cuts)
 
     if not params.print_diagnostics and params.scan_for_oom_batches:
@@ -1248,7 +1245,6 @@ def run(rank, world_size, args):
             model=model,
             train_dl=train_dl,
             optimizer=optimizer,
-            graph_compiler=graph_compiler,
             params=params,
         )
 
@@ -1273,7 +1269,6 @@ def run(rank, world_size, args):
             model_avg=model_avg,
             optimizer=optimizer,
             scheduler=scheduler,
-            graph_compiler=graph_compiler,
             train_dl=train_dl,
             valid_dl=valid_dl,
             scaler=scaler,
@@ -1307,7 +1302,6 @@ def run(rank, world_size, args):
 def display_and_save_batch(
     batch: dict,
     params: AttributeDict,
-    graph_compiler: CharCtcTrainingGraphCompiler,
 ) -> None:
     """Display the batch statistics and save the batch into disk.
 
@@ -1317,8 +1311,6 @@ def display_and_save_batch(
         for the content in it.
       params:
         Parameters for training. See :func:`get_params`.
-      graph_compiler:
-        The compiler to encode texts to ids.
     """
     from lhotse.utils import uuid4
 
@@ -1332,8 +1324,8 @@ def display_and_save_batch(
     logging.info(f"features shape: {features.shape}")
 
     texts = supervisions["text"]
-    y = graph_compiler.texts_to_ids(texts)
-    num_tokens = sum(len(i) for i in y)
+    tokens = [c.supervisions[0].tokens for c in supervisions["cut"]]
+    num_tokens = sum(len(i) for i in tokens)
     logging.info(f"num tokens: {num_tokens}")
 
 
@@ -1341,7 +1333,6 @@ def scan_pessimistic_batches_for_oom(
     model: Union[nn.Module, DDP],
     train_dl: torch.utils.data.DataLoader,
     optimizer: torch.optim.Optimizer,
-    graph_compiler: CharCtcTrainingGraphCompiler,
     params: AttributeDict,
 ):
     from lhotse.dataset import find_pessimistic_batches
@@ -1357,7 +1348,6 @@ def scan_pessimistic_batches_for_oom(
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
-                    graph_compiler=graph_compiler,
                     batch=batch,
                     is_training=True,
                 )
@@ -1372,7 +1362,7 @@ def scan_pessimistic_batches_for_oom(
                     f"Failing criterion: {criterion} "
                     f"(={crit_values[criterion]}) ..."
                 )
-            display_and_save_batch(batch, params=params, graph_compiler=graph_compiler)
+            display_and_save_batch(batch, params=params)
             raise
         logging.info(
             f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
@@ -1385,6 +1375,7 @@ def main():
     args = parser.parse_args()
     args.lang_dir = Path(args.lang_dir)
     args.exp_dir = Path(args.exp_dir)
+    args.return_cuts = True
 
     world_size = args.world_size
     assert world_size >= 1

From 171cf8c9fe41666c7d70ba58c0481ec6675d8941 Mon Sep 17 00:00:00 2001
From: Fangjun Kuang <csukuangfj@gmail.com>
Date: Wed, 9 Apr 2025 11:52:37 +0800
Subject: [PATCH 02/22] Avoid redundant computation in PiecewiseLinear. (#1915)

---
 egs/librispeech/ASR/zipformer/scaling.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/egs/librispeech/ASR/zipformer/scaling.py b/egs/librispeech/ASR/zipformer/scaling.py
index d345c2931..6d6281903 100644
--- a/egs/librispeech/ASR/zipformer/scaling.py
+++ b/egs/librispeech/ASR/zipformer/scaling.py
@@ -160,8 +160,10 @@ class PiecewiseLinear(object):
                     extra_x_vals.append(extra_x_val)
             if len(extra_x_vals) > 0:
                 x_vals = sorted(set(x_vals + extra_x_vals))
-        y_vals1 = [self(x) for x in x_vals]
-        y_vals2 = [p(x) for x in x_vals]
+
+            y_vals1 = [self(x) for x in x_vals]
+            y_vals2 = [p(x) for x in x_vals]
+
         return (
             PiecewiseLinear(*zip(x_vals, y_vals1)),
             PiecewiseLinear(*zip(x_vals, y_vals2)),

From 300a821f58abdb9975a3743caf1a78953e97711b Mon Sep 17 00:00:00 2001
From: Fangjun Kuang <csukuangfj@gmail.com>
Date: Thu, 10 Apr 2025 10:30:37 +0800
Subject: [PATCH 03/22] Fix aishell training (#1916)

---
 egs/aishell/ASR/zipformer/train.py      | 5 ++---
 egs/aishell/ASR/zipformer/train_bbpe.py | 5 ++---
 2 files changed, 4 insertions(+), 6 deletions(-)

diff --git a/egs/aishell/ASR/zipformer/train.py b/egs/aishell/ASR/zipformer/train.py
index cd253c597..dddfe52fa 100755
--- a/egs/aishell/ASR/zipformer/train.py
+++ b/egs/aishell/ASR/zipformer/train.py
@@ -1343,8 +1343,7 @@ def main():
         run(rank=0, world_size=1, args=args)
 
 
-torch.set_num_threads(1)
-torch.set_num_interop_threads(1)
-
 if __name__ == "__main__":
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
     main()
diff --git a/egs/aishell/ASR/zipformer/train_bbpe.py b/egs/aishell/ASR/zipformer/train_bbpe.py
index 46a5506db..dbc262c5c 100755
--- a/egs/aishell/ASR/zipformer/train_bbpe.py
+++ b/egs/aishell/ASR/zipformer/train_bbpe.py
@@ -935,8 +935,7 @@ def main():
         run(rank=0, world_size=1, args=args)
 
 
-torch.set_num_threads(1)
-torch.set_num_interop_threads(1)
-
 if __name__ == "__main__":
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
     main()

From 64c53640857d0b9c3fd63070c2f741d374051ce9 Mon Sep 17 00:00:00 2001
From: math345 <math345@qq.com>
Date: Thu, 10 Apr 2025 11:37:28 +0800
Subject: [PATCH 04/22] Fix bug: When resuming training from a checkpoint,
 model_avg was not assigned, resulting in a None error. (#1914)

---
 egs/wenetspeech/KWS/zipformer/finetune.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/egs/wenetspeech/KWS/zipformer/finetune.py b/egs/wenetspeech/KWS/zipformer/finetune.py
index cd437da4c..249209352 100755
--- a/egs/wenetspeech/KWS/zipformer/finetune.py
+++ b/egs/wenetspeech/KWS/zipformer/finetune.py
@@ -593,6 +593,9 @@ def run(rank, world_size, args):
 
     if params.continue_finetune:
         assert params.start_epoch > 0, params.start_epoch
+        if rank == 0:
+            # model_avg is only used with rank 0
+            model_avg = copy.deepcopy(model).to(torch.float64)
         checkpoints = load_checkpoint_if_available(
             params=params, model=model, model_avg=model_avg
         )

From 5ec95e5482867de02eece6615ff51662c7ed9b37 Mon Sep 17 00:00:00 2001
From: Yifan Yang <64255737+yfyeung@users.noreply.github.com>
Date: Wed, 23 Apr 2025 16:18:38 +0800
Subject: [PATCH 05/22] Fix SpeechLLM recipe (#1926)

---
 egs/speech_llm/ASR_LLM/RESULTS.md |  8 ++++----
 egs/speech_llm/ASR_LLM/prepare.sh | 13 ++++++++-----
 egs/speech_llm/ASR_LLM/shared     |  1 +
 3 files changed, 13 insertions(+), 9 deletions(-)
 mode change 100644 => 100755 egs/speech_llm/ASR_LLM/prepare.sh
 create mode 120000 egs/speech_llm/ASR_LLM/shared

diff --git a/egs/speech_llm/ASR_LLM/RESULTS.md b/egs/speech_llm/ASR_LLM/RESULTS.md
index 830c70397..01c48a82e 100644
--- a/egs/speech_llm/ASR_LLM/RESULTS.md
+++ b/egs/speech_llm/ASR_LLM/RESULTS.md
@@ -42,8 +42,8 @@ huggingface-cli download --local-dir models/whisper    yuekai/icefall_asr_aishel
 # For multi-hans fine-tuned whisper model
 # huggingface-cli download --local-dir models/whisper    yuekai/icefall_asr_multi-hans-zh_whisper v1.1/whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt
 
-# huggingface-clie download  --local-dir models/qwen     Qwen/Qwen2-7B-Instruct
-huggingface-clie download  --local-dir models/qwen     Qwen/Qwen2-1.5B-Instruct
+# huggingface-cli download  --local-dir models/qwen     Qwen/Qwen2-7B-Instruct
+huggingface-cli download  --local-dir models/qwen     Qwen/Qwen2-1.5B-Instruct
 
 # First, we only train the projector and freeze other modules.
 torchrun --nproc_per_node 8 ./whisper_llm_zh/train.py \
@@ -57,7 +57,7 @@ torchrun --nproc_per_node 8 ./whisper_llm_zh/train.py \
   --use-flash-attn True \
   --use-lora False --unfreeze-llm False
 
-# Then we jointly train the projector and LLM LoRA modules.
+# Then, we jointly train the projector and LLM LoRA modules.
 torchrun --nproc_per_node 8 ./whisper_llm_zh/train.py \
   --max-duration 200 \
   --exp-dir ./whisper_llm_zh/exp_test \
@@ -81,7 +81,7 @@ huggingface-cli download --local-dir models/whisper    yuekai/icefall_asr_aishel
 # For multi-hans fine-tuned whisper model
 # huggingface-cli download --local-dir models/whisper    yuekai/icefall_asr_multi-hans-zh_whisper v1.1/whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt
 
-huggingface-clie download  --local-dir models/qwen     Qwen/Qwen2-7B-Instruct
+huggingface-cli download  --local-dir models/qwen     Qwen/Qwen2-7B-Instruct
 
 mkdir -p whisper_llm_zh/exp_aishell_whisper_qwen2_1.5B
 ln -s models/checkpoint/epoch-10-avg-5.pt whisper_llm_zh/exp_aishell_whisper_qwen2_1.5B/epoch-999.pt
diff --git a/egs/speech_llm/ASR_LLM/prepare.sh b/egs/speech_llm/ASR_LLM/prepare.sh
old mode 100644
new mode 100755
index 6f5ed5448..8ca3c1c36
--- a/egs/speech_llm/ASR_LLM/prepare.sh
+++ b/egs/speech_llm/ASR_LLM/prepare.sh
@@ -7,6 +7,9 @@ set -eou pipefail
 
 stage=0
 stop_stage=0
+
+. shared/parse_options.sh || exit 1
+
 # All files generated by this script are saved in "data".
 # You can safely remove "data" and rerun this script to regenerate it.
 mkdir -p data
@@ -23,7 +26,7 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
 
   # pip install huggingface_hub['cli']
   # for aishell 1
-  huggingface-cli download --local-dir data yuekai/aishell_whisper_fbank_lhotse
+  huggingface-cli download --repo-type dataset --local-dir data yuekai/aishell_whisper_fbank_lhotse
 
 fi
 
@@ -31,9 +34,9 @@ if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
   log "stage 1: Download whisper-large-v2 multi-hans-zh fbank feature from huggingface"
 
   # for multi-hans-zh
-  huggingface-cli download --local-dir data/fbank yuekai/wenetspeech_whisper_fbank_lhotse
-  huggingface-cli download --local-dir data/fbank yuekai/multi_hans_zh_whisper_fbank_lhotse
-  huggingface-cli download --local-dir data/fbank yuekai/alimeeting_aishell4_training_whisper_fbank_lhotse
+  huggingface-cli download --repo-type dataset --local-dir data/fbank yuekai/wenetspeech_whisper_fbank_lhotse
+  huggingface-cli download --repo-type dataset --local-dir data/fbank yuekai/multi_hans_zh_whisper_fbank_lhotse
+  huggingface-cli download --repo-type dataset --local-dir data/fbank yuekai/alimeeting_aishell4_training_whisper_fbank_lhotse
 fi
 
 if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
@@ -41,6 +44,6 @@ if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
 
   # for speechio test sets
   mkdir data_speechio
-  huggingface-cli download --local-dir data_speechio yuekai/icefall_asr_speechio
+  huggingface-cli download --repo-type model --local-dir data_speechio yuekai/icefall_asr_speechio
   mv data_speechio/fbank/* data/fbank
 fi
diff --git a/egs/speech_llm/ASR_LLM/shared b/egs/speech_llm/ASR_LLM/shared
new file mode 120000
index 000000000..4cbd91a7e
--- /dev/null
+++ b/egs/speech_llm/ASR_LLM/shared
@@ -0,0 +1 @@
+../../../icefall/shared
\ No newline at end of file

From cc2e64a6aa11c82fe3925c224e9bf1536edfb5e4 Mon Sep 17 00:00:00 2001
From: Fangjun Kuang <csukuangfj@gmail.com>
Date: Thu, 24 Apr 2025 17:04:46 +0800
Subject: [PATCH 06/22] Fix convert_texts_into_ids() in the tedlium3 recipe.
 (#1929)

---
 egs/tedlium3/ASR/pruned_transducer_stateless/train.py | 2 +-
 egs/tedlium3/ASR/transducer_stateless/train.py        | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/egs/tedlium3/ASR/pruned_transducer_stateless/train.py b/egs/tedlium3/ASR/pruned_transducer_stateless/train.py
index 170f37767..2455f3630 100755
--- a/egs/tedlium3/ASR/pruned_transducer_stateless/train.py
+++ b/egs/tedlium3/ASR/pruned_transducer_stateless/train.py
@@ -422,7 +422,7 @@ def compute_loss(
 
     texts = batch["supervisions"]["text"]
     unk_id = params.unk_id
-    y = convert_texts_into_ids(texts, unk_id, sp=sp)
+    y = convert_texts_into_ids(texts, sp=sp)
     y = k2.RaggedTensor(y).to(device)
 
     with torch.set_grad_enabled(is_training):
diff --git a/egs/tedlium3/ASR/transducer_stateless/train.py b/egs/tedlium3/ASR/transducer_stateless/train.py
index 6fed32e81..c6fa34e70 100755
--- a/egs/tedlium3/ASR/transducer_stateless/train.py
+++ b/egs/tedlium3/ASR/transducer_stateless/train.py
@@ -397,7 +397,7 @@ def compute_loss(
 
     texts = batch["supervisions"]["text"]
     unk_id = params.unk_id
-    y = convert_texts_into_ids(texts, unk_id, sp=sp)
+    y = convert_texts_into_ids(texts, sp=sp)
     y = k2.RaggedTensor(y).to(device)
 
     with torch.set_grad_enabled(is_training):

From cd7caf12df92ac21f67a4027cf80b940ee15bef6 Mon Sep 17 00:00:00 2001
From: Yifan Yang <64255737+yfyeung@users.noreply.github.com>
Date: Wed, 30 Apr 2025 11:41:00 +0800
Subject: [PATCH 07/22] Fix speech_llm recipe (#1936)

* fix training/decoding scripts, cleanup unused code, and ensure compliance with style checks

---------

Co-authored-by: Your Name <you@example.com>
Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>
---
 egs/speech_llm/ASR_LLM/RESULTS.md             |  15 ++-
 .../ASR_LLM/whisper_llm_zh/decode.py          | 104 ++++++------------
 .../ASR_LLM/whisper_llm_zh/train.py           |  97 ++++------------
 3 files changed, 60 insertions(+), 156 deletions(-)

diff --git a/egs/speech_llm/ASR_LLM/RESULTS.md b/egs/speech_llm/ASR_LLM/RESULTS.md
index 01c48a82e..42dce80c5 100644
--- a/egs/speech_llm/ASR_LLM/RESULTS.md
+++ b/egs/speech_llm/ASR_LLM/RESULTS.md
@@ -55,7 +55,8 @@ torchrun --nproc_per_node 8 ./whisper_llm_zh/train.py \
   --deepspeed \
   --deepspeed_config ./whisper_llm_zh/ds_config_zero1.json \
   --use-flash-attn True \
-  --use-lora False --unfreeze-llm False
+  --use-lora False \
+  --unfreeze-llm False
 
 # Then, we jointly train the projector and LLM LoRA modules.
 torchrun --nproc_per_node 8 ./whisper_llm_zh/train.py \
@@ -67,7 +68,8 @@ torchrun --nproc_per_node 8 ./whisper_llm_zh/train.py \
   --deepspeed \
   --deepspeed_config ./whisper_llm_zh/ds_config_zero1.json \
   --use-flash-attn True \
-  --use-lora True --unfreeze-llm True
+  --use-lora True \
+  --unfreeze-llm True \
   --pretrained-model-path ./whisper_llm_zh/exp_test/epoch-3.pt
 ```
 
@@ -77,11 +79,11 @@ mkdir -p models/whisper models/qwen models/checkpoint
 huggingface-cli download --local-dir models/checkpoint yuekai/icefall_asr_aishell_whisper_qwen2_1.5B
 
 # For aishell fine-tuned whisper model
-huggingface-cli download --local-dir models/whisper    yuekai/icefall_asr_aishell_whisper exp_large_v2/whisper-large-v2-aishell1-epoch-10-avg-6.pt
+huggingface-cli download --local-dir models/whisper yuekai/icefall_asr_aishell_whisper exp_large_v2/whisper-large-v2-aishell1-epoch-10-avg-6.pt
 # For multi-hans fine-tuned whisper model
-# huggingface-cli download --local-dir models/whisper    yuekai/icefall_asr_multi-hans-zh_whisper v1.1/whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt
+# huggingface-cli download --local-dir models/whisper yuekai/icefall_asr_multi-hans-zh_whisper v1.1/whisper-large-v2-multi-hans-zh-epoch-3-avg-10.pt
 
-huggingface-cli download  --local-dir models/qwen     Qwen/Qwen2-7B-Instruct
+huggingface-cli download --local-dir models/qwen Qwen/Qwen2-7B-Instruct
 
 mkdir -p whisper_llm_zh/exp_aishell_whisper_qwen2_1.5B
 ln -s models/checkpoint/epoch-10-avg-5.pt whisper_llm_zh/exp_aishell_whisper_qwen2_1.5B/epoch-999.pt
@@ -94,5 +96,6 @@ python3 ./whisper_llm_zh/decode.py \
   --epoch 999 --avg 1 \
   --manifest-dir data/fbank \
   --use-flash-attn True \
-  --use-lora True --dataset aishell
+  --use-lora True \
+  --dataset aishell
 ```
diff --git a/egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py b/egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py
index 882ce4fbf..3036b471e 100755
--- a/egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py
+++ b/egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py
@@ -66,7 +66,7 @@ from train import DEFAULT_SPEECH_TOKEN
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
 
-from icefall.checkpoint import average_checkpoints_with_averaged_model, load_checkpoint
+from icefall.checkpoint import load_checkpoint
 from icefall.env import get_env_info
 from icefall.utils import (
     AttributeDict,
@@ -357,43 +357,6 @@ def decode_dataset(
     Returns:
         Return a dict, whose key may be "beam-search".
     """
-
-    def normalize_text_alimeeting(text: str, normalize: str = "m2met") -> str:
-        """
-        Text normalization similar to M2MeT challenge baseline.
-        See: https://github.com/yufan-aslp/AliMeeting/blob/main/asr/local/text_normalize.pl
-        """
-        if normalize == "none":
-            return text
-        elif normalize == "m2met":
-            import re
-
-            text = text.replace(" ", "")
-            text = text.replace("<sil>", "")
-            text = text.replace("<%>", "")
-            text = text.replace("<->", "")
-            text = text.replace("<$>", "")
-            text = text.replace("<#>", "")
-            text = text.replace("<_>", "")
-            text = text.replace("<space>", "")
-            text = text.replace("`", "")
-            text = text.replace("&", "")
-            text = text.replace(",", "")
-            if re.search("[a-zA-Z]", text):
-                text = text.upper()
-            text = text.replace("Ａ", "A")
-            text = text.replace("ａ", "A")
-            text = text.replace("ｂ", "B")
-            text = text.replace("ｃ", "C")
-            text = text.replace("ｋ", "K")
-            text = text.replace("ｔ", "T")
-            text = text.replace("，", "")
-            text = text.replace("丶", "")
-            text = text.replace("。", "")
-            text = text.replace("、", "")
-            text = text.replace("？", "")
-            return text
-
     results = []
 
     num_cuts = 0
@@ -406,6 +369,7 @@ def decode_dataset(
     results = defaultdict(list)
     for batch_idx, batch in enumerate(dl):
         texts = batch["supervisions"]["text"]
+        texts = [list("".join(text.split())) for text in texts]
         cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
 
         hyps_dict = decode_one_batch(
@@ -418,12 +382,8 @@ def decode_dataset(
         for lm_scale, hyps in hyps_dict.items():
             this_batch = []
             assert len(hyps) == len(texts)
-            for cut_id, hyp_words, ref_text in zip(cut_ids, hyps, texts):
-                ref_text = normalize_text_alimeeting(ref_text)
-                ref_words = ref_text.split()
-                print(f"ref: {ref_text}")
-                print(f"hyp: {''.join(hyp_words)}")
-                this_batch.append((cut_id, ref_words, hyp_words))
+            for cut_id, hyp_text, ref_text in zip(cut_ids, hyps, texts):
+                this_batch.append((cut_id, ref_text, hyp_text))
 
             results[lm_scale].extend(this_batch)
 
@@ -439,40 +399,38 @@ def decode_dataset(
 def save_results(
     params: AttributeDict,
     test_set_name: str,
-    results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
+    results_dict: Dict[str, List[Tuple[List[int], List[int]]]],
 ):
-
-    enable_log = True
     test_set_wers = dict()
     for key, results in results_dict.items():
         recog_path = (
-            params.exp_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
         )
         results = sorted(results)
-        store_transcripts(filename=recog_path, texts=results)
-        if enable_log:
-            logging.info(f"The transcripts are stored in {recog_path}")
+        store_transcripts(filename=recog_path, texts=results, char_level=True)
+        logging.info(f"The transcripts are stored in {recog_path}")
 
-        # The following prints out WERs, per-word error statistics and aligned
+        # The following prints out CERs, per-word error statistics and aligned
         # ref/hyp pairs.
         errs_filename = (
-            params.exp_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
         )
-        # we compute CER for aishell dataset.
-        results_char = []
-        for res in results:
-            results_char.append((res[0], list("".join(res[1])), list("".join(res[2]))))
         with open(errs_filename, "w") as f:
             wer = write_error_stats(
-                f, f"{test_set_name}-{key}", results_char, enable_log=enable_log
+                f,
+                f"{test_set_name}-{key}",
+                results,
+                enable_log=True,
+                compute_CER=True,
             )
             test_set_wers[key] = wer
 
-        if enable_log:
-            logging.info("Wrote detailed error stats to {}".format(errs_filename))
+        logging.info("Wrote detailed error stats to {}".format(errs_filename))
 
     test_set_wers = sorted(test_set_wers.items(), key=lambda x: x[1])
-    errs_info = params.exp_dir / f"cer-summary-{test_set_name}-{params.suffix}.txt"
+    errs_info = (
+        params.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+    )
     with open(errs_info, "w") as f:
         print("settings\tCER", file=f)
         for key, val in test_set_wers:
@@ -495,9 +453,13 @@ def main():
 
     params = get_params()
     params.update(vars(args))
+
+    params.res_dir = params.exp_dir / f"{params.method}"
+
     params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
     setup_logger(
-        f"{params.exp_dir}/log-{params.method}-beam{params.beam_size}/log-decode-{params.suffix}"
+        params.res_dir
+        / f"log-decode-{params.method}-beam{params.beam_size}-{params.suffix}"
     )
 
     logging.info("Decoding started")
@@ -574,23 +536,20 @@ def main():
     if params.avg > 1:
         start = params.epoch - params.avg + 1
         assert start >= 1, start
-        checkpoint = torch.load(
-            f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
-        )
-        assert "model" not in checkpoint
         # deepspeed converted checkpoint only contains model state_dict
         filenames = [
-            f"{params.exp_dir}/epoch-{epoch}.pt"
+            f"{params.exp_dir}/epoch-{epoch}/pytorch_model.bin"
             for epoch in range(start, params.epoch + 1)
         ]
         avg_checkpoint = average_checkpoints(filenames)
         model.load_state_dict(avg_checkpoint, strict=False)
 
-        filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt"
-        torch.save(avg_checkpoint, filename)
+        # filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt"
+        # torch.save(avg_checkpoint, filename)
     else:
         checkpoint = torch.load(
-            f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
+            f"{params.exp_dir}/epoch-{params.epoch}/pytorch_model.bin",
+            map_location="cpu",
         )
         model.load_state_dict(checkpoint, strict=False)
 
@@ -643,8 +602,7 @@ def main():
     logging.info("Done!")
 
 
-torch.set_num_threads(1)
-torch.set_num_interop_threads(1)
-
 if __name__ == "__main__":
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
     main()
diff --git a/egs/speech_llm/ASR_LLM/whisper_llm_zh/train.py b/egs/speech_llm/ASR_LLM/whisper_llm_zh/train.py
index 5f224c984..7947a60a5 100755
--- a/egs/speech_llm/ASR_LLM/whisper_llm_zh/train.py
+++ b/egs/speech_llm/ASR_LLM/whisper_llm_zh/train.py
@@ -1,6 +1,7 @@
 #!/usr/bin/env python3
 # Copyright    2023  Xiaomi Corp.        (authors: Xiaoyu Yang)
 #              2024  Yuekai Zhang
+#              2025  Yifan  Yang
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -42,47 +43,32 @@ torchrun --nproc_per_node 8 ./whisper_llm_zh/train.py \
 """
 
 import argparse
-import copy
 import logging
 import os
-import random
 import warnings
 from pathlib import Path
-from shutil import copyfile
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Dict, Optional, Tuple
 
 import deepspeed
-import k2
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 import transformers
 import whisper
 from asr_datamodule import AsrDataModule
 from deepspeed.utils.zero_to_fp32 import convert_zero_checkpoint_to_fp32_state_dict
-from label_smoothing import LabelSmoothingLoss
-from lhotse import CutSet, load_manifest
 from lhotse.cut import Cut
-from lhotse.dataset.sampling.base import CutSampler
 from lhotse.utils import fix_random_seed
 from model import IGNORE_TOKEN_ID, SPEECH_LLM, EncoderProjector
 from multi_dataset import MultiDataset
-from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
+from peft import LoraConfig, get_peft_model
 from torch import Tensor
 from torch.utils.tensorboard import SummaryWriter
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
 
-from icefall import diagnostics
 from icefall.dist import get_rank, get_world_size
 from icefall.env import get_env_info
-from icefall.utils import (
-    AttributeDict,
-    MetricsTracker,
-    filter_uneven_sized_batch,
-    setup_logger,
-    str2bool,
-)
+from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
 
 DEFAULT_SPEECH_TOKEN = "<speech>"
 
@@ -286,13 +272,6 @@ def compute_loss(
     Returns:
         Return a tuple of two elements. The first element is the loss tensor.
     """
-    # For the uneven-sized batch, the total duration after padding would possibly
-    # cause OOM. Hence, for each batch, which is sorted descendingly by length,
-    # we simply drop the last few shortest samples, so that the retained total frames
-    # (after padding) would not exceed `allowed_max_frames`:
-    # `allowed_max_frames = int(max_frames * (1.0 + allowed_excess_duration_ratio))`,
-    # where `max_frames = max_duration * 1000 // frame_shift_ms`.
-    # We set allowed_excess_duration_ratio=0.1.
 
     def preprocess(
         messages,
@@ -347,46 +326,6 @@ def compute_loss(
 
         return input_ids, attention_mask, target_ids
 
-    def normalize_text_alimeeting(text: str, normalize: str = "m2met") -> str:
-        """
-        Text normalization similar to M2MeT challenge baseline.
-        See: https://github.com/yufan-aslp/AliMeeting/blob/main/asr/local/text_normalize.pl
-        """
-        if normalize == "none":
-            return text
-        elif normalize == "m2met":
-            import re
-
-            text = text.replace(" ", "")
-            text = text.replace("<sil>", "")
-            text = text.replace("<%>", "")
-            text = text.replace("<->", "")
-            text = text.replace("<$>", "")
-            text = text.replace("<#>", "")
-            text = text.replace("<_>", "")
-            text = text.replace("<space>", "")
-            text = text.replace("`", "")
-            text = text.replace("&", "")
-            text = text.replace(",", "")
-            if re.search("[a-zA-Z]", text):
-                text = text.upper()
-            text = text.replace("Ａ", "A")
-            text = text.replace("ａ", "A")
-            text = text.replace("ｂ", "B")
-            text = text.replace("ｃ", "C")
-            text = text.replace("ｋ", "K")
-            text = text.replace("ｔ", "T")
-            text = text.replace("，", "")
-            text = text.replace("丶", "")
-            text = text.replace("。", "")
-            text = text.replace("、", "")
-            text = text.replace("？", "")
-            return text
-
-    max_frames = params.max_duration * 1000 // params.frame_shift_ms
-    allowed_max_frames = int(max_frames * (1.0 + params.allowed_excess_duration_ratio))
-    batch = filter_uneven_sized_batch(batch, allowed_max_frames)
-
     device = next(model.parameters()).device
     feature = batch["inputs"]
 
@@ -397,11 +336,10 @@ def compute_loss(
     batch_idx_train = params.batch_idx_train
     supervisions = batch["supervisions"]
     texts = batch["supervisions"]["text"]
-    # remove spaces in texts
-    texts = [normalize_text_alimeeting(text) for text in texts]
 
     messages = []
     for i, text in enumerate(texts):
+        text = text.replace(" ", "")
         message = [
             {"role": "user", "content": f"{DEFAULT_SPEECH_TOKEN}请转写音频为文字"},
             {"role": "assistant", "content": text},
@@ -516,14 +454,17 @@ def train_one_epoch(
         The rank of the node in DDP training. If no DDP is used, it should
         be set to 0.
     """
-    model.encoder_projector.train()
+    model.train()
+    model.encoder.eval()
+    if not params.unfreeze_llm:
+        model.llm.eval()
 
     tot_loss = MetricsTracker()
 
     for batch_idx, batch in enumerate(train_dl):
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
-        if batch_idx % params.valid_interval == 0 and not params.print_diagnostics:
+        if batch_idx % params.valid_interval == 0:
             logging.info("Computing validation loss")
             valid_info = compute_validation_loss(
                 params=params,
@@ -533,6 +474,9 @@ def train_one_epoch(
                 world_size=world_size,
             )
             model.train()
+            model.encoder.eval()
+            if not params.unfreeze_llm:
+                model.llm.eval()
             logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
             logging.info(
                 f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
@@ -648,7 +592,6 @@ def run(rank, world_size, args):
     speech_encoder_dim = whisper_model.dims.n_audio_state
     for name, param in speech_encoder.named_parameters():
         param.requires_grad = False
-    speech_encoder.eval()
 
     tokenizer = AutoTokenizer.from_pretrained(params.llm_path_or_name)
     if params.use_flash_attn:
@@ -671,7 +614,6 @@ def run(rank, world_size, args):
     if not params.unfreeze_llm:
         for name, param in llm.named_parameters():
             param.requires_grad = False
-        llm.eval()
     else:
         if params.use_lora:
             lora_config = LoraConfig(
@@ -728,7 +670,7 @@ def run(rank, world_size, args):
     logging.info(f"Device: {device}")
     model.to(device)
 
-    assert params.deepspeed and world_size > 1
+    assert params.deepspeed
     logging.info("Using DeepSpeed")
     model, optimizer, _, scheduler = deepspeed.initialize(
         args=params, model=model, model_parameters=model.parameters()
@@ -764,7 +706,7 @@ def run(rank, world_size, args):
     if params.sampler_state_dict_path:
         sampler_state_dict = torch.load(params.sampler_state_dict_path)
         sampler_state_dict["max_duration"] = params.max_duration
-    # TODO: load sampler state dict
+
     train_dl = data_module.train_dataloaders(
         train_cuts, sampler_state_dict=sampler_state_dict
     )
@@ -806,15 +748,15 @@ def run(rank, world_size, args):
 
         model.save_checkpoint(
             save_dir=params.exp_dir,
-            tag=f"epoch-{params.cur_epoch}",
+            tag=f"zero-epoch-{params.cur_epoch}",
             client_state={},
             exclude_frozen_parameters=True,
         )
         if rank == 0:
             convert_zero_checkpoint_to_fp32_state_dict(
                 params.exp_dir,
-                f"{params.exp_dir}/epoch-{params.cur_epoch}.pt",
-                tag=f"epoch-{params.cur_epoch}",
+                f"{params.exp_dir}/epoch-{params.cur_epoch}",
+                tag=f"zero-epoch-{params.cur_epoch}",
                 exclude_frozen_parameters=True,
             )
             # save sampler state dict into checkpoint
@@ -824,7 +766,7 @@ def run(rank, world_size, args):
                 f"{params.exp_dir}/epoch-{params.cur_epoch}-sampler.pt",
             )
 
-            os.system(f"rm -rf {params.exp_dir}/epoch-{params.cur_epoch}")
+            os.system(f"rm -rf {params.exp_dir}/zero-epoch-{params.cur_epoch}")
 
     logging.info("Done!")
 
@@ -865,6 +807,7 @@ def main():
 
     torch.set_num_threads(1)
     torch.set_num_interop_threads(1)
+    warnings.filterwarnings("ignore", category=FutureWarning)
     run(rank=rank, world_size=world_size, args=args)
 
 

From 4627969ccd151caf335c0fb6a2bfbe8bf609e04f Mon Sep 17 00:00:00 2001
From: Yifan Yang <64255737+yfyeung@users.noreply.github.com>
Date: Mon, 12 May 2025 14:19:53 +0800
Subject: [PATCH 08/22] fix bug: undefined name 'partial' (#1941)

---
 egs/wenetspeech/KWS/zipformer/finetune.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/egs/wenetspeech/KWS/zipformer/finetune.py b/egs/wenetspeech/KWS/zipformer/finetune.py
index 249209352..b1abfd79e 100755
--- a/egs/wenetspeech/KWS/zipformer/finetune.py
+++ b/egs/wenetspeech/KWS/zipformer/finetune.py
@@ -69,6 +69,7 @@ import argparse
 import copy
 import logging
 import warnings
+from functools import partial
 from pathlib import Path
 from typing import List, Optional, Tuple, Union
 

From e79833aad278f09792deceab5962b09ae4f56378 Mon Sep 17 00:00:00 2001
From: Yifan Yang <64255737+yfyeung@users.noreply.github.com>
Date: Mon, 12 May 2025 19:28:48 +0800
Subject: [PATCH 09/22] ensure SwooshL/SwooshR output dtype matches input dtype
 (#1940)

---
 egs/librispeech/ASR/zipformer/scaling.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/egs/librispeech/ASR/zipformer/scaling.py b/egs/librispeech/ASR/zipformer/scaling.py
index 6d6281903..11375385e 100644
--- a/egs/librispeech/ASR/zipformer/scaling.py
+++ b/egs/librispeech/ASR/zipformer/scaling.py
@@ -1403,9 +1403,9 @@ class SwooshL(torch.nn.Module):
             zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
             return logaddexp(zero, x - 4.0) - 0.08 * x - 0.035
         if not x.requires_grad:
-            return k2.swoosh_l_forward(x)
+            return k2.swoosh_l_forward(x).to(x.dtype)
         else:
-            return k2.swoosh_l(x)
+            return k2.swoosh_l(x).to(x.dtype)
         # return SwooshLFunction.apply(x)
 
 
@@ -1477,9 +1477,9 @@ class SwooshR(torch.nn.Module):
             zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
             return logaddexp(zero, x - 1.0) - 0.08 * x - 0.313261687
         if not x.requires_grad:
-            return k2.swoosh_r_forward(x)
+            return k2.swoosh_r_forward(x).to(x.dtype)
         else:
-            return k2.swoosh_r(x)
+            return k2.swoosh_r(x).to(x.dtype)
         # return SwooshRFunction.apply(x)
 
 

From fd8f8780fac68d959df0bf521f1b76aac35f88fd Mon Sep 17 00:00:00 2001
From: Fangjun Kuang <csukuangfj@gmail.com>
Date: Wed, 21 May 2025 12:04:57 +0800
Subject: [PATCH 10/22] Fix logging torch.dtype. (#1947)

---
 icefall/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/icefall/utils.py b/icefall/utils.py
index aab479e56..ffb926566 100644
--- a/icefall/utils.py
+++ b/icefall/utils.py
@@ -186,7 +186,7 @@ class AttributeDict(dict):
         tmp = {}
         for k, v in self.items():
             # PosixPath is ont JSON serializable
-            if isinstance(v, pathlib.Path) or isinstance(v, torch.device):
+            if isinstance(v, (pathlib.Path, torch.device, torch.dtype)):
                 v = str(v)
             tmp[k] = v
         return json.dumps(tmp, indent=indent, sort_keys=True)

From 30e7ea4b5a0449bfac4e7fa5eef9a23238230013 Mon Sep 17 00:00:00 2001
From: Tianxiang Zhao <162714929+Redemption-ZTX@users.noreply.github.com>
Date: Thu, 22 May 2025 12:05:01 +0800
Subject: [PATCH 11/22] Fix a bug in finetune.py --use-mux (#1949)

---
 egs/librispeech/ASR/zipformer/finetune.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/egs/librispeech/ASR/zipformer/finetune.py b/egs/librispeech/ASR/zipformer/finetune.py
index 2ff631914..2c869a57a 100755
--- a/egs/librispeech/ASR/zipformer/finetune.py
+++ b/egs/librispeech/ASR/zipformer/finetune.py
@@ -140,8 +140,8 @@ def add_finetune_arguments(parser: argparse.ArgumentParser):
         type=str2bool,
         default=False,
         help="""
-        Whether to adapt. If true, we will mix 5% of the new data
-        with 95% of the original data to fine-tune. This is useful
+        Whether to adapt. If true, we will mix 5%% of the new data
+        with 95%% of the original data to fine-tune. This is useful
         if you want to maintain the performance on the original domain
         """,
     )
@@ -1134,7 +1134,7 @@ def train_one_epoch(
                 f"Epoch {params.cur_epoch}, "
                 f"batch {batch_idx}, loss[{loss_info}], "
                 f"tot_loss[{tot_loss}], batch size: {batch_size}, "
-                f"lr: {cur_lr:.2e}, "
+                f"lr: {cur_lr: .2e}, "
                 + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
             )
 

From 021e1a88469f5ca8299daa4000d05a5dd89b8623 Mon Sep 17 00:00:00 2001
From: Mahsa Yarmohammadi <35142904+mahsa7823@users.noreply.github.com>
Date: Thu, 22 May 2025 10:06:35 -0400
Subject: [PATCH 12/22] Add acknowledgment to README (#1950)

---
 README.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/README.md b/README.md
index 0e550ffb1..498f7e3b4 100644
--- a/README.md
+++ b/README.md
@@ -383,3 +383,7 @@ Please see: [![Open In Colab](https://colab.research.google.com/assets/colab-bad
 [vctk]: egs/vctk/TTS
 [ljspeech]: egs/ljspeech/TTS
 [libritts_tts]: egs/libritts/TTS
+
+## Acknowledgements
+
+Some contributors to this project were supported by Xiaomi Corporation. Others were supported by National Science Foundation CCRI award 2120435.  This is not an exhaustive list of sources of support.

From ffb7d0563526f42123d2c65c284646a5c375b74f Mon Sep 17 00:00:00 2001
From: Zengwei Yao <yaozengwei@outlook.com>
Date: Tue, 27 May 2025 12:09:59 +0800
Subject: [PATCH 13/22] refactor branch exchange in cr-ctc (#1954)

---
 egs/librispeech/ASR/zipformer/model.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/egs/librispeech/ASR/zipformer/model.py b/egs/librispeech/ASR/zipformer/model.py
index c7dbe1e0a..f2791e51f 100644
--- a/egs/librispeech/ASR/zipformer/model.py
+++ b/egs/librispeech/ASR/zipformer/model.py
@@ -210,10 +210,10 @@ class AsrModel(nn.Module):
         )
 
         # Compute consistency regularization loss
-        exchanged_targets = ctc_output.detach().chunk(2, dim=0)
-        exchanged_targets = torch.cat(
-            [exchanged_targets[1], exchanged_targets[0]], dim=0
-        )  # exchange: [x1, x2] -> [x2, x1]
+        batch_size = ctc_output.shape[0]
+        assert batch_size % 2 == 0, batch_size
+        # exchange: [x1, x2] -> [x2, x1]
+        exchanged_targets = torch.roll(ctc_output.detach(), batch_size // 2, dims=0)
         cr_loss = nn.functional.kl_div(
             input=ctc_output,
             target=exchanged_targets,

From 06539d2b9d5b4945ba9ce6edaf301eda13748b6b Mon Sep 17 00:00:00 2001
From: Wei Kang <wkang.pku@gmail.com>
Date: Tue, 17 Jun 2025 20:17:12 +0800
Subject: [PATCH 14/22] Add Zipvoice (#1964)

* Add ZipVoice - a flow-matching based zero-shot TTS model.
---
 egs/zipvoice/README.md                        |  411 ++++
 egs/zipvoice/assets/prompt-en.wav             |  Bin 0 -> 675496 bytes
 egs/zipvoice/local/compute_fbank.py           |  288 +++
 egs/zipvoice/local/evaluate_sim.py            |  508 +++++
 egs/zipvoice/local/evaluate_utmos.py          |  294 +++
 egs/zipvoice/local/evaluate_wer_hubert.py     |  172 ++
 egs/zipvoice/local/evaluate_wer_seedtts.py    |  181 ++
 egs/zipvoice/local/feature.py                 |    1 +
 .../local/prepare_token_file_emilia.py        |   90 +
 .../local/prepare_token_file_libritts.py      |   31 +
 egs/zipvoice/local/preprocess_emilia.py       |  156 ++
 egs/zipvoice/local/tokenizer.py               |    1 +
 egs/zipvoice/local/validate_manifest.py       |   70 +
 egs/zipvoice/scripts/evaluate.sh              |  102 +
 egs/zipvoice/scripts/prepare_emilia.sh        |  126 ++
 egs/zipvoice/scripts/prepare_libritts.sh      |   97 +
 egs/zipvoice/zipvoice/checkpoint.py           |  142 ++
 egs/zipvoice/zipvoice/feature.py              |  135 ++
 .../zipvoice/generate_averaged_model.py       |  209 ++
 egs/zipvoice/zipvoice/infer.py                |  586 +++++
 egs/zipvoice/zipvoice/model.py                |  578 +++++
 egs/zipvoice/zipvoice/optim.py                | 1256 +++++++++++
 egs/zipvoice/zipvoice/scaling.py              | 1910 +++++++++++++++++
 egs/zipvoice/zipvoice/solver.py               |  277 +++
 egs/zipvoice/zipvoice/tokenizer.py            |  570 +++++
 egs/zipvoice/zipvoice/train_distill.py        | 1043 +++++++++
 egs/zipvoice/zipvoice/train_flow.py           | 1108 ++++++++++
 egs/zipvoice/zipvoice/tts_datamodule.py       |  456 ++++
 egs/zipvoice/zipvoice/utils.py                |  219 ++
 egs/zipvoice/zipvoice/zipformer.py            | 1648 ++++++++++++++
 egs/zipvoice/zipvoice/zipvoice_infer.py       |  645 ++++++
 31 files changed, 13310 insertions(+)
 create mode 100644 egs/zipvoice/README.md
 create mode 100644 egs/zipvoice/assets/prompt-en.wav
 create mode 100644 egs/zipvoice/local/compute_fbank.py
 create mode 100644 egs/zipvoice/local/evaluate_sim.py
 create mode 100644 egs/zipvoice/local/evaluate_utmos.py
 create mode 100644 egs/zipvoice/local/evaluate_wer_hubert.py
 create mode 100644 egs/zipvoice/local/evaluate_wer_seedtts.py
 create mode 120000 egs/zipvoice/local/feature.py
 create mode 100644 egs/zipvoice/local/prepare_token_file_emilia.py
 create mode 100644 egs/zipvoice/local/prepare_token_file_libritts.py
 create mode 100644 egs/zipvoice/local/preprocess_emilia.py
 create mode 120000 egs/zipvoice/local/tokenizer.py
 create mode 100644 egs/zipvoice/local/validate_manifest.py
 create mode 100644 egs/zipvoice/scripts/evaluate.sh
 create mode 100755 egs/zipvoice/scripts/prepare_emilia.sh
 create mode 100755 egs/zipvoice/scripts/prepare_libritts.sh
 create mode 100644 egs/zipvoice/zipvoice/checkpoint.py
 create mode 100644 egs/zipvoice/zipvoice/feature.py
 create mode 100644 egs/zipvoice/zipvoice/generate_averaged_model.py
 create mode 100644 egs/zipvoice/zipvoice/infer.py
 create mode 100644 egs/zipvoice/zipvoice/model.py
 create mode 100644 egs/zipvoice/zipvoice/optim.py
 create mode 100644 egs/zipvoice/zipvoice/scaling.py
 create mode 100644 egs/zipvoice/zipvoice/solver.py
 create mode 100644 egs/zipvoice/zipvoice/tokenizer.py
 create mode 100644 egs/zipvoice/zipvoice/train_distill.py
 create mode 100644 egs/zipvoice/zipvoice/train_flow.py
 create mode 100644 egs/zipvoice/zipvoice/tts_datamodule.py
 create mode 100644 egs/zipvoice/zipvoice/utils.py
 create mode 100644 egs/zipvoice/zipvoice/zipformer.py
 create mode 100644 egs/zipvoice/zipvoice/zipvoice_infer.py

diff --git a/egs/zipvoice/README.md b/egs/zipvoice/README.md
new file mode 100644
index 000000000..4bca60301
--- /dev/null
+++ b/egs/zipvoice/README.md
@@ -0,0 +1,411 @@
+## ZipVoice: Fast and High-Quality Zero-Shot Text-to-Speech with Flow Matching
+
+
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](http://arxiv.org/abs/2506.13053)
+[![demo](https://img.shields.io/badge/GitHub-Demo%20page-orange.svg)](https://zipvoice.github.io/)
+
+
+## Overview
+ZipVoice is a high-quality zero-shot TTS model with a small model size and fast inference speed.
+#### Key features:
+
+- Small and fast: only 123M parameters.
+
+- High-quality: state-of-the-art voice cloning performance in speaker similarity, intelligibility, and naturalness.
+
+- Multi-lingual: support Chinese and English.
+
+
+## News
+**2025/06/16**: 🔥 ZipVoice is released.
+
+
+## Installation
+
+* Clone icefall repository and change to zipvoice directory:
+
+```bash
+git clone https://github.com/k2-fsa/icefall.git
+cd icefall/egs/zipvoice
+```
+
+* Create a Python virtual environment (optional but recommended):
+
+```bash
+python3 -m venv venv
+source venv/bin/activate
+```
+
+* Install the required packages:
+
+```bash
+# Install pytorch and k2.
+# If you want to use different versions, please refer to https://k2-fsa.org/get-started/k2/ for details.
+# For users in China mainland, please refer to https://k2-fsa.org/zh-CN/get-started/k2/
+
+pip install torch==2.5.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
+pip install k2==1.24.4.dev20250208+cuda12.1.torch2.5.1 -f https://k2-fsa.github.io/k2/cuda.html
+
+# Install other dependencies.
+pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html
+pip install -r requirements.txt
+```
+
+## Usage
+
+To generate speech with our pre-trained ZipVoice or ZipVoice-Distill models, use the following commands (Required models will be downloaded from HuggingFace):
+
+### 1. Inference of a single sentence:
+```bash
+python3 zipvoice/zipvoice_infer.py \
+    --model-name "zipvoice_distill" \
+    --prompt-wav prompt.wav \
+    --prompt-text "I am the transcription of the prompt wav." \
+    --text "I am the text to be synthesized." \
+    --res-wav-path result.wav
+
+# Example with a pre-defined prompt wav and text
+python3 zipvoice/zipvoice_infer.py \
+    --model-name "zipvoice_distill" \
+    --prompt-wav assets/prompt-en.wav \
+    --prompt-text "Some call me nature, others call me mother nature. I've been here for over four point five billion years, twenty two thousand five hundred times longer than you." \
+    --text "Welcome to use our tts model, have fun!" \
+    --res-wav-path result.wav
+```
+
+### 2. Inference of a list of sentences:
+```bash
+python3 zipvoice/zipvoice_infer.py \
+    --model-name "zipvoice_distill" \
+    --test-list test.tsv \
+    --res-dir results/test
+```
+
+- `--model-name` can be `zipvoice` or `zipvoice_distill`, which are models before and after distillation, respectively.
+- Each line of `test.tsv` is in the format of `{wav_name}\t{prompt_transcription}\t{prompt_wav}\t{text}`.
+
+
+> **Note:**  If you having trouble connecting to HuggingFace, try:
+```bash
+export HF_ENDPOINT=https://hf-mirror.com
+```
+
+## Training Your Own Model
+
+The following steps show how to train a model from scratch on Emilia and LibriTTS datasets, respectively.
+
+### 0. Install dependencies for training
+
+```bash
+pip install -r ../../requirements.txt
+```
+
+### 1. Data Preparation
+
+#### 1.1. Prepare the Emilia dataset
+
+```bash
+bash scripts/prepare_emilia.sh
+```
+
+See [scripts/prepare_emilia.sh](scripts/prepare_emilia.sh) for step by step instructions.
+
+#### 1.2 Prepare the LibriTTS dataset
+
+```bash
+bash scripts/prepare_libritts.sh
+```
+
+See [scripts/prepare_libritts.sh](scripts/prepare_libritts.sh) for step by step instructions.
+
+### 2. Training
+
+#### 2.1 Traininig on Emilia
+
+<details>
+<summary>Expand to view training steps</summary>
+
+##### 2.1.1 Train the ZipVoice model
+
+- Training:
+
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 zipvoice/train_flow.py \
+        --world-size 8 \
+        --use-fp16 1 \
+        --dataset emilia \
+        --max-duration 500 \
+        --lr-hours 30000 \
+        --lr-batches 7500 \
+        --token-file "data/tokens_emilia.txt" \
+        --manifest-dir "data/fbank" \
+        --num-epochs 11 \
+        --exp-dir zipvoice/exp_zipvoice
+```
+
+-  Average the checkpoints to produce the final model:
+
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 zipvoice/generate_averaged_model.py \
+      --epoch 11 \
+      --avg 4 \
+      --distill 0 \
+      --token-file data/tokens_emilia.txt \
+      --dataset "emilia" \
+      --exp-dir ./zipvoice/exp_zipvoice
+# The generated model is zipvoice/exp_zipvoice/epoch-11-avg-4.pt
+```
+
+##### 2.1.2. Train the ZipVoice-Distill model (Optional)
+
+- The first-stage distillation:
+
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 zipvoice/train_distill.py \
+        --world-size 8 \
+        --use-fp16 1 \
+        --tensorboard 1 \
+        --dataset "emilia" \
+        --base-lr 0.0005 \
+        --max-duration 500 \
+        --token-file "data/tokens_emilia.txt" \
+        --manifest-dir "data/fbank" \
+        --teacher-model zipvoice/exp_zipvoice/epoch-11-avg-4.pt \
+        --num-updates 60000 \
+        --distill-stage "first" \
+        --exp-dir zipvoice/exp_zipvoice_distill_1stage
+```
+
+- Average checkpoints for the second-stage initialization:
+
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 zipvoice/generate_averaged_model.py \
+      --iter 60000 \
+      --avg 7 \
+      --distill 1 \
+      --token-file data/tokens_emilia.txt \
+      --dataset "emilia" \
+      --exp-dir ./zipvoice/exp_zipvoice_distill_1stage
+# The generated model is zipvoice/exp_zipvoice_distill_1stage/iter-60000-avg-7.pt
+```
+
+-  The second-stage distillation:
+
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 zipvoice/train_distill.py \
+        --world-size 8 \
+        --use-fp16 1 \
+        --tensorboard 1 \
+        --dataset "emilia" \
+        --base-lr 0.0001 \
+        --max-duration 200 \
+        --token-file "data/tokens_emilia.txt" \
+        --manifest-dir "data/fbank" \
+        --teacher-model zipvoice/exp_zipvoice_distill_1stage/iter-60000-avg-7.pt \
+        --num-updates 2000 \
+        --distill-stage "second" \
+        --exp-dir zipvoice/exp_zipvoice_distill_new
+```
+</details>
+
+
+#### 2.2 Traininig on LibriTTS
+
+<details>
+<summary>Expand to view training steps</summary>
+
+##### 2.2.1 Train the ZipVoice model
+
+- Training:
+
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 zipvoice/train_flow.py \
+        --world-size 8 \
+        --use-fp16 1 \
+        --dataset libritts \
+        --max-duration 250 \
+        --lr-epochs 10 \
+        --lr-batches 7500 \
+        --token-file "data/tokens_libritts.txt" \
+        --manifest-dir "data/fbank" \
+        --num-epochs 60 \
+        --exp-dir zipvoice/exp_zipvoice_libritts
+```
+
+- Average the checkpoints to produce the final model:
+
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 zipvoice/generate_averaged_model.py \
+      --epoch 60 \
+      --avg 10 \
+      --distill 0 \
+      --token-file data/tokens_libritts.txt \
+      --dataset "libritts" \
+      --exp-dir ./zipvoice/exp_zipvoice_libritts
+# The generated model is zipvoice/exp_zipvoice_libritts/epoch-60-avg-10.pt
+```
+
+##### 2.1.2 Train the ZipVoice-Distill model (Optional)
+
+- The first-stage distillation:
+
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 zipvoice/train_distill.py \
+        --world-size 8 \
+        --use-fp16 1 \
+        --tensorboard 1 \
+        --dataset "libritts" \
+        --base-lr 0.001 \
+        --max-duration 250 \
+        --token-file "data/tokens_libritts.txt" \
+        --manifest-dir "data/fbank" \
+        --teacher-model zipvoice/exp_zipvoice_libritts/epoch-60-avg-10.pt \
+        --num-epochs 6 \
+        --distill-stage "first" \
+        --exp-dir zipvoice/exp_zipvoice_distill_1stage_libritts
+```
+
+- Average checkpoints for the second-stage initialization:
+
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 ./zipvoice/generate_averaged_model.py \
+      --epoch 6 \
+      --avg 3 \
+      --distill 1 \
+      --token-file data/tokens_libritts.txt \
+      --dataset "libritts" \
+      --exp-dir ./zipvoice/exp_zipvoice_distill_1stage_libritts
+# The generated model is zipvoice/exp_zipvoice_distill_1stage_libritts/epoch-6-avg-3.pt
+```
+
+- The second-stage distillation:
+
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 zipvoice/train_distill.py \
+        --world-size 8 \
+        --use-fp16 1 \
+        --tensorboard 1 \
+        --dataset "libritts" \
+        --base-lr 0.001 \
+        --max-duration 250 \
+        --token-file "data/tokens_libritts.txt" \
+        --manifest-dir "data/fbank" \
+        --teacher-model zipvoice/exp_zipvoice_distill_1stage_libritts/epoch-6-avg-3.pt \
+        --num-epochs 6 \
+        --distill-stage "second" \
+        --exp-dir zipvoice/exp_zipvoice_distill_libritts
+```
+
+- Average checkpoints to produce the final model:
+
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 ./zipvoice/generate_averaged_model.py \
+      --epoch 6 \
+      --avg 3 \
+      --distill 1 \
+      --token-file data/tokens_libritts.txt \
+      --dataset "libritts" \
+      --exp-dir ./zipvoice/exp_zipvoice_distill_libritts
+# The generated model is ./zipvoice/exp_zipvoice_distill_libritts/epoch-6-avg-3.pt
+```
+</details>
+
+
+### 3. Inference with the trained model
+
+#### 3.1 Inference with the model trained on Emilia
+<details>
+<summary>Expand to view inference commands.</summary>
+
+##### 3.1.1 ZipVoice model before distill:
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 zipvoice/infer.py \
+      --checkpoint zipvoice/exp_zipvoice/epoch-11-avg-4.pt \
+      --distill 0 \
+      --token-file "data/tokens_emilia.txt" \
+      --test-list test.tsv \
+      --res-dir results/test \
+      --num-step 16 \
+      --guidance-scale 1
+```
+
+##### 3.1.2 ZipVoice-Distill model before distill:
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 zipvoice/infer.py \
+      --checkpoint zipvoice/exp_zipvoice_distill/checkpoint-2000.pt \
+      --distill 1 \
+      --token-file "data/tokens_emilia.txt" \
+      --test-list test.tsv \
+      --res-dir results/test_distill \
+      --num-step 8 \
+      --guidance-scale 3
+```
+</details>
+
+
+#### 3.2 Inference with the model trained on LibriTTS
+
+<details>
+<summary>Expand to view inference commands.</summary>
+
+##### 3.2.1 ZipVoice model before distill:
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 zipvoice/infer.py \
+      --checkpoint zipvoice/exp_zipvoice_libritts/epoch-60-avg-10.pt \
+      --distill 0 \
+      --token-file "data/tokens_libritts.txt" \
+      --test-list test.tsv \
+      --res-dir results/test_libritts \
+      --num-step 8 \
+      --guidance-scale 1 \
+      --target-rms 1.0 \
+      --t-shift 0.7
+```
+
+##### 3.2.2 ZipVoice-Distill model before distill
+
+```bash
+export PYTHONPATH=../../:$PYTHONPATH
+python3 zipvoice/infer.py \
+      --checkpoint zipvoice/exp_zipvoice_distill/epoch-6-avg-3.pt \
+      --distill 1 \
+      --token-file "data/tokens_libritts.txt" \
+      --test-list test.tsv \
+      --res-dir results/test_distill_libritts \
+      --num-step 4 \
+      --guidance-scale 3 \
+      --target-rms 1.0 \
+      --t-shift 0.7
+```
+</details>
+
+### 4. Evaluation on benchmarks
+
+See [local/evaluate.sh](local/evaluate.sh) for details of objective metrics evaluation
+on three test sets, i.e., LibriSpeech-PC test-clean, Seed-TTS test-en and Seed-TTS test-zh.
+
+
+## Citation
+
+```bibtex
+@article{zhu-2025-zipvoice,
+      title={ZipVoice: Fast and High-Quality Zero-Shot Text-to-Speech with Flow Matching}, 
+      author={Han Zhu and Wei Kang and Zengwei Yao and Liyong Guo and Fangjun Kuang and Zhaoqing Li and Weiji Zhuang and Long Lin and Daniel Povey}
+      journal={arXiv preprint arXiv:2506.13053},
+      year={2025},
+}
+```
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HcmV?d00001

diff --git a/egs/zipvoice/local/compute_fbank.py b/egs/zipvoice/local/compute_fbank.py
new file mode 100644
index 000000000..09a154229
--- /dev/null
+++ b/egs/zipvoice/local/compute_fbank.py
@@ -0,0 +1,288 @@
+#!/usr/bin/env python3
+# Copyright    2025  Xiaomi Corp.        (authors: Wei Kang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import logging
+import os
+from pathlib import Path
+from typing import Optional
+from concurrent.futures import ProcessPoolExecutor as Pool
+
+import torch
+from lhotse import (
+    CutSet,
+    LilcomChunkyWriter,
+    load_manifest_lazy,
+    set_audio_duration_mismatch_tolerance,
+)
+
+from feature import TorchAudioFbank, TorchAudioFbankConfig
+import lhotse
+
+# Torch's multithreaded behavior needs to be disabled or
+# it wastes a lot of CPU and slow things down.
+# Do this outside of main() in case it needs to take effect
+# even when we are not invoking the main (e.g. when spawning subprocesses).
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+
+def str2bool(v):
+    """Used in argparse.ArgumentParser.add_argument to indicate
+    that a type is a bool type and user can enter
+
+        - yes, true, t, y, 1, to represent True
+        - no, false, f, n, 0, to represent False
+
+    See https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse  # noqa
+    """
+    if isinstance(v, bool):
+        return v
+    if v.lower() in ("yes", "true", "t", "y", "1"):
+        return True
+    elif v.lower() in ("no", "false", "f", "n", "0"):
+        return False
+    else:
+        raise argparse.ArgumentTypeError("Boolean value expected.")
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--sampling-rate",
+        type=int,
+        default=24000,
+        help="The target sampling rate, the audio will be resampled to this sampling_rate.",
+    )
+
+    parser.add_argument(
+        "--frame-shift",
+        type=int,
+        default=256,
+        help="Frame shift in samples",
+    )
+
+    parser.add_argument(
+        "--frame-length",
+        type=int,
+        default=1024,
+        help="Frame length in samples",
+    )
+
+    parser.add_argument(
+        "--num-mel-bins",
+        type=int,
+        default=100,
+        help="The num of mel filters.",
+    )
+
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        help="Dataset name.",
+    )
+
+    parser.add_argument(
+        "--subset",
+        type=str,
+        help="The subset of the dataset.",
+    )
+
+    parser.add_argument(
+        "--source-dir",
+        type=str,
+        default="data/manifests",
+        help="The source directory of manifest files.",
+    )
+
+    parser.add_argument(
+        "--dest-dir",
+        type=str,
+        default="data/fbank",
+        help="The destination directory of manifest files.",
+    )
+
+    parser.add_argument(
+        "--split-cuts",
+        type=str2bool,
+        default=False,
+        help="Whether to use splited cuts.",
+    )
+
+    parser.add_argument(
+        "--split-begin",
+        type=int,
+        help="Start idx of splited cuts.",
+    )
+
+    parser.add_argument(
+        "--split-end",
+        type=int,
+        help="End idx of splited cuts.",
+    )
+
+    parser.add_argument(
+        "--batch-duration",
+        type=int,
+        default=1000,
+        help="The batch duration when computing the features.",
+    )
+
+    parser.add_argument(
+        "--num-jobs", type=int, default=20, help="The number of extractor workers."
+    )
+
+    return parser.parse_args()
+
+
+def compute_fbank_split_single(params, idx):
+    lhotse.set_audio_duration_mismatch_tolerance(0.1)  # for emilia
+    src_dir = Path(params.source_dir)
+    output_dir = Path(params.dest_dir)
+    num_mel_bins = params.num_mel_bins
+
+    if not src_dir.exists():
+        logging.error(f"{src_dir} not exists")
+        return
+
+    if not output_dir.exists():
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+    num_digits = 8
+
+    config = TorchAudioFbankConfig(
+        sampling_rate=params.sampling_rate,
+        n_mels=params.num_mel_bins,
+        n_fft=params.frame_length,
+        hop_length=params.frame_shift,
+    )
+    extractor = TorchAudioFbank(config)
+
+    prefix = params.dataset
+    subset = params.subset
+    suffix = "jsonl.gz"
+
+    idx = f"{idx}".zfill(num_digits)
+    cuts_filename = f"{prefix}_cuts_{subset}.{idx}.{suffix}"
+
+    if (src_dir / cuts_filename).is_file():
+        logging.info(f"Loading manifests {src_dir / cuts_filename}")
+        cut_set = load_manifest_lazy(src_dir / cuts_filename)
+    else:
+        logging.warning(f"Raw {cuts_filename} not exists, skipping")
+        return
+
+    cut_set = cut_set.resample(params.sampling_rate)
+
+    if (output_dir / cuts_filename).is_file():
+        logging.info(f"{cuts_filename} already exists - skipping.")
+        return
+
+    logging.info(f"Processing {subset}.{idx} of {prefix}")
+
+    cut_set = cut_set.compute_and_store_features_batch(
+        extractor=extractor,
+        storage_path=f"{output_dir}/{prefix}_feats_{subset}_{idx}",
+        num_workers=4,
+        batch_duration=params.batch_duration,
+        storage_type=LilcomChunkyWriter,
+        overwrite=True,
+    )
+    cut_set.to_file(output_dir / cuts_filename)
+
+
+def compute_fbank_split(params):
+    if params.split_end < params.split_begin:
+        logging.warning(
+            f"Split begin should be smaller than split end, given "
+            f"{params.split_begin} -> {params.split_end}."
+        )
+
+    with Pool(max_workers=params.num_jobs) as pool:
+        futures = [
+            pool.submit(compute_fbank_split_single, params, i)
+            for i in range(params.split_begin, params.split_end)
+        ]
+        for f in futures:
+            f.result()
+            f.done()
+
+
+def compute_fbank(params):
+    src_dir = Path(params.source_dir)
+    output_dir = Path(params.dest_dir)
+    num_jobs = params.num_jobs
+    num_mel_bins = params.num_mel_bins
+
+    prefix = params.dataset
+    subset = params.subset
+    suffix = "jsonl.gz"
+
+    cut_set_name = f"{prefix}_cuts_{subset}.{suffix}"
+
+    if (src_dir / cut_set_name).is_file():
+        logging.info(f"Loading manifests {src_dir / cut_set_name}")
+        cut_set = load_manifest_lazy(src_dir / cut_set_name)
+    else:
+        recordings = load_manifest_lazy(
+            src_dir / f"{prefix}_recordings_{subset}.{suffix}"
+        )
+        supervisions = load_manifest_lazy(
+            src_dir / f"{prefix}_supervisions_{subset}.{suffix}"
+        )
+        cut_set = CutSet.from_manifests(
+            recordings=recordings,
+            supervisions=supervisions,
+        )
+
+    cut_set = cut_set.resample(params.sampling_rate)
+
+    config = TorchAudioFbankConfig(
+        sampling_rate=params.sampling_rate,
+        n_mels=params.num_mel_bins,
+        n_fft=params.frame_length,
+        hop_length=params.frame_shift,
+    )
+    extractor = TorchAudioFbank(config)
+
+    cuts_filename = f"{prefix}_cuts_{subset}.{suffix}"
+    if (output_dir / cuts_filename).is_file():
+        logging.info(f"{prefix} {subset} already exists - skipping.")
+        return
+    logging.info(f"Processing {subset} of {prefix}")
+
+    cut_set = cut_set.compute_and_store_features(
+        extractor=extractor,
+        storage_path=f"{output_dir}/{prefix}_feats_{subset}",
+        num_jobs=num_jobs,
+        storage_type=LilcomChunkyWriter,
+    )
+    cut_set.to_file(output_dir / cuts_filename)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    args = get_args()
+    logging.info(vars(args))
+    if args.split_cuts:
+        compute_fbank_split(params=args)
+    else:
+        compute_fbank(params=args)
diff --git a/egs/zipvoice/local/evaluate_sim.py b/egs/zipvoice/local/evaluate_sim.py
new file mode 100644
index 000000000..df439cf2c
--- /dev/null
+++ b/egs/zipvoice/local/evaluate_sim.py
@@ -0,0 +1,508 @@
+"""
+Calculate pairwise Speaker Similarity betweeen two speech directories.
+SV model wavlm_large_finetune.pth is downloaded from
+    https://github.com/microsoft/UniSpeech/tree/main/downstreams/speaker_verification
+SSL model wavlm_large.pt is downloaded from
+    https://huggingface.co/s3prl/converted_ckpts/resolve/main/wavlm_large.pt
+"""
+import argparse
+import logging
+import os
+
+import librosa
+import numpy as np
+import soundfile as sf
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+logging.basicConfig(level=logging.INFO)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--eval-path", type=str, help="path of the evaluated speech directory"
+    )
+    parser.add_argument(
+        "--test-list",
+        type=str,
+        help="path of the file list that contains the corresponding "
+        "relationship between the prompt and evaluated speech. "
+        "The first column is the wav name and the third column is the prompt speech",
+    )
+    parser.add_argument(
+        "--sv-model-path",
+        type=str,
+        default="model/UniSpeech/wavlm_large_finetune.pth",
+        help="path of the wavlm-based ECAPA-TDNN model",
+    )
+    parser.add_argument(
+        "--ssl-model-path",
+        type=str,
+        default="model/s3prl/wavlm_large.pt",
+        help="path of the wavlm SSL model",
+    )
+    return parser
+
+
+class SpeakerSimilarity:
+    def __init__(
+        self,
+        sv_model_path="model/UniSpeech/wavlm_large_finetune.pth",
+        ssl_model_path="model/s3prl/wavlm_large.pt",
+    ):
+        """
+        Initialize
+        """
+        self.sample_rate = 16000
+        self.channels = 1
+        self.device = (
+            torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+        )
+        logging.info("[Speaker Similarity] Using device: {}".format(self.device))
+        self.model = ECAPA_TDNN_WAVLLM(
+            feat_dim=1024,
+            channels=512,
+            emb_dim=256,
+            sr=16000,
+            ssl_model_path=ssl_model_path,
+        )
+        state_dict = torch.load(
+            sv_model_path, map_location=lambda storage, loc: storage
+        )
+        self.model.load_state_dict(state_dict["model"], strict=False)
+        self.model.to(self.device)
+        self.model.eval()
+
+    def get_embeddings(self, wav_list, dtype="float32"):
+        """
+        Get embeddings
+        """
+
+        def _load_speech_task(fname, sample_rate):
+
+            wav_data, sr = sf.read(fname, dtype=dtype)
+            if sr != sample_rate:
+                wav_data = librosa.resample(
+                    wav_data, orig_sr=sr, target_sr=self.sample_rate
+                )
+            wav_data = torch.from_numpy(wav_data)
+
+            return wav_data
+
+        embd_lst = []
+        for file_path in tqdm(wav_list):
+            speech = _load_speech_task(file_path, self.sample_rate)
+            speech = speech.to(self.device)
+            with torch.no_grad():
+                embd = self.model([speech])
+            embd_lst.append(embd)
+
+        return embd_lst
+
+    def score(
+        self,
+        eval_path,
+        test_list,
+        dtype="float32",
+    ):
+        """
+        Computes the Speaker Similarity (SIM-o) between two directories of speech files.
+
+        Parameters:
+        - eval_path (str): Path to the directory containing evaluation speech files.
+        - test_list (str): Path to the file containing the corresponding relationship
+             between prompt and evaluated speech.
+        - dtype (str, optional): Data type for loading speech. Default is "float32".
+
+        Returns:
+        - float: The Speaker Similarity (SIM-o) score between the two directories
+            of speech files.
+        """
+        prompt_wavs = []
+        eval_wavs = []
+        with open(test_list, "r") as fr:
+            lines = fr.readlines()
+            for line in lines:
+                wav_name, prompt_text, prompt_wav, text = line.strip().split("\t")
+                prompt_wavs.append(prompt_wav)
+                eval_wavs.append(os.path.join(eval_path, wav_name + ".wav"))
+        embds_prompt = self.get_embeddings(prompt_wavs, dtype=dtype)
+
+        embds_eval = self.get_embeddings(eval_wavs, dtype=dtype)
+
+        # Check if embeddings are empty
+        if len(embds_prompt) == 0:
+            logging.info("[Speaker Similarity] real set dir is empty, exiting...")
+            return -1
+        if len(embds_eval) == 0:
+            logging.info("[Speaker Similarity] eval set dir is empty, exiting...")
+            return -1
+
+        scores = []
+        for real_embd, eval_embd in zip(embds_prompt, embds_eval):
+            scores.append(
+                torch.nn.functional.cosine_similarity(real_embd, eval_embd, dim=-1)
+                .detach()
+                .cpu()
+                .numpy()
+            )
+
+        return np.mean(scores)
+
+
+# part of the code is borrowed from https://github.com/lawlict/ECAPA-TDNN
+
+""" Res2Conv1d + BatchNorm1d + ReLU
+"""
+
+
+class Res2Conv1dReluBn(nn.Module):
+    """
+    in_channels == out_channels == channels
+    """
+
+    def __init__(
+        self,
+        channels,
+        kernel_size=1,
+        stride=1,
+        padding=0,
+        dilation=1,
+        bias=True,
+        scale=4,
+    ):
+        super().__init__()
+        assert channels % scale == 0, "{} % {} != 0".format(channels, scale)
+        self.scale = scale
+        self.width = channels // scale
+        self.nums = scale if scale == 1 else scale - 1
+
+        self.convs = []
+        self.bns = []
+        for i in range(self.nums):
+            self.convs.append(
+                nn.Conv1d(
+                    self.width,
+                    self.width,
+                    kernel_size,
+                    stride,
+                    padding,
+                    dilation,
+                    bias=bias,
+                )
+            )
+            self.bns.append(nn.BatchNorm1d(self.width))
+        self.convs = nn.ModuleList(self.convs)
+        self.bns = nn.ModuleList(self.bns)
+
+    def forward(self, x):
+        out = []
+        spx = torch.split(x, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            # Order: conv -> relu -> bn
+            sp = self.convs[i](sp)
+            sp = self.bns[i](F.relu(sp))
+            out.append(sp)
+        if self.scale != 1:
+            out.append(spx[self.nums])
+        out = torch.cat(out, dim=1)
+
+        return out
+
+
+""" Conv1d + BatchNorm1d + ReLU
+"""
+
+
+class Conv1dReluBn(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size=1,
+        stride=1,
+        padding=0,
+        dilation=1,
+        bias=True,
+    ):
+        super().__init__()
+        self.conv = nn.Conv1d(
+            in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias
+        )
+        self.bn = nn.BatchNorm1d(out_channels)
+
+    def forward(self, x):
+        return self.bn(F.relu(self.conv(x)))
+
+
+""" The SE connection of 1D case.
+"""
+
+
+class SE_Connect(nn.Module):
+    def __init__(self, channels, se_bottleneck_dim=128):
+        super().__init__()
+        self.linear1 = nn.Linear(channels, se_bottleneck_dim)
+        self.linear2 = nn.Linear(se_bottleneck_dim, channels)
+
+    def forward(self, x):
+        out = x.mean(dim=2)
+        out = F.relu(self.linear1(out))
+        out = torch.sigmoid(self.linear2(out))
+        out = x * out.unsqueeze(2)
+
+        return out
+
+
+""" SE-Res2Block of the ECAPA-TDNN architecture.
+"""
+
+
+# def SE_Res2Block(channels, kernel_size, stride, padding, dilation, scale):
+#     return nn.Sequential(
+#         Conv1dReluBn(channels, 512, kernel_size=1, stride=1, padding=0),
+#         Res2Conv1dReluBn(512, kernel_size, stride, padding, dilation, scale=scale),
+#         Conv1dReluBn(512, channels, kernel_size=1, stride=1, padding=0),
+#         SE_Connect(channels)
+#     )
+
+
+class SE_Res2Block(nn.Module):
+    def __init__(
+        self,
+        in_channels,
+        out_channels,
+        kernel_size,
+        stride,
+        padding,
+        dilation,
+        scale,
+        se_bottleneck_dim,
+    ):
+        super().__init__()
+        self.Conv1dReluBn1 = Conv1dReluBn(
+            in_channels, out_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.Res2Conv1dReluBn = Res2Conv1dReluBn(
+            out_channels, kernel_size, stride, padding, dilation, scale=scale
+        )
+        self.Conv1dReluBn2 = Conv1dReluBn(
+            out_channels, out_channels, kernel_size=1, stride=1, padding=0
+        )
+        self.SE_Connect = SE_Connect(out_channels, se_bottleneck_dim)
+
+        self.shortcut = None
+        if in_channels != out_channels:
+            self.shortcut = nn.Conv1d(
+                in_channels=in_channels,
+                out_channels=out_channels,
+                kernel_size=1,
+            )
+
+    def forward(self, x):
+        residual = x
+        if self.shortcut:
+            residual = self.shortcut(x)
+
+        x = self.Conv1dReluBn1(x)
+        x = self.Res2Conv1dReluBn(x)
+        x = self.Conv1dReluBn2(x)
+        x = self.SE_Connect(x)
+
+        return x + residual
+
+
+""" Attentive weighted mean and standard deviation pooling.
+"""
+
+
+class AttentiveStatsPool(nn.Module):
+    def __init__(self, in_dim, attention_channels=128, global_context_att=False):
+        super().__init__()
+        self.global_context_att = global_context_att
+
+        # Use Conv1d with stride == 1 rather than Linear,
+        #  then we don't need to transpose inputs.
+        if global_context_att:
+            self.linear1 = nn.Conv1d(
+                in_dim * 3, attention_channels, kernel_size=1
+            )  # equals W and b in the paper
+        else:
+            self.linear1 = nn.Conv1d(
+                in_dim, attention_channels, kernel_size=1
+            )  # equals W and b in the paper
+        self.linear2 = nn.Conv1d(
+            attention_channels, in_dim, kernel_size=1
+        )  # equals V and k in the paper
+
+    def forward(self, x):
+
+        if self.global_context_att:
+            context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
+            context_std = torch.sqrt(
+                torch.var(x, dim=-1, keepdim=True) + 1e-10
+            ).expand_as(x)
+            x_in = torch.cat((x, context_mean, context_std), dim=1)
+        else:
+            x_in = x
+
+        # DON'T use ReLU here! In experiments, I find ReLU hard to converge.
+        alpha = torch.tanh(self.linear1(x_in))
+        # alpha = F.relu(self.linear1(x_in))
+        alpha = torch.softmax(self.linear2(alpha), dim=2)
+        mean = torch.sum(alpha * x, dim=2)
+        residuals = torch.sum(alpha * (x**2), dim=2) - mean**2
+        std = torch.sqrt(residuals.clamp(min=1e-9))
+        return torch.cat([mean, std], dim=1)
+
+
+class ECAPA_TDNN_WAVLLM(nn.Module):
+    def __init__(
+        self,
+        feat_dim=80,
+        channels=512,
+        emb_dim=192,
+        global_context_att=False,
+        sr=16000,
+        ssl_model_path=None,
+    ):
+        super().__init__()
+        self.sr = sr
+
+        if ssl_model_path is None:
+            self.feature_extract = torch.hub.load("s3prl/s3prl", "wavlm_large")
+        else:
+            self.feature_extract = torch.hub.load(
+                os.path.dirname(ssl_model_path),
+                "wavlm_local",
+                source="local",
+                ckpt=ssl_model_path,
+            )
+
+        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
+            self.feature_extract.model.encoder.layers[23].self_attn, "fp32_attention"
+        ):
+            self.feature_extract.model.encoder.layers[
+                23
+            ].self_attn.fp32_attention = False
+        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
+            self.feature_extract.model.encoder.layers[11].self_attn, "fp32_attention"
+        ):
+            self.feature_extract.model.encoder.layers[
+                11
+            ].self_attn.fp32_attention = False
+
+        self.feat_num = self.get_feat_num()
+        self.feature_weight = nn.Parameter(torch.zeros(self.feat_num))
+
+        self.instance_norm = nn.InstanceNorm1d(feat_dim)
+        # self.channels = [channels] * 4 + [channels * 3]
+        self.channels = [channels] * 4 + [1536]
+
+        self.layer1 = Conv1dReluBn(feat_dim, self.channels[0], kernel_size=5, padding=2)
+        self.layer2 = SE_Res2Block(
+            self.channels[0],
+            self.channels[1],
+            kernel_size=3,
+            stride=1,
+            padding=2,
+            dilation=2,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+        self.layer3 = SE_Res2Block(
+            self.channels[1],
+            self.channels[2],
+            kernel_size=3,
+            stride=1,
+            padding=3,
+            dilation=3,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+        self.layer4 = SE_Res2Block(
+            self.channels[2],
+            self.channels[3],
+            kernel_size=3,
+            stride=1,
+            padding=4,
+            dilation=4,
+            scale=8,
+            se_bottleneck_dim=128,
+        )
+
+        # self.conv = nn.Conv1d(self.channels[-1], self.channels[-1], kernel_size=1)
+        cat_channels = channels * 3
+        self.conv = nn.Conv1d(cat_channels, self.channels[-1], kernel_size=1)
+        self.pooling = AttentiveStatsPool(
+            self.channels[-1],
+            attention_channels=128,
+            global_context_att=global_context_att,
+        )
+        self.bn = nn.BatchNorm1d(self.channels[-1] * 2)
+        self.linear = nn.Linear(self.channels[-1] * 2, emb_dim)
+
+    def get_feat_num(self):
+        self.feature_extract.eval()
+        wav = [torch.randn(self.sr).to(next(self.feature_extract.parameters()).device)]
+        with torch.no_grad():
+            features = self.feature_extract(wav)
+        select_feature = features["hidden_states"]
+        if isinstance(select_feature, (list, tuple)):
+            return len(select_feature)
+        else:
+            return 1
+
+    def get_feat(self, x):
+        with torch.no_grad():
+            x = self.feature_extract([sample for sample in x])
+
+        x = x["hidden_states"]
+        if isinstance(x, (list, tuple)):
+            x = torch.stack(x, dim=0)
+        else:
+            x = x.unsqueeze(0)
+        norm_weights = (
+            F.softmax(self.feature_weight, dim=-1)
+            .unsqueeze(-1)
+            .unsqueeze(-1)
+            .unsqueeze(-1)
+        )
+        x = (norm_weights * x).sum(dim=0)
+        x = torch.transpose(x, 1, 2) + 1e-6
+
+        x = self.instance_norm(x)
+        return x
+
+    def forward(self, x):
+        x = self.get_feat(x)
+
+        out1 = self.layer1(x)
+        out2 = self.layer2(out1)
+        out3 = self.layer3(out2)
+        out4 = self.layer4(out3)
+
+        out = torch.cat([out2, out3, out4], dim=1)
+        out = F.relu(self.conv(out))
+        out = self.bn(self.pooling(out))
+        out = self.linear(out)
+
+        return out
+
+
+if __name__ == "__main__":
+    parser = get_parser()
+    args = parser.parse_args()
+    SIM = SpeakerSimilarity(
+        sv_model_path=args.sv_model_path, ssl_model_path=args.ssl_model_path
+    )
+    score = SIM.score(args.eval_path, args.test_list)
+    logging.info(f"SIM-o score: {score:.3f}")
diff --git a/egs/zipvoice/local/evaluate_utmos.py b/egs/zipvoice/local/evaluate_utmos.py
new file mode 100644
index 000000000..369e139c1
--- /dev/null
+++ b/egs/zipvoice/local/evaluate_utmos.py
@@ -0,0 +1,294 @@
+"""
+Calculate UTMOS score with automatic Mean Opinion Score (MOS) prediction system
+adapted from https://huggingface.co/spaces/sarulab-speech/UTMOS-demo
+
+# Download model checkpoints
+wget https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/epoch%3D3-step%3D7459.ckpt -P model/huggingface/utmos/utmos.pt
+wget https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/wav2vec_small.pt -P model/huggingface/utmos/wav2vec_small.pt
+"""
+
+import argparse
+import logging
+import os
+
+import fairseq
+import librosa
+import numpy as np
+import pytorch_lightning as pl
+import soundfile as sf
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+
+logging.basicConfig(level=logging.INFO)
+
+
+def get_parser():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--wav-path", type=str, help="path of the evaluated speech directory"
+    )
+    parser.add_argument(
+        "--utmos-model-path",
+        type=str,
+        default="model/huggingface/utmos/utmos.pt",
+        help="path of the UTMOS model",
+    )
+    parser.add_argument(
+        "--ssl-model-path",
+        type=str,
+        default="model/huggingface/utmos/wav2vec_small.pt",
+        help="path of the wav2vec SSL model",
+    )
+    return parser
+
+
+class UTMOSScore:
+    """Predicting score for each audio clip."""
+
+    def __init__(self, utmos_model_path, ssl_model_path):
+        self.sample_rate = 16000
+        self.device = (
+            torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+        )
+        self.model = (
+            BaselineLightningModule.load_from_checkpoint(
+                utmos_model_path, ssl_model_path=ssl_model_path
+            )
+            .eval()
+            .to(self.device)
+        )
+
+    def score(self, wavs: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            wavs: waveforms to be evaluated. When len(wavs) == 1 or 2,
+                the model processes the input as a single audio clip. The model
+                performs batch processing when len(wavs) == 3.
+        """
+        if len(wavs.shape) == 1:
+            out_wavs = wavs.unsqueeze(0).unsqueeze(0)
+        elif len(wavs.shape) == 2:
+            out_wavs = wavs.unsqueeze(0)
+        elif len(wavs.shape) == 3:
+            out_wavs = wavs
+        else:
+            raise ValueError("Dimension of input tensor needs to be <= 3.")
+        bs = out_wavs.shape[0]
+        batch = {
+            "wav": out_wavs,
+            "domains": torch.zeros(bs, dtype=torch.int).to(self.device),
+            "judge_id": torch.ones(bs, dtype=torch.int).to(self.device) * 288,
+        }
+        with torch.no_grad():
+            output = self.model(batch)
+
+        return output.mean(dim=1).squeeze(1).cpu().detach() * 2 + 3
+
+    def score_dir(self, dir, dtype="float32"):
+        def _load_speech_task(fname, sample_rate):
+
+            wav_data, sr = sf.read(fname, dtype=dtype)
+            if sr != sample_rate:
+                wav_data = librosa.resample(
+                    wav_data, orig_sr=sr, target_sr=self.sample_rate
+                )
+            wav_data = torch.from_numpy(wav_data)
+
+            return wav_data
+
+        score_lst = []
+        for fname in tqdm(os.listdir(dir)):
+            speech = _load_speech_task(os.path.join(dir, fname), self.sample_rate)
+            speech = speech.to(self.device)
+            with torch.no_grad():
+                score = self.score(speech)
+            score_lst.append(score.item())
+        return np.mean(score_lst)
+
+
+def load_ssl_model(ckpt_path="wav2vec_small.pt"):
+    SSL_OUT_DIM = 768
+    model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task(
+        [ckpt_path]
+    )
+    ssl_model = model[0]
+    ssl_model.remove_pretraining_modules()
+    return SSL_model(ssl_model, SSL_OUT_DIM)
+
+
+class BaselineLightningModule(pl.LightningModule):
+    def __init__(self, ssl_model_path):
+        super().__init__()
+        self.construct_model(ssl_model_path)
+        self.save_hyperparameters()
+
+    def construct_model(self, ssl_model_path):
+        self.feature_extractors = nn.ModuleList(
+            [
+                load_ssl_model(ckpt_path=ssl_model_path),
+                DomainEmbedding(3, 128),
+            ]
+        )
+        output_dim = sum(
+            [
+                feature_extractor.get_output_dim()
+                for feature_extractor in self.feature_extractors
+            ]
+        )
+        output_layers = [
+            LDConditioner(judge_dim=128, num_judges=3000, input_dim=output_dim)
+        ]
+        output_dim = output_layers[-1].get_output_dim()
+        output_layers.append(
+            Projection(
+                hidden_dim=2048,
+                activation=torch.nn.ReLU(),
+                range_clipping=False,
+                input_dim=output_dim,
+            )
+        )
+
+        self.output_layers = nn.ModuleList(output_layers)
+
+    def forward(self, inputs):
+        outputs = {}
+        for feature_extractor in self.feature_extractors:
+            outputs.update(feature_extractor(inputs))
+        x = outputs
+        for output_layer in self.output_layers:
+            x = output_layer(x, inputs)
+        return x
+
+
+class SSL_model(nn.Module):
+    def __init__(self, ssl_model, ssl_out_dim) -> None:
+        super(SSL_model, self).__init__()
+        self.ssl_model, self.ssl_out_dim = ssl_model, ssl_out_dim
+
+    def forward(self, batch):
+        wav = batch["wav"]
+        wav = wav.squeeze(1)  # [batches, wav_len]
+        res = self.ssl_model(wav, mask=False, features_only=True)
+        x = res["x"]
+        return {"ssl-feature": x}
+
+    def get_output_dim(self):
+        return self.ssl_out_dim
+
+
+class DomainEmbedding(nn.Module):
+    def __init__(self, n_domains, domain_dim) -> None:
+        super().__init__()
+        self.embedding = nn.Embedding(n_domains, domain_dim)
+        self.output_dim = domain_dim
+
+    def forward(self, batch):
+        return {"domain-feature": self.embedding(batch["domains"])}
+
+    def get_output_dim(self):
+        return self.output_dim
+
+
+class LDConditioner(nn.Module):
+    """
+    Conditions ssl output by listener embedding
+    """
+
+    def __init__(self, input_dim, judge_dim, num_judges=None):
+        super().__init__()
+        self.input_dim = input_dim
+        self.judge_dim = judge_dim
+        self.num_judges = num_judges
+        assert num_judges != None
+        self.judge_embedding = nn.Embedding(num_judges, self.judge_dim)
+        # concat [self.output_layer, phoneme features]
+
+        self.decoder_rnn = nn.LSTM(
+            input_size=self.input_dim + self.judge_dim,
+            hidden_size=512,
+            num_layers=1,
+            batch_first=True,
+            bidirectional=True,
+        )  # linear?
+        self.out_dim = self.decoder_rnn.hidden_size * 2
+
+    def get_output_dim(self):
+        return self.out_dim
+
+    def forward(self, x, batch):
+        judge_ids = batch["judge_id"]
+        if "phoneme-feature" in x.keys():
+            concatenated_feature = torch.cat(
+                (
+                    x["ssl-feature"],
+                    x["phoneme-feature"]
+                    .unsqueeze(1)
+                    .expand(-1, x["ssl-feature"].size(1), -1),
+                ),
+                dim=2,
+            )
+        else:
+            concatenated_feature = x["ssl-feature"]
+        if "domain-feature" in x.keys():
+            concatenated_feature = torch.cat(
+                (
+                    concatenated_feature,
+                    x["domain-feature"]
+                    .unsqueeze(1)
+                    .expand(-1, concatenated_feature.size(1), -1),
+                ),
+                dim=2,
+            )
+        if judge_ids != None:
+            concatenated_feature = torch.cat(
+                (
+                    concatenated_feature,
+                    self.judge_embedding(judge_ids)
+                    .unsqueeze(1)
+                    .expand(-1, concatenated_feature.size(1), -1),
+                ),
+                dim=2,
+            )
+            decoder_output, (h, c) = self.decoder_rnn(concatenated_feature)
+        return decoder_output
+
+
+class Projection(nn.Module):
+    def __init__(self, input_dim, hidden_dim, activation, range_clipping=False):
+        super(Projection, self).__init__()
+        self.range_clipping = range_clipping
+        output_dim = 1
+        if range_clipping:
+            self.proj = nn.Tanh()
+
+        self.net = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim),
+            activation,
+            nn.Dropout(0.3),
+            nn.Linear(hidden_dim, output_dim),
+        )
+        self.output_dim = output_dim
+
+    def forward(self, x, batch):
+        output = self.net(x)
+
+        # range clipping
+        if self.range_clipping:
+            return self.proj(output) * 2.0 + 3
+        else:
+            return output
+
+    def get_output_dim(self):
+        return self.output_dim
+
+
+if __name__ == "__main__":
+    parser = get_parser()
+    args = parser.parse_args()
+    UTMOS = UTMOSScore(
+        utmos_model_path=args.utmos_model_path, ssl_model_path=args.ssl_model_path
+    )
+    score = UTMOS.score_dir(args.wav_path)
+    logging.info(f"UTMOS score: {score:.2f}")
diff --git a/egs/zipvoice/local/evaluate_wer_hubert.py b/egs/zipvoice/local/evaluate_wer_hubert.py
new file mode 100644
index 000000000..d30346e67
--- /dev/null
+++ b/egs/zipvoice/local/evaluate_wer_hubert.py
@@ -0,0 +1,172 @@
+"""
+Calculate WER with Hubert models.
+"""
+import argparse
+import os
+import re
+from pathlib import Path
+
+import librosa
+import numpy as np
+import soundfile as sf
+import torch
+from jiwer import compute_measures
+from tqdm import tqdm
+from transformers import pipeline
+
+
+def get_parser():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--wav-path", type=str, help="path of the speech directory")
+    parser.add_argument(
+        "--decode-path",
+        type=str,
+        default=None,
+        help="path of the output file of WER information",
+    )
+    parser.add_argument(
+        "--model-path",
+        type=str,
+        default=None,
+        help="path of the local hubert model, e.g., model/huggingface/hubert-large-ls960-ft",
+    )
+    parser.add_argument(
+        "--test-list",
+        type=str,
+        default="test.tsv",
+        help="path of the transcript tsv file, where the first column "
+        "is the wav name and the last column is the transcript",
+    )
+    parser.add_argument(
+        "--batch-size", type=int, default=16, help="decoding batch size"
+    )
+    return parser
+
+
+def post_process(text: str):
+    text = text.replace("‘", "'")
+    text = text.replace("’", "'")
+    text = re.sub(r"[^a-zA-Z0-9']", " ", text.lower())
+    text = re.sub(r"\s+", " ", text)
+    text = text.strip()
+    return text
+
+
+def process_one(hypo, truth):
+    truth = post_process(truth)
+    hypo = post_process(hypo)
+
+    measures = compute_measures(truth, hypo)
+    word_num = len(truth.split(" "))
+    wer = measures["wer"]
+    subs = measures["substitutions"]
+    dele = measures["deletions"]
+    inse = measures["insertions"]
+    return (truth, hypo, wer, subs, dele, inse, word_num)
+
+
+class SpeechEvalDataset(torch.utils.data.Dataset):
+    def __init__(self, wav_path: str, test_list: str):
+        super().__init__()
+        self.wav_name = []
+        self.wav_paths = []
+        self.transcripts = []
+        with Path(test_list).open("r", encoding="utf8") as f:
+            meta = [item.split("\t") for item in f.read().rstrip().split("\n")]
+        for item in meta:
+            self.wav_name.append(item[0])
+            self.wav_paths.append(Path(wav_path, item[0] + ".wav"))
+            self.transcripts.append(item[-1])
+
+    def __len__(self):
+        return len(self.wav_paths)
+
+    def __getitem__(self, index: int):
+        wav, sampling_rate = sf.read(self.wav_paths[index])
+        item = {
+            "array": librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000),
+            "sampling_rate": 16000,
+            "reference": self.transcripts[index],
+            "wav_name": self.wav_name[index],
+        }
+        return item
+
+
+def main(test_list, wav_path, model_path, decode_path, batch_size, device):
+
+    if model_path is not None:
+        pipe = pipeline(
+            "automatic-speech-recognition",
+            model=model_path,
+            device=device,
+            tokenizer=model_path,
+        )
+    else:
+        pipe = pipeline(
+            "automatic-speech-recognition",
+            model="facebook/hubert-large-ls960-ft",
+            device=device,
+        )
+
+    dataset = SpeechEvalDataset(wav_path, test_list)
+
+    bar = tqdm(
+        pipe(
+            dataset,
+            generate_kwargs={"language": "english", "task": "transcribe"},
+            batch_size=batch_size,
+        ),
+        total=len(dataset),
+    )
+
+    wers = []
+    inses = []
+    deles = []
+    subses = []
+    word_nums = 0
+    if decode_path:
+        decode_dir = os.path.dirname(decode_path)
+        if not os.path.exists(decode_dir):
+            os.makedirs(decode_dir)
+        fout = open(decode_path, "w")
+    for out in bar:
+        wav_name = out["wav_name"][0]
+        transcription = post_process(out["text"].strip())
+        text_ref = post_process(out["reference"][0].strip())
+        truth, hypo, wer, subs, dele, inse, word_num = process_one(
+            transcription, text_ref
+        )
+        if decode_path:
+            fout.write(f"{wav_name}\t{wer}\t{truth}\t{hypo}\t{inse}\t{dele}\t{subs}\n")
+        wers.append(float(wer))
+        inses.append(float(inse))
+        deles.append(float(dele))
+        subses.append(float(subs))
+        word_nums += word_num
+
+    wer = round((np.sum(subses) + np.sum(deles) + np.sum(inses)) / word_nums * 100, 3)
+    subs = round(np.mean(subses) * 100, 3)
+    dele = round(np.mean(deles) * 100, 3)
+    inse = round(np.mean(inses) * 100, 3)
+    print(f"WER: {wer}%\n")
+    if decode_path:
+        fout.write(f"WER: {wer}%\n")
+        fout.flush()
+
+
+if __name__ == "__main__":
+    parser = get_parser()
+    args = parser.parse_args()
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+    else:
+        device = torch.device("cpu")
+    main(
+        args.test_list,
+        args.wav_path,
+        args.model_path,
+        args.decode_path,
+        args.batch_size,
+        device,
+    )
diff --git a/egs/zipvoice/local/evaluate_wer_seedtts.py b/egs/zipvoice/local/evaluate_wer_seedtts.py
new file mode 100644
index 000000000..f7e256387
--- /dev/null
+++ b/egs/zipvoice/local/evaluate_wer_seedtts.py
@@ -0,0 +1,181 @@
+"""
+Calculate WER with Whisper-large-v3 or Paraformer models, 
+following Seed-TTS https://github.com/BytedanceSpeech/seed-tts-eval
+"""
+
+import argparse
+import os
+import string
+
+import numpy as np
+import scipy
+import soundfile as sf
+import torch
+import zhconv
+from funasr import AutoModel
+from jiwer import compute_measures
+from tqdm import tqdm
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+from zhon.hanzi import punctuation
+
+
+def get_parser():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--wav-path", type=str, help="path of the speech directory")
+    parser.add_argument(
+        "--decode-path",
+        type=str,
+        default=None,
+        help="path of the output file of WER information",
+    )
+    parser.add_argument(
+        "--model-path",
+        type=str,
+        default=None,
+        help="path of the local whisper and paraformer model, "
+        "e.g., whisper: model/huggingface/whisper-large-v3/, "
+        "paraformer: model/huggingface/paraformer-zh/",
+    )
+    parser.add_argument(
+        "--test-list",
+        type=str,
+        default="test.tsv",
+        help="path of the transcript tsv file, where the first column "
+        "is the wav name and the last column is the transcript",
+    )
+    parser.add_argument("--lang", type=str, help="decoded language, zh or en")
+    return parser
+
+
+def load_en_model(model_path):
+    if model_path is None:
+        model_path = "openai/whisper-large-v3"
+    processor = WhisperProcessor.from_pretrained(model_path)
+    model = WhisperForConditionalGeneration.from_pretrained(model_path)
+    return processor, model
+
+
+def load_zh_model(model_path):
+    if model_path is None:
+        model_path = "paraformer-zh"
+    model = AutoModel(model=model_path)
+    return model
+
+
+def process_one(hypo, truth, lang):
+    punctuation_all = punctuation + string.punctuation
+    for x in punctuation_all:
+        if x == "'":
+            continue
+        truth = truth.replace(x, "")
+        hypo = hypo.replace(x, "")
+
+    truth = truth.replace("  ", " ")
+    hypo = hypo.replace("  ", " ")
+
+    if lang == "zh":
+        truth = " ".join([x for x in truth])
+        hypo = " ".join([x for x in hypo])
+    elif lang == "en":
+        truth = truth.lower()
+        hypo = hypo.lower()
+    else:
+        raise NotImplementedError
+
+    measures = compute_measures(truth, hypo)
+    word_num = len(truth.split(" "))
+    wer = measures["wer"]
+    subs = measures["substitutions"]
+    dele = measures["deletions"]
+    inse = measures["insertions"]
+    return (truth, hypo, wer, subs, dele, inse, word_num)
+
+
+def main(test_list, wav_path, model_path, decode_path, lang, device):
+    if lang == "en":
+        processor, model = load_en_model(model_path)
+        model.to(device)
+    elif lang == "zh":
+        model = load_zh_model(model_path)
+    params = []
+    for line in open(test_list).readlines():
+        line = line.strip()
+        items = line.split("\t")
+        wav_name, text_ref = items[0], items[-1]
+        file_path = os.path.join(wav_path, wav_name + ".wav")
+        assert os.path.exists(file_path), f"{file_path}"
+
+        params.append((file_path, text_ref))
+    wers = []
+    inses = []
+    deles = []
+    subses = []
+    word_nums = 0
+    if decode_path:
+        decode_dir = os.path.dirname(decode_path)
+        if not os.path.exists(decode_dir):
+            os.makedirs(decode_dir)
+        fout = open(decode_path, "w")
+    for wav_path, text_ref in tqdm(params):
+        if lang == "en":
+            wav, sr = sf.read(wav_path)
+            if sr != 16000:
+                wav = scipy.signal.resample(wav, int(len(wav) * 16000 / sr))
+            input_features = processor(
+                wav, sampling_rate=16000, return_tensors="pt"
+            ).input_features
+            input_features = input_features.to(device)
+            forced_decoder_ids = processor.get_decoder_prompt_ids(
+                language="english", task="transcribe"
+            )
+            predicted_ids = model.generate(
+                input_features, forced_decoder_ids=forced_decoder_ids
+            )
+            transcription = processor.batch_decode(
+                predicted_ids, skip_special_tokens=True
+            )[0]
+        elif lang == "zh":
+            res = model.generate(input=wav_path, batch_size_s=300, disable_pbar=True)
+            transcription = res[0]["text"]
+            transcription = zhconv.convert(transcription, "zh-cn")
+
+        truth, hypo, wer, subs, dele, inse, word_num = process_one(
+            transcription, text_ref, lang
+        )
+        if decode_path:
+            fout.write(f"{wav_path}\t{wer}\t{truth}\t{hypo}\t{inse}\t{dele}\t{subs}\n")
+        wers.append(float(wer))
+        inses.append(float(inse))
+        deles.append(float(dele))
+        subses.append(float(subs))
+        word_nums += word_num
+
+    wer_avg = round(np.mean(wers) * 100, 3)
+    wer = round((np.sum(subses) + np.sum(deles) + np.sum(inses)) / word_nums * 100, 3)
+    subs = round(np.mean(subses) * 100, 3)
+    dele = round(np.mean(deles) * 100, 3)
+    inse = round(np.mean(inses) * 100, 3)
+    print(f"Seed-TTS WER: {wer_avg}%\n")
+    print(f"WER: {wer}%\n")
+    if decode_path:
+        fout.write(f"SeedTTS WER: {wer_avg}%\n")
+        fout.write(f"WER: {wer}%\n")
+        fout.flush()
+
+
+if __name__ == "__main__":
+    parser = get_parser()
+    args = parser.parse_args()
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+    else:
+        device = torch.device("cpu")
+    main(
+        args.test_list,
+        args.wav_path,
+        args.model_path,
+        args.decode_path,
+        args.lang,
+        device,
+    )
diff --git a/egs/zipvoice/local/feature.py b/egs/zipvoice/local/feature.py
new file mode 120000
index 000000000..08ef7d228
--- /dev/null
+++ b/egs/zipvoice/local/feature.py
@@ -0,0 +1 @@
+../zipvoice/feature.py
\ No newline at end of file
diff --git a/egs/zipvoice/local/prepare_token_file_emilia.py b/egs/zipvoice/local/prepare_token_file_emilia.py
new file mode 100644
index 000000000..68af8d397
--- /dev/null
+++ b/egs/zipvoice/local/prepare_token_file_emilia.py
@@ -0,0 +1,90 @@
+#!/usr/bin/env python3
+# Copyright         2024  Xiaomi Corp.        (authors: Zengwei Yao,
+#                                                       Wei Kang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+This file generates the file that maps tokens to IDs.
+"""
+
+import argparse
+import logging
+from pathlib import Path
+from typing import List
+
+from piper_phonemize import get_espeak_map
+from pypinyin.contrib.tone_convert import to_finals_tone3, to_initials
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--tokens",
+        type=Path,
+        default=Path("data/tokens_emilia.txt"),
+        help="Path to the dict that maps the text tokens to IDs",
+    )
+
+    parser.add_argument(
+        "--pinyin",
+        type=Path,
+        default=Path("local/pinyin.txt"),
+        help="Path to the all unique pinyin",
+    )
+
+    return parser.parse_args()
+
+
+def get_pinyin_tokens(pinyin: Path) -> List[str]:
+    phones = set()
+    with open(pinyin, "r") as f:
+        for line in f:
+            x = line.strip()
+            initial = to_initials(x, strict=False)
+            # don't want to share tokens with espeak tokens, so use tone3 style
+            finals = to_finals_tone3(x, strict=False, neutral_tone_with_five=True)
+            if initial != "":
+                # don't want to share tokens with espeak tokens, so add a '0' after each initial
+                phones.add(initial + "0")
+            if finals != "":
+                phones.add(finals)
+    return sorted(phones)
+
+
+def get_token2id(args):
+    """Get a dict that maps token to IDs, and save it to the given filename."""
+    all_tokens = get_espeak_map()  # token: [token_id]
+    all_tokens = {token: token_id[0] for token, token_id in all_tokens.items()}
+    # sort by token_id
+    all_tokens = sorted(all_tokens.items(), key=lambda x: x[1])
+
+    all_pinyin = get_pinyin_tokens(args.pinyin)
+    with open(args.tokens, "w", encoding="utf-8") as f:
+        for token, token_id in all_tokens:
+            f.write(f"{token} {token_id}\n")
+        num_espeak_tokens = len(all_tokens)
+        for i, pinyin in enumerate(all_pinyin):
+            f.write(f"{pinyin} {num_espeak_tokens + i}\n")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    args = get_args()
+    get_token2id(args)
diff --git a/egs/zipvoice/local/prepare_token_file_libritts.py b/egs/zipvoice/local/prepare_token_file_libritts.py
new file mode 100644
index 000000000..374b02613
--- /dev/null
+++ b/egs/zipvoice/local/prepare_token_file_libritts.py
@@ -0,0 +1,31 @@
+import re
+from collections import Counter
+
+from lhotse import load_manifest_lazy
+
+
+def prepare_tokens(manifest_file, token_file):
+    counter = Counter()
+    manifest = load_manifest_lazy(manifest_file)
+    for cut in manifest:
+        line = re.sub(r"\s+", " ", cut.supervisions[0].text)
+        counter.update(line)
+
+    unique_chars = set(counter.keys())
+
+    if "_" in unique_chars:
+        unique_chars.remove("_")
+
+    sorted_chars = sorted(unique_chars, key=lambda char: counter[char], reverse=True)
+
+    result = ["_"] + sorted_chars
+
+    with open(token_file, "w", encoding="utf-8") as file:
+        for index, char in enumerate(result):
+            file.write(f"{char} {index}\n")
+
+
+if __name__ == "__main__":
+    manifest_file = "data/fbank_libritts/libritts_cuts_train-all-shuf.jsonl.gz"
+    output_token_file = "data/tokens_libritts.txt"
+    prepare_tokens(manifest_file, output_token_file)
diff --git a/egs/zipvoice/local/preprocess_emilia.py b/egs/zipvoice/local/preprocess_emilia.py
new file mode 100644
index 000000000..bd76446aa
--- /dev/null
+++ b/egs/zipvoice/local/preprocess_emilia.py
@@ -0,0 +1,156 @@
+#!/usr/bin/env python3
+# Copyright         2024  Xiaomi Corp.        (authors: Zengwei Yao,
+#                                                       Zengrui Jin,
+#                                                       Wei Kang)
+#                   2024  Tsinghua University (authors: Zengrui Jin,)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+This file reads the texts in given manifest and save the cleaned new cuts.
+"""
+
+import argparse
+import logging
+import glob
+import os
+from pathlib import Path
+from typing import List
+
+from lhotse import CutSet, load_manifest_lazy
+from concurrent.futures import ProcessPoolExecutor as Pool
+
+from tokenizer import (
+    is_alphabet,
+    is_chinese,
+    is_hangul,
+    is_japanese,
+    tokenize_by_CJK_char,
+)
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--subset",
+        type=str,
+        help="Subset of emilia, (ZH, EN, etc.)",
+    )
+
+    parser.add_argument(
+        "--jobs",
+        type=int,
+        default=20,
+        help="Number of jobs to processing.",
+    )
+
+    parser.add_argument(
+        "--source-dir",
+        type=str,
+        default="data/manifests/splits_raw",
+        help="The source directory of manifest files.",
+    )
+
+    parser.add_argument(
+        "--dest-dir",
+        type=str,
+        default="data/manifests/splits",
+        help="The destination directory of manifest files.",
+    )
+
+    return parser.parse_args()
+
+
+def preprocess_emilia(file_name: str, input_dir: Path, output_dir: Path):
+    logging.info(f"Processing {file_name}")
+    if (output_dir / file_name).is_file():
+        logging.info(f"{file_name} exists, skipping.")
+        return
+
+    def _filter_cut(cut):
+        text = cut.supervisions[0].text
+        duration = cut.supervisions[0].duration
+        chinese = []
+        english = []
+
+        # only contains chinese and space and alphabets
+        clean_chars = []
+        for x in text:
+            if is_hangul(x):
+                logging.warning(f"Delete cut with text containing Korean : {text}")
+                return False
+            if is_japanese(x):
+                logging.warning(f"Delete cut with text containing Japanese : {text}")
+                return False
+            if is_chinese(x):
+                chinese.append(x)
+                clean_chars.append(x)
+            if is_alphabet(x):
+                english.append(x)
+                clean_chars.append(x)
+            if x == " ":
+                clean_chars.append(x)
+        if len(english) + len(chinese) == 0:
+            logging.warning(f"Delete cut with text has no valid chars : {text}")
+            return False
+
+        words = tokenize_by_CJK_char("".join(clean_chars))
+        for i in range(len(words) - 10):
+            if words[i : i + 10].count(words[i]) == 10:
+                logging.warning(f"Delete cut with text with too much repeats : {text}")
+                return False
+        # word speed, 20 - 600 / minute
+        if duration < len(words) / 600 * 60 or duration > len(words) / 20 * 60:
+            logging.warning(
+                f"Delete cut with audio text mismatch, duration : {duration}s, "
+                f"words : {len(words)}, text : {text}"
+            )
+            return False
+        return True
+
+    try:
+        cut_set = load_manifest_lazy(input_dir / file_name)
+        cut_set = cut_set.filter(_filter_cut)
+        cut_set.to_file(output_dir / file_name)
+    except Exception as e:
+        logging.error(f"Manifest {file_name} failed with error: {e}")
+        os.remove(str(output_dir / file_name))
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    args = get_args()
+
+    input_dir = Path(args.source_dir)
+    output_dir = Path(args.dest_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    cut_files = glob.glob(f"{args.source_dir}/emilia_cuts_{args.subset}.*.jsonl.gz")
+
+    with Pool(max_workers=args.jobs) as pool:
+        futures = [
+            pool.submit(
+                preprocess_emilia, filename.split("/")[-1], input_dir, output_dir
+            )
+            for filename in cut_files
+        ]
+        for f in futures:
+            f.result()
+            f.done()
+    logging.info("Processing done.")
diff --git a/egs/zipvoice/local/tokenizer.py b/egs/zipvoice/local/tokenizer.py
new file mode 120000
index 000000000..024e340cc
--- /dev/null
+++ b/egs/zipvoice/local/tokenizer.py
@@ -0,0 +1 @@
+../zipvoice/tokenizer.py
\ No newline at end of file
diff --git a/egs/zipvoice/local/validate_manifest.py b/egs/zipvoice/local/validate_manifest.py
new file mode 100644
index 000000000..68159ae03
--- /dev/null
+++ b/egs/zipvoice/local/validate_manifest.py
@@ -0,0 +1,70 @@
+#!/usr/bin/env python3
+# Copyright    2022-2023  Xiaomi Corp.        (authors: Fangjun Kuang,
+#                                                       Zengwei Yao)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This script checks the following assumptions of the generated manifest:
+
+- Single supervision per cut
+
+We will add more checks later if needed.
+
+Usage example:
+
+    python3 ./local/validate_manifest.py \
+            ./data/spectrogram/ljspeech_cuts_all.jsonl.gz
+
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+from lhotse import CutSet, load_manifest_lazy
+from lhotse.dataset.speech_synthesis import validate_for_tts
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "manifest",
+        type=Path,
+        help="Path to the manifest file",
+    )
+
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+
+    manifest = args.manifest
+    logging.info(f"Validating {manifest}")
+
+    assert manifest.is_file(), f"{manifest} does not exist"
+    cut_set = load_manifest_lazy(manifest)
+    assert isinstance(cut_set, CutSet), type(cut_set)
+
+    validate_for_tts(cut_set)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    main()
diff --git a/egs/zipvoice/scripts/evaluate.sh b/egs/zipvoice/scripts/evaluate.sh
new file mode 100644
index 000000000..fbc0eed9a
--- /dev/null
+++ b/egs/zipvoice/scripts/evaluate.sh
@@ -0,0 +1,102 @@
+export CUDA_VISIBLE_DEVICES="0"
+export PYTHONWARNINGS=ignore
+export PYTHONPATH=../../:$PYTHONPATH
+
+# Uncomment this if you have trouble connecting to HuggingFace
+# export HF_ENDPOINT=https://hf-mirror.com
+
+start_stage=1
+end_stage=3
+
+# Models used for SIM-o evaluation.
+# SV model wavlm_large_finetune.pth is downloaded from https://github.com/microsoft/UniSpeech/tree/main/downstreams/speaker_verification
+# SSL model wavlm_large.pt is downloaded from https://huggingface.co/s3prl/converted_ckpts/resolve/main/wavlm_large.pt
+sv_model_path=model/UniSpeech/wavlm_large_finetune.pth
+wavlm_model_path=model/s3prl/wavlm_large.pt
+
+# Models used for UTMOS evaluation.
+# wget https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/epoch%3D3-step%3D7459.ckpt -P model/huggingface/utmos/utmos.pt
+# wget https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/wav2vec_small.pt -P model/huggingface/utmos/wav2vec_small.pt
+utmos_model_path=model/huggingface/utmos/utmos.pt
+wav2vec_model_path=model/huggingface/utmos/wav2vec_small.pt
+
+
+if [ $start_stage -le 1 ] && [ $end_stage -ge 1 ]; then 
+
+        echo "=====Evaluate for Seed-TTS test-en======="
+        test_list=testset/test_seedtts_en.tsv
+        wav_path=results/zipvoice_seedtts_en
+
+        echo $wav_path
+        echo "-----Computing SIM-o-----"
+        python3 local/evaluate_sim.py \
+                --sv-model-path ${sv_model_path} \
+                --ssl-model-path ${wavlm_model_path} \
+                --eval-path ${wav_path} \
+                --test-list ${test_list}
+
+        echo "-----Computing WER-----"
+        python3 local/evaluate_wer_seedtts.py \
+                --test-list ${test_list} \
+                --wav-path ${wav_path} \
+                --lang "en" 
+
+        echo "-----Computing UTSMOS-----"
+        python3 local/evaluate_utmos.py \
+                --wav-path ${wav_path} \
+                --utmos-model-path ${utmos_model_path} \
+                --ssl-model-path ${wav2vec_model_path}
+
+fi
+
+if [ $start_stage -le 2 ] && [ $end_stage -ge 2 ]; then 
+        echo "=====Evaluate for Seed-TTS test-zh======="
+        test_list=testset/test_seedtts_zh.tsv
+        wav_path=results/zipvoice_seedtts_zh
+
+        echo $wav_path
+        echo "-----Computing SIM-o-----"
+        python3 local/evaluate_sim.py \
+                --sv-model-path ${sv_model_path} \
+                --ssl-model-path ${wavlm_model_path} \
+                --eval-path ${wav_path} \
+                --test-list ${test_list}
+
+        echo "-----Computing WER-----"
+        python3 local/evaluate_wer_seedtts.py \
+                --test-list ${test_list} \
+                --wav-path ${wav_path} \
+                --lang "zh" 
+
+        echo "-----Computing UTSMOS-----"
+        python3 local/evaluate_utmos.py \
+                --wav-path ${wav_path} \
+                --utmos-model-path ${utmos_model_path} \
+                --ssl-model-path ${wav2vec_model_path}
+fi
+
+if [ $start_stage -le 3 ] && [ $end_stage -ge 3 ]; then 
+        echo "=====Evaluate for Librispeech test-clean======="
+        test_list=testset/test_librispeech_pc_test_clean.tsv
+        wav_path=results/zipvoice_librispeech_test_clean
+
+        echo $wav_path
+        echo "-----Computing SIM-o-----"
+        python3 local/evaluate_sim.py \
+                --sv-model-path ${sv_model_path} \
+                --ssl-model-path ${wavlm_model_path} \
+                --eval-path ${wav_path} \
+                --test-list ${test_list}
+
+        echo "-----Computing WER-----"
+        python3 local/evaluate_wer_hubert.py \
+                --test-list ${test_list} \
+                --wav-path ${wav_path} \
+
+        echo "-----Computing UTSMOS-----"
+        python3 local/evaluate_utmos.py \
+                --wav-path ${wav_path} \
+                --utmos-model-path ${utmos_model_path} \
+                --ssl-model-path ${wav2vec_model_path}
+
+fi
\ No newline at end of file
diff --git a/egs/zipvoice/scripts/prepare_emilia.sh b/egs/zipvoice/scripts/prepare_emilia.sh
new file mode 100755
index 000000000..bf19ed1a5
--- /dev/null
+++ b/egs/zipvoice/scripts/prepare_emilia.sh
@@ -0,0 +1,126 @@
+#!/usr/bin/env bash
+
+# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+set -eou pipefail
+
+stage=0
+stop_stage=5
+sampling_rate=24000
+nj=32
+
+dl_dir=$PWD/download
+
+# All files generated by this script are saved in "data".
+# You can safely remove "data" and rerun this script to regenerate it.
+mkdir -p data
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+log "dl_dir: $dl_dir"
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Download data"
+
+  # Your download directory should look like this:
+  #
+  #    download/Amphion___Emilia
+  #    ├── metafile.yaml
+  #    ├── raw
+  #    │   ├── DE
+  #    │   ├── EN
+  #    │   ├── FR
+  #    │   ├── JA
+  #    │   ├── KO
+  #    │   ├── openemilia_45batches.tar.gz
+  #    │   ├── openemilia_all.tar.gz
+  #    │   └── ZH
+  #    └── README.md
+
+  if [ ! -d $dl_dir/Amphion___Emilia/raw ]; then
+    log "Please refer https://openxlab.org.cn/datasets/Amphion/Emilia to download the dataset."
+    exit(-1)
+  fi
+
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Prepare emilia manifests (EN and ZH only)"
+  # We assume that you have downloaded the Emilia corpus
+  # to $dl_dir/Amphion___Emilia
+  # see stage 0 for the directory structure
+  mkdir -p data/manifests
+  if [ ! -e data/manifests/.emilia.done ]; then
+    lhotse prepare emilia --lang en --num-jobs ${nj} $dl_dir/Amphion___Emilia data/manifests
+    lhotse prepare emilia --lang zh --num-jobs ${nj} $dl_dir/Amphion___Emilia data/manifests
+    touch data/manifests/.emilia.done
+  fi
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Preprocess Emilia dataset, mainly for cleaning"
+  mkdir -p data/manifests/splits_raw
+  if [ ! -e data/manifests/split_raw/.emilia.split.done ]; then
+    lhotse split-lazy data/manifests/emilia_cuts_EN.jsonl.gz data/manifests/splits_raw 10000
+    lhotse split-lazy data/manifests/emilia_cuts_ZH.jsonl.gz data/manifests/splits_raw 10000
+    touch data/manifests/splits_raw/.emilia.split.done
+  fi
+
+  mkdir -p data/manifests/splits
+
+  if [ ! -e data/manifests/splits/.emilia.preprocess.done ]; then
+    python local/preprocess_emilia.py --subset EN
+    python local/preprocess_emilia.py --subset ZH
+    touch data/manifests/splits/.emilia.preprocess.done
+  fi
+
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 3: Extract Fbank for Emilia"
+  mkdir -p data/fbank/emilia_splits
+  if [ ! -e data/fbank/emilia_splits/.emilia.fbank.done ]; then
+    # You can speed up the extraction by distributing splits to multiple machines.
+    for subset in EN ZH; do
+      python local/compute_fbank.py \
+        --source-dir data/manifests/splits \
+        --dest-dir data/fbank/emilia_splits \
+        --dataset emilia \
+        --subset ${subset} \
+        --splits-cuts 1 \
+        --split-begin 0 \
+        --split-end 2000 \
+        --num-jobs ${nj}
+    done
+    touch data/fbank/emilia_splits/.emilia.fbank.done
+  fi
+
+  if [ ! -e data/fbank/emilia_cuts_EN.jsonl.gz ]; then
+    log "Combining EN fbank cuts and spliting EN dev set"
+    gunzip -c data/fbank/emilia_splits/emilia_cuts_EN.*.jsonl.gz > data/fbank/emilia_cuts_EN.jsonl
+    head -n 1500 data/fbank/emilia_cuts_EN.jsonl | gzip -c > data/fbank/emilia_cuts_EN_dev.jsonl.gz
+    sed -i '1,1500d' data/fbank/emilia_cuts_EN.jsonl
+    gzip data/fbank/emilia_cuts_EN.jsonl
+  fi
+
+  if [ ! -e data/fbank/emilia_cuts_ZH.jsonl.gz ]; then
+    log "Combining ZH fbank cuts and spliting ZH dev set"
+    gunzip -c data/fbank/emilia_splits/emilia_cuts_ZH.*.jsonl.gz > data/fbank/emilia_cuts_ZH.jsonl
+    head -n 1500 data/fbank/emilia_cuts_ZH.jsonl | gzip -c > data/fbank/emilia_cuts_ZH_dev.jsonl.gz
+    sed -i '1,1500d' data/fbank/emilia_cuts_ZH.jsonl
+    gzip data/fbank/emilia_cuts_ZH.jsonl
+  fi
+
+fi
+
+if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
+  log "Stage 4: Generate token file"
+  if [ ! -e data/tokens_emilia.txt ]; then
+    ./local/prepare_token_file_emilia.py --tokens data/tokens_emilia.txt
+  fi
+fi
diff --git a/egs/zipvoice/scripts/prepare_libritts.sh b/egs/zipvoice/scripts/prepare_libritts.sh
new file mode 100755
index 000000000..6d643145e
--- /dev/null
+++ b/egs/zipvoice/scripts/prepare_libritts.sh
@@ -0,0 +1,97 @@
+#!/usr/bin/env bash
+
+# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
+export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
+
+set -eou pipefail
+
+stage=0
+stop_stage=5
+sampling_rate=24000
+nj=20
+
+dl_dir=$PWD/download
+
+# All files generated by this script are saved in "data".
+# You can safely remove "data" and rerun this script to regenerate it.
+mkdir -p data
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+log "dl_dir: $dl_dir"
+
+if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
+  log "Stage 0: Download data"
+
+  # If you have pre-downloaded it to /path/to/LibriTTS,
+  # you can create a symlink
+  #
+  #   ln -sfv /path/to/LibriTTS $dl_dir/LibriTTS
+  #
+  if [ ! -d $dl_dir/LibriTTS ]; then
+    lhotse download libritts $dl_dir
+  fi
+
+fi
+
+if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
+  log "Stage 1: Prepare LibriTTS manifest"
+  # We assume that you have downloaded the LibriTTS corpus
+  # to $dl_dir/LibriTTS
+  mkdir -p data/manifests_libritts
+  if [ ! -e data/manifests_libritts/.libritts.done ]; then
+    lhotse prepare libritts --num-jobs ${nj} $dl_dir/LibriTTS data/manifests
+    touch data/manifests/.libritts.done
+  fi
+fi
+
+if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
+  log "Stage 2: Compute Fbank for LibriTTS"
+  mkdir -p data/fbank
+
+  if [ ! -e data/fbank/.libritts.done ]; then
+    for subset in train-clean-100 train-clean-360 train-other-500 dev-clean test-clean; do
+      python local/compute_fbank.py \
+        --source-dir data/manifests \
+        --dest-dir data/fbank \
+        --dataset libritts \
+        --subset ${subset} \
+        --sampling-rate $sampling_rate \
+        --num-jobs ${nj}
+    done
+    touch data/fbank/.libritts.done
+  fi
+
+  # Here we shuffle and combine the train-clean-100, train-clean-360 and
+  # train-other-500 together to form the training set.
+  if [ ! -f data/fbank/libritts_cuts_train-all-shuf.jsonl.gz ]; then
+    cat <(gunzip -c data/fbank/libritts_cuts_train-clean-100.jsonl.gz) \
+      <(gunzip -c data/fbank/libritts_cuts_train-clean-360.jsonl.gz) \
+      <(gunzip -c data/fbank/libritts_cuts_train-other-500.jsonl.gz) | \
+      shuf | gzip -c > data/fbank/libritts_cuts_train-all-shuf.jsonl.gz
+  fi
+
+  if [ ! -f data/fbank/libritts_cuts_train-clean-460.jsonl.gz ]; then
+    cat <(gunzip -c data/fbank/libritts_cuts_train-clean-100.jsonl.gz) \
+      <(gunzip -c data/fbank/libritts_cuts_train-clean-360.jsonl.gz) | \
+      shuf | gzip -c > data/fbank/libritts_cuts_train-clean-460.jsonl.gz
+  fi
+
+  if [ ! -e data/fbank/.libritts-validated.done ]; then
+    log "Validating data/fbank for LibriTTS"
+    ./local/validate_manifest.py \
+      data/fbank/libritts_cuts_train-all-shuf.jsonl.gz
+    touch data/fbank/.libritts-validated.done
+  fi
+fi
+
+if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
+  log "Stage 3: Generate token file"
+  if [ ! -e data/tokens_libritts.txt ]; then
+    ./local/prepare_token_file_libritts.py --tokens data/tokens_libritts.txt
+  fi
+fi
diff --git a/egs/zipvoice/zipvoice/checkpoint.py b/egs/zipvoice/zipvoice/checkpoint.py
new file mode 100644
index 000000000..e3acd57dd
--- /dev/null
+++ b/egs/zipvoice/zipvoice/checkpoint.py
@@ -0,0 +1,142 @@
+# Copyright  2021-2022  Xiaomi Corporation  (authors: Fangjun Kuang,
+#                                                     Zengwei Yao)
+#
+# See ../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import logging
+from pathlib import Path
+from typing import Any, Dict, Optional, Union
+
+import torch
+import torch.nn as nn
+from lhotse.dataset.sampling.base import CutSampler
+from torch.cuda.amp import GradScaler
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.optim import Optimizer
+
+# use duck typing for LRScheduler since we have different possibilities, see
+# our class LRScheduler.
+LRSchedulerType = object
+
+
+def save_checkpoint(
+    filename: Path,
+    model: Union[nn.Module, DDP],
+    model_avg: Optional[nn.Module] = None,
+    model_ema: Optional[nn.Module] = None,
+    params: Optional[Dict[str, Any]] = None,
+    optimizer: Optional[Optimizer] = None,
+    scheduler: Optional[LRSchedulerType] = None,
+    scaler: Optional[GradScaler] = None,
+    sampler: Optional[CutSampler] = None,
+    rank: int = 0,
+) -> None:
+    """Save training information to a file.
+
+    Args:
+      filename:
+        The checkpoint filename.
+      model:
+        The model to be saved. We only save its `state_dict()`.
+      model_avg:
+        The stored model averaged from the start of training.
+      model_ema:
+        The EMA version of model.
+      params:
+        User defined parameters, e.g., epoch, loss.
+      optimizer:
+        The optimizer to be saved. We only save its `state_dict()`.
+      scheduler:
+        The scheduler to be saved. We only save its `state_dict()`.
+      scalar:
+        The GradScaler to be saved. We only save its `state_dict()`.
+      sampler:
+        The sampler used in the labeled training dataset. We only
+          save its `state_dict()`.
+      rank:
+        Used in DDP. We save checkpoint only for the node whose
+          rank is 0.
+    Returns:
+      Return None.
+    """
+    if rank != 0:
+        return
+
+    logging.info(f"Saving checkpoint to {filename}")
+
+    if isinstance(model, DDP):
+        model = model.module
+
+    checkpoint = {
+        "model": model.state_dict(),
+        "optimizer": optimizer.state_dict() if optimizer is not None else None,
+        "scheduler": scheduler.state_dict() if scheduler is not None else None,
+        "grad_scaler": scaler.state_dict() if scaler is not None else None,
+        "sampler": sampler.state_dict() if sampler is not None else None,
+    }
+
+    if model_avg is not None:
+        checkpoint["model_avg"] = model_avg.to(torch.float32).state_dict()
+    if model_ema is not None:
+        checkpoint["model_ema"] = model_ema.to(torch.float32).state_dict()
+
+    if params:
+        for k, v in params.items():
+            assert k not in checkpoint
+            checkpoint[k] = v
+
+    torch.save(checkpoint, filename)
+
+
+def load_checkpoint(
+    filename: Path,
+    model: Optional[nn.Module] = None,
+    model_avg: Optional[nn.Module] = None,
+    model_ema: Optional[nn.Module] = None,
+    strict: bool = False,
+) -> Dict[str, Any]:
+    logging.info(f"Loading checkpoint from {filename}")
+    checkpoint = torch.load(filename, map_location="cpu")
+
+    if model is not None:
+
+        if next(iter(checkpoint["model"])).startswith("module."):
+            logging.info("Loading checkpoint saved by DDP")
+
+            dst_state_dict = model.state_dict()
+            src_state_dict = checkpoint["model"]
+            for key in dst_state_dict.keys():
+                src_key = "{}.{}".format("module", key)
+                dst_state_dict[key] = src_state_dict.pop(src_key)
+            assert len(src_state_dict) == 0
+            model.load_state_dict(dst_state_dict, strict=strict)
+        else:
+            logging.info("Loading checkpoint")
+            model.load_state_dict(checkpoint["model"], strict=strict)
+
+        checkpoint.pop("model")
+
+    if model_avg is not None and "model_avg" in checkpoint:
+        logging.info("Loading averaged model")
+        model_avg.load_state_dict(checkpoint["model_avg"], strict=strict)
+        checkpoint.pop("model_avg")
+
+    if model_ema is not None and "model_ema" in checkpoint:
+        logging.info("Loading ema model")
+        model_ema.load_state_dict(checkpoint["model_ema"], strict=strict)
+        checkpoint.pop("model_ema")
+
+    return checkpoint
diff --git a/egs/zipvoice/zipvoice/feature.py b/egs/zipvoice/zipvoice/feature.py
new file mode 100644
index 000000000..e7d484d10
--- /dev/null
+++ b/egs/zipvoice/zipvoice/feature.py
@@ -0,0 +1,135 @@
+#!/usr/bin/env python3
+# Copyright         2024  Xiaomi Corp.        (authors: Han Zhu)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+from typing import Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torchaudio
+from lhotse.features.base import FeatureExtractor, register_extractor
+from lhotse.utils import Seconds, compute_num_frames
+
+
+class MelSpectrogramFeatures(nn.Module):
+    def __init__(
+        self,
+        sampling_rate=24000,
+        n_mels=100,
+        n_fft=1024,
+        hop_length=256,
+    ):
+        super().__init__()
+
+        self.mel_spec = torchaudio.transforms.MelSpectrogram(
+            sample_rate=sampling_rate,
+            n_fft=n_fft,
+            hop_length=hop_length,
+            n_mels=n_mels,
+            center=True,
+            power=1,
+        )
+
+    def forward(self, inp):
+        assert len(inp.shape) == 2
+
+        mel = self.mel_spec(inp)
+        logmel = mel.clamp(min=1e-7).log()
+        return logmel
+
+
+@dataclass
+class TorchAudioFbankConfig:
+    sampling_rate: int
+    n_mels: int
+    n_fft: int
+    hop_length: int
+
+
+@register_extractor
+class TorchAudioFbank(FeatureExtractor):
+
+    name = "TorchAudioFbank"
+    config_type = TorchAudioFbankConfig
+
+    def __init__(self, config):
+        super().__init__(config=config)
+
+    def _feature_fn(self, sample):
+        fbank = MelSpectrogramFeatures(
+            sampling_rate=self.config.sampling_rate,
+            n_mels=self.config.n_mels,
+            n_fft=self.config.n_fft,
+            hop_length=self.config.hop_length,
+        )
+
+        return fbank(sample)
+
+    @property
+    def device(self) -> Union[str, torch.device]:
+        return self.config.device
+
+    def feature_dim(self, sampling_rate: int) -> int:
+        return self.config.n_mels
+
+    def extract(
+        self,
+        samples: Union[np.ndarray, torch.Tensor],
+        sampling_rate: int,
+    ) -> Union[np.ndarray, torch.Tensor]:
+        # Check for sampling rate compatibility.
+        expected_sr = self.config.sampling_rate
+        assert sampling_rate == expected_sr, (
+            f"Mismatched sampling rate: extractor expects {expected_sr}, "
+            f"got {sampling_rate}"
+        )
+        is_numpy = False
+        if not isinstance(samples, torch.Tensor):
+            samples = torch.from_numpy(samples)
+            is_numpy = True
+
+        if len(samples.shape) == 1:
+            samples = samples.unsqueeze(0)
+        assert samples.ndim == 2, samples.shape
+        assert samples.shape[0] == 1, samples.shape
+
+        mel = self._feature_fn(samples).squeeze().t()
+
+        assert mel.ndim == 2, mel.shape
+        assert mel.shape[1] == self.config.n_mels, mel.shape
+
+        num_frames = compute_num_frames(
+            samples.shape[1] / sampling_rate, self.frame_shift, sampling_rate
+        )
+
+        if mel.shape[0] > num_frames:
+            mel = mel[:num_frames]
+        elif mel.shape[0] < num_frames:
+            mel = mel.unsqueeze(0)
+            mel = torch.nn.functional.pad(
+                mel, (0, 0, 0, num_frames - mel.shape[1]), mode="replicate"
+            ).squeeze(0)
+
+        if is_numpy:
+            return mel.cpu().numpy()
+        else:
+            return mel
+
+    @property
+    def frame_shift(self) -> Seconds:
+        return self.config.hop_length / self.config.sampling_rate
diff --git a/egs/zipvoice/zipvoice/generate_averaged_model.py b/egs/zipvoice/zipvoice/generate_averaged_model.py
new file mode 100644
index 000000000..e1b7ca7c6
--- /dev/null
+++ b/egs/zipvoice/zipvoice/generate_averaged_model.py
@@ -0,0 +1,209 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2022 Xiaomi Corporation
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Usage:
+This script loads checkpoints and averages them.
+
+(1) Average ZipVoice models before distill:
+    python3 ./zipvoice/generate_averaged_model.py \
+        --epoch 11 \
+        --avg 4 \
+        --distill 0 \
+        --token-file data/tokens_emilia.txt \
+        --exp-dir ./zipvoice/exp_zipvoice
+
+    It will generate a file `epoch-11-avg-14.pt` in the given `exp_dir`.
+    You can later load it by `torch.load("epoch-11-avg-4.pt")`.
+
+(2) Average ZipVoice-Distill models (the first stage model):
+
+    python3 ./zipvoice/generate_averaged_model.py \
+        --iter 60000 \
+        --avg 7 \
+        --distill 1 \
+        --token-file data/tokens_emilia.txt \
+        --exp-dir ./zipvoice/exp_zipvoice_distill_1stage
+"""
+
+import argparse
+from pathlib import Path
+
+import torch
+from model import get_distill_model, get_model
+from tokenizer import TokenizerEmilia, TokenizerLibriTTS
+from train_flow import add_model_arguments, get_params
+
+from icefall.checkpoint import average_checkpoints_with_averaged_model, find_checkpoints
+from icefall.utils import str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=11,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=4,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' or --iter",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="zipvoice/exp_zipvoice",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--distill",
+        type=str2bool,
+        default=False,
+        help="Whether to use distill model. ",
+    )
+
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="emilia",
+        choices=["emilia", "libritts"],
+        help="The used training dataset for the model to inference",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    if params.dataset == "emilia":
+        tokenizer = TokenizerEmilia(
+            token_file=params.token_file, token_type=params.token_type
+        )
+    elif params.dataset == "libritts":
+        tokenizer = TokenizerLibriTTS(
+            token_file=params.token_file, token_type=params.token_type
+        )
+
+    params.vocab_size = tokenizer.vocab_size
+    params.pad_id = tokenizer.pad_id
+
+    params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    print("Script started")
+
+    params.device = torch.device("cpu")
+    print(f"Device: {params.device}")
+
+    print("About to create model")
+    if params.distill:
+        model = get_distill_model(params)
+    else:
+        model = get_model(params)
+
+    if params.iter > 0:
+        filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+            : params.avg + 1
+        ]
+        if len(filenames) == 0:
+            raise ValueError(
+                f"No checkpoints found for" f" --iter {params.iter}, --avg {params.avg}"
+            )
+        elif len(filenames) < params.avg + 1:
+            raise ValueError(
+                f"Not enough checkpoints ({len(filenames)}) found for"
+                f" --iter {params.iter}, --avg {params.avg}"
+            )
+        filename_start = filenames[-1]
+        filename_end = filenames[0]
+        print(
+            "Calculating the averaged model over iteration checkpoints"
+            f" from {filename_start} (excluded) to {filename_end}"
+        )
+        model.to(params.device)
+        model.load_state_dict(
+            average_checkpoints_with_averaged_model(
+                filename_start=filename_start,
+                filename_end=filename_end,
+                device=params.device,
+            ),
+            strict=True,
+        )
+    else:
+        assert params.avg > 0, params.avg
+        start = params.epoch - params.avg
+        assert start >= 1, start
+        filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+        filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+        print(
+            f"Calculating the averaged model over epoch range from "
+            f"{start} (excluded) to {params.epoch}"
+        )
+        model.to(params.device)
+        model.load_state_dict(
+            average_checkpoints_with_averaged_model(
+                filename_start=filename_start,
+                filename_end=filename_end,
+                device=params.device,
+            ),
+            strict=True,
+        )
+    if params.iter > 0:
+        filename = params.exp_dir / f"iter-{params.iter}-avg-{params.avg}.pt"
+    else:
+        filename = params.exp_dir / f"epoch-{params.epoch}-avg-{params.avg}.pt"
+    torch.save({"model": model.state_dict()}, filename)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    print(f"Number of model parameters: {num_param}")
+
+    print("Done!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/egs/zipvoice/zipvoice/infer.py b/egs/zipvoice/zipvoice/infer.py
new file mode 100644
index 000000000..2819d3c85
--- /dev/null
+++ b/egs/zipvoice/zipvoice/infer.py
@@ -0,0 +1,586 @@
+#!/usr/bin/env python3
+# Copyright         2024  Xiaomi Corp.        (authors: Wei Kang
+#                                                       Han Zhu)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script loads checkpoints to generate waveforms.
+This script is supposed to be used with the model trained by yourself.
+If you want to use the pre-trained checkpoints provided by us, please refer to zipvoice_infer.py.
+
+(1) Usage with a pre-trained checkpoint:
+
+    (a) ZipVoice model before distill:
+        python3 zipvoice/infer.py \
+            --checkpoint zipvoice/exp_zipvoice/epoch-11-avg-4.pt \
+            --distill 0 \
+            --token-file "data/tokens_emilia.txt" \
+            --test-list test.tsv \
+            --res-dir results/test \
+            --num-step 16 \
+            --guidance-scale 1
+
+    (b) ZipVoice-Distill:
+        python3 zipvoice/infer.py \
+            --checkpoint zipvoice/exp_zipvoice_distill/checkpoint-2000.pt \
+            --distill 1 \
+            --token-file "data/tokens_emilia.txt" \
+            --test-list test.tsv \
+            --res-dir results/test_distill \
+            --num-step 8 \
+            --guidance-scale 3
+
+(2) Usage with a directory of checkpoints (may requires checkpoint averaging):
+
+    (a) ZipVoice model before distill:
+        python3 flow_match/infer.py \
+            --exp-dir zipvoice/exp_zipvoice \
+            --epoch 11 \
+            --avg 4 \
+            --distill 0 \
+            --token-file "data/tokens_emilia.txt" \
+            --test-list test.tsv \
+            --res-dir results \
+            --num-step 16 \
+            --guidance-scale 1
+
+    (b) ZipVoice-Distill:
+        python3 flow_match/infer.py \
+            --exp-dir zipvoice/exp_zipvoice_distill/ \
+            --iter 2000 \
+            --avg 0 \
+            --distill 1 \
+            --token-file "data/tokens_emilia.txt" \
+            --test-list test.tsv \
+            --res-dir results \
+            --num-step 8 \
+            --guidance-scale 3
+"""
+
+
+import argparse
+import datetime as dt
+import logging
+import os
+from pathlib import Path
+from typing import Optional
+
+import numpy as np
+import soundfile as sf
+import torch
+import torch.nn as nn
+import torchaudio
+from checkpoint import load_checkpoint
+from feature import TorchAudioFbank, TorchAudioFbankConfig
+from lhotse.utils import fix_random_seed
+from model import get_distill_model, get_model
+from tokenizer import TokenizerEmilia, TokenizerLibriTTS
+from train_flow import add_model_arguments, get_params
+from vocos import Vocos
+
+from icefall.checkpoint import average_checkpoints_with_averaged_model, find_checkpoints
+from icefall.utils import AttributeDict, setup_logger, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        default=None,
+        help="The checkpoint for inference. "
+        "If it is None, will use checkpoints under exp_dir",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="zipvoice/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=0,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        You can specify --avg to use more checkpoints for model averaging.""",
+    )
+
+    parser.add_argument(
+        "--iter",
+        type=int,
+        default=0,
+        help="""If positive, --epoch is ignored and it
+        will use the checkpoint exp_dir/checkpoint-iter.pt.
+        You can specify --avg to use more checkpoints for model averaging.
+        """,
+    )
+
+    parser.add_argument(
+        "--avg",
+        type=int,
+        default=4,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' or '--iter', avg=0 means no avg",
+    )
+
+    parser.add_argument(
+        "--vocoder-path",
+        type=str,
+        default=None,
+        help="The local vocos vocoder path, downloaded from huggingface, "
+        "will download the vocodoer from huggingface if it is None.",
+    )
+
+    parser.add_argument(
+        "--distill",
+        type=str2bool,
+        default=False,
+        help="Whether it is a distilled TTS model.",
+    )
+
+    parser.add_argument(
+        "--test-list",
+        type=str,
+        default=None,
+        help="The list of prompt speech, prompt_transcription, "
+        "and text to synthesize in the format of "
+        "'{wav_name}\t{prompt_transcription}\t{prompt_wav}\t{text}'.",
+    )
+
+    parser.add_argument(
+        "--res-dir",
+        type=str,
+        default="results",
+        help="Path name of the generated wavs dir",
+    )
+
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="emilia",
+        choices=["emilia", "libritts"],
+        help="The used training dataset for the model to inference",
+    )
+
+    parser.add_argument(
+        "--guidance-scale",
+        type=float,
+        default=1.0,
+        help="The scale of classifier-free guidance during inference.",
+    )
+
+    parser.add_argument(
+        "--num-step",
+        type=int,
+        default=16,
+        help="The number of sampling steps.",
+    )
+
+    parser.add_argument(
+        "--feat-scale",
+        type=float,
+        default=0.1,
+        help="The scale factor of fbank feature",
+    )
+
+    parser.add_argument(
+        "--speed",
+        type=float,
+        default=1.0,
+        help="Control speech speed, 1.0 means normal, >1.0 means speed up",
+    )
+
+    parser.add_argument(
+        "--t-shift",
+        type=float,
+        default=0.5,
+        help="Shift t to smaller ones if t_shift < 1.0",
+    )
+
+    parser.add_argument(
+        "--target-rms",
+        type=float,
+        default=0.1,
+        help="Target speech normalization rms value",
+    )
+
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=666,
+        help="Random seed",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def get_vocoder(vocos_local_path: Optional[str] = None):
+    if vocos_local_path:
+        vocos_local_path = "model/huggingface/vocos-mel-24khz/"
+        vocoder = Vocos.from_hparams(f"{vocos_local_path}/config.yaml")
+        state_dict = torch.load(
+            f"{vocos_local_path}/pytorch_model.bin",
+            weights_only=True,
+            map_location="cpu",
+        )
+        vocoder.load_state_dict(state_dict)
+    else:
+        vocoder = Vocos.from_pretrained("charactr/vocos-mel-24khz")
+    return vocoder
+
+
+def generate_sentence(
+    save_path: str,
+    prompt_text: str,
+    prompt_wav: str,
+    text: str,
+    model: nn.Module,
+    vocoder: nn.Module,
+    tokenizer: TokenizerEmilia,
+    feature_extractor: TorchAudioFbank,
+    device: torch.device,
+    num_step: int = 16,
+    guidance_scale: float = 1.0,
+    speed: float = 1.0,
+    t_shift: float = 0.5,
+    target_rms: float = 0.1,
+    feat_scale: float = 0.1,
+    sampling_rate: int = 24000,
+):
+    """
+    Generate waveform of a text based on a given prompt
+        waveform and its transcription.
+
+    Args:
+        save_path (str): Path to save the generated wav.
+        prompt_text (str): Transcription of the prompt wav.
+        prompt_wav (str): Path to the prompt wav file.
+        text (str): Text to be synthesized into a waveform.
+        model (nn.Module): The model used for generation.
+        vocoder (nn.Module): The vocoder used to convert features to waveforms.
+        tokenizer (TokenizerEmilia): The tokenizer used to convert text to tokens.
+        feature_extractor (TorchAudioFbank): The feature extractor used to
+            extract acoustic features.
+        device (torch.device): The device on which computations are performed.
+        num_step (int, optional): Number of steps for decoding. Defaults to 16.
+        guidance_scale (float, optional): Scale for classifier-free guidance.
+            Defaults to 1.0.
+        speed (float, optional): Speed control. Defaults to 1.0.
+        t_shift (float, optional): Time shift. Defaults to 0.5.
+        target_rms (float, optional): Target RMS for waveform normalization.
+            Defaults to 0.1.
+        feat_scale (float, optional): Scale for features.
+            Defaults to 0.1.
+        sampling_rate (int, optional): Sampling rate for the waveform.
+            Defaults to 24000.
+    Returns:
+        metrics (dict): Dictionary containing time and real-time
+            factor metrics for processing.
+    """
+    # Convert text to tokens
+    tokens = tokenizer.texts_to_token_ids([text])
+    prompt_tokens = tokenizer.texts_to_token_ids([prompt_text])
+
+    # Load and preprocess prompt wav
+    prompt_wav, prompt_sampling_rate = torchaudio.load(prompt_wav)
+    prompt_rms = torch.sqrt(torch.mean(torch.square(prompt_wav)))
+    if prompt_rms < target_rms:
+        prompt_wav = prompt_wav * target_rms / prompt_rms
+
+    if prompt_sampling_rate != sampling_rate:
+        resampler = torchaudio.transforms.Resample(
+            orig_freq=prompt_sampling_rate, new_freq=sampling_rate
+        )
+        prompt_wav = resampler(prompt_wav)
+
+    # Extract features from prompt wav
+    prompt_features = feature_extractor.extract(
+        prompt_wav, sampling_rate=sampling_rate
+    ).to(device)
+    prompt_features = prompt_features.unsqueeze(0) * feat_scale
+    prompt_features_lens = torch.tensor([prompt_features.size(1)], device=device)
+
+    # Start timing
+    start_t = dt.datetime.now()
+
+    # Generate features
+    (
+        pred_features,
+        pred_features_lens,
+        pred_prompt_features,
+        pred_prompt_features_lens,
+    ) = model.sample(
+        tokens=tokens,
+        prompt_tokens=prompt_tokens,
+        prompt_features=prompt_features,
+        prompt_features_lens=prompt_features_lens,
+        speed=speed,
+        t_shift=t_shift,
+        duration="predict",
+        num_step=num_step,
+        guidance_scale=guidance_scale,
+    )
+
+    # Postprocess predicted features
+    pred_features = pred_features.permute(0, 2, 1) / feat_scale  # (B, C, T)
+
+    # Start vocoder processing
+    start_vocoder_t = dt.datetime.now()
+    wav = vocoder.decode(pred_features).squeeze(1).clamp(-1, 1)
+
+    # Calculate processing times and real-time factors
+    t = (dt.datetime.now() - start_t).total_seconds()
+    t_no_vocoder = (start_vocoder_t - start_t).total_seconds()
+    t_vocoder = (dt.datetime.now() - start_vocoder_t).total_seconds()
+    wav_seconds = wav.shape[-1] / sampling_rate
+    rtf = t / wav_seconds
+    rtf_no_vocoder = t_no_vocoder / wav_seconds
+    rtf_vocoder = t_vocoder / wav_seconds
+    metrics = {
+        "t": t,
+        "t_no_vocoder": t_no_vocoder,
+        "t_vocoder": t_vocoder,
+        "wav_seconds": wav_seconds,
+        "rtf": rtf,
+        "rtf_no_vocoder": rtf_no_vocoder,
+        "rtf_vocoder": rtf_vocoder,
+    }
+
+    # Adjust wav volume if necessary
+    if prompt_rms < target_rms:
+        wav = wav * prompt_rms / target_rms
+    wav = wav[0].cpu().numpy()
+    sf.write(save_path, wav, sampling_rate)
+
+    return metrics
+
+
+def generate(
+    params: AttributeDict,
+    model: nn.Module,
+    vocoder: nn.Module,
+    tokenizer: TokenizerEmilia,
+):
+    total_t = []
+    total_t_no_vocoder = []
+    total_t_vocoder = []
+    total_wav_seconds = []
+
+    config = TorchAudioFbankConfig(
+        sampling_rate=params.sampling_rate,
+        n_mels=100,
+        n_fft=1024,
+        hop_length=256,
+    )
+    feature_extractor = TorchAudioFbank(config)
+
+    with open(params.test_list, "r") as fr:
+        lines = fr.readlines()
+
+    for i, line in enumerate(lines):
+        wav_name, prompt_text, prompt_wav, text = line.strip().split("\t")
+        save_path = f"{params.wav_dir}/{wav_name}.wav"
+        metrics = generate_sentence(
+            save_path=save_path,
+            prompt_text=prompt_text,
+            prompt_wav=prompt_wav,
+            text=text,
+            model=model,
+            vocoder=vocoder,
+            tokenizer=tokenizer,
+            feature_extractor=feature_extractor,
+            device=params.device,
+            num_step=params.num_step,
+            guidance_scale=params.guidance_scale,
+            speed=params.speed,
+            t_shift=params.t_shift,
+            target_rms=params.target_rms,
+            feat_scale=params.feat_scale,
+            sampling_rate=params.sampling_rate,
+        )
+        print(f"[Sentence: {i}] RTF: {metrics['rtf']:.4f}")
+        total_t.append(metrics["t"])
+        total_t_no_vocoder.append(metrics["t_no_vocoder"])
+        total_t_vocoder.append(metrics["t_vocoder"])
+        total_wav_seconds.append(metrics["wav_seconds"])
+
+    print(f"Average RTF: " f"{np.sum(total_t)/np.sum(total_wav_seconds):.4f}")
+    print(
+        f"Average RTF w/o vocoder: "
+        f"{np.sum(total_t_no_vocoder)/np.sum(total_wav_seconds):.4f}"
+    )
+    print(
+        f"Average RTF vocoder: "
+        f"{np.sum(total_t_vocoder)/np.sum(total_wav_seconds):.4f}"
+    )
+
+
+@torch.inference_mode()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    if params.iter > 0:
+        params.suffix = (
+            f"wavs-iter-{params.iter}-avg"
+            f"-{params.avg}-step-{params.num_step}-scale-{params.guidance_scale}"
+        )
+    elif params.epoch > 0:
+        params.suffix = (
+            f"wavs-epoch-{params.epoch}-avg"
+            f"-{params.avg}-step-{params.num_step}-scale-{params.guidance_scale}"
+        )
+    else:
+        params.suffix = "wavs"
+
+    setup_logger(f"{params.res_dir}/log-infer-{params.suffix}")
+    logging.info("Decoding started")
+
+    if torch.cuda.is_available():
+        params.device = torch.device("cuda", 0)
+    else:
+        params.device = torch.device("cpu")
+
+    logging.info(f"Device: {params.device}")
+
+    if params.dataset == "emilia":
+        tokenizer = TokenizerEmilia(
+            token_file=params.token_file, token_type=params.token_type
+        )
+    elif params.dataset == "libritts":
+        tokenizer = TokenizerLibriTTS(
+            token_file=params.token_file, token_type=params.token_type
+        )
+
+    params.vocab_size = tokenizer.vocab_size
+    params.pad_id = tokenizer.pad_id
+
+    logging.info(params)
+    fix_random_seed(params.seed)
+
+    logging.info("About to create model")
+    if params.distill:
+        model = get_distill_model(params)
+    else:
+        model = get_model(params)
+
+    if params.checkpoint:
+        load_checkpoint(params.checkpoint, model, strict=True)
+    else:
+        if params.avg == 0:
+            if params.iter > 0:
+                load_checkpoint(
+                    f"{params.exp_dir}/checkpoint-{params.iter}.pt", model, strict=True
+                )
+            else:
+                load_checkpoint(
+                    f"{params.exp_dir}/epoch-{params.epoch}.pt", model, strict=True
+                )
+        else:
+            if params.iter > 0:
+                filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                    : params.avg + 1
+                ]
+                if len(filenames) == 0:
+                    raise ValueError(
+                        f"No checkpoints found for"
+                        f" --iter {params.iter}, --avg {params.avg}"
+                    )
+                elif len(filenames) < params.avg + 1:
+                    raise ValueError(
+                        f"Not enough checkpoints ({len(filenames)}) found for"
+                        f" --iter {params.iter}, --avg {params.avg}"
+                    )
+                filename_start = filenames[-1]
+                filename_end = filenames[0]
+                logging.info(
+                    "Calculating the averaged model over iteration checkpoints"
+                    f" from {filename_start} (excluded) to {filename_end}"
+                )
+                model.to(params.device)
+                model.load_state_dict(
+                    average_checkpoints_with_averaged_model(
+                        filename_start=filename_start,
+                        filename_end=filename_end,
+                        device=params.device,
+                    ),
+                    strict=True,
+                )
+            else:
+                assert params.avg > 0, params.avg
+                start = params.epoch - params.avg
+                assert start >= 1, start
+                filename_start = f"{params.exp_dir}/epoch-{start}.pt"
+                filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
+                logging.info(
+                    f"Calculating the averaged model over epoch range from "
+                    f"{start} (excluded) to {params.epoch}"
+                )
+                model.to(params.device)
+                model.load_state_dict(
+                    average_checkpoints_with_averaged_model(
+                        filename_start=filename_start,
+                        filename_end=filename_end,
+                        device=params.device,
+                    ),
+                    strict=True,
+                )
+
+    model = model.to(params.device)
+    model.eval()
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    vocoder = get_vocoder(params.vocoder_path)
+    vocoder = vocoder.to(params.device)
+    vocoder.eval()
+    num_param = sum([p.numel() for p in vocoder.parameters()])
+    logging.info(f"Number of vocoder parameters: {num_param}")
+
+    params.wav_dir = f"{params.res_dir}/{params.suffix}"
+    os.makedirs(params.wav_dir, exist_ok=True)
+
+    assert (
+        params.test_list is not None
+    ), "Please provide --test-list for speech synthesize."
+    generate(
+        params=params,
+        model=model,
+        vocoder=vocoder,
+        tokenizer=tokenizer,
+    )
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
+    main()
diff --git a/egs/zipvoice/zipvoice/model.py b/egs/zipvoice/zipvoice/model.py
new file mode 100644
index 000000000..25c7973b2
--- /dev/null
+++ b/egs/zipvoice/zipvoice/model.py
@@ -0,0 +1,578 @@
+# Copyright    2024    Xiaomi Corp.        (authors:  Wei Kang
+#                                                     Han Zhu)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import List, Optional
+
+import torch
+import torch.nn as nn
+from scaling import ScheduledFloat
+from solver import EulerSolver
+from torch.nn.parallel import DistributedDataParallel as DDP
+from utils import (
+    AttributeDict,
+    condition_time_mask,
+    get_tokens_index,
+    make_pad_mask,
+    pad_labels,
+    prepare_avg_tokens_durations,
+    to_int_tuple,
+)
+from zipformer import TTSZipformer
+
+
+def get_model(params: AttributeDict) -> nn.Module:
+    """Get the normal TTS model."""
+
+    fm_decoder = get_fm_decoder_model(params)
+    text_encoder = get_text_encoder_model(params)
+
+    model = TtsModel(
+        fm_decoder=fm_decoder,
+        text_encoder=text_encoder,
+        text_embed_dim=params.text_embed_dim,
+        feat_dim=params.feat_dim,
+        vocab_size=params.vocab_size,
+        pad_id=params.pad_id,
+    )
+    return model
+
+
+def get_distill_model(params: AttributeDict) -> nn.Module:
+    """Get the distillation TTS model."""
+
+    fm_decoder = get_fm_decoder_model(params, distill=True)
+    text_encoder = get_text_encoder_model(params)
+
+    model = DistillTTSModelTrainWrapper(
+        fm_decoder=fm_decoder,
+        text_encoder=text_encoder,
+        text_embed_dim=params.text_embed_dim,
+        feat_dim=params.feat_dim,
+        vocab_size=params.vocab_size,
+        pad_id=params.pad_id,
+    )
+    return model
+
+
+def get_fm_decoder_model(params: AttributeDict, distill: bool = False) -> nn.Module:
+    """Get the Zipformer-based FM decoder model."""
+
+    encoder = TTSZipformer(
+        in_dim=params.feat_dim * 3,
+        out_dim=params.feat_dim,
+        downsampling_factor=to_int_tuple(params.fm_decoder_downsampling_factor),
+        num_encoder_layers=to_int_tuple(params.fm_decoder_num_layers),
+        cnn_module_kernel=to_int_tuple(params.fm_decoder_cnn_module_kernel),
+        encoder_dim=params.fm_decoder_dim,
+        feedforward_dim=params.fm_decoder_feedforward_dim,
+        num_heads=params.fm_decoder_num_heads,
+        query_head_dim=params.query_head_dim,
+        pos_head_dim=params.pos_head_dim,
+        value_head_dim=params.value_head_dim,
+        pos_dim=params.pos_dim,
+        dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)),
+        warmup_batches=4000.0,
+        use_time_embed=True,
+        time_embed_dim=192,
+        use_guidance_scale_embed=distill,
+    )
+    return encoder
+
+
+def get_text_encoder_model(params: AttributeDict) -> nn.Module:
+    """Get the Zipformer-based text encoder model."""
+
+    encoder = TTSZipformer(
+        in_dim=params.text_embed_dim,
+        out_dim=params.feat_dim,
+        downsampling_factor=to_int_tuple(params.text_encoder_downsampling_factor),
+        num_encoder_layers=to_int_tuple(params.text_encoder_num_layers),
+        cnn_module_kernel=to_int_tuple(params.text_encoder_cnn_module_kernel),
+        encoder_dim=params.text_encoder_dim,
+        feedforward_dim=params.text_encoder_feedforward_dim,
+        num_heads=params.text_encoder_num_heads,
+        query_head_dim=params.query_head_dim,
+        pos_head_dim=params.pos_head_dim,
+        value_head_dim=params.value_head_dim,
+        pos_dim=params.pos_dim,
+        dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)),
+        warmup_batches=4000.0,
+        use_time_embed=False,
+    )
+    return encoder
+
+
+class TtsModel(nn.Module):
+    """The normal TTS model."""
+
+    def __init__(
+        self,
+        fm_decoder: nn.Module,
+        text_encoder: nn.Module,
+        text_embed_dim: int,
+        feat_dim: int,
+        vocab_size: int,
+        pad_id: int = 0,
+    ):
+        """
+        Args:
+            fm_decoder: the flow-matching encoder model, inputs are the
+                input condition embeddings and noisy acoustic features,
+                outputs are better acoustic features.
+            text_encoder: the text encoder model. input are text
+                embeddings, output are contextualized text embeddings.
+            text_embed_dim: dimension of text embedding.
+            feat_dim: dimension of acoustic features.
+            vocab_size: vocabulary size.
+            pad_id: padding id.
+        """
+        super().__init__()
+
+        self.feat_dim = feat_dim
+        self.text_embed_dim = text_embed_dim
+        self.pad_id = pad_id
+
+        self.fm_decoder = fm_decoder
+
+        self.text_encoder = text_encoder
+
+        self.embed = nn.Embedding(vocab_size, text_embed_dim)
+
+        self.distill = False
+
+    def forward_fm_decoder(
+        self,
+        t: torch.Tensor,
+        xt: torch.Tensor,
+        text_condition: torch.Tensor,
+        speech_condition: torch.Tensor,
+        padding_mask: Optional[torch.Tensor] = None,
+        guidance_scale: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Compute velocity.
+        Args:
+            t:  A tensor of shape (N, 1, 1) or a tensor of a float,
+                in the range of (0, 1).
+            xt: the input of the current timestep, including condition
+                embeddings and noisy acoustic features.
+            text_condition: the text condition embeddings, with the
+                shape (batch, seq_len, emb_dim).
+            speech_condition: the speech condition embeddings, with the
+                shape (batch, seq_len, emb_dim).
+            padding_mask: The mask for padding, True means masked
+                position, with the shape (N, T).
+            guidance_scale: The guidance scale in classifier-free guidance,
+                which is a tensor of shape (N, 1, 1) or a tensor of a float.
+
+        Returns:
+            predicted velocity, with the shape (batch, seq_len, emb_dim).
+        """
+        assert t.dim() in (0, 3)
+        # Handle t with the shape (N, 1, 1):
+        # squeeze the last dimension if it's size is 1.
+        while t.dim() > 1 and t.size(-1) == 1:
+            t = t.squeeze(-1)
+        if guidance_scale is not None:
+            while guidance_scale.dim() > 1 and guidance_scale.size(-1) == 1:
+                guidance_scale = guidance_scale.squeeze(-1)
+        # Handle t with a single value: expand to the size of batch size.
+        if t.dim() == 0:
+            t = t.repeat(xt.shape[0])
+        if guidance_scale is not None and guidance_scale.dim() == 0:
+            guidance_scale = guidance_scale.repeat(xt.shape[0])
+
+        xt = torch.cat([xt, text_condition, speech_condition], dim=2)
+        vt = self.fm_decoder(
+            x=xt, t=t, padding_mask=padding_mask, guidance_scale=guidance_scale
+        )
+        return vt
+
+    def forward_text_embed(
+        self,
+        tokens: List[List[int]],
+    ):
+        """
+        Get the text embeddings.
+        Args:
+            tokens: a list of list of token ids.
+        Returns:
+            embed: the text embeddings, shape (batch, seq_len, emb_dim).
+            tokens_lens: the length of each token sequence, shape (batch,).
+        """
+        device = (
+            self.device if isinstance(self, DDP) else next(self.parameters()).device
+        )
+        tokens_padded = pad_labels(tokens, pad_id=self.pad_id, device=device)  # (B, S)
+        embed = self.embed(tokens_padded)  # (B, S, C)
+        tokens_lens = torch.tensor(
+            [len(token) for token in tokens], dtype=torch.int64, device=device
+        )
+        tokens_padding_mask = make_pad_mask(tokens_lens, embed.shape[1])  # (B, S)
+
+        embed = self.text_encoder(
+            x=embed, t=None, padding_mask=tokens_padding_mask
+        )  # (B, S, C)
+        return embed, tokens_lens
+
+    def forward_text_condition(
+        self,
+        embed: torch.Tensor,
+        tokens_lens: torch.Tensor,
+        features_lens: torch.Tensor,
+    ):
+        """
+        Get the text condition with the same length of the acoustic feature.
+        Args:
+            embed: the text embeddings, shape (batch, token_seq_len, emb_dim).
+            tokens_lens: the length of each token sequence, shape (batch,).
+            features_lens: the length of each acoustic feature sequence,
+                shape (batch,).
+        Returns:
+            text_condition: the text condition, shape
+                (batch, feature_seq_len, emb_dim).
+            padding_mask: the padding mask of text condition, shape
+                (batch, feature_seq_len).
+        """
+
+        num_frames = int(features_lens.max())
+
+        padding_mask = make_pad_mask(features_lens, max_len=num_frames)  # (B, T)
+
+        tokens_durations = prepare_avg_tokens_durations(features_lens, tokens_lens)
+
+        tokens_index = get_tokens_index(tokens_durations, num_frames).to(
+            embed.device
+        )  # (B, T)
+
+        text_condition = torch.gather(
+            embed,
+            dim=1,
+            index=tokens_index.unsqueeze(-1).expand(
+                embed.size(0), num_frames, embed.size(-1)
+            ),
+        )  # (B, T, F)
+        return text_condition, padding_mask
+
+    def forward_text_train(
+        self,
+        tokens: List[List[int]],
+        features_lens: torch.Tensor,
+    ):
+        """
+        Process text for training, given text tokens and real feature lengths.
+        """
+        embed, tokens_lens = self.forward_text_embed(tokens)
+        text_condition, padding_mask = self.forward_text_condition(
+            embed, tokens_lens, features_lens
+        )
+        return (
+            text_condition,
+            padding_mask,
+        )
+
+    def forward_text_inference_gt_duration(
+        self,
+        tokens: List[List[int]],
+        features_lens: torch.Tensor,
+        prompt_tokens: List[List[int]],
+        prompt_features_lens: torch.Tensor,
+    ):
+        """
+        Process text for inference, given text tokens, real feature lengths and prompts.
+        """
+        tokens = [
+            prompt_token + token for prompt_token, token in zip(prompt_tokens, tokens)
+        ]
+        features_lens = prompt_features_lens + features_lens
+        embed, tokens_lens = self.forward_text_embed(tokens)
+        text_condition, padding_mask = self.forward_text_condition(
+            embed, tokens_lens, features_lens
+        )
+        return text_condition, padding_mask
+
+    def forward_text_inference_ratio_duration(
+        self,
+        tokens: List[List[int]],
+        prompt_tokens: List[List[int]],
+        prompt_features_lens: torch.Tensor,
+        speed: float,
+    ):
+        """
+        Process text for inference, given text tokens and prompts,
+        feature lengths are predicted with the ratio of token numbers.
+        """
+        device = (
+            self.device if isinstance(self, DDP) else next(self.parameters()).device
+        )
+
+        cat_tokens = [
+            prompt_token + token for prompt_token, token in zip(prompt_tokens, tokens)
+        ]
+
+        prompt_tokens_lens = torch.tensor(
+            [len(token) for token in prompt_tokens], dtype=torch.int64, device=device
+        )
+
+        cat_embed, cat_tokens_lens = self.forward_text_embed(cat_tokens)
+
+        features_lens = torch.ceil(
+            (prompt_features_lens / prompt_tokens_lens * cat_tokens_lens / speed)
+        ).to(dtype=torch.int64)
+
+        text_condition, padding_mask = self.forward_text_condition(
+            cat_embed, cat_tokens_lens, features_lens
+        )
+        return text_condition, padding_mask
+
+    def forward(
+        self,
+        tokens: List[List[int]],
+        features: torch.Tensor,
+        features_lens: torch.Tensor,
+        noise: torch.Tensor,
+        t: torch.Tensor,
+        condition_drop_ratio: float = 0.0,
+    ) -> torch.Tensor:
+        """Forward pass of the model for training.
+        Args:
+            tokens: a list of list of token ids.
+            features: the acoustic features, with the shape (batch, seq_len, feat_dim).
+            features_lens: the length of each acoustic feature sequence, shape (batch,).
+            noise: the intitial noise, with the shape (batch, seq_len, feat_dim).
+            t: the time step, with the shape (batch, 1, 1).
+            condition_drop_ratio: the ratio of dropped text condition.
+        Returns:
+            fm_loss: the flow-matching loss.
+        """
+
+        (text_condition, padding_mask,) = self.forward_text_train(
+            tokens=tokens,
+            features_lens=features_lens,
+        )
+
+        speech_condition_mask = condition_time_mask(
+            features_lens=features_lens,
+            mask_percent=(0.7, 1.0),
+            max_len=features.size(1),
+        )
+        speech_condition = torch.where(speech_condition_mask.unsqueeze(-1), 0, features)
+
+        if condition_drop_ratio > 0.0:
+            drop_mask = (
+                torch.rand(text_condition.size(0), 1, 1).to(text_condition.device)
+                > condition_drop_ratio
+            )
+            text_condition = text_condition * drop_mask
+
+        xt = features * t + noise * (1 - t)
+        ut = features - noise  # (B, T, F)
+
+        vt = self.forward_fm_decoder(
+            t=t,
+            xt=xt,
+            text_condition=text_condition,
+            speech_condition=speech_condition,
+            padding_mask=padding_mask,
+        )
+
+        loss_mask = speech_condition_mask & (~padding_mask)
+        fm_loss = torch.mean((vt[loss_mask] - ut[loss_mask]) ** 2)
+
+        return fm_loss
+
+    def sample(
+        self,
+        tokens: List[List[int]],
+        prompt_tokens: List[List[int]],
+        prompt_features: torch.Tensor,
+        prompt_features_lens: torch.Tensor,
+        features_lens: Optional[torch.Tensor] = None,
+        speed: float = 1.0,
+        t_shift: float = 1.0,
+        duration: str = "predict",
+        num_step: int = 5,
+        guidance_scale: float = 0.5,
+    ) -> torch.Tensor:
+        """
+        Generate acoustic features, given text tokens, prompts feature
+            and prompt transcription's text tokens.
+        Args:
+            tokens: a list of list of text tokens.
+            prompt_tokens: a list of list of prompt tokens.
+            prompt_features: the prompt feature with the shape
+                (batch_size, seq_len, feat_dim).
+            prompt_features_lens: the length of each prompt feature,
+                with the shape (batch_size,).
+            features_lens: the length of the predicted eature, with the
+                shape (batch_size,). It is used only when duration is "real".
+            duration: "real" or "predict". If "real", the predicted
+                feature length is given by features_lens.
+            num_step: the number of steps to use in the ODE solver.
+            guidance_scale: the guidance scale for classifier-free guidance.
+            distill: whether to use the distillation model for sampling.
+        """
+
+        assert duration in ["real", "predict"]
+
+        if duration == "predict":
+            (
+                text_condition,
+                padding_mask,
+            ) = self.forward_text_inference_ratio_duration(
+                tokens=tokens,
+                prompt_tokens=prompt_tokens,
+                prompt_features_lens=prompt_features_lens,
+                speed=speed,
+            )
+        else:
+            assert features_lens is not None
+            text_condition, padding_mask = self.forward_text_inference_gt_duration(
+                tokens=tokens,
+                features_lens=features_lens,
+                prompt_tokens=prompt_tokens,
+                prompt_features_lens=prompt_features_lens,
+            )
+        batch_size, num_frames, _ = text_condition.shape
+
+        speech_condition = torch.nn.functional.pad(
+            prompt_features, (0, 0, 0, num_frames - prompt_features.size(1))
+        )  # (B, T, F)
+
+        # False means speech condition positions.
+        speech_condition_mask = make_pad_mask(prompt_features_lens, num_frames)
+        speech_condition = torch.where(
+            speech_condition_mask.unsqueeze(-1),
+            torch.zeros_like(speech_condition),
+            speech_condition,
+        )
+
+        x0 = torch.randn(
+            batch_size, num_frames, self.feat_dim, device=text_condition.device
+        )
+        solver = EulerSolver(self, distill=self.distill, func_name="forward_fm_decoder")
+
+        x1 = solver.sample(
+            x=x0,
+            text_condition=text_condition,
+            speech_condition=speech_condition,
+            padding_mask=padding_mask,
+            num_step=num_step,
+            guidance_scale=guidance_scale,
+            t_shift=t_shift,
+        )
+        x1_wo_prompt_lens = (~padding_mask).sum(-1) - prompt_features_lens
+        x1_prompt = torch.zeros(
+            x1.size(0), prompt_features_lens.max(), x1.size(2), device=x1.device
+        )
+        x1_wo_prompt = torch.zeros(
+            x1.size(0), x1_wo_prompt_lens.max(), x1.size(2), device=x1.device
+        )
+        for i in range(x1.size(0)):
+            x1_wo_prompt[i, : x1_wo_prompt_lens[i], :] = x1[
+                i,
+                prompt_features_lens[i] : prompt_features_lens[i]
+                + x1_wo_prompt_lens[i],
+            ]
+            x1_prompt[i, : prompt_features_lens[i], :] = x1[
+                i, : prompt_features_lens[i]
+            ]
+
+        return x1_wo_prompt, x1_wo_prompt_lens, x1_prompt, prompt_features_lens
+
+    def sample_intermediate(
+        self,
+        tokens: List[List[int]],
+        features: torch.Tensor,
+        features_lens: torch.Tensor,
+        noise: torch.Tensor,
+        speech_condition_mask: torch.Tensor,
+        t_start: torch.Tensor,
+        t_end: torch.Tensor,
+        num_step: int = 1,
+        guidance_scale: torch.Tensor = None,
+    ) -> torch.Tensor:
+        """
+        Generate acoustic features in intermediate timesteps.
+        Args:
+            tokens: List of list of token ids.
+            features: The acoustic features, with the shape (batch, seq_len, feat_dim).
+            features_lens: The length of each acoustic feature sequence,
+                with the shape (batch,).
+            noise: The initial noise, with the shape (batch, seq_len, feat_dim).
+            speech_condition_mask: The mask for speech condition, True means
+                non-condition positions, with the shape (batch, seq_len).
+            t_start: The start timestep, with the shape (batch, 1, 1).
+            t_end: The end timestep, with the shape (batch, 1, 1).
+            num_step: The number of steps for sampling.
+            guidance_scale: The scale for classifier-free guidance inference,
+                with the shape (batch, 1, 1).
+            distill: Whether to use distillation model.
+        """
+        (text_condition, padding_mask,) = self.forward_text_train(
+            tokens=tokens,
+            features_lens=features_lens,
+        )
+
+        speech_condition = torch.where(speech_condition_mask.unsqueeze(-1), 0, features)
+
+        solver = EulerSolver(self, distill=self.distill, func_name="forward_fm_decoder")
+
+        x_t_end = solver.sample(
+            x=noise,
+            text_condition=text_condition,
+            speech_condition=speech_condition,
+            padding_mask=padding_mask,
+            num_step=num_step,
+            guidance_scale=guidance_scale,
+            t_start=t_start,
+            t_end=t_end,
+        )
+        x_t_end_lens = (~padding_mask).sum(-1)
+        return x_t_end, x_t_end_lens
+
+
+class DistillTTSModelTrainWrapper(TtsModel):
+    """Wrapper for training the distilled TTS model."""
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.distill = True
+
+    def forward(
+        self,
+        tokens: List[List[int]],
+        features: torch.Tensor,
+        features_lens: torch.Tensor,
+        noise: torch.Tensor,
+        speech_condition_mask: torch.Tensor,
+        t_start: torch.Tensor,
+        t_end: torch.Tensor,
+        num_step: int = 1,
+        guidance_scale: torch.Tensor = None,
+    ) -> torch.Tensor:
+
+        return self.sample_intermediate(
+            tokens=tokens,
+            features=features,
+            features_lens=features_lens,
+            noise=noise,
+            speech_condition_mask=speech_condition_mask,
+            t_start=t_start,
+            t_end=t_end,
+            num_step=num_step,
+            guidance_scale=guidance_scale,
+        )
diff --git a/egs/zipvoice/zipvoice/optim.py b/egs/zipvoice/zipvoice/optim.py
new file mode 100644
index 000000000..daf17556a
--- /dev/null
+++ b/egs/zipvoice/zipvoice/optim.py
@@ -0,0 +1,1256 @@
+# Copyright      2022  Xiaomi Corp.        (authors: Daniel Povey)
+#
+# See ../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import contextlib
+import logging
+import random
+from collections import defaultdict
+from typing import Dict, List, Optional, Tuple, Union
+
+import torch
+from lhotse.utils import fix_random_seed
+from torch import Tensor
+from torch.optim import Optimizer
+
+
+class BatchedOptimizer(Optimizer):
+    """
+    This class adds to class Optimizer the capability to optimize parameters in batches:
+    it will stack the parameters and their grads for you so the optimizer can work
+    on tensors with an extra leading dimension.  This is intended for speed with GPUs,
+    as it reduces the number of kernels launched in the optimizer.
+
+    Args:
+      params:
+    """
+
+    def __init__(self, params, defaults):
+        super(BatchedOptimizer, self).__init__(params, defaults)
+
+    @contextlib.contextmanager
+    def batched_params(self, param_group, group_params_names):
+        """
+        This function returns (technically, yields) a list of
+          of tuples (p, state), where
+        p is a `fake` parameter that is stacked (over axis 0) from real parameters
+        that share the same shape, and its gradient is also stacked;
+        `state` is the state corresponding to this batch of parameters
+        (it will be physically located in the "state" for one of the real
+        parameters, the last one that has any particular shape and dtype).
+
+        This function is decorated as a context manager so that it can
+        write parameters back to their "real" locations.
+
+        The idea is, instead of doing:
+        <code>
+          for p in group["params"]:
+             state = self.state[p]
+             ...
+        </code>
+        you can do:
+        <code>
+          with self.batched_params(group["params"]) as batches:
+             for p, state, p_names in batches:
+                 ...
+        </code>
+
+        Args:
+          group: a parameter group, which is a list of parameters; should be
+                one of self.param_groups.
+          group_params_names: name for each parameter in group,
+                which is List[str].
+        """
+        batches = defaultdict(
+            list
+        )  # `batches` maps from tuple (dtype_as_str,*shape) to list of nn.Parameter
+        batches_names = defaultdict(
+            list
+        )  # `batches` maps from tuple (dtype_as_str,*shape) to list of str
+
+        assert len(param_group) == len(group_params_names)
+        for p, named_p in zip(param_group, group_params_names):
+            key = (str(p.dtype), *p.shape)
+            batches[key].append(p)
+            batches_names[key].append(named_p)
+
+        batches_names_keys = list(batches_names.keys())
+        sorted_idx = sorted(
+            range(len(batches_names)), key=lambda i: batches_names_keys[i]
+        )
+        batches_names = [batches_names[batches_names_keys[idx]] for idx in sorted_idx]
+        batches = [batches[batches_names_keys[idx]] for idx in sorted_idx]
+
+        stacked_params_dict = dict()
+
+        # turn batches into a list, in deterministic order.
+        # tuples will contain tuples of (stacked_param, state, stacked_params_names),
+        # one for each batch in `batches`.
+        tuples = []
+
+        for batch, batch_names in zip(batches, batches_names):
+            p = batch[0]
+            # we arbitrarily store the state in the
+            # state corresponding to the 1st parameter in the
+            # group.  class Optimizer will take care of saving/loading state.
+            state = self.state[p]
+            p_stacked = torch.stack(batch)
+            grad = torch.stack(
+                [torch.zeros_like(p) if p.grad is None else p.grad for p in batch]
+            )
+            p_stacked.grad = grad
+            stacked_params_dict[key] = p_stacked
+            tuples.append((p_stacked, state, batch_names))
+
+        yield tuples  # <-- calling code will do the actual optimization here!
+
+        for ((stacked_params, _state, _names), batch) in zip(tuples, batches):
+            for i, p in enumerate(batch):  # batch is list of Parameter
+                p.copy_(stacked_params[i])
+
+
+def basic_step(group, p, state, grad):
+    # computes basic Adam update using beta2 (dividing by gradient stddev) only.  no momentum yet.
+    lr = group["lr"]
+    if p.numel() == p.shape[0]:
+        lr = lr * group["scalar_lr_scale"]
+    beta2 = group["betas"][1]
+    eps = group["eps"]
+    # p shape: (batch_size,) or (batch_size, 1, [1,..])
+    try:
+        exp_avg_sq = state[
+            "exp_avg_sq"
+        ]  # shape: (batch_size,) or (batch_size, 1, [1,..])
+    except KeyError:
+        exp_avg_sq = torch.zeros(*p.shape, device=p.device, dtype=torch.float)
+        state["exp_avg_sq"] = exp_avg_sq
+
+    exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+
+    # bias_correction2 is like in Adam.
+    # slower update at the start will help stability anyway.
+    bias_correction2 = 1 - beta2 ** (state["step"] + 1)
+    if bias_correction2 < 0.99:
+        # note: not in-place.
+        exp_avg_sq = exp_avg_sq * (1.0 / bias_correction2)
+    denom = exp_avg_sq.sqrt().add_(eps)
+
+    return -lr * grad / denom
+
+
+def scaling_step(group, p, state, grad):
+    delta = basic_step(group, p, state, grad)
+    if p.numel() == p.shape[0]:
+        return delta  # there is no scaling for scalar parameters.  (p.shape[0] is the batch of parameters.)
+
+    step = state["step"]
+    size_update_period = group["size_update_period"]
+
+    try:
+        param_rms = state["param_rms"]
+        scale_grads = state["scale_grads"]
+        scale_exp_avg_sq = state["scale_exp_avg_sq"]
+    except KeyError:
+        # we know p.ndim > 1 because we'd have returned above if not, so don't worry
+        # about the speial case of dim=[] that pytorch treats inconsistently.
+        param_rms = (p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt()
+        param_rms = param_rms.to(torch.float)
+        scale_exp_avg_sq = torch.zeros_like(param_rms)
+        scale_grads = torch.zeros(
+            size_update_period, *param_rms.shape, dtype=torch.float, device=p.device
+        )
+        state["param_rms"] = param_rms
+        state["scale_grads"] = scale_grads
+        state["scale_exp_avg_sq"] = scale_exp_avg_sq
+
+    # on every step, update the gradient w.r.t. the scale of the parameter, we
+    # store these as a batch and periodically update the size (for speed only, to
+    # avoid too many operations).
+    scale_grads[step % size_update_period] = (p * grad).sum(
+        dim=list(range(1, p.ndim)), keepdim=True
+    )
+
+    # periodically recompute the value of param_rms.
+    if step % size_update_period == size_update_period - 1:
+        param_rms.copy_((p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt())
+
+    param_min_rms = group["param_min_rms"]
+
+    # scale the step size by param_rms.  This is the most important "scaling" part of
+    # ScaledAdam
+    delta *= param_rms.clamp(min=param_min_rms)
+
+    if step % size_update_period == size_update_period - 1 and step > 0:
+        # This block updates the size of parameter by adding a step ("delta") value in
+        # the direction of either shrinking or growing it.
+        beta2 = group["betas"][1]
+        size_lr = group["lr"] * group["scalar_lr_scale"]
+        param_max_rms = group["param_max_rms"]
+        eps = group["eps"]
+        batch_size = p.shape[0]
+        # correct beta2 for the size update period: we will have
+        # faster decay at this level.
+        beta2_corr = beta2**size_update_period
+        scale_exp_avg_sq.mul_(beta2_corr).add_(
+            (scale_grads**2).mean(dim=0),  # mean over dim `size_update_period`
+            alpha=1 - beta2_corr,
+        )  # shape is (batch_size, 1, 1, ...)
+
+        # The 1st time we reach here is when size_step == 1.
+        size_step = (step + 1) // size_update_period
+        bias_correction2 = 1 - beta2_corr**size_step
+
+        denom = scale_exp_avg_sq.sqrt() + eps
+
+        scale_step = (
+            -size_lr * (bias_correction2**0.5) * scale_grads.sum(dim=0) / denom
+        )
+
+        is_too_small = param_rms < param_min_rms
+
+        # when the param gets too small, just don't shrink it any further.
+        scale_step.masked_fill_(is_too_small, 0.0)
+
+        # The following may help prevent instability: don't allow the scale step to be too large in
+        # either direction.
+        scale_step.clamp_(min=-0.1, max=0.1)
+
+        # and ensure the parameter rms after update never exceeds param_max_rms.
+        # We have to look at the trained model for parameters at or around the
+        # param_max_rms, because sometimes they can indicate a problem with the
+        # topology or settings.
+        scale_step = torch.minimum(scale_step, (param_max_rms - param_rms) / param_rms)
+
+        delta.add_(p * scale_step)
+
+    return delta
+
+
+def momentum_step(group, p, state, grad):
+    delta = scaling_step(group, p, state, grad)
+    beta1 = group["betas"][0]
+    try:
+        stored_delta = state["delta"]
+    except KeyError:
+        stored_delta = torch.zeros(*p.shape, device=p.device, dtype=torch.float)
+        state["delta"] = stored_delta
+    stored_delta.mul_(beta1)
+    stored_delta.add_(delta, alpha=(1 - beta1))
+    # we don't bother doing the "bias correction" part of Adam for beta1 because this is just
+    # an edge effect that affects the first 10 or so batches; and the effect of not doing it
+    # is just to do a slower update for the first few batches, which will help stability.
+    return stored_delta
+
+
+class ScaledAdam(BatchedOptimizer):
+    """
+     Implements 'Scaled Adam', a variant of Adam where we scale each parameter's update
+     proportional to the norm of that parameter; and also learn the scale of the parameter,
+     in log space, subject to upper and lower limits (as if we had factored each parameter as
+     param = underlying_param * log_scale.exp())
+
+
+     Args:
+          params:  The parameters or param_groups to optimize (like other Optimizer subclasses)
+                   Unlike common optimizers, which accept model.parameters() or groups of parameters(),
+                   this optimizer could accept model.named_parameters() or groups of named_parameters().
+                   See comments of function _get_names_of_parameters for its 4 possible cases.
+              lr:  The learning rate.  We will typically use a learning rate schedule that starts
+                   at 0.03 and decreases over time, i.e. much higher than other common
+                   optimizers.
+     clipping_scale: (e.g. 2.0)
+                   A scale for gradient-clipping: if specified, the normalized gradients
+                   over the whole model will be clipped to have 2-norm equal to
+                   `clipping_scale` times the median 2-norm over the most recent period
+                   of `clipping_update_period` minibatches.  By "normalized gradients",
+                   we mean after multiplying by the rms parameter value for this tensor
+                   [for non-scalars]; this is appropriate because our update is scaled
+                   by this quantity.
+            betas: beta1,beta2 are momentum constants for regular momentum, and moving sum-sq grad.
+                   Must satisfy 0 < beta <= beta2 < 1.
+     scalar_lr_scale: A scaling factor on the learning rate, that we use to update the                   scale of each parameter tensor and scalar parameters of the mode..
+                   If each parameter were decomposed
+                   as p * p_scale.exp(), where (p**2).mean().sqrt() == 1.0, scalar_lr_scale
+                   would be a the scaling factor on the learning rate of p_scale.
+              eps:  A general-purpose epsilon to prevent division by zero
+    param_min_rms: Minimum root-mean-square value of parameter tensor, for purposes of
+                   learning the scale on the parameters (we'll constrain the rms of each non-scalar
+                   parameter tensor to be >= this value)
+    param_max_rms: Maximum root-mean-square value of parameter tensor, for purposes of
+                   learning the scale on the parameters (we'll constrain the rms of each non-scalar
+                   parameter tensor to be <= this value)
+       scalar_max: Maximum absolute value for scalar parameters (applicable if your
+                   model has any parameters with numel() == 1).
+    size_update_period: The periodicity, in steps, with which we update the size (scale)
+                   of the parameter tensor.  This is provided to save a little time
+                   in the update.
+     clipping_update_period: if clipping_scale is specified, this is the period
+    """
+
+    def __init__(
+        self,
+        params,
+        lr=3e-02,
+        clipping_scale=None,
+        betas=(0.9, 0.98),
+        scalar_lr_scale=0.1,
+        eps=1.0e-08,
+        param_min_rms=1.0e-05,
+        param_max_rms=3.0,
+        scalar_max=10.0,
+        size_update_period=4,
+        clipping_update_period=100,
+    ):
+
+        defaults = dict(
+            lr=lr,
+            clipping_scale=clipping_scale,
+            betas=betas,
+            scalar_lr_scale=scalar_lr_scale,
+            eps=eps,
+            param_min_rms=param_min_rms,
+            param_max_rms=param_max_rms,
+            scalar_max=scalar_max,
+            size_update_period=size_update_period,
+            clipping_update_period=clipping_update_period,
+        )
+
+        # If params only contains parameters or group of parameters,
+        # i.e when parameter names are not given,
+        # this flag will be set to False in funciton _get_names_of_parameters.
+        self.show_dominant_parameters = True
+        param_groups, parameters_names = self._get_names_of_parameters(params)
+        super(ScaledAdam, self).__init__(param_groups, defaults)
+        assert len(self.param_groups) == len(parameters_names)
+        self.parameters_names = parameters_names
+
+    def _get_names_of_parameters(
+        self, params_or_named_params
+    ) -> Tuple[List[Dict], List[List[str]]]:
+        """
+        Args:
+          params_or_named_params: according to the way ScaledAdam is initialized in train.py,
+            this argument could be one of following 4 cases,
+            case 1, a generator of parameter, e.g.:
+              optimizer = ScaledAdam(model.parameters(), lr=params.base_lr, clipping_scale=3.0)
+
+            case 2, a list of parameter groups with different config, e.g.:
+              model_param_groups = [
+                      {'params': model.encoder.parameters(), 'lr': 0.05},
+                      {'params': model.decoder.parameters(), 'lr': 0.01},
+                      {'params': model.joiner.parameters(), 'lr': 0.03},
+                      ]
+              optimizer = ScaledAdam(model_param_groups, lr=params.base_lr, clipping_scale=3.0)
+
+            case 3, a generator of named_parameter, e.g.:
+              optimizer = ScaledAdam(model.named_parameters(), lr=params.base_lr, clipping_scale=3.0)
+
+            case 4, a list of named_parameter groups with different config, e.g.:
+              model_named_param_groups = [
+                      {'named_params': model.encoder.named_parameters(), 'lr': 0.05},
+                      {'named_params': model.decoder.named_parameters(), 'lr': 0.01},
+                      {'named_params': model.joiner.named_parameters(), 'lr': 0.03},
+                      ]
+              optimizer = ScaledAdam(model_named_param_groups, lr=params.base_lr, clipping_scale=3.0)
+
+          For case 1 and case 2, input params is used to initialize the underlying torch.optimizer.
+          For case 3 and case 4, firstly, names and params are extracted from input named_params,
+            then, these extracted params are used to initialize the underlying torch.optimizer,
+            and these extracted names are mainly used by function
+            `_show_gradient_dominating_parameter`
+
+        Returns:
+          Returns a tuple containing 2 elements:
+            - `param_groups` with type List[Dict], each Dict element is a parameter group.
+              An example of `param_groups` could be:
+              [
+                  {'params': `one iterable of Parameter`, 'lr': 0.05},
+                  {'params': `another iterable of Parameter`, 'lr': 0.08},
+                  {'params': `a third iterable of Parameter`, 'lr': 0.1},
+              ]
+            - `param_gruops_names` with type List[List[str]],
+               each `List[str]` is for a group['params'] in param_groups,
+               and each `str` is the name of a parameter.
+               A dummy name "foo" is related to each parameter,
+               if input are params without names, i.e. case 1 or case 2.
+        """
+        # variable naming convention in this function:
+        #   p is short for param.
+        #   np is short for named_param.
+        #   p_or_np is short for param_or_named_param.
+        #   cur is short for current.
+        #   group is a dict, e.g. {'params': iterable of parameter, 'lr': 0.05, other fields}.
+        #   groups is a List[group]
+
+        iterable_or_groups = list(params_or_named_params)
+        if len(iterable_or_groups) == 0:
+            raise ValueError("optimizer got an empty parameter list")
+
+        # The first value of returned tuple.  A list of dicts containing at
+        # least 'params' as a key.
+        param_groups = []
+
+        # The second value of returned tuple,
+        # a List[List[str]], each sub-List is for a group.
+        param_groups_names = []
+
+        if not isinstance(iterable_or_groups[0], dict):
+            # case 1 or case 3,
+            # the input is an iterable of parameter or named parameter.
+            param_iterable_cur_group = []
+            param_names_cur_group = []
+            for p_or_np in iterable_or_groups:
+                if isinstance(p_or_np, tuple):
+                    # case 3
+                    name, param = p_or_np
+                else:
+                    # case 1
+                    assert isinstance(p_or_np, torch.Tensor)
+                    param = p_or_np
+                    # Assign a dummy name as a placeholder
+                    name = "foo"
+                    self.show_dominant_parameters = False
+                param_iterable_cur_group.append(param)
+                param_names_cur_group.append(name)
+            param_groups.append({"params": param_iterable_cur_group})
+            param_groups_names.append(param_names_cur_group)
+        else:
+            # case 2 or case 4
+            # the input is groups of parameter or named parameter.
+            for cur_group in iterable_or_groups:
+                if "named_params" in cur_group:
+                    name_list = [x[0] for x in cur_group["named_params"]]
+                    p_list = [x[1] for x in cur_group["named_params"]]
+                    del cur_group["named_params"]
+                    cur_group["params"] = p_list
+                else:
+                    assert "params" in cur_group
+                    name_list = ["foo" for _ in cur_group["params"]]
+                param_groups.append(cur_group)
+                param_groups_names.append(name_list)
+
+        return param_groups, param_groups_names
+
+    def __setstate__(self, state):
+        super(ScaledAdam, self).__setstate__(state)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        batch = True
+
+        for group, group_params_names in zip(self.param_groups, self.parameters_names):
+
+            with self.batched_params(group["params"], group_params_names) as batches:
+
+                # batches is list of pairs (stacked_param, state).  stacked_param is like
+                # a regular parameter, and will have a .grad, but the 1st dim corresponds to
+                # a stacking dim, it is not a real dim.
+
+                if (
+                    len(batches[0][1]) == 0
+                ):  # if len(first state) == 0: not yet initialized
+                    clipping_scale = 1
+                else:
+                    clipping_scale = self._get_clipping_scale(group, batches)
+
+                for p, state, _ in batches:
+                    # Perform optimization step.
+                    # grad is not going to be None, we handled that when creating the batches.
+                    grad = p.grad
+                    if grad.is_sparse:
+                        raise RuntimeError(
+                            "ScaledAdam optimizer does not support sparse gradients"
+                        )
+
+                    try:
+                        cur_step = state["step"]
+                    except KeyError:
+                        state["step"] = 0
+                        cur_step = 0
+
+                    grad = (
+                        p.grad if clipping_scale == 1.0 else p.grad.mul_(clipping_scale)
+                    )
+                    p += momentum_step(group, p.detach(), state, grad)
+
+                    if p.numel() == p.shape[0]:  # scalar parameter
+                        scalar_max = group["scalar_max"]
+                        p.clamp_(min=-scalar_max, max=scalar_max)
+
+                    state["step"] = cur_step + 1
+
+        return loss
+
+    def _get_clipping_scale(
+        self, group: dict, tuples: List[Tuple[Tensor, dict, List[str]]]
+    ) -> float:
+        """
+        Returns a scalar factor <= 1.0 that dictates gradient clipping, i.e. we will scale the gradients
+        by this amount before applying the rest of the update.
+
+        Args:
+           group: the parameter group, an item in self.param_groups
+           tuples: a list of tuples of (param, state, param_names)
+                where param is a batched set of parameters,
+                with a .grad (1st dim is batch dim)
+                and state is the state-dict where optimization parameters are kept.
+                param_names is a List[str] while each str is name for a parameter
+                in batched set of parameters "param".
+        """
+        assert len(tuples) >= 1
+        clipping_scale = group["clipping_scale"]
+        (first_p, first_state, _) = tuples[0]
+        step = first_state["step"]
+        if clipping_scale is None or step == 0:
+            # no clipping.  return early on step == 0 because the other
+            # parameters' state won't have been initialized yet.
+            return 1.0
+        clipping_update_period = group["clipping_update_period"]
+        scalar_lr_scale = group["scalar_lr_scale"]
+
+        tot_sumsq = torch.tensor(0.0, device=first_p.device)
+        for (p, state, param_names) in tuples:
+            grad = p.grad
+            if grad.is_sparse:
+                raise RuntimeError(
+                    "ScaledAdam optimizer does not support sparse gradients"
+                )
+            if p.numel() == p.shape[0]:  # a batch of scalars
+                tot_sumsq += (grad**2).sum() * (
+                    scalar_lr_scale**2
+                )  # sum() to change shape [1] to []
+            else:
+                tot_sumsq += ((grad * state["param_rms"]) ** 2).sum()
+
+        tot_norm = tot_sumsq.sqrt()
+        if "model_norms" not in first_state:
+            first_state["model_norms"] = torch.zeros(
+                clipping_update_period, device=p.device
+            )
+        first_state["model_norms"][step % clipping_update_period] = tot_norm
+
+        irregular_estimate_steps = [
+            i for i in [10, 20, 40] if i < clipping_update_period
+        ]
+        if step % clipping_update_period == 0 or step in irregular_estimate_steps:
+            # Print some stats.
+            # We don't reach here if step == 0 because we would have returned
+            # above.
+            sorted_norms = first_state["model_norms"].sort()[0].to("cpu")
+            if step in irregular_estimate_steps:
+                sorted_norms = sorted_norms[-step:]
+            num_norms = sorted_norms.numel()
+            quartiles = []
+            for n in range(0, 5):
+                index = min(num_norms - 1, (num_norms // 4) * n)
+                quartiles.append(sorted_norms[index].item())
+
+            median = quartiles[2]
+            if median - median != 0:
+                raise RuntimeError("Too many grads were not finite")
+            threshold = clipping_scale * median
+            if step in irregular_estimate_steps:
+                # use larger thresholds on first few steps of estimating threshold,
+                # as norm may be changing rapidly.
+                threshold = threshold * 2.0
+            first_state["model_norm_threshold"] = threshold
+            percent_clipped = (
+                first_state["num_clipped"] * 100.0 / num_norms
+                if "num_clipped" in first_state
+                else 0.0
+            )
+            first_state["num_clipped"] = 0
+            quartiles = " ".join(["%.3e" % x for x in quartiles])
+            logging.warning(
+                f"Clipping_scale={clipping_scale}, grad-norm quartiles {quartiles}, "
+                f"threshold={threshold:.3e}, percent-clipped={percent_clipped:.1f}"
+            )
+
+        try:
+            model_norm_threshold = first_state["model_norm_threshold"]
+        except KeyError:
+            return 1.0  # threshold has not yet been set.
+
+        ans = min(1.0, (model_norm_threshold / (tot_norm + 1.0e-20)).item())
+        if ans != ans:  # e.g. ans is nan
+            ans = 0.0
+        if ans < 1.0:
+            first_state["num_clipped"] += 1
+        if ans < 0.5:
+            logging.warning(
+                f"Scaling gradients by {ans}, model_norm_threshold={model_norm_threshold}"
+            )
+            if self.show_dominant_parameters:
+                assert p.shape[0] == len(param_names)
+                self._show_gradient_dominating_parameter(
+                    tuples, tot_sumsq, group["scalar_lr_scale"]
+                )
+                self._show_param_with_unusual_grad(tuples)
+
+        if ans == 0.0:
+            for (p, state, param_names) in tuples:
+                p.grad.zero_()  # get rid of infinity()
+
+        return ans
+
+    def _show_param_with_unusual_grad(
+        self,
+        tuples: List[Tuple[Tensor, dict, List[str]]],
+    ):
+        """
+        Print information about parameter which has the largest ratio of grad-on-this-batch
+        divided by normal grad size.
+           tuples: a list of tuples of (param, state, param_names)
+                where param is a batched set of parameters,
+                with a .grad (1st dim is batch dim)
+                and state is the state-dict where optimization parameters are kept.
+                param_names is a List[str] while each str is name for a parameter
+                in batched set of parameters "param".
+        """
+        largest_ratio = 0.0
+        largest_name = ""
+        # ratios_names is a list of 3-tuples: (grad_ratio, param_name, tensor)
+        ratios_names = []
+        for (p, state, batch_param_names) in tuples:
+            dims = list(range(1, p.ndim))
+
+            def mean(x):
+                # workaround for bad interface of torch's "mean" for when dims is the empty list.
+                if len(dims) > 0:
+                    return x.mean(dim=dims)
+                else:
+                    return x
+
+            grad_ratio = (
+                (mean(p.grad**2) / state["exp_avg_sq"].mean(dim=dims))
+                .sqrt()
+                .to("cpu")
+            )
+
+            ratios_names += zip(
+                grad_ratio.tolist(), batch_param_names, p.grad.unbind(dim=0)
+            )
+
+        ratios_names = sorted(ratios_names, reverse=True)
+        ratios_names = ratios_names[:10]
+        ratios_names = [
+            (ratio, name, largest_index(tensor))
+            for (ratio, name, tensor) in ratios_names
+        ]
+
+        logging.debug(
+            f"Parameters with most larger-than-usual grads, with ratios, are: {ratios_names}"
+        )
+
+    def _show_gradient_dominating_parameter(
+        self,
+        tuples: List[Tuple[Tensor, dict, List[str]]],
+        tot_sumsq: Tensor,
+        scalar_lr_scale: float,
+    ):
+        """
+        Show information of parameter which dominates tot_sumsq.
+
+        Args:
+           tuples: a list of tuples of (param, state, param_names)
+                where param is a batched set of parameters,
+                with a .grad (1st dim is batch dim)
+                and state is the state-dict where optimization parameters are kept.
+                param_names is a List[str] while each str is name for a parameter
+                in batched set of parameters "param".
+            tot_sumsq: sumsq of all parameters. Though it's could be calculated
+                from tuples, we still pass it to save some time.
+        """
+        all_sumsq_orig = {}
+        for (p, state, batch_param_names) in tuples:
+            # p is a stacked batch parameters.
+            batch_grad = p.grad
+            if p.numel() == p.shape[0]:  # a batch of scalars
+                # Dummy values used by following `zip` statement.
+                batch_rms_orig = torch.full(
+                    p.shape, scalar_lr_scale, device=batch_grad.device
+                )
+            else:
+                batch_rms_orig = state["param_rms"]
+            batch_sumsq_orig = (batch_grad * batch_rms_orig) ** 2
+            if batch_grad.ndim > 1:
+                # need to guard it with if-statement because sum() sums over
+                # all dims if dim == ().
+                batch_sumsq_orig = batch_sumsq_orig.sum(
+                    dim=list(range(1, batch_grad.ndim))
+                )
+            for name, sumsq_orig, rms, grad in zip(
+                batch_param_names, batch_sumsq_orig, batch_rms_orig, batch_grad
+            ):
+
+                proportion_orig = sumsq_orig / tot_sumsq
+                all_sumsq_orig[name] = (proportion_orig, sumsq_orig, rms, grad)
+
+        sorted_by_proportion = {
+            k: v
+            for k, v in sorted(
+                all_sumsq_orig.items(), key=lambda item: item[1][0], reverse=True
+            )
+        }
+        dominant_param_name = next(iter(sorted_by_proportion))
+        (
+            dominant_proportion,
+            dominant_sumsq,
+            dominant_rms,
+            dominant_grad,
+        ) = sorted_by_proportion[dominant_param_name]
+        logging.debug(
+            f"Parameter dominating tot_sumsq {dominant_param_name}"
+            f" with proportion {dominant_proportion:.2f},"
+            f" where dominant_sumsq=(grad_sumsq*orig_rms_sq)"
+            f"={dominant_sumsq:.3e},"
+            f" grad_sumsq={(dominant_grad**2).sum():.3e},"
+            f" orig_rms_sq={(dominant_rms**2).item():.3e}"
+        )
+
+
+def largest_index(x: Tensor):
+    x = x.contiguous()
+    argmax = x.abs().argmax().item()
+    return [(argmax // x.stride(i)) % x.size(i) for i in range(x.ndim)]
+
+
+class LRScheduler(object):
+    """
+    Base-class for learning rate schedulers where the learning-rate depends on both the
+    batch and the epoch.
+    """
+
+    def __init__(self, optimizer: Optimizer, verbose: bool = False):
+        # Attach optimizer
+        if not isinstance(optimizer, Optimizer):
+            raise TypeError("{} is not an Optimizer".format(type(optimizer).__name__))
+        self.optimizer = optimizer
+        self.verbose = verbose
+
+        for group in optimizer.param_groups:
+            group.setdefault("base_lr", group["lr"])
+
+        self.base_lrs = [group["base_lr"] for group in optimizer.param_groups]
+
+        self.epoch = 0
+        self.batch = 0
+
+    def state_dict(self):
+        """Returns the state of the scheduler as a :class:`dict`.
+
+        It contains an entry for every variable in self.__dict__ which
+        is not the optimizer.
+        """
+        return {
+            # the user might try to override the base_lr, so don't include this in the state.
+            # previously they were included.
+            # "base_lrs": self.base_lrs,
+            "epoch": self.epoch,
+            "batch": self.batch,
+        }
+
+    def load_state_dict(self, state_dict):
+        """Loads the schedulers state.
+
+        Args:
+            state_dict (dict): scheduler state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        # the things with base_lrs are a work-around for a previous problem
+        # where base_lrs were written with the state dict.
+        base_lrs = self.base_lrs
+        self.__dict__.update(state_dict)
+        self.base_lrs = base_lrs
+
+    def get_last_lr(self) -> List[float]:
+        """Return last computed learning rate by current scheduler.  Will be a list of float."""
+        return self._last_lr
+
+    def get_lr(self):
+        # Compute list of learning rates from self.epoch and self.batch and
+        # self.base_lrs; this must be overloaded by the user.
+        # e.g. return [some_formula(self.batch, self.epoch, base_lr) for base_lr in self.base_lrs ]
+        raise NotImplementedError
+
+    def step_batch(self, batch: Optional[int] = None) -> None:
+        # Step the batch index, or just set it.  If `batch` is specified, it
+        # must be the batch index from the start of training, i.e. summed over
+        # all epochs.
+        # You can call this in any order; if you don't provide 'batch', it should
+        # of course be called once per batch.
+        if batch is not None:
+            self.batch = batch
+        else:
+            self.batch = self.batch + 1
+        self._set_lrs()
+
+    def step_epoch(self, epoch: Optional[int] = None):
+        # Step the epoch index, or just set it.  If you provide the 'epoch' arg,
+        # you should call this at the start of the epoch; if you don't provide the 'epoch'
+        # arg, you should call it at the end of the epoch.
+        if epoch is not None:
+            self.epoch = epoch
+        else:
+            self.epoch = self.epoch + 1
+        self._set_lrs()
+
+    def _set_lrs(self):
+        values = self.get_lr()
+        assert len(values) == len(self.optimizer.param_groups)
+
+        for i, data in enumerate(zip(self.optimizer.param_groups, values)):
+            param_group, lr = data
+            param_group["lr"] = lr
+            self.print_lr(self.verbose, i, lr)
+        self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
+
+    def print_lr(self, is_verbose, group, lr):
+        """Display the current learning rate."""
+        if is_verbose:
+            logging.warning(
+                f"Epoch={self.epoch}, batch={self.batch}: adjusting learning rate"
+                f" of group {group} to {lr:.4e}."
+            )
+
+
+class Eden(LRScheduler):
+    """
+    Eden scheduler.
+    The basic formula (before warmup) is:
+      lr = base_lr * (((batch**2 + lr_batches**2) / lr_batches**2) ** -0.25 *
+                     (((epoch**2 + lr_epochs**2) / lr_epochs**2) ** -0.25)) * warmup
+    where `warmup` increases from linearly 0.5 to 1 over `warmup_batches` batches
+    and then stays constant at 1.
+
+    If you don't have the concept of epochs, or one epoch takes a very long time,
+    you can replace the notion of 'epoch' with some measure of the amount of data
+    processed, e.g. hours of data or frames of data, with 'lr_epochs' being set to
+    some measure representing "quite a lot of data": say, one fifth or one third
+    of an entire training run, but it doesn't matter much.  You could also use
+    Eden2 which has only the notion of batches.
+
+    We suggest base_lr = 0.04 (passed to optimizer) if used with ScaledAdam
+
+    Args:
+        optimizer: the optimizer to change the learning rates on
+        lr_batches: the number of batches after which we start significantly
+              decreasing the learning rate, suggest 5000.
+        lr_epochs: the number of epochs after which we start significantly
+              decreasing the learning rate, suggest 6 if you plan to do e.g.
+              20 to 40 epochs, but may need smaller number if dataset is huge
+              and you will do few epochs.
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        lr_batches: Union[int, float],
+        lr_epochs: Union[int, float],
+        warmup_batches: Union[int, float] = 500.0,
+        warmup_start: float = 0.5,
+        verbose: bool = False,
+    ):
+        super(Eden, self).__init__(optimizer, verbose)
+        self.lr_batches = lr_batches
+        self.lr_epochs = lr_epochs
+        self.warmup_batches = warmup_batches
+
+        assert 0.0 <= warmup_start <= 1.0, warmup_start
+        self.warmup_start = warmup_start
+
+    def get_lr(self):
+        factor = (
+            (self.batch**2 + self.lr_batches**2) / self.lr_batches**2
+        ) ** -0.25 * (
+            ((self.epoch**2 + self.lr_epochs**2) / self.lr_epochs**2) ** -0.25
+        )
+        warmup_factor = (
+            1.0
+            if self.batch >= self.warmup_batches
+            else self.warmup_start
+            + (1.0 - self.warmup_start) * (self.batch / self.warmup_batches)
+            # else 0.5 + 0.5 * (self.batch / self.warmup_batches)
+        )
+
+        return [x * factor * warmup_factor for x in self.base_lrs]
+
+
+class Eden2(LRScheduler):
+    """
+    Eden2 scheduler, simpler than Eden because it does not use the notion of epoch,
+    only batches.
+
+    The basic formula (before warmup) is:
+      lr = base_lr * ((batch**2 + lr_batches**2) / lr_batches**2) ** -0.5) * warmup
+
+    where `warmup` increases from linearly 0.5 to 1 over `warmup_batches` batches
+    and then stays constant at 1.
+
+
+     E.g. suggest base_lr = 0.04 (passed to optimizer) if used with ScaledAdam
+
+    Args:
+        optimizer: the optimizer to change the learning rates on
+        lr_batches: the number of batches after which we start significantly
+              decreasing the learning rate, suggest 5000.
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        lr_batches: Union[int, float],
+        warmup_batches: Union[int, float] = 500.0,
+        warmup_start: float = 0.5,
+        verbose: bool = False,
+    ):
+        super().__init__(optimizer, verbose)
+        self.lr_batches = lr_batches
+        self.warmup_batches = warmup_batches
+
+        assert 0.0 <= warmup_start <= 1.0, warmup_start
+        self.warmup_start = warmup_start
+
+    def get_lr(self):
+        factor = (
+            (self.batch**2 + self.lr_batches**2) / self.lr_batches**2
+        ) ** -0.5
+        warmup_factor = (
+            1.0
+            if self.batch >= self.warmup_batches
+            else self.warmup_start
+            + (1.0 - self.warmup_start) * (self.batch / self.warmup_batches)
+            # else 0.5 + 0.5 * (self.batch / self.warmup_batches)
+        )
+
+        return [x * factor * warmup_factor for x in self.base_lrs]
+
+
+class FixedLRScheduler(LRScheduler):
+    """
+    Fixed learning rate scheduler.
+
+    Args:
+        optimizer: the optimizer to change the learning rates on
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        verbose: bool = False,
+    ):
+        super(FixedLRScheduler, self).__init__(optimizer, verbose)
+
+    def get_lr(self):
+
+        return [x for x in self.base_lrs]
+
+
+def _test_eden():
+    m = torch.nn.Linear(100, 100)
+    optim = ScaledAdam(m.parameters(), lr=0.03)
+
+    scheduler = Eden(optim, lr_batches=100, lr_epochs=2, verbose=True)
+
+    for epoch in range(10):
+        scheduler.step_epoch(epoch)  # sets epoch to `epoch`
+
+        for step in range(20):
+            x = torch.randn(200, 100).detach()
+            x.requires_grad = True
+            y = m(x)
+            dy = torch.randn(200, 100).detach()
+            f = (y * dy).sum()
+            f.backward()
+
+            optim.step()
+            scheduler.step_batch()
+            optim.zero_grad()
+
+    logging.info(f"last lr = {scheduler.get_last_lr()}")
+    logging.info(f"state dict = {scheduler.state_dict()}")
+
+
+# This is included mostly as a baseline for ScaledAdam.
+class Eve(Optimizer):
+    """
+    Implements Eve algorithm.  This is a modified version of AdamW with a special
+    way of setting the weight-decay / shrinkage-factor, which is designed to make the
+    rms of the parameters approach a particular target_rms (default: 0.1).  This is
+    for use with networks with 'scaled' versions of modules (see scaling.py), which
+    will be close to invariant to the absolute scale on the parameter matrix.
+
+    The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
+    The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
+    Eve is unpublished so far.
+
+    Arguments:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, optional): learning rate (default: 1e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        weight_decay (float, optional): weight decay coefficient (default: 3e-4;
+            this value means that the weight would decay significantly after
+            about 3k minibatches.  Is not multiplied by learning rate, but
+            is conditional on RMS-value of parameter being > target_rms.
+        target_rms (float, optional): target root-mean-square value of
+           parameters, if they fall below this we will stop applying weight decay.
+
+
+    .. _Adam: A Method for Stochastic Optimization:
+        https://arxiv.org/abs/1412.6980
+    .. _Decoupled Weight Decay Regularization:
+        https://arxiv.org/abs/1711.05101
+    .. _On the Convergence of Adam and Beyond:
+        https://openreview.net/forum?id=ryQu7f-RZ
+    """
+
+    def __init__(
+        self,
+        params,
+        lr=1e-3,
+        betas=(0.9, 0.98),
+        eps=1e-8,
+        weight_decay=1e-3,
+        target_rms=0.1,
+    ):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
+        if not 0 <= weight_decay <= 0.1:
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+        if not 0 < target_rms <= 10.0:
+            raise ValueError("Invalid target_rms value: {}".format(target_rms))
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            target_rms=target_rms,
+        )
+        super(Eve, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(Eve, self).__setstate__(state)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            for p in group["params"]:
+                if p.grad is None:
+                    continue
+
+                # Perform optimization step
+                grad = p.grad
+                if grad.is_sparse:
+                    raise RuntimeError("AdamW does not support sparse gradients")
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state["step"] = 0
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+
+                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
+
+                beta1, beta2 = group["betas"]
+
+                state["step"] += 1
+                bias_correction1 = 1 - beta1 ** state["step"]
+                bias_correction2 = 1 - beta2 ** state["step"]
+
+                # Decay the first and second moment running average coefficient
+                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+                denom = (exp_avg_sq.sqrt() * (bias_correction2**-0.5)).add_(
+                    group["eps"]
+                )
+
+                step_size = group["lr"] / bias_correction1
+                target_rms = group["target_rms"]
+                weight_decay = group["weight_decay"]
+
+                if p.numel() > 1:
+                    # avoid applying this weight-decay on "scaling factors"
+                    # (which are scalar).
+                    is_above_target_rms = p.norm() > (target_rms * (p.numel() ** 0.5))
+                    p.mul_(1 - (weight_decay * is_above_target_rms))
+
+                p.addcdiv_(exp_avg, denom, value=-step_size)
+
+                if random.random() < 0.0005:
+                    step = (exp_avg / denom) * step_size
+                    logging.info(
+                        f"Delta rms = {(step**2).mean().item()}, shape = {step.shape}"
+                    )
+
+        return loss
+
+
+def _test_scaled_adam(hidden_dim: int):
+    import timeit
+
+    from scaling import ScaledLinear
+
+    E = 100
+    B = 4
+    T = 2
+    logging.info("in test_eve_cain")
+    # device = torch.device('cuda')
+    device = torch.device("cpu")
+    dtype = torch.float32
+
+    fix_random_seed(42)
+    # these input_magnitudes and output_magnitudes are to test that
+    # Abel is working as we expect and is able to adjust scales of
+    # different dims differently.
+    input_magnitudes = (1.0 * torch.randn(E, dtype=dtype, device=device)).exp()
+    output_magnitudes = (1.0 * torch.randn(E, dtype=dtype, device=device)).exp()
+
+    for iter in [1, 0]:
+        fix_random_seed(42)
+        Linear = torch.nn.Linear if iter == 0 else ScaledLinear
+
+        m = torch.nn.Sequential(
+            Linear(E, hidden_dim),
+            torch.nn.PReLU(),
+            Linear(hidden_dim, hidden_dim),
+            torch.nn.PReLU(),
+            Linear(hidden_dim, E),
+        ).to(device)
+
+        train_pairs = [
+            (
+                100.0
+                * torch.randn(B, T, E, device=device, dtype=dtype)
+                * input_magnitudes,
+                torch.randn(B, T, E, device=device, dtype=dtype) * output_magnitudes,
+            )
+            for _ in range(20)
+        ]
+
+        if iter == 0:
+            optim = Eve(m.parameters(), lr=0.003)
+        elif iter == 1:
+            optim = ScaledAdam(m.named_parameters(), lr=0.03, clipping_scale=2.0)
+        scheduler = Eden(optim, lr_batches=200, lr_epochs=5, verbose=False)
+
+        start = timeit.default_timer()
+        avg_loss = 0.0
+        for epoch in range(180):
+            scheduler.step_epoch()
+            # if epoch == 100 and iter in [2,3]:
+            #    optim.reset_speedup()  # check it doesn't crash.
+
+            # if epoch == 130:
+            #    opts = diagnostics.TensorDiagnosticOptions(
+            #        512
+            #    )  # allow 4 megabytes per sub-module
+            #    diagnostic = diagnostics.attach_diagnostics(m, opts)
+
+            for n, (x, y) in enumerate(train_pairs):
+                y_out = m(x)
+                loss = ((y_out - y) ** 2).mean() * 100.0
+                if epoch == 0 and n == 0:
+                    avg_loss = loss.item()
+                else:
+                    avg_loss = 0.98 * avg_loss + 0.02 * loss.item()
+                if n == 0 and epoch % 5 == 0:
+                    # norm1 = '%.2e' % (m[0].weight**2).mean().sqrt().item()
+                    # norm1b = '%.2e' % (m[0].bias**2).mean().sqrt().item()
+                    # norm2 = '%.2e' % (m[2].weight**2).mean().sqrt().item()
+                    # norm2b = '%.2e' % (m[2].bias**2).mean().sqrt().item()
+                    # scale1 = '%.2e' % (m[0].weight_scale.exp().item())
+                    # scale1b = '%.2e' % (m[0].bias_scale.exp().item())
+                    # scale2 = '%.2e' % (m[2].weight_scale.exp().item())
+                    # scale2b = '%.2e' % (m[2].bias_scale.exp().item())
+                    lr = scheduler.get_last_lr()[0]
+                    logging.info(
+                        f"Iter {iter}, epoch {epoch}, batch {n}, avg_loss {avg_loss:.4g}, lr={lr:.4e}"
+                    )  # , norms={norm1,norm1b,norm2,norm2b}") # scales={scale1,scale1b,scale2,scale2b}
+                loss.log().backward()
+                optim.step()
+                optim.zero_grad()
+                scheduler.step_batch()
+
+        # diagnostic.print_diagnostics()
+
+        stop = timeit.default_timer()
+        logging.info(f"Iter={iter}, Time taken: {stop - start}")
+
+        logging.info(f"last lr = {scheduler.get_last_lr()}")
+        # logging.info("state dict = ", scheduler.state_dict())
+        # logging.info("optim state_dict = ", optim.state_dict())
+        logging.info(f"input_magnitudes = {input_magnitudes}")
+        logging.info(f"output_magnitudes = {output_magnitudes}")
+
+
+if __name__ == "__main__":
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
+    logging.getLogger().setLevel(logging.INFO)
+    import subprocess
+
+    s = subprocess.check_output(
+        "git status -uno .; git log -1; git diff HEAD .", shell=True
+    )
+    logging.info(s)
+    import sys
+
+    if len(sys.argv) > 1:
+        hidden_dim = int(sys.argv[1])
+    else:
+        hidden_dim = 200
+
+    _test_scaled_adam(hidden_dim)
+    _test_eden()
diff --git a/egs/zipvoice/zipvoice/scaling.py b/egs/zipvoice/zipvoice/scaling.py
new file mode 100644
index 000000000..5211e3a76
--- /dev/null
+++ b/egs/zipvoice/zipvoice/scaling.py
@@ -0,0 +1,1910 @@
+# Copyright    2022-2023  Xiaomi Corp.        (authors: Daniel Povey)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import logging
+import math
+import random
+from typing import Optional, Tuple, Union
+
+import k2
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+custom_bwd = lambda func: torch.amp.custom_bwd(func, device_type="cuda")
+custom_fwd = lambda func: torch.amp.custom_fwd(func, device_type="cuda")
+
+
+def logaddexp_onnx(x: Tensor, y: Tensor) -> Tensor:
+    max_value = torch.max(x, y)
+    diff = torch.abs(x - y)
+    return max_value + torch.log1p(torch.exp(-diff))
+
+
+# RuntimeError: Exporting the operator logaddexp to ONNX opset version
+# 14 is not supported. Please feel free to request support or submit
+# a pull request on PyTorch GitHub.
+#
+# The following function is to solve the above error when exporting
+# models to ONNX via torch.jit.trace()
+def logaddexp(x: Tensor, y: Tensor) -> Tensor:
+    # Caution(fangjun): Put torch.jit.is_scripting() before
+    # torch.onnx.is_in_onnx_export();
+    # otherwise, it will cause errors for torch.jit.script().
+    #
+    # torch.logaddexp() works for both torch.jit.script() and
+    # torch.jit.trace() but it causes errors for ONNX export.
+    #
+    if torch.jit.is_scripting():
+        # Note: We cannot use torch.jit.is_tracing() here as it also
+        # matches torch.onnx.export().
+        return torch.logaddexp(x, y)
+    elif torch.onnx.is_in_onnx_export():
+        return logaddexp_onnx(x, y)
+    else:
+        # for torch.jit.trace()
+        return torch.logaddexp(x, y)
+
+
+class PiecewiseLinear(object):
+    """
+    Piecewise linear function, from float to float, specified as nonempty list of (x,y) pairs with
+    the x values in order.  x values <[initial x] or >[final x] are map to [initial y], [final y]
+    respectively.
+    """
+
+    def __init__(self, *args):
+        assert len(args) >= 1, len(args)
+        if len(args) == 1 and isinstance(args[0], PiecewiseLinear):
+            self.pairs = list(args[0].pairs)
+        else:
+            self.pairs = [(float(x), float(y)) for x, y in args]
+        for x, y in self.pairs:
+            assert isinstance(x, (float, int)), type(x)
+            assert isinstance(y, (float, int)), type(y)
+
+        for i in range(len(self.pairs) - 1):
+            assert self.pairs[i + 1][0] > self.pairs[i][0], (
+                i,
+                self.pairs[i],
+                self.pairs[i + 1],
+            )
+
+    def __str__(self):
+        # e.g. 'PiecewiseLinear((0., 10.), (100., 0.))'
+        return f"PiecewiseLinear({str(self.pairs)[1:-1]})"
+
+    def __call__(self, x):
+        if x <= self.pairs[0][0]:
+            return self.pairs[0][1]
+        elif x >= self.pairs[-1][0]:
+            return self.pairs[-1][1]
+        else:
+            cur_x, cur_y = self.pairs[0]
+            for i in range(1, len(self.pairs)):
+                next_x, next_y = self.pairs[i]
+                if x >= cur_x and x <= next_x:
+                    return cur_y + (next_y - cur_y) * (x - cur_x) / (next_x - cur_x)
+                cur_x, cur_y = next_x, next_y
+            assert False
+
+    def __mul__(self, alpha):
+        return PiecewiseLinear(*[(x, y * alpha) for x, y in self.pairs])
+
+    def __add__(self, x):
+        if isinstance(x, (float, int)):
+            return PiecewiseLinear(*[(p[0], p[1] + x) for p in self.pairs])
+        s, x = self.get_common_basis(x)
+        return PiecewiseLinear(
+            *[(sp[0], sp[1] + xp[1]) for sp, xp in zip(s.pairs, x.pairs)]
+        )
+
+    def max(self, x):
+        if isinstance(x, (float, int)):
+            x = PiecewiseLinear((0, x))
+        s, x = self.get_common_basis(x, include_crossings=True)
+        return PiecewiseLinear(
+            *[(sp[0], max(sp[1], xp[1])) for sp, xp in zip(s.pairs, x.pairs)]
+        )
+
+    def min(self, x):
+        if isinstance(x, float) or isinstance(x, int):
+            x = PiecewiseLinear((0, x))
+        s, x = self.get_common_basis(x, include_crossings=True)
+        return PiecewiseLinear(
+            *[(sp[0], min(sp[1], xp[1])) for sp, xp in zip(s.pairs, x.pairs)]
+        )
+
+    def __eq__(self, other):
+        return self.pairs == other.pairs
+
+    def get_common_basis(self, p: "PiecewiseLinear", include_crossings: bool = False):
+        """
+        Returns (self_mod, p_mod) which are equivalent piecewise linear
+        functions to self and p, but with the same x values.
+
+          p: the other piecewise linear function
+          include_crossings: if true, include in the x values positions
+              where the functions indicate by this and p crosss.
+        """
+        assert isinstance(p, PiecewiseLinear), type(p)
+
+        # get sorted x-values without repetition.
+        x_vals = sorted(set([x for x, _ in self.pairs] + [x for x, _ in p.pairs]))
+        y_vals1 = [self(x) for x in x_vals]
+        y_vals2 = [p(x) for x in x_vals]
+
+        if include_crossings:
+            extra_x_vals = []
+            for i in range(len(x_vals) - 1):
+                if (y_vals1[i] > y_vals2[i]) != (y_vals1[i + 1] > y_vals2[i + 1]):
+                    # if the two lines in this subsegment potentially cross each other..
+                    diff_cur = abs(y_vals1[i] - y_vals2[i])
+                    diff_next = abs(y_vals1[i + 1] - y_vals2[i + 1])
+                    # `pos`, between 0 and 1, gives the relative x position,
+                    # with 0 being x_vals[i] and 1 being x_vals[i+1].
+                    pos = diff_cur / (diff_cur + diff_next)
+                    extra_x_val = x_vals[i] + pos * (x_vals[i + 1] - x_vals[i])
+                    extra_x_vals.append(extra_x_val)
+            if len(extra_x_vals) > 0:
+                x_vals = sorted(set(x_vals + extra_x_vals))
+        y_vals1 = [self(x) for x in x_vals]
+        y_vals2 = [p(x) for x in x_vals]
+        return (
+            PiecewiseLinear(*zip(x_vals, y_vals1)),
+            PiecewiseLinear(*zip(x_vals, y_vals2)),
+        )
+
+
+class ScheduledFloat(torch.nn.Module):
+    """
+    This object is a torch.nn.Module only because we want it to show up in [top_level module].modules();
+    it does not have a working forward() function.  You are supposed to cast it to float, as
+    in, float(parent_module.whatever), and use it as something like a dropout prob.
+
+    It is a floating point value whose value changes depending on the batch count of the
+    training loop.  It is a piecewise linear function where you specify the (x,y) pairs
+    in sorted order on x; x corresponds to the batch index.  For batch-index values before the
+    first x or after the last x, we just use the first or last y value.
+
+    Example:
+       self.dropout = ScheduledFloat((0.0, 0.2), (4000.0, 0.0), default=0.0)
+
+    `default` is used when self.batch_count is not set or not in training mode or in
+     torch.jit scripting mode.
+    """
+
+    def __init__(self, *args, default: float = 0.0):
+        super().__init__()
+        # self.batch_count and self.name will be written to in the training loop.
+        self.batch_count = None
+        self.name = None
+        self.default = default
+        self.schedule = PiecewiseLinear(*args)
+
+    def extra_repr(self) -> str:
+        return (
+            f"batch_count={self.batch_count}, schedule={str(self.schedule.pairs[1:-1])}"
+        )
+
+    def __float__(self):
+        batch_count = self.batch_count
+        if (
+            batch_count is None
+            or not self.training
+            or torch.jit.is_scripting()
+            or torch.jit.is_tracing()
+        ):
+            return float(self.default)
+        else:
+            ans = self.schedule(self.batch_count)
+            if random.random() < 0.0002:
+                logging.debug(
+                    f"ScheduledFloat: name={self.name}, batch_count={self.batch_count}, ans={ans}"
+                )
+            return ans
+
+    def __add__(self, x):
+        if isinstance(x, float) or isinstance(x, int):
+            return ScheduledFloat(self.schedule + x, default=self.default)
+        else:
+            return ScheduledFloat(
+                self.schedule + x.schedule, default=self.default + x.default
+            )
+
+    def max(self, x):
+        if isinstance(x, float) or isinstance(x, int):
+            return ScheduledFloat(self.schedule.max(x), default=self.default)
+        else:
+            return ScheduledFloat(
+                self.schedule.max(x.schedule), default=max(self.default, x.default)
+            )
+
+
+FloatLike = Union[float, ScheduledFloat]
+
+
+def random_cast_to_half(x: Tensor, min_abs: float = 5.0e-06) -> Tensor:
+    """
+    A randomized way of casting a floating point value to half precision.
+    """
+    if x.dtype == torch.float16:
+        return x
+    x_abs = x.abs()
+    is_too_small = x_abs < min_abs
+    # for elements where is_too_small is true, random_val will contain +-min_abs with
+    # probability (x.abs() / min_abs), and 0.0 otherwise.  [so this preserves expectations,
+    # for those elements].
+    random_val = min_abs * x.sign() * (torch.rand_like(x) * min_abs < x_abs)
+    return torch.where(is_too_small, random_val, x).to(torch.float16)
+
+
+class CutoffEstimator:
+    """
+    Estimates cutoffs of an arbitrary numerical quantity such that a specified
+    proportion of items will be above the cutoff on average.
+
+      p is the proportion of items that should be above the cutoff.
+    """
+
+    def __init__(self, p: float):
+        self.p = p
+        # total count of items
+        self.count = 0
+        # total count of items that were above the cutoff
+        self.count_above = 0
+        # initial cutoff value
+        self.cutoff = 0
+
+    def __call__(self, x: float) -> bool:
+        """
+        Returns true if x is above the cutoff.
+        """
+        ans = x > self.cutoff
+        self.count += 1
+        if ans:
+            self.count_above += 1
+        cur_p = self.count_above / self.count
+        delta_p = cur_p - self.p
+        if (delta_p > 0) == ans:
+            q = abs(delta_p)
+            self.cutoff = x * q + self.cutoff * (1 - q)
+        return ans
+
+
+class SoftmaxFunction(torch.autograd.Function):
+    """
+    Tries to handle half-precision derivatives in a randomized way that should
+    be more accurate for training than the default behavior.
+    """
+
+    @staticmethod
+    def forward(ctx, x: Tensor, dim: int):
+        ans = x.softmax(dim=dim)
+        # if x dtype is float16, x.softmax() returns a float32 because
+        # (presumably) that op does not support float16, and autocast
+        # is enabled.
+        if torch.is_autocast_enabled():
+            ans = ans.to(torch.float16)
+        ctx.save_for_backward(ans)
+        ctx.x_dtype = x.dtype
+        ctx.dim = dim
+        return ans
+
+    @staticmethod
+    def backward(ctx, ans_grad: Tensor):
+        (ans,) = ctx.saved_tensors
+        with torch.amp.autocast("cuda", enabled=False):
+            ans_grad = ans_grad.to(torch.float32)
+            ans = ans.to(torch.float32)
+            x_grad = ans_grad * ans
+            x_grad = x_grad - ans * x_grad.sum(dim=ctx.dim, keepdim=True)
+            return x_grad, None
+
+
+def softmax(x: Tensor, dim: int):
+    if not x.requires_grad or torch.jit.is_scripting() or torch.jit.is_tracing():
+        return x.softmax(dim=dim)
+
+    return SoftmaxFunction.apply(x, dim)
+
+
+class MaxEigLimiterFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        coeffs: Tensor,
+        direction: Tensor,
+        channel_dim: int,
+        grad_scale: float,
+    ) -> Tensor:
+        ctx.channel_dim = channel_dim
+        ctx.grad_scale = grad_scale
+        ctx.save_for_backward(x.detach(), coeffs.detach(), direction.detach())
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad, *args):
+        with torch.enable_grad():
+            (x_orig, coeffs, new_direction) = ctx.saved_tensors
+            x_orig.requires_grad = True
+            num_channels = x_orig.shape[ctx.channel_dim]
+            x = x_orig.transpose(ctx.channel_dim, -1).reshape(-1, num_channels)
+            new_direction.requires_grad = False
+            x = x - x.mean(dim=0)
+            x_var = (x**2).mean()
+            x_residual = x - coeffs * new_direction
+            x_residual_var = (x_residual**2).mean()
+            # `variance_proportion` is the proportion of the variance accounted for
+            # by the top eigen-direction.  This is to be minimized.
+            variance_proportion = (x_var - x_residual_var) / (x_var + 1.0e-20)
+            variance_proportion.backward()
+        x_orig_grad = x_orig.grad
+        x_extra_grad = (
+            x_orig.grad
+            * ctx.grad_scale
+            * x_grad.norm()
+            / (x_orig_grad.norm() + 1.0e-20)
+        )
+        return x_grad + x_extra_grad.detach(), None, None, None, None
+
+
+class BiasNormFunction(torch.autograd.Function):
+    # This computes:
+    #   scales = (torch.mean((x - bias) ** 2, keepdim=True)) ** -0.5 * log_scale.exp()
+    #   return x * scales
+    # (after unsqueezing the bias), but it does it in a memory-efficient way so that
+    # it can just store the returned value (chances are, this will also be needed for
+    # some other reason, related to the next operation, so we can save memory).
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        bias: Tensor,
+        log_scale: Tensor,
+        channel_dim: int,
+        store_output_for_backprop: bool,
+    ) -> Tensor:
+        assert bias.ndim == 1
+        if channel_dim < 0:
+            channel_dim = channel_dim + x.ndim
+        ctx.store_output_for_backprop = store_output_for_backprop
+        ctx.channel_dim = channel_dim
+        for _ in range(channel_dim + 1, x.ndim):
+            bias = bias.unsqueeze(-1)
+        scales = (
+            torch.mean((x - bias) ** 2, dim=channel_dim, keepdim=True) ** -0.5
+        ) * log_scale.exp()
+        ans = x * scales
+        ctx.save_for_backward(
+            ans.detach() if store_output_for_backprop else x,
+            scales.detach(),
+            bias.detach(),
+            log_scale.detach(),
+        )
+        return ans
+
+    @staticmethod
+    def backward(ctx, ans_grad: Tensor) -> Tensor:
+        ans_or_x, scales, bias, log_scale = ctx.saved_tensors
+        if ctx.store_output_for_backprop:
+            x = ans_or_x / scales
+        else:
+            x = ans_or_x
+        x = x.detach()
+        x.requires_grad = True
+        bias.requires_grad = True
+        log_scale.requires_grad = True
+        with torch.enable_grad():
+            # recompute scales from x, bias and log_scale.
+            scales = (
+                torch.mean((x - bias) ** 2, dim=ctx.channel_dim, keepdim=True) ** -0.5
+            ) * log_scale.exp()
+            ans = x * scales
+            ans.backward(gradient=ans_grad)
+        return x.grad, bias.grad.flatten(), log_scale.grad, None, None
+
+
+class BiasNorm(torch.nn.Module):
+    """
+    This is intended to be a simpler, and hopefully cheaper, replacement for
+    LayerNorm.  The observation this is based on, is that Transformer-type
+    networks, especially with pre-norm, sometimes seem to set one of the
+    feature dimensions to a large constant value (e.g. 50), which "defeats"
+    the LayerNorm because the output magnitude is then not strongly dependent
+    on the other (useful) features.  Presumably the weight and bias of the
+    LayerNorm are required to allow it to do this.
+
+    Instead, we give the BiasNorm a trainable bias that it can use when
+    computing the scale for normalization.  We also give it a (scalar)
+    trainable scale on the output.
+
+
+    Args:
+       num_channels: the number of channels, e.g. 512.
+       channel_dim: the axis/dimension corresponding to the channel,
+         interpreted as an offset from the input's ndim if negative.
+         This is NOT the num_channels; it should typically be one of
+         {-2, -1, 0, 1, 2, 3}.
+      log_scale: the initial log-scale that we multiply the output by; this
+         is learnable.
+      log_scale_min: FloatLike, minimum allowed value of log_scale
+      log_scale_max: FloatLike, maximum allowed value of log_scale
+      store_output_for_backprop: only possibly affects memory use; recommend
+         to set to True if you think the output of this module is more likely
+         than the input of this module to be required to be stored for the
+         backprop.
+    """
+
+    def __init__(
+        self,
+        num_channels: int,
+        channel_dim: int = -1,  # CAUTION: see documentation.
+        log_scale: float = 1.0,
+        log_scale_min: float = -1.5,
+        log_scale_max: float = 1.5,
+        store_output_for_backprop: bool = False,
+    ) -> None:
+        super(BiasNorm, self).__init__()
+        self.num_channels = num_channels
+        self.channel_dim = channel_dim
+        self.log_scale = nn.Parameter(torch.tensor(log_scale))
+        self.bias = nn.Parameter(torch.zeros(num_channels))
+
+        self.log_scale_min = log_scale_min
+        self.log_scale_max = log_scale_max
+
+        self.store_output_for_backprop = store_output_for_backprop
+
+    def forward(self, x: Tensor) -> Tensor:
+        assert x.shape[self.channel_dim] == self.num_channels
+
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            channel_dim = self.channel_dim
+            if channel_dim < 0:
+                channel_dim += x.ndim
+            bias = self.bias
+            for _ in range(channel_dim + 1, x.ndim):
+                bias = bias.unsqueeze(-1)
+            scales = (
+                torch.mean((x - bias) ** 2, dim=channel_dim, keepdim=True) ** -0.5
+            ) * self.log_scale.exp()
+            return x * scales
+
+        log_scale = limit_param_value(
+            self.log_scale,
+            min=float(self.log_scale_min),
+            max=float(self.log_scale_max),
+            training=self.training,
+        )
+
+        return BiasNormFunction.apply(
+            x, self.bias, log_scale, self.channel_dim, self.store_output_for_backprop
+        )
+
+
+def ScaledLinear(*args, initial_scale: float = 1.0, **kwargs) -> nn.Linear:
+    """
+    Behaves like a constructor of a modified version of nn.Linear
+    that gives an easy way to set the default initial parameter scale.
+
+    Args:
+        Accepts the standard args and kwargs that nn.Linear accepts
+        e.g. in_features, out_features, bias=False.
+
+        initial_scale: you can override this if you want to increase
+           or decrease the initial magnitude of the module's output
+           (affects the initialization of weight_scale and bias_scale).
+           Another option, if you want to do something like this, is
+           to re-initialize the parameters.
+    """
+    ans = nn.Linear(*args, **kwargs)
+    with torch.no_grad():
+        ans.weight[:] *= initial_scale
+        if ans.bias is not None:
+            torch.nn.init.uniform_(ans.bias, -0.1 * initial_scale, 0.1 * initial_scale)
+    return ans
+
+
+def ScaledConv1d(*args, initial_scale: float = 1.0, **kwargs) -> nn.Conv1d:
+    """
+    Behaves like a constructor of a modified version of nn.Conv1d
+    that gives an easy way to set the default initial parameter scale.
+
+    Args:
+        Accepts the standard args and kwargs that nn.Linear accepts
+        e.g. in_features, out_features, bias=False.
+
+        initial_scale: you can override this if you want to increase
+           or decrease the initial magnitude of the module's output
+           (affects the initialization of weight_scale and bias_scale).
+           Another option, if you want to do something like this, is
+           to re-initialize the parameters.
+    """
+    ans = nn.Conv1d(*args, **kwargs)
+    with torch.no_grad():
+        ans.weight[:] *= initial_scale
+        if ans.bias is not None:
+            torch.nn.init.uniform_(ans.bias, -0.1 * initial_scale, 0.1 * initial_scale)
+    return ans
+
+
+def ScaledConv2d(*args, initial_scale: float = 1.0, **kwargs) -> nn.Conv2d:
+    """
+    Behaves like a constructor of a modified version of nn.Conv2d
+    that gives an easy way to set the default initial parameter scale.
+
+    Args:
+        Accepts the standard args and kwargs that nn.Linear accepts
+        e.g. in_features, out_features, bias=False, but:
+    NO PADDING-RELATED ARGS.
+
+        initial_scale: you can override this if you want to increase
+           or decrease the initial magnitude of the module's output
+           (affects the initialization of weight_scale and bias_scale).
+           Another option, if you want to do something like this, is
+           to re-initialize the parameters.
+    """
+    ans = nn.Conv2d(*args, **kwargs)
+    with torch.no_grad():
+        ans.weight[:] *= initial_scale
+        if ans.bias is not None:
+            torch.nn.init.uniform_(ans.bias, -0.1 * initial_scale, 0.1 * initial_scale)
+    return ans
+
+
+class ChunkCausalDepthwiseConv1d(torch.nn.Module):
+    """
+    Behaves like a depthwise 1d convolution, except that it is causal in
+    a chunkwise way, as if we had a block-triangular attention mask.
+    The chunk size is provided at test time (it should probably be
+    kept in sync with the attention mask).
+
+    This has a little more than twice the parameters of a conventional
+    depthwise conv1d module: we implement it by having one
+    depthwise convolution, of half the width, that is causal (via
+    right-padding); and one depthwise convolution that is applied only
+    within chunks, that we multiply by a scaling factor which depends
+    on the position within the chunk.
+
+    Args:
+        Accepts the standard args and kwargs that nn.Linear accepts
+        e.g. in_features, out_features, bias=False.
+
+        initial_scale: you can override this if you want to increase
+           or decrease the initial magnitude of the module's output
+           (affects the initialization of weight_scale and bias_scale).
+           Another option, if you want to do something like this, is
+           to re-initialize the parameters.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        kernel_size: int,
+        initial_scale: float = 1.0,
+        bias: bool = True,
+    ):
+        super().__init__()
+        assert kernel_size % 2 == 1
+
+        half_kernel_size = (kernel_size + 1) // 2
+        # will pad manually, on one side.
+        self.causal_conv = nn.Conv1d(
+            in_channels=channels,
+            out_channels=channels,
+            groups=channels,
+            kernel_size=half_kernel_size,
+            padding=0,
+            bias=True,
+        )
+
+        self.chunkwise_conv = nn.Conv1d(
+            in_channels=channels,
+            out_channels=channels,
+            groups=channels,
+            kernel_size=kernel_size,
+            padding=kernel_size // 2,
+            bias=bias,
+        )
+
+        # first row is correction factors added to the scale near the left edge of the chunk,
+        # second row is correction factors added to the scale near the right edge of the chunk,
+        # both of these are added to a default scale of 1.0.
+        self.chunkwise_conv_scale = nn.Parameter(torch.zeros(2, channels, kernel_size))
+        self.kernel_size = kernel_size
+
+        with torch.no_grad():
+            self.causal_conv.weight[:] *= initial_scale
+            self.chunkwise_conv.weight[:] *= initial_scale
+            if bias:
+                torch.nn.init.uniform_(
+                    self.causal_conv.bias, -0.1 * initial_scale, 0.1 * initial_scale
+                )
+
+    def forward(self, x: Tensor, chunk_size: int = -1) -> Tensor:
+        """
+             Forward function.  Args:
+               x: a Tensor of shape (batch_size, channels, seq_len)
+        chunk_size: the chunk size, in frames; does not have to divide seq_len exactly.
+        """
+        (batch_size, num_channels, seq_len) = x.shape
+
+        # half_kernel_size = self.kernel_size + 1 // 2
+        # left_pad is half_kernel_size - 1 where half_kernel_size is the size used
+        # in the causal conv.  It's the amount by which we must pad on the left,
+        # to make the convolution causal.
+        left_pad = self.kernel_size // 2
+
+        if chunk_size < 0 or chunk_size > seq_len:
+            chunk_size = seq_len
+        right_pad = -seq_len % chunk_size
+
+        x = torch.nn.functional.pad(x, (left_pad, right_pad))
+
+        x_causal = self.causal_conv(x[..., : left_pad + seq_len])
+        assert x_causal.shape == (batch_size, num_channels, seq_len)
+
+        x_chunk = x[..., left_pad:]
+        num_chunks = x_chunk.shape[2] // chunk_size
+        x_chunk = x_chunk.reshape(batch_size, num_channels, num_chunks, chunk_size)
+        x_chunk = x_chunk.permute(0, 2, 1, 3).reshape(
+            batch_size * num_chunks, num_channels, chunk_size
+        )
+        x_chunk = self.chunkwise_conv(x_chunk)  # does not change shape
+
+        chunk_scale = self._get_chunk_scale(chunk_size)
+
+        x_chunk = x_chunk * chunk_scale
+        x_chunk = x_chunk.reshape(
+            batch_size, num_chunks, num_channels, chunk_size
+        ).permute(0, 2, 1, 3)
+        x_chunk = x_chunk.reshape(batch_size, num_channels, num_chunks * chunk_size)[
+            ..., :seq_len
+        ]
+
+        return x_chunk + x_causal
+
+    def _get_chunk_scale(self, chunk_size: int):
+        """Returns tensor of shape (num_channels, chunk_size) that will be used to
+        scale the output of self.chunkwise_conv."""
+        left_edge = self.chunkwise_conv_scale[0]
+        right_edge = self.chunkwise_conv_scale[1]
+        if chunk_size < self.kernel_size:
+            left_edge = left_edge[:, :chunk_size]
+            right_edge = right_edge[:, -chunk_size:]
+        else:
+            t = chunk_size - self.kernel_size
+            channels = left_edge.shape[0]
+            pad = torch.zeros(
+                channels, t, device=left_edge.device, dtype=left_edge.dtype
+            )
+            left_edge = torch.cat((left_edge, pad), dim=-1)
+            right_edge = torch.cat((pad, right_edge), dim=-1)
+        return 1.0 + (left_edge + right_edge)
+
+    def streaming_forward(
+        self,
+        x: Tensor,
+        cache: Tensor,
+    ) -> Tuple[Tensor, Tensor]:
+        """Streaming Forward function.
+
+        Args:
+            x: a Tensor of shape (batch_size, channels, seq_len)
+            cache: cached left context of shape (batch_size, channels, left_pad)
+        """
+        (batch_size, num_channels, seq_len) = x.shape
+
+        # left_pad is half_kernel_size - 1 where half_kernel_size is the size used
+        # in the causal conv.  It's the amount by which we must pad on the left,
+        # to make the convolution causal.
+        left_pad = self.kernel_size // 2
+
+        # Pad cache
+        assert cache.shape[-1] == left_pad, (cache.shape[-1], left_pad)
+        x = torch.cat([cache, x], dim=2)
+        # Update cache
+        cache = x[..., -left_pad:]
+
+        x_causal = self.causal_conv(x)
+        assert x_causal.shape == (batch_size, num_channels, seq_len)
+
+        x_chunk = x[..., left_pad:]
+        x_chunk = self.chunkwise_conv(x_chunk)  # does not change shape
+
+        chunk_scale = self._get_chunk_scale(chunk_size=seq_len)
+        x_chunk = x_chunk * chunk_scale
+
+        return x_chunk + x_causal, cache
+
+
+class BalancerFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        min_mean: float,
+        max_mean: float,
+        min_rms: float,
+        max_rms: float,
+        grad_scale: float,
+        channel_dim: int,
+    ) -> Tensor:
+        if channel_dim < 0:
+            channel_dim += x.ndim
+        ctx.channel_dim = channel_dim
+        ctx.save_for_backward(x)
+        ctx.config = (min_mean, max_mean, min_rms, max_rms, grad_scale, channel_dim)
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None, None, None]:
+        (x,) = ctx.saved_tensors
+        (min_mean, max_mean, min_rms, max_rms, grad_scale, channel_dim) = ctx.config
+
+        try:
+            with torch.enable_grad():
+                with torch.amp.autocast("cuda", enabled=False):
+                    x = x.to(torch.float32)
+                    x = x.detach()
+                    x.requires_grad = True
+                    mean_dims = [i for i in range(x.ndim) if i != channel_dim]
+                    uncentered_var = (x**2).mean(dim=mean_dims, keepdim=True)
+                    mean = x.mean(dim=mean_dims, keepdim=True)
+                    stddev = (uncentered_var - (mean * mean)).clamp(min=1.0e-20).sqrt()
+                    rms = uncentered_var.clamp(min=1.0e-20).sqrt()
+
+                    m = mean / stddev
+                    # part of loss that relates to mean / stddev
+                    m_loss = (m - m.clamp(min=min_mean, max=max_mean)).abs()
+
+                    # put a much larger scale on the RMS-max-limit loss, so that if both it and the
+                    # m_loss are violated we fix the RMS loss first.
+                    rms_clamped = rms.clamp(min=min_rms, max=max_rms)
+                    r_loss = (rms_clamped / rms).log().abs()
+
+                    loss = m_loss + r_loss
+
+                    loss.backward(gradient=torch.ones_like(loss))
+                    loss_grad = x.grad
+                    loss_grad_rms = (
+                        (loss_grad**2)
+                        .mean(dim=mean_dims, keepdim=True)
+                        .sqrt()
+                        .clamp(min=1.0e-20)
+                    )
+
+                    loss_grad = loss_grad * (grad_scale / loss_grad_rms)
+
+                    x_grad_float = x_grad.to(torch.float32)
+                    # scale each element of loss_grad by the absolute value of the corresponding
+                    # element of x_grad, which we view as a noisy estimate of its magnitude for that
+                    # (frame and dimension).  later we can consider factored versions.
+                    x_grad_mod = x_grad_float + (x_grad_float.abs() * loss_grad)
+                    x_grad = x_grad_mod.to(x_grad.dtype)
+        except Exception as e:
+            logging.info(
+                f"Caught exception in Balancer backward: {e}, size={list(x_grad.shape)}, will continue."
+            )
+
+        return x_grad, None, None, None, None, None, None
+
+
+class Balancer(torch.nn.Module):
+    """
+    Modifies the backpropped derivatives of a function to try to encourage, for
+    each channel, that it is positive at least a proportion `threshold` of the
+    time.  It does this by multiplying negative derivative values by up to
+    (1+max_factor), and positive derivative values by up to (1-max_factor),
+    interpolated from 1 at the threshold to those extremal values when none
+    of the inputs are positive.
+
+    Args:
+           num_channels: the number of channels
+           channel_dim: the dimension/axis corresponding to the channel, e.g.
+               -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative.
+           min_positive: the minimum, per channel, of the proportion of the time
+               that (x > 0), below which we start to modify the derivatives.
+           max_positive: the maximum, per channel, of the proportion of the time
+               that (x > 0), above which we start to modify the derivatives.
+           scale_gain_factor: determines the 'gain' with which we increase the
+              change in gradient once the constraints on min_abs and max_abs
+              are violated.
+           min_abs:  the minimum average-absolute-value difference from the mean
+               value per channel, which we allow, before we start to modify
+               the derivatives to prevent this.
+           max_abs:  the maximum average-absolute-value difference from the mean
+               value per channel, which we allow, before we start to modify
+               the derivatives to prevent this.
+         prob: determines the minimum probability with which we modify the
+             gradients for the {min,max}_positive and {min,max}_abs constraints,
+             on each forward().  This is done randomly to prevent all layers
+             from doing it at the same time.
+    """
+
+    def __init__(
+        self,
+        num_channels: int,
+        channel_dim: int,
+        min_positive: FloatLike = 0.05,
+        max_positive: FloatLike = 0.95,
+        min_abs: FloatLike = 0.2,
+        max_abs: FloatLike = 100.0,
+        grad_scale: FloatLike = 0.04,
+        prob: Optional[FloatLike] = None,
+    ):
+        super().__init__()
+
+        if prob is None:
+            prob = ScheduledFloat((0.0, 0.5), (8000.0, 0.125), default=0.4)
+        self.prob = prob
+        # 5% of the time we will return and do nothing because memory usage is
+        # too high.
+        self.mem_cutoff = CutoffEstimator(0.05)
+
+        # actually self.num_channels is no longer needed except for an assertion.
+        self.num_channels = num_channels
+        self.channel_dim = channel_dim
+        self.min_positive = min_positive
+        self.max_positive = max_positive
+        self.min_abs = min_abs
+        self.max_abs = max_abs
+        self.grad_scale = grad_scale
+
+    def forward(self, x: Tensor) -> Tensor:
+        if (
+            torch.jit.is_scripting()
+            or not x.requires_grad
+            or (x.is_cuda and self.mem_cutoff(torch.cuda.memory_allocated()))
+        ):
+            return _no_op(x)
+
+        prob = float(self.prob)
+        if random.random() < prob:
+            # The following inner-functions convert from the way we historically specified
+            # these limitations, as limits on the absolute value and the proportion of positive
+            # values, to limits on the RMS value and the (mean / stddev).
+            def _abs_to_rms(x):
+                # for normally distributed data, if the expected absolute value is x, the
+                # expected rms value will be sqrt(pi/2) * x.
+                return 1.25331413732 * x
+
+            def _proportion_positive_to_mean(x):
+                def _atanh(x):
+                    eps = 1.0e-10
+                    # eps is to prevent crashes if x is exactly 0 or 1.
+                    # we'll just end up returning a fairly large value.
+                    return (math.log(1 + x + eps) - math.log(1 - x + eps)) / 2.0
+
+                def _approx_inverse_erf(x):
+                    # 1 / (sqrt(pi) * ln(2)),
+                    # see https://math.stackexchange.com/questions/321569/approximating-the-error-function-erf-by-analytical-functions
+                    # this approximation is extremely crude and gets progressively worse for
+                    # x very close to -1 or +1, but we mostly care about the "middle" region
+                    # e.g. _approx_inverse_erf(0.05) = 0.0407316414078772,
+                    # and math.erf(0.0407316414078772) = 0.045935330944660666,
+                    # which is pretty close to 0.05.
+                    return 0.8139535143 * _atanh(x)
+
+                # first convert x from the range 0..1 to the range -1..1 which the error
+                # function returns
+                x = -1 + (2 * x)
+                return _approx_inverse_erf(x)
+
+            min_mean = _proportion_positive_to_mean(float(self.min_positive))
+            max_mean = _proportion_positive_to_mean(float(self.max_positive))
+            min_rms = _abs_to_rms(float(self.min_abs))
+            max_rms = _abs_to_rms(float(self.max_abs))
+            grad_scale = float(self.grad_scale)
+
+            assert x.shape[self.channel_dim] == self.num_channels
+
+            return BalancerFunction.apply(
+                x, min_mean, max_mean, min_rms, max_rms, grad_scale, self.channel_dim
+            )
+        else:
+            return _no_op(x)
+
+
+def penalize_abs_values_gt(
+    x: Tensor, limit: float, penalty: float, name: str = None
+) -> Tensor:
+    """
+    Returns x unmodified, but in backprop will put a penalty for the excess of
+    the absolute values of elements of x over the limit "limit".  E.g. if
+    limit == 10.0, then if x has any values over 10 it will get a penalty.
+
+    Caution: the value of this penalty will be affected by grad scaling used
+    in automatic mixed precision training.  For this reasons we use this,
+    it shouldn't really matter, or may even be helpful; we just use this
+    to disallow really implausible values of scores to be given to softmax.
+
+    The name is for randomly printed debug info.
+    """
+    x_sign = x.sign()
+    over_limit = (x.abs() - limit) > 0
+    # The following is a memory efficient way to penalize the absolute values of
+    # x that's over the limit.  (The memory efficiency comes when you think
+    # about which items torch needs to cache for the autograd, and which ones it
+    # can throw away).  The numerical value of aux_loss as computed here will
+    # actually be larger than it should be, by limit * over_limit.sum(), but it
+    # has the same derivative as the real aux_loss which is penalty * (x.abs() -
+    # limit).relu().
+    aux_loss = penalty * ((x_sign * over_limit).to(torch.int8) * x)
+    # note: we don't do sum() here on aux)_loss, but it's as if we had done
+    # sum() due to how with_loss() works.
+    x = with_loss(x, aux_loss, name)
+    # you must use x for something, or this will be ineffective.
+    return x
+
+
+def _diag(x: Tensor):  # like .diag(), but works for tensors with 3 dims.
+    if x.ndim == 2:
+        return x.diag()
+    else:
+        (batch, dim, dim) = x.shape
+        x = x.reshape(batch, dim * dim)
+        x = x[:, :: dim + 1]
+        assert x.shape == (batch, dim)
+        return x
+
+
+def _whitening_metric(x: Tensor, num_groups: int):
+    """
+    Computes the "whitening metric", a value which will be 1.0 if all the eigenvalues of
+    of the centered feature covariance are the same within each group's covariance matrix
+    and also between groups.
+    Args:
+        x: a Tensor of shape (*, num_channels)
+     num_groups:  the number of groups of channels, a number >=1 that divides num_channels
+    Returns:
+        Returns a scalar Tensor that will be 1.0 if the data is "perfectly white" and
+    greater than 1.0 otherwise.
+    """
+    assert x.dtype != torch.float16
+    x = x.reshape(-1, x.shape[-1])
+    (num_frames, num_channels) = x.shape
+    assert num_channels % num_groups == 0
+    channels_per_group = num_channels // num_groups
+    x = x.reshape(num_frames, num_groups, channels_per_group).transpose(0, 1)
+    # x now has shape (num_groups, num_frames, channels_per_group)
+    # subtract the mean so we use the centered, not uncentered, covariance.
+    # My experience has been that when we "mess with the gradients" like this,
+    # it's better not do anything that tries to move the mean around, because
+    # that can easily cause instability.
+    x = x - x.mean(dim=1, keepdim=True)
+    # x_covar: (num_groups, channels_per_group, channels_per_group)
+    x_covar = torch.matmul(x.transpose(1, 2), x)
+    x_covar_mean_diag = _diag(x_covar).mean()
+    # the following expression is what we'd get if we took the matrix product
+    # of each covariance and measured the mean of its trace, i.e.
+    # the same as _diag(torch.matmul(x_covar, x_covar)).mean().
+    x_covarsq_mean_diag = (x_covar**2).sum() / (num_groups * channels_per_group)
+    # this metric will be >= 1.0; the larger it is, the less 'white' the data was.
+    metric = x_covarsq_mean_diag / (x_covar_mean_diag**2 + 1.0e-20)
+    return metric
+
+
+class WhiteningPenaltyFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: Tensor, module: nn.Module) -> Tensor:
+        ctx.save_for_backward(x)
+        ctx.module = module
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad: Tensor):
+        (x_orig,) = ctx.saved_tensors
+        w = ctx.module
+
+        try:
+            with torch.enable_grad():
+                with torch.amp.autocast("cuda", enabled=False):
+                    x_detached = x_orig.to(torch.float32).detach()
+                    x_detached.requires_grad = True
+
+                    metric = _whitening_metric(x_detached, w.num_groups)
+
+                    if random.random() < 0.005 or __name__ == "__main__":
+                        logging.debug(
+                            f"Whitening: name={w.name}, num_groups={w.num_groups}, num_channels={x_orig.shape[-1]}, "
+                            f"metric={metric.item():.2f} vs. limit={float(w.whitening_limit)}"
+                        )
+
+                    if metric < float(w.whitening_limit):
+                        w.prob = w.min_prob
+                        return x_grad, None
+                    else:
+                        w.prob = w.max_prob
+                        metric.backward()
+                        penalty_grad = x_detached.grad
+                        scale = w.grad_scale * (
+                            x_grad.to(torch.float32).norm()
+                            / (penalty_grad.norm() + 1.0e-20)
+                        )
+                        penalty_grad = penalty_grad * scale
+                        return x_grad + penalty_grad.to(x_grad.dtype), None
+        except Exception as e:
+            logging.info(
+                f"Caught exception in Whiten backward: {e}, size={list(x_grad.shape)}, will continue."
+            )
+        return x_grad, None
+
+
+class Whiten(nn.Module):
+    def __init__(
+        self,
+        num_groups: int,
+        whitening_limit: FloatLike,
+        prob: Union[float, Tuple[float, float]],
+        grad_scale: FloatLike,
+    ):
+        """
+        Args:
+          num_groups: the number of groups to divide the channel dim into before
+            whitening.  We will attempt to make the feature covariance
+            within each group, after mean subtraction, as "white" as possible,
+            while having the same trace across all groups.
+         whitening_limit: a value greater than 1.0, that dictates how much
+           freedom we have to violate the constraints.  1.0 would mean perfectly
+           white, with exactly the same trace across groups; larger values
+           give more freedom.  E.g. 2.0.
+         prob: the probability with which we apply the gradient modification
+           (also affects the grad scale).  May be supplied as a float,
+           or as a pair (min_prob, max_prob)
+
+          grad_scale: determines the scale on the gradient term from this object,
+            relative to the rest of the gradient on the attention weights.
+            E.g. 0.02 (you may want to use smaller values than this if prob is large)
+        """
+        super(Whiten, self).__init__()
+        assert num_groups >= 1
+        assert float(whitening_limit) >= 1
+        assert grad_scale >= 0
+        self.num_groups = num_groups
+        self.whitening_limit = whitening_limit
+        self.grad_scale = grad_scale
+
+        if isinstance(prob, float):
+            prob = (prob, prob)
+        (self.min_prob, self.max_prob) = prob
+        assert 0 < self.min_prob <= self.max_prob <= 1
+        self.prob = self.max_prob
+        self.name = None  # will be set in training loop
+
+    def forward(self, x: Tensor) -> Tensor:
+        """
+        In the forward pass, this function just returns the input unmodified.
+        In the backward pass, it will modify the gradients to ensure that the
+        distribution in each group has close to (lambda times I) as the covariance
+        after mean subtraction, with the same lambda across groups.
+        For whitening_limit > 1, there will be more freedom to violate this
+        constraint.
+
+        Args:
+           x: the input of shape (*, num_channels)
+
+        Returns:
+            x, unmodified.   You should make sure
+        you use the returned value, or the graph will be freed
+        and nothing will happen in backprop.
+        """
+        grad_scale = float(self.grad_scale)
+        if not x.requires_grad or random.random() > self.prob or grad_scale == 0:
+            return _no_op(x)
+        else:
+            return WhiteningPenaltyFunction.apply(x, self)
+
+
+class WithLoss(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: Tensor, y: Tensor, name: str):
+        ctx.y_shape = y.shape
+        if random.random() < 0.002 and name is not None:
+            loss_sum = y.sum().item()
+            logging.debug(f"WithLoss: name={name}, loss-sum={loss_sum:.3e}")
+        return x
+
+    @staticmethod
+    def backward(ctx, ans_grad: Tensor):
+        return (
+            ans_grad,
+            torch.ones(ctx.y_shape, dtype=ans_grad.dtype, device=ans_grad.device),
+            None,
+        )
+
+
+def with_loss(x, y, name):
+    # returns x but adds y.sum() to the loss function.
+    return WithLoss.apply(x, y, name)
+
+
+class ScaleGradFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: Tensor, alpha: float) -> Tensor:
+        ctx.alpha = alpha
+        return x
+
+    @staticmethod
+    def backward(ctx, grad: Tensor):
+        return grad * ctx.alpha, None
+
+
+def scale_grad(x: Tensor, alpha: float):
+    return ScaleGradFunction.apply(x, alpha)
+
+
+class ScaleGrad(nn.Module):
+    def __init__(self, alpha: float):
+        super().__init__()
+        self.alpha = alpha
+
+    def forward(self, x: Tensor) -> Tensor:
+        if torch.jit.is_scripting() or torch.jit.is_tracing() or not self.training:
+            return x
+        return scale_grad(x, self.alpha)
+
+
+class LimitParamValue(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: Tensor, min: float, max: float):
+        ctx.save_for_backward(x)
+        assert max >= min
+        ctx.min = min
+        ctx.max = max
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad: Tensor):
+        (x,) = ctx.saved_tensors
+        # where x < ctx.min, ensure all grads are negative (this will tend to make
+        # x more positive).
+        x_grad = x_grad * torch.where(
+            torch.logical_and(x_grad > 0, x < ctx.min), -1.0, 1.0
+        )
+        # where x > ctx.max, ensure all grads are positive (this will tend to make
+        # x more negative).
+        x_grad *= torch.where(torch.logical_and(x_grad < 0, x > ctx.max), -1.0, 1.0)
+        return x_grad, None, None
+
+
+def limit_param_value(
+    x: Tensor, min: float, max: float, prob: float = 0.6, training: bool = True
+):
+    # You apply this to (typically) an nn.Parameter during training to ensure that its
+    # (elements mostly) stays within a supplied range.  This is done by modifying the
+    # gradients in backprop.
+    # It's not necessary to do this on every batch: do it only some of the time,
+    # to save a little time.
+    if training and random.random() < prob:
+        return LimitParamValue.apply(x, min, max)
+    else:
+        return x
+
+
+def _no_op(x: Tensor) -> Tensor:
+    if torch.jit.is_scripting() or torch.jit.is_tracing():
+        return x
+    else:
+        # a no-op function that will have a node in the autograd graph,
+        # to avoid certain bugs relating to backward hooks
+        return x.chunk(1, dim=-1)[0]
+
+
+class Identity(torch.nn.Module):
+    def __init__(self):
+        super(Identity, self).__init__()
+
+    def forward(self, x):
+        return _no_op(x)
+
+
+class DoubleSwishFunction(torch.autograd.Function):
+    """
+      double_swish(x) = x * torch.sigmoid(x-1)
+
+    This is a definition, originally motivated by its close numerical
+    similarity to swish(swish(x)), where swish(x) =  x * sigmoid(x).
+
+    Memory-efficient derivative computation:
+     double_swish(x) = x * s, where s(x) = torch.sigmoid(x-1)
+     double_swish'(x) = d/dx double_swish(x) =  x * s'(x) + x' * s(x) = x * s'(x) + s(x).
+     Now, s'(x) = s(x) * (1-s(x)).
+     double_swish'(x) =  x * s'(x) + s(x).
+                      =  x * s(x) * (1-s(x)) + s(x).
+                     = double_swish(x) * (1-s(x)) + s(x)
+     ... so we just need to remember s(x) but not x itself.
+    """
+
+    @staticmethod
+    def forward(ctx, x: Tensor) -> Tensor:
+        requires_grad = x.requires_grad
+        if x.dtype == torch.float16:
+            x = x.to(torch.float32)
+
+        s = torch.sigmoid(x - 1.0)
+        y = x * s
+
+        if requires_grad:
+            deriv = y * (1 - s) + s
+
+            # notes on derivative of x * sigmoid(x - 1):
+            # https://www.wolframalpha.com/input?i=d%2Fdx+%28x+*+sigmoid%28x-1%29%29
+            # min \simeq -0.043638.  Take floor as -0.044 so it's a lower bund
+            # max \simeq 1.1990.   Take ceil to be 1.2 so it's an upper bound.
+            # the combination of "+ torch.rand_like(deriv)" and casting to torch.uint8 (which
+            # floors), should be expectation-preserving.
+            floor = -0.044
+            ceil = 1.2
+            d_scaled = (deriv - floor) * (255.0 / (ceil - floor)) + torch.rand_like(
+                deriv
+            )
+            if __name__ == "__main__":
+                # for self-testing only.
+                assert d_scaled.min() >= 0.0
+                assert d_scaled.max() < 256.0
+            d_int = d_scaled.to(torch.uint8)
+            ctx.save_for_backward(d_int)
+        if x.dtype == torch.float16 or torch.is_autocast_enabled():
+            y = y.to(torch.float16)
+        return y
+
+    @staticmethod
+    def backward(ctx, y_grad: Tensor) -> Tensor:
+        (d,) = ctx.saved_tensors
+        # the same constants as used in forward pass.
+        floor = -0.043637
+        ceil = 1.2
+
+        d = d * ((ceil - floor) / 255.0) + floor
+        return y_grad * d
+
+
+class DoubleSwish(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x: Tensor) -> Tensor:
+        """Return double-swish activation function which is an approximation to Swish(Swish(x)),
+        that we approximate closely with x * sigmoid(x-1).
+        """
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            return x * torch.sigmoid(x - 1.0)
+        return DoubleSwishFunction.apply(x)
+
+
+# Dropout2 is just like normal dropout, except it supports schedules on the dropout rates.
+class Dropout2(nn.Module):
+    def __init__(self, p: FloatLike):
+        super().__init__()
+        self.p = p
+
+    def forward(self, x: Tensor) -> Tensor:
+        return torch.nn.functional.dropout(x, p=float(self.p), training=self.training)
+
+
+class MulForDropout3(torch.autograd.Function):
+    # returns (x * y * alpha) where alpha is a float and y doesn't require
+    # grad and is zero-or-one.
+    @staticmethod
+    @custom_fwd
+    def forward(ctx, x, y, alpha):
+        assert not y.requires_grad
+        ans = x * y * alpha
+        ctx.save_for_backward(ans)
+        ctx.alpha = alpha
+        return ans
+
+    @staticmethod
+    @custom_bwd
+    def backward(ctx, ans_grad):
+        (ans,) = ctx.saved_tensors
+        x_grad = ctx.alpha * ans_grad * (ans != 0)
+        return x_grad, None, None
+
+
+# Dropout3 is just like normal dropout, except it supports schedules on the dropout rates,
+# and it lets you choose one dimension to share the dropout mask over
+class Dropout3(nn.Module):
+    def __init__(self, p: FloatLike, shared_dim: int):
+        super().__init__()
+        self.p = p
+        self.shared_dim = shared_dim
+
+    def forward(self, x: Tensor) -> Tensor:
+        p = float(self.p)
+        if not self.training or p == 0:
+            return _no_op(x)
+        scale = 1.0 / (1 - p)
+        rand_shape = list(x.shape)
+        rand_shape[self.shared_dim] = 1
+        mask = torch.rand(*rand_shape, device=x.device) > p
+        ans = MulForDropout3.apply(x, mask, scale)
+        return ans
+
+
+class SwooshLFunction(torch.autograd.Function):
+    """
+    swoosh_l(x) =  log(1 + exp(x-4)) - 0.08*x - 0.035
+    """
+
+    @staticmethod
+    def forward(ctx, x: Tensor) -> Tensor:
+        requires_grad = x.requires_grad
+        if x.dtype == torch.float16:
+            x = x.to(torch.float32)
+
+        zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
+
+        coeff = -0.08
+
+        with torch.amp.autocast("cuda", enabled=False):
+            with torch.enable_grad():
+                x = x.detach()
+                x.requires_grad = True
+                y = torch.logaddexp(zero, x - 4.0) + coeff * x - 0.035
+
+                if not requires_grad:
+                    return y
+
+                y.backward(gradient=torch.ones_like(y))
+
+                grad = x.grad
+                floor = coeff
+                ceil = 1.0 + coeff + 0.005
+
+                d_scaled = (grad - floor) * (255.0 / (ceil - floor)) + torch.rand_like(
+                    grad
+                )
+                if __name__ == "__main__":
+                    # for self-testing only.
+                    assert d_scaled.min() >= 0.0
+                    assert d_scaled.max() < 256.0
+
+                d_int = d_scaled.to(torch.uint8)
+                ctx.save_for_backward(d_int)
+                if x.dtype == torch.float16 or torch.is_autocast_enabled():
+                    y = y.to(torch.float16)
+                return y
+
+    @staticmethod
+    def backward(ctx, y_grad: Tensor) -> Tensor:
+        (d,) = ctx.saved_tensors
+        # the same constants as used in forward pass.
+
+        coeff = -0.08
+        floor = coeff
+        ceil = 1.0 + coeff + 0.005
+        d = d * ((ceil - floor) / 255.0) + floor
+        return y_grad * d
+
+
+class SwooshL(torch.nn.Module):
+    def forward(self, x: Tensor) -> Tensor:
+        """Return Swoosh-L activation."""
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
+            return logaddexp(zero, x - 4.0) - 0.08 * x - 0.035
+        if not x.requires_grad:
+            return k2.swoosh_l_forward(x)
+        else:
+            return k2.swoosh_l(x)
+        # return SwooshLFunction.apply(x)
+
+
+class SwooshLOnnx(torch.nn.Module):
+    def forward(self, x: Tensor) -> Tensor:
+        """Return Swoosh-L activation."""
+        zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
+        return logaddexp_onnx(zero, x - 4.0) - 0.08 * x - 0.035
+
+
+class SwooshRFunction(torch.autograd.Function):
+    """
+     swoosh_r(x) =  log(1 + exp(x-1)) - 0.08*x - 0.313261687
+
+    derivatives are between -0.08 and 0.92.
+    """
+
+    @staticmethod
+    def forward(ctx, x: Tensor) -> Tensor:
+        requires_grad = x.requires_grad
+
+        if x.dtype == torch.float16:
+            x = x.to(torch.float32)
+
+        zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
+
+        with torch.amp.autocast("cuda", enabled=False):
+            with torch.enable_grad():
+                x = x.detach()
+                x.requires_grad = True
+                y = torch.logaddexp(zero, x - 1.0) - 0.08 * x - 0.313261687
+
+                if not requires_grad:
+                    return y
+                y.backward(gradient=torch.ones_like(y))
+
+                grad = x.grad
+                floor = -0.08
+                ceil = 0.925
+
+                d_scaled = (grad - floor) * (255.0 / (ceil - floor)) + torch.rand_like(
+                    grad
+                )
+                if __name__ == "__main__":
+                    # for self-testing only.
+                    assert d_scaled.min() >= 0.0
+                    assert d_scaled.max() < 256.0
+
+                d_int = d_scaled.to(torch.uint8)
+                ctx.save_for_backward(d_int)
+                if x.dtype == torch.float16 or torch.is_autocast_enabled():
+                    y = y.to(torch.float16)
+                return y
+
+    @staticmethod
+    def backward(ctx, y_grad: Tensor) -> Tensor:
+        (d,) = ctx.saved_tensors
+        # the same constants as used in forward pass.
+        floor = -0.08
+        ceil = 0.925
+        d = d * ((ceil - floor) / 255.0) + floor
+        return y_grad * d
+
+
+class SwooshR(torch.nn.Module):
+    def forward(self, x: Tensor) -> Tensor:
+        """Return Swoosh-R activation."""
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
+            return logaddexp(zero, x - 1.0) - 0.08 * x - 0.313261687
+        if not x.requires_grad:
+            return k2.swoosh_r_forward(x)
+        else:
+            return k2.swoosh_r(x)
+        # return SwooshRFunction.apply(x)
+
+
+class SwooshROnnx(torch.nn.Module):
+    def forward(self, x: Tensor) -> Tensor:
+        """Return Swoosh-R activation."""
+        zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
+        return logaddexp_onnx(zero, x - 1.0) - 0.08 * x - 0.313261687
+
+
+# simple version of SwooshL that does not redefine the backprop, used in
+# ActivationDropoutAndLinearFunction.
+def SwooshLForward(x: Tensor):
+    x_offset = x - 4.0
+    log_sum = (1.0 + x_offset.exp()).log().to(x.dtype)
+    log_sum = torch.where(log_sum == float("inf"), x_offset, log_sum)
+    return log_sum - 0.08 * x - 0.035
+
+
+# simple version of SwooshR that does not redefine the backprop, used in
+# ActivationDropoutAndLinearFunction.
+def SwooshRForward(x: Tensor):
+    x_offset = x - 1.0
+    log_sum = (1.0 + x_offset.exp()).log().to(x.dtype)
+    log_sum = torch.where(log_sum == float("inf"), x_offset, log_sum)
+    return log_sum - 0.08 * x - 0.313261687
+
+
+class ActivationDropoutAndLinearFunction(torch.autograd.Function):
+    @staticmethod
+    @custom_fwd
+    def forward(
+        ctx,
+        x: Tensor,
+        weight: Tensor,
+        bias: Optional[Tensor],
+        activation: str,
+        dropout_p: float,
+        dropout_shared_dim: Optional[int],
+    ):
+        if dropout_p != 0.0:
+            dropout_shape = list(x.shape)
+            if dropout_shared_dim is not None:
+                dropout_shape[dropout_shared_dim] = 1
+            # else it won't be very memory efficient.
+            dropout_mask = (1.0 / (1.0 - dropout_p)) * (
+                torch.rand(*dropout_shape, device=x.device, dtype=x.dtype) > dropout_p
+            )
+        else:
+            dropout_mask = None
+
+        ctx.save_for_backward(x, weight, bias, dropout_mask)
+
+        ctx.activation = activation
+
+        forward_activation_dict = {
+            "SwooshL": k2.swoosh_l_forward,
+            "SwooshR": k2.swoosh_r_forward,
+        }
+        # it will raise a KeyError if this fails.  This will be an error.  We let it
+        # propagate to the user.
+        activation_func = forward_activation_dict[activation]
+        x = activation_func(x)
+        if dropout_mask is not None:
+            x = x * dropout_mask
+        x = torch.nn.functional.linear(x, weight, bias)
+        return x
+
+    @staticmethod
+    @custom_bwd
+    def backward(ctx, ans_grad: Tensor):
+        saved = ctx.saved_tensors
+        (x, weight, bias, dropout_mask) = saved
+
+        forward_and_deriv_activation_dict = {
+            "SwooshL": k2.swoosh_l_forward_and_deriv,
+            "SwooshR": k2.swoosh_r_forward_and_deriv,
+        }
+        # the following lines a KeyError if the activation is unrecognized.
+        # This will be an error.  We let it propagate to the user.
+        func = forward_and_deriv_activation_dict[ctx.activation]
+
+        y, func_deriv = func(x)
+        if dropout_mask is not None:
+            y = y * dropout_mask
+        # now compute derivative of y w.r.t. weight and bias..
+        # y: (..., in_channels), ans_grad: (..., out_channels),
+        (out_channels, in_channels) = weight.shape
+
+        in_channels = y.shape[-1]
+        g = ans_grad.reshape(-1, out_channels)
+        weight_deriv = torch.matmul(g.t(), y.reshape(-1, in_channels))
+        y_deriv = torch.matmul(ans_grad, weight)
+        bias_deriv = None if bias is None else g.sum(dim=0)
+        x_deriv = y_deriv * func_deriv
+        if dropout_mask is not None:
+            # order versus func_deriv does not matter
+            x_deriv = x_deriv * dropout_mask
+
+        return x_deriv, weight_deriv, bias_deriv, None, None, None
+
+
+class ActivationDropoutAndLinear(torch.nn.Module):
+    """
+     This merges an activation function followed by dropout and then a nn.Linear module;
+     it does so in a memory efficient way so that it only stores the input to the whole
+     module.  If activation == SwooshL and dropout_shared_dim != None, this will be
+     equivalent to:
+       nn.Sequential(SwooshL(),
+                     Dropout3(dropout_p, shared_dim=dropout_shared_dim),
+                     ScaledLinear(in_channels, out_channels, bias=bias,
+                                  initial_scale=initial_scale))
+    If dropout_shared_dim is None, the dropout would be equivalent to
+    Dropout2(dropout_p).  Note: Dropout3 will be more memory efficient as the dropout
+    mask is smaller.
+
+     Args:
+        in_channels: number of input channels, e.g. 256
+        out_channels: number of output channels, e.g. 256
+        bias: if true, have a bias
+        activation: the activation function, for now just support SwooshL.
+        dropout_p: the dropout probability or schedule (happens after nonlinearity).
+        dropout_shared_dim: the dimension, if any, across which the dropout mask is
+             shared (e.g. the time dimension).  If None, this may be less memory
+             efficient if there are modules before this one that cache the input
+             for their backprop (e.g. Balancer or Whiten).
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        bias: bool = True,
+        activation: str = "SwooshL",
+        dropout_p: FloatLike = 0.0,
+        dropout_shared_dim: Optional[int] = -1,
+        initial_scale: float = 1.0,
+    ):
+        super().__init__()
+        # create a temporary module of nn.Linear that we'll steal the
+        # weights and bias from
+        l = ScaledLinear(
+            in_channels, out_channels, bias=bias, initial_scale=initial_scale
+        )
+
+        self.weight = l.weight
+        # register_parameter properly handles making it a parameter when l.bias
+        # is None. I think there is some reason for doing it this way rather
+        # than just setting it to None but I don't know what it is, maybe
+        # something to do with exporting the module..
+        self.register_parameter("bias", l.bias)
+
+        self.activation = activation
+        self.dropout_p = dropout_p
+        self.dropout_shared_dim = dropout_shared_dim
+
+    def forward(self, x: Tensor):
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            if self.activation == "SwooshL":
+                x = SwooshLForward(x)
+            elif self.activation == "SwooshR":
+                x = SwooshRForward(x)
+            else:
+                assert False, self.activation
+            return torch.nn.functional.linear(x, self.weight, self.bias)
+
+        return ActivationDropoutAndLinearFunction.apply(
+            x,
+            self.weight,
+            self.bias,
+            self.activation,
+            float(self.dropout_p),
+            self.dropout_shared_dim,
+        )
+
+
+def convert_num_channels(x: Tensor, num_channels: int) -> Tensor:
+    if num_channels <= x.shape[-1]:
+        return x[..., :num_channels]
+    else:
+        shape = list(x.shape)
+        shape[-1] = num_channels - shape[-1]
+        zeros = torch.zeros(shape, dtype=x.dtype, device=x.device)
+        return torch.cat((x, zeros), dim=-1)
+
+
+def _test_whiten():
+    for proportion in [0.1, 0.5, 10.0]:
+        logging.info(f"_test_whiten(): proportion = {proportion}")
+        x = torch.randn(100, 128)
+        direction = torch.randn(128)
+        coeffs = torch.randn(100, 1)
+        x += proportion * direction * coeffs
+
+        x.requires_grad = True
+
+        m = Whiten(
+            1, 5.0, prob=1.0, grad_scale=0.1  # num_groups  # whitening_limit,
+        )  # grad_scale
+
+        for _ in range(4):
+            y = m(x)
+
+        y_grad = torch.randn_like(x)
+        y.backward(gradient=y_grad)
+
+        if proportion < 0.2:
+            assert torch.allclose(x.grad, y_grad)
+        elif proportion > 1.0:
+            assert not torch.allclose(x.grad, y_grad)
+
+
+def _test_balancer_sign():
+    probs = torch.arange(0, 1, 0.01)
+    N = 1000
+    x = 1.0 * ((2.0 * (torch.rand(probs.numel(), N) < probs.unsqueeze(-1))) - 1.0)
+    x = x.detach()
+    x.requires_grad = True
+    m = Balancer(
+        probs.numel(),
+        channel_dim=0,
+        min_positive=0.05,
+        max_positive=0.95,
+        min_abs=0.0,
+        prob=1.0,
+    )
+
+    y_grad = torch.sign(torch.randn(probs.numel(), N))
+
+    y = m(x)
+    y.backward(gradient=y_grad)
+    print("_test_balancer_sign: x = ", x)
+    print("_test_balancer_sign: y grad = ", y_grad)
+    print("_test_balancer_sign: x grad = ", x.grad)
+
+
+def _test_balancer_magnitude():
+    magnitudes = torch.arange(0, 1, 0.01)
+    N = 1000
+    x = torch.sign(torch.randn(magnitudes.numel(), N)) * magnitudes.unsqueeze(-1)
+    x = x.detach()
+    x.requires_grad = True
+    m = Balancer(
+        magnitudes.numel(),
+        channel_dim=0,
+        min_positive=0.0,
+        max_positive=1.0,
+        min_abs=0.2,
+        max_abs=0.7,
+        prob=1.0,
+    )
+
+    y_grad = torch.sign(torch.randn(magnitudes.numel(), N))
+
+    y = m(x)
+    y.backward(gradient=y_grad)
+    print("_test_balancer_magnitude: x = ", x)
+    print("_test_balancer_magnitude: y grad = ", y_grad)
+    print("_test_balancer_magnitude: x grad = ", x.grad)
+
+
+def _test_double_swish_deriv():
+    x = torch.randn(10, 12, dtype=torch.double) * 3.0
+    x.requires_grad = True
+    m = DoubleSwish()
+
+    tol = (1.2 - (-0.043637)) / 255.0
+    torch.autograd.gradcheck(m, x, atol=tol)
+
+    # for self-test.
+    x = torch.randn(1000, 1000, dtype=torch.double) * 3.0
+    x.requires_grad = True
+    y = m(x)
+
+
+def _test_swooshl_deriv():
+    x = torch.randn(10, 12, dtype=torch.double) * 3.0
+    x.requires_grad = True
+    m = SwooshL()
+
+    tol = 1.0 / 255.0
+    torch.autograd.gradcheck(m, x, atol=tol, eps=0.01)
+
+    # for self-test.
+    x = torch.randn(1000, 1000, dtype=torch.double) * 3.0
+    x.requires_grad = True
+    y = m(x)
+
+
+def _test_swooshr_deriv():
+    x = torch.randn(10, 12, dtype=torch.double) * 3.0
+    x.requires_grad = True
+    m = SwooshR()
+
+    tol = 1.0 / 255.0
+    torch.autograd.gradcheck(m, x, atol=tol, eps=0.01)
+
+    # for self-test.
+    x = torch.randn(1000, 1000, dtype=torch.double) * 3.0
+    x.requires_grad = True
+    y = m(x)
+
+
+def _test_softmax():
+    a = torch.randn(2, 10, dtype=torch.float64)
+    b = a.clone()
+    a.requires_grad = True
+    b.requires_grad = True
+    a.softmax(dim=1)[:, 0].sum().backward()
+    print("a grad = ", a.grad)
+    softmax(b, dim=1)[:, 0].sum().backward()
+    print("b grad = ", b.grad)
+    assert torch.allclose(a.grad, b.grad)
+
+
+def _test_piecewise_linear():
+    p = PiecewiseLinear((0, 10.0))
+    for x in [-100, 0, 100]:
+        assert p(x) == 10.0
+    p = PiecewiseLinear((0, 10.0), (1, 0.0))
+    for x, y in [(-100, 10.0), (0, 10.0), (0.5, 5.0), (1, 0.0), (2, 0.0)]:
+        print("x, y = ", x, y)
+        assert p(x) == y, (x, p(x), y)
+
+    q = PiecewiseLinear((0.5, 15.0), (0.6, 1.0))
+    x_vals = [-1.0, 0.0, 0.1, 0.2, 0.5, 0.6, 0.7, 0.9, 1.0, 2.0]
+    pq = p.max(q)
+    for x in x_vals:
+        y1 = max(p(x), q(x))
+        y2 = pq(x)
+        assert abs(y1 - y2) < 0.001
+    pq = p.min(q)
+    for x in x_vals:
+        y1 = min(p(x), q(x))
+        y2 = pq(x)
+        assert abs(y1 - y2) < 0.001
+    pq = p + q
+    for x in x_vals:
+        y1 = p(x) + q(x)
+        y2 = pq(x)
+        assert abs(y1 - y2) < 0.001
+
+
+def _test_activation_dropout_and_linear():
+    in_channels = 20
+    out_channels = 30
+
+    for bias in [True, False]:
+        # actually we don't test for dropout_p != 0.0 because forward functions will give
+        # different answers.  This is because we are using the k2 implementation of
+        # swoosh_l an swoosh_r inside SwooshL() and SwooshR(), and they call randn()
+        # internally, messing up the random state.
+        for dropout_p in [0.0]:
+            for activation in ["SwooshL", "SwooshR"]:
+                m1 = nn.Sequential(
+                    SwooshL() if activation == "SwooshL" else SwooshR(),
+                    Dropout3(p=dropout_p, shared_dim=-1),
+                    ScaledLinear(
+                        in_channels, out_channels, bias=bias, initial_scale=0.5
+                    ),
+                )
+                m2 = ActivationDropoutAndLinear(
+                    in_channels,
+                    out_channels,
+                    bias=bias,
+                    initial_scale=0.5,
+                    activation=activation,
+                    dropout_p=dropout_p,
+                )
+                with torch.no_grad():
+                    m2.weight[:] = m1[2].weight
+                    if bias:
+                        m2.bias[:] = m1[2].bias
+                # make sure forward gives same result.
+                x1 = torch.randn(10, in_channels)
+                x1.requires_grad = True
+
+                # TEMP.
+                assert torch.allclose(
+                    SwooshRFunction.apply(x1), SwooshRForward(x1), atol=1.0e-03
+                )
+
+                x2 = x1.clone().detach()
+                x2.requires_grad = True
+                seed = 10
+                torch.manual_seed(seed)
+                y1 = m1(x1)
+                y_grad = torch.randn_like(y1)
+                y1.backward(gradient=y_grad)
+                torch.manual_seed(seed)
+                y2 = m2(x2)
+                y2.backward(gradient=y_grad)
+
+                print(
+                    f"bias = {bias}, dropout_p = {dropout_p}, activation = {activation}"
+                )
+                print("y1 = ", y1)
+                print("y2 = ", y2)
+                assert torch.allclose(y1, y2, atol=0.02)
+                assert torch.allclose(m1[2].weight.grad, m2.weight.grad, atol=1.0e-05)
+                if bias:
+                    assert torch.allclose(m1[2].bias.grad, m2.bias.grad, atol=1.0e-05)
+                print("x1.grad = ", x1.grad)
+                print("x2.grad = ", x2.grad)
+
+                def isclose(a, b):
+                    # return true if cosine similarity is > 0.9.
+                    return (a * b).sum() > 0.9 * (
+                        (a**2).sum() * (b**2).sum()
+                    ).sqrt()
+
+                # the SwooshL() implementation has a noisy gradient due to 1-byte
+                # storage of it.
+                assert isclose(x1.grad, x2.grad)
+
+
+if __name__ == "__main__":
+    logging.getLogger().setLevel(logging.DEBUG)
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
+    _test_piecewise_linear()
+    _test_softmax()
+    _test_whiten()
+    _test_balancer_sign()
+    _test_balancer_magnitude()
+    _test_double_swish_deriv()
+    _test_swooshr_deriv()
+    _test_swooshl_deriv()
+    _test_activation_dropout_and_linear()
diff --git a/egs/zipvoice/zipvoice/solver.py b/egs/zipvoice/zipvoice/solver.py
new file mode 100644
index 000000000..a1e316ec8
--- /dev/null
+++ b/egs/zipvoice/zipvoice/solver.py
@@ -0,0 +1,277 @@
+#!/usr/bin/env python3
+# Copyright         2024  Xiaomi Corp.        (authors: Han Zhu)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Optional, Union
+
+import torch
+
+
+class DiffusionModel(torch.nn.Module):
+    """A wrapper of diffusion models for inference.
+    Args:
+        model: The diffusion model.
+        distill: Whether it is a distillation model.
+    """
+
+    def __init__(
+        self,
+        model: torch.nn.Module,
+        distill: bool = False,
+        func_name: str = "forward_fm_decoder",
+    ):
+        super().__init__()
+        self.model = model
+        self.distill = distill
+        self.func_name = func_name
+        self.model_func = getattr(self.model, func_name)
+
+    def forward(
+        self,
+        t: torch.Tensor,
+        x: torch.Tensor,
+        text_condition: torch.Tensor,
+        speech_condition: torch.Tensor,
+        padding_mask: Optional[torch.Tensor] = None,
+        guidance_scale: Union[float, torch.Tensor] = 0.0,
+        **kwargs
+    ) -> torch.Tensor:
+        """
+        Forward function that Handles the classifier-free guidance.
+        Args:
+            t: The current timestep, a tensor of shape (batch, 1, 1) or a tensor of a single float.
+            x: The initial value, with the shape (batch, seq_len, emb_dim).
+            text_condition: The text_condition of the diffision model, with the shape (batch, seq_len, emb_dim).
+            speech_condition: The speech_condition of the diffision model, with the shape (batch, seq_len, emb_dim).
+            padding_mask: The mask for padding; True means masked position, with the shape (batch, seq_len).
+            guidance_scale: The scale of classifier-free guidance, a float or a tensor of shape (batch, 1, 1).
+        Retrun:
+            The prediction with the shape (batch, seq_len, emb_dim).
+        """
+        if not torch.is_tensor(guidance_scale):
+            guidance_scale = torch.tensor(
+                guidance_scale, dtype=t.dtype, device=t.device
+            )
+        if self.distill:
+            return self.model_func(
+                t=t,
+                xt=x,
+                text_condition=text_condition,
+                speech_condition=speech_condition,
+                padding_mask=padding_mask,
+                guidance_scale=guidance_scale,
+                **kwargs
+            )
+
+        if (guidance_scale == 0.0).all():
+            return self.model_func(
+                t=t,
+                xt=x,
+                text_condition=text_condition,
+                speech_condition=speech_condition,
+                padding_mask=padding_mask,
+                **kwargs
+            )
+        else:
+            if t.dim() != 0:
+                t = torch.cat([t] * 2, dim=0)
+
+            x = torch.cat([x] * 2, dim=0)
+            padding_mask = torch.cat([padding_mask] * 2, dim=0)
+
+            text_condition = torch.cat(
+                [torch.zeros_like(text_condition), text_condition], dim=0
+            )
+
+            if t.dim() == 0:
+                if t > 0.5:
+                    speech_condition = torch.cat(
+                        [torch.zeros_like(speech_condition), speech_condition], dim=0
+                    )
+                else:
+                    guidance_scale = guidance_scale * 2
+                    speech_condition = torch.cat(
+                        [speech_condition, speech_condition], dim=0
+                    )
+            else:
+                assert t.dim() > 0, t
+                larger_t_index = (t > 0.5).squeeze(1).squeeze(1)
+                zero_speech_condition = torch.cat(
+                    [torch.zeros_like(speech_condition), speech_condition], dim=0
+                )
+                speech_condition = torch.cat(
+                    [speech_condition, speech_condition], dim=0
+                )
+                speech_condition[larger_t_index] = zero_speech_condition[larger_t_index]
+                guidance_scale[~larger_t_index[: larger_t_index.size(0) // 2]] *= 2
+
+            data_uncond, data_cond = self.model_func(
+                t=t,
+                xt=x,
+                text_condition=text_condition,
+                speech_condition=speech_condition,
+                padding_mask=padding_mask,
+                **kwargs
+            ).chunk(2, dim=0)
+
+            res = (1 + guidance_scale) * data_cond - guidance_scale * data_uncond
+            return res
+
+
+class EulerSolver:
+    def __init__(
+        self,
+        model: torch.nn.Module,
+        distill: bool = False,
+        func_name: str = "forward_fm_decoder",
+    ):
+        """Construct a Euler Solver
+        Args:
+            model: The diffusion model.
+            distill: Whether it is distillation model.
+        """
+
+        self.model = DiffusionModel(model, distill=distill, func_name=func_name)
+
+    def sample(
+        self,
+        x: torch.Tensor,
+        text_condition: torch.Tensor,
+        speech_condition: torch.Tensor,
+        padding_mask: torch.Tensor,
+        num_step: int = 10,
+        guidance_scale: Union[float, torch.Tensor] = 0.0,
+        t_start: Union[float, torch.Tensor] = 0.0,
+        t_end: Union[float, torch.Tensor] = 1.0,
+        t_shift: float = 1.0,
+        **kwargs
+    ) -> torch.Tensor:
+        """
+        Compute the sample at time `t_end` by Euler Solver.
+        Args:
+            x: The initial value at time `t_start`, with the shape (batch, seq_len, emb_dim).
+            text_condition: The text condition of the diffision mode, with the shape (batch, seq_len, emb_dim).
+            speech_condition: The speech condition of the diffision model, with the shape (batch, seq_len, emb_dim).
+            padding_mask: The mask for padding; True means masked position, with the shape (batch, seq_len).
+            num_step: The number of ODE steps.
+            guidance_scale: The scale for classifier-free guidance, which is
+                a float or a tensor with the shape (batch, 1, 1).
+            t_start: the start timestep in the range of [0, 1],
+                which is a float or a tensor with the shape (batch, 1, 1).
+            t_end: the end time_step in the range of [0, 1],
+                which is a float or a tensor with the shape (batch, 1, 1).
+            t_shift: shift the t toward smaller numbers so that the sampling
+                will emphasize low SNR region. Should be in the range of (0, 1].
+                The shifting will be more significant when the number is smaller.
+
+        Returns:
+            The approximated solution at time `t_end`.
+        """
+        device = x.device
+
+        if torch.is_tensor(t_start) and t_start.dim() > 0:
+            timesteps = get_time_steps_batch(
+                t_start=t_start,
+                t_end=t_end,
+                num_step=num_step,
+                t_shift=t_shift,
+                device=device,
+            )
+        else:
+            timesteps = get_time_steps(
+                t_start=t_start,
+                t_end=t_end,
+                num_step=num_step,
+                t_shift=t_shift,
+                device=device,
+            )
+        for step in range(num_step):
+            v = self.model(
+                t=timesteps[step],
+                x=x,
+                text_condition=text_condition,
+                speech_condition=speech_condition,
+                padding_mask=padding_mask,
+                guidance_scale=guidance_scale,
+                **kwargs
+            )
+            x = x + v * (timesteps[step + 1] - timesteps[step])
+        return x
+
+
+def get_time_steps(
+    t_start: float = 0.0,
+    t_end: float = 1.0,
+    num_step: int = 10,
+    t_shift: float = 1.0,
+    device: torch.device = torch.device("cpu"),
+) -> torch.Tensor:
+    """Compute the intermediate time steps for sampling.
+
+    Args:
+        t_start: The starting time of the sampling (default is 0).
+        t_end: The starting time of the sampling (default is 1).
+        num_step: The number of sampling.
+        t_shift: shift the t toward smaller numbers so that the sampling
+            will emphasize low SNR region. Should be in the range of (0, 1].
+            The shifting will be more significant when the number is smaller.
+        device: A torch device.
+    Returns:
+        The time step with the shape (num_step + 1,).
+    """
+
+    timesteps = torch.linspace(t_start, t_end, num_step + 1).to(device)
+
+    timesteps = t_shift * timesteps / (1 + (t_shift - 1) * timesteps)
+
+    return timesteps
+
+
+def get_time_steps_batch(
+    t_start: torch.Tensor,
+    t_end: torch.Tensor,
+    num_step: int = 10,
+    t_shift: float = 1.0,
+    device: torch.device = torch.device("cpu"),
+) -> torch.Tensor:
+    """Compute the intermediate time steps for sampling in the batch mode.
+
+    Args:
+        t_start: The starting time of the sampling (default is 0), with the shape (batch, 1, 1).
+        t_end: The starting time of the sampling (default is 1), with the shape (batch, 1, 1).
+        num_step: The number of sampling.
+        t_shift: shift the t toward smaller numbers so that the sampling
+            will emphasize low SNR region. Should be in the range of (0, 1].
+            The shifting will be more significant when the number is smaller.
+        device: A torch device.
+    Returns:
+        The time step with the shape (num_step + 1, N, 1, 1).
+    """
+    while t_start.dim() > 1 and t_start.size(-1) == 1:
+        t_start = t_start.squeeze(-1)
+    while t_end.dim() > 1 and t_end.size(-1) == 1:
+        t_end = t_end.squeeze(-1)
+    assert t_start.dim() == t_end.dim() == 1
+
+    timesteps_shape = (num_step + 1, t_start.size(0))
+    timesteps = torch.zeros(timesteps_shape, device=device)
+
+    for i in range(t_start.size(0)):
+        timesteps[:, i] = torch.linspace(t_start[i], t_end[i], steps=num_step + 1)
+
+    timesteps = t_shift * timesteps / (1 + (t_shift - 1) * timesteps)
+
+    return timesteps.unsqueeze(-1).unsqueeze(-1)
diff --git a/egs/zipvoice/zipvoice/tokenizer.py b/egs/zipvoice/zipvoice/tokenizer.py
new file mode 100644
index 000000000..87af061e6
--- /dev/null
+++ b/egs/zipvoice/zipvoice/tokenizer.py
@@ -0,0 +1,570 @@
+# Copyright      2023-2024  Xiaomi Corp.        (authors: Zengwei Yao
+#                                                         Han Zhu,
+#                                                         Wei Kang)
+#
+# See ../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import re
+import unicodedata
+from functools import reduce
+from typing import Dict, List, Optional
+
+import cn2an
+import inflect
+import jieba
+from pypinyin import Style, lazy_pinyin
+from pypinyin.contrib.tone_convert import to_finals_tone3, to_initials
+
+try:
+    from piper_phonemize import phonemize_espeak
+except Exception as ex:
+    raise RuntimeError(
+        f"{ex}\nPlease run\n"
+        "pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html"
+    )
+
+_inflect = inflect.engine()
+_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
+_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
+_percent_number_re = re.compile(r"([0-9\.\,]*[0-9]+%)")
+_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
+_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
+_fraction_re = re.compile(r"([0-9]+)/([0-9]+)")
+_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
+_number_re = re.compile(r"[0-9]+")
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [
+    (re.compile("\\b%s\\b" % x[0], re.IGNORECASE), x[1])
+    for x in [
+        ("mrs", "misess"),
+        ("mr", "mister"),
+        ("dr", "doctor"),
+        ("st", "saint"),
+        ("co", "company"),
+        ("jr", "junior"),
+        ("maj", "major"),
+        ("gen", "general"),
+        ("drs", "doctors"),
+        ("rev", "reverend"),
+        ("lt", "lieutenant"),
+        ("hon", "honorable"),
+        ("sgt", "sergeant"),
+        ("capt", "captain"),
+        ("esq", "esquire"),
+        ("ltd", "limited"),
+        ("col", "colonel"),
+        ("ft", "fort"),
+        ("etc", "et cetera"),
+        ("btw", "by the way"),
+    ]
+]
+
+
+def intersperse(sequence, item=0):
+    result = [item] * (len(sequence) * 2 + 1)
+    result[1::2] = sequence
+    return result
+
+
+def expand_abbreviations(text):
+    for regex, replacement in _abbreviations:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def _remove_commas(m):
+    return m.group(1).replace(",", "")
+
+
+def _expand_decimal_point(m):
+    return m.group(1).replace(".", " point ")
+
+
+def _expand_percent(m):
+    return m.group(1).replace("%", " percent ")
+
+
+def _expand_dollars(m):
+    match = m.group(1)
+    parts = match.split(".")
+    if len(parts) > 2:
+        return " " + match + " dollars "  # Unexpected format
+    dollars = int(parts[0]) if parts[0] else 0
+    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+    if dollars and cents:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        cent_unit = "cent" if cents == 1 else "cents"
+        return " %s %s, %s %s " % (dollars, dollar_unit, cents, cent_unit)
+    elif dollars:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        return " %s %s " % (dollars, dollar_unit)
+    elif cents:
+        cent_unit = "cent" if cents == 1 else "cents"
+        return " %s %s " % (cents, cent_unit)
+    else:
+        return " zero dollars "
+
+
+def fraction_to_words(numerator, denominator):
+    if numerator == 1 and denominator == 2:
+        return " one half "
+    if numerator == 1 and denominator == 4:
+        return " one quarter "
+    if denominator == 2:
+        return " " + _inflect.number_to_words(numerator) + " halves "
+    if denominator == 4:
+        return " " + _inflect.number_to_words(numerator) + " quarters "
+    return (
+        " "
+        + _inflect.number_to_words(numerator)
+        + " "
+        + _inflect.ordinal(_inflect.number_to_words(denominator))
+        + " "
+    )
+
+
+def _expand_fraction(m):
+    numerator = int(m.group(1))
+    denominator = int(m.group(2))
+    return fraction_to_words(numerator, denominator)
+
+
+def _expand_ordinal(m):
+    return " " + _inflect.number_to_words(m.group(0)) + " "
+
+
+def _expand_number(m):
+    num = int(m.group(0))
+    if num > 1000 and num < 3000:
+        if num == 2000:
+            return " two thousand "
+        elif num > 2000 and num < 2010:
+            return " two thousand " + _inflect.number_to_words(num % 100) + " "
+        elif num % 100 == 0:
+            return " " + _inflect.number_to_words(num // 100) + " hundred "
+        else:
+            return (
+                " "
+                + _inflect.number_to_words(num, andword="", zero="oh", group=2).replace(
+                    ", ", " "
+                )
+                + " "
+            )
+    else:
+        return " " + _inflect.number_to_words(num, andword="") + " "
+
+
+# Normalize numbers pronunciation
+def normalize_numbers(text):
+    text = re.sub(_comma_number_re, _remove_commas, text)
+    text = re.sub(_pounds_re, r"\1 pounds", text)
+    text = re.sub(_dollars_re, _expand_dollars, text)
+    text = re.sub(_fraction_re, _expand_fraction, text)
+    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+    text = re.sub(_percent_number_re, _expand_percent, text)
+    text = re.sub(_ordinal_re, _expand_ordinal, text)
+    text = re.sub(_number_re, _expand_number, text)
+    return text
+
+
+# Convert numbers to Chinese pronunciation
+def number_to_chinese(text):
+    text = cn2an.transform(text, "an2cn")
+    return text
+
+
+def map_punctuations(text):
+    text = text.replace("，", ",")
+    text = text.replace("。", ".")
+    text = text.replace("！", "!")
+    text = text.replace("？", "?")
+    text = text.replace("；", ";")
+    text = text.replace("：", ":")
+    text = text.replace("、", ",")
+    text = text.replace("‘", "'")
+    text = text.replace("“", '"')
+    text = text.replace("”", '"')
+    text = text.replace("’", "'")
+    text = text.replace("⋯", "…")
+    text = text.replace("···", "…")
+    text = text.replace("・・・", "…")
+    text = text.replace("...", "…")
+    return text
+
+
+def is_chinese(char):
+    if char >= "\u4e00" and char <= "\u9fa5":
+        return True
+    else:
+        return False
+
+
+def is_alphabet(char):
+    if (char >= "\u0041" and char <= "\u005a") or (
+        char >= "\u0061" and char <= "\u007a"
+    ):
+        return True
+    else:
+        return False
+
+
+def is_hangul(char):
+    letters = unicodedata.normalize("NFD", char)
+    return all(
+        ["\u1100" <= c <= "\u11ff" or "\u3131" <= c <= "\u318e" for c in letters]
+    )
+
+
+def is_japanese(char):
+    return any(
+        [
+            start <= char <= end
+            for start, end in [
+                ("\u3041", "\u3096"),
+                ("\u30a0", "\u30ff"),
+                ("\uff5f", "\uff9f"),
+                ("\u31f0", "\u31ff"),
+                ("\u3220", "\u3243"),
+                ("\u3280", "\u337f"),
+            ]
+        ]
+    )
+
+
+def get_segment(text: str) -> List[str]:
+    # sentence --> [ch_part, en_part, ch_part, ...]
+    # example :
+    # input : 我们是小米人,是吗? Yes I think so!霍...啦啦啦
+    # output : [('我们是小米人,是吗? ', 'zh'), ('Yes I think so!', 'en'), ('霍...啦啦啦', 'zh')]
+    segments = []
+    types = []
+    flag = 0
+    temp_seg = ""
+    temp_lang = ""
+
+    for i, ch in enumerate(text):
+        if is_chinese(ch):
+            types.append("zh")
+        elif is_alphabet(ch):
+            types.append("en")
+        else:
+            types.append("other")
+
+    assert len(types) == len(text)
+
+    for i in range(len(types)):
+        # find the first char of the seg
+        if flag == 0:
+            temp_seg += text[i]
+            temp_lang = types[i]
+            flag = 1
+        else:
+            if temp_lang == "other":
+                if types[i] == temp_lang:
+                    temp_seg += text[i]
+                else:
+                    temp_seg += text[i]
+                    temp_lang = types[i]
+            else:
+                if types[i] == temp_lang:
+                    temp_seg += text[i]
+                elif types[i] == "other":
+                    temp_seg += text[i]
+                else:
+                    segments.append((temp_seg, temp_lang))
+                    temp_seg = text[i]
+                    temp_lang = types[i]
+                    flag = 1
+
+    segments.append((temp_seg, temp_lang))
+    return segments
+
+
+def preprocess(text: str) -> str:
+    text = map_punctuations(text)
+    return text
+
+
+def tokenize_ZH(text: str) -> List[str]:
+    try:
+        text = number_to_chinese(text)
+        segs = list(jieba.cut(text))
+        full = lazy_pinyin(
+            segs, style=Style.TONE3, tone_sandhi=True, neutral_tone_with_five=True
+        )
+        phones = []
+        for x in full:
+            # valid pinyin (in tone3 style) is alphabet + 1 number in [1-5].
+            if not (x[0:-1].isalpha() and x[-1] in ("1", "2", "3", "4", "5")):
+                phones.append(x)
+                continue
+            initial = to_initials(x, strict=False)
+            # don't want to share tokens with espeak tokens, so use tone3 style
+            final = to_finals_tone3(x, strict=False, neutral_tone_with_five=True)
+            if initial != "":
+                # don't want to share tokens with espeak tokens, so add a '0' after each initial
+                phones.append(initial + "0")
+            if final != "":
+                phones.append(final)
+        return phones
+    except:
+        return []
+
+
+def tokenize_EN(text: str) -> List[str]:
+    try:
+        text = expand_abbreviations(text)
+        text = normalize_numbers(text)
+        tokens = phonemize_espeak(text, "en-us")
+        tokens = reduce(lambda x, y: x + y, tokens)
+        return tokens
+    except:
+        return []
+
+
+class TokenizerEmilia(object):
+    def __init__(self, token_file: Optional[str] = None, token_type="phone"):
+        """
+        Args:
+          tokens: the file that contains information that maps tokens to ids,
+            which is a text file with '{token} {token_id}' per line.
+        """
+        assert (
+            token_type == "phone"
+        ), f"Only support phone tokenizer for Emilia, but get {token_type}."
+        self.has_tokens = False
+        if token_file is None:
+            logging.debug(
+                "Initialize Tokenizer without tokens file, will fail when map to ids."
+            )
+            return
+        self.token2id: Dict[str, int] = {}
+        with open(token_file, "r", encoding="utf-8") as f:
+            for line in f.readlines():
+                info = line.rstrip().split("\t")
+                token, id = info[0], int(info[1])
+                assert token not in self.token2id, token
+                self.token2id[token] = id
+        self.pad_id = self.token2id["_"]  # padding
+
+        self.vocab_size = len(self.token2id)
+        self.has_tokens = True
+
+    def texts_to_token_ids(
+        self,
+        texts: List[str],
+    ) -> List[List[int]]:
+        return self.tokens_to_token_ids(self.texts_to_tokens(texts))
+
+    def texts_to_tokens(
+        self,
+        texts: List[str],
+    ) -> List[List[str]]:
+        """
+        Args:
+          texts:
+            A list of transcripts.
+        Returns:
+          Return a list of a list of tokens [utterance][token]
+        """
+        for i in range(len(texts)):
+            # Text normalization
+            texts[i] = preprocess(texts[i])
+
+        phoneme_list = []
+        for text in texts:
+            # now only en and ch
+            segments = get_segment(text)
+            all_phoneme = []
+            for index in range(len(segments)):
+                seg = segments[index]
+                if seg[1] == "zh":
+                    phoneme = tokenize_ZH(seg[0])
+                else:
+                    if seg[1] != "en":
+                        logging.warning(
+                            f"The lang should be en, given {seg[1]}, skipping segment : {seg}"
+                        )
+                        continue
+                    phoneme = tokenize_EN(seg[0])
+                all_phoneme += phoneme
+        phoneme_list.append(all_phoneme)
+        return phoneme_list
+
+    def tokens_to_token_ids(
+        self,
+        tokens: List[List[str]],
+        intersperse_blank: bool = False,
+    ) -> List[List[int]]:
+        """
+        Args:
+          tokens_list:
+            A list of token list, each corresponding to one utterance.
+          intersperse_blank:
+            Whether to intersperse blanks in the token sequence.
+
+        Returns:
+          Return a list of token id list [utterance][token_id]
+        """
+        assert self.has_tokens, "Please initialize Tokenizer with a tokens file."
+        token_ids = []
+
+        for tks in tokens:
+            ids = []
+            for t in tks:
+                if t not in self.token2id:
+                    logging.warning(f"Skip OOV {t}")
+                    continue
+                ids.append(self.token2id[t])
+
+            if intersperse_blank:
+                ids = intersperse(ids, self.pad_id)
+
+            token_ids.append(ids)
+
+        return token_ids
+
+
+class TokenizerLibriTTS(object):
+    def __init__(self, token_file: str, token_type: str):
+        """
+        Args:
+          type: the type of tokenizer, e.g., bpe, char, phone.
+          tokens: the file that contains information that maps tokens to ids,
+            which is a text file with '{token} {token_id}' per line if type is
+            char or phone, otherwise it is a bpe_model file.
+        """
+        self.type = token_type
+        assert token_type in ["bpe", "char", "phone"]
+        # Parse token file
+
+        if token_type == "bpe":
+            import sentencepiece as spm
+
+            self.sp = spm.SentencePieceProcessor()
+            self.sp.load(token_file)
+            self.pad_id = self.sp.piece_to_id("<pad>")
+            self.vocab_size = self.sp.get_piece_size()
+        else:
+            self.token2id: Dict[str, int] = {}
+            with open(token_file, "r", encoding="utf-8") as f:
+                for line in f.readlines():
+                    info = line.rstrip().split("\t")
+                    token, id = info[0], int(info[1])
+                    assert token not in self.token2id, token
+                    self.token2id[token] = id
+            self.pad_id = self.token2id["_"]  # padding
+            self.vocab_size = len(self.token2id)
+        try:
+            from tacotron_cleaner.cleaners import custom_english_cleaners as cleaner
+        except Exception as ex:
+            raise RuntimeError(
+                f"{ex}\nPlease run\n"
+                "pip install espnet_tts_frontend`, refer to https://github.com/espnet/espnet_tts_frontend/"
+            )
+        self.cleaner = cleaner
+
+    def texts_to_token_ids(
+        self,
+        texts: List[str],
+        lang: str = "en-us",
+    ) -> List[List[int]]:
+        """
+        Args:
+          texts:
+            A list of transcripts.
+          intersperse_blank:
+            Whether to intersperse blanks in the token sequence.
+            Used when alignment is from MAS.
+          lang:
+            Language argument passed to phonemize_espeak().
+
+        Returns:
+          Return a list of token id list [utterance][token_id]
+        """
+        for i in range(len(texts)):
+            # Text normalization
+            texts[i] = self.cleaner(texts[i])
+
+        if self.type == "bpe":
+            token_ids_list = self.sp.encode(texts)
+
+        elif self.type == "phone":
+            token_ids_list = []
+            for text in texts:
+                tokens_list = phonemize_espeak(text.lower(), lang)
+                tokens = []
+                for t in tokens_list:
+                    tokens.extend(t)
+                token_ids = []
+                for t in tokens:
+                    if t not in self.token2id:
+                        logging.warning(f"Skip OOV {t}")
+                        continue
+                    token_ids.append(self.token2id[t])
+
+                token_ids_list.append(token_ids)
+        else:
+            token_ids_list = []
+            for text in texts:
+                token_ids = []
+                for t in text:
+                    if t not in self.token2id:
+                        logging.warning(f"Skip OOV {t}")
+                        continue
+                    token_ids.append(self.token2id[t])
+
+                token_ids_list.append(token_ids)
+
+        return token_ids_list
+
+    def tokens_to_token_ids(
+        self,
+        tokens_list: List[str],
+    ) -> List[List[int]]:
+        """
+        Args:
+          tokens_list:
+            A list of token list, each corresponding to one utterance.
+
+        Returns:
+          Return a list of token id list [utterance][token_id]
+        """
+        token_ids_list = []
+
+        for tokens in tokens_list:
+            token_ids = []
+            for t in tokens:
+                if t not in self.token2id:
+                    logging.warning(f"Skip OOV {t}")
+                    continue
+                token_ids.append(self.token2id[t])
+
+            token_ids_list.append(token_ids)
+
+        return token_ids_list
+
+
+if __name__ == "__main__":
+    text = "我们是5年小米人,是吗? Yes I think so! mr king, 5 years, from 2019 to 2024. 霍...啦啦啦超过90%的人咯...?!9204"
+    tokenizer = Tokenizer()
+    tokens = tokenizer.texts_to_tokens([text])
+    print(f"tokens : {tokens}")
+    tokens2 = "|".join(tokens[0])
+    print(f"tokens2 : {tokens2}")
+    tokens2 = tokens2.split("|")
+    assert tokens[0] == tokens2
diff --git a/egs/zipvoice/zipvoice/train_distill.py b/egs/zipvoice/zipvoice/train_distill.py
new file mode 100644
index 000000000..9e52a3790
--- /dev/null
+++ b/egs/zipvoice/zipvoice/train_distill.py
@@ -0,0 +1,1043 @@
+#!/usr/bin/env python3
+# Copyright         2024  Xiaomi Corp.        (authors: Han Zhu)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+"""
+This script trains a ZipVoice-Distill model starting from a ZipVoice model.
+It has two distillation stages.
+
+Usage:
+
+(1) The first distillation stage with a fixed ZipVoice model as the teacher.
+python3 zipvoice/train_distill.py \
+        --world-size 8 \
+        --use-fp16 1 \
+        --tensorboard 1 \
+        --dataset "emilia" \
+        --base-lr 0.0005 \
+        --max-duration 500 \
+        --token-file "data/tokens_emilia.txt" \
+        --manifest-dir "data/fbank" \
+        --teacher-model zipvoice/exp_zipvoice/epoch-11-avg-4.pt \
+        --num-updates 60000 \
+        --distill-stage "first" \
+        --exp-dir zipvoice/exp_zipvoice_distill_1stage
+
+(2) The second distillation stage with a EMA model as the teacher.
+python3 zipvoice/train_distill.py \
+        --world-size 8 \
+        --use-fp16 1 \
+        --tensorboard 1 \
+        --dataset "emilia" \
+        --base-lr 0.0001 \
+        --max-duration 500 \
+        --token-file "data/tokens_emilia.txt" \
+        --manifest-dir "data/fbank" \
+        --teacher-model zipvoice/exp_zipvoice_distill_1stage/iter-60000-avg-7.pt \
+        --num-updates 2000 \
+        --distill-stage "second" \
+        --exp-dir zipvoice/exp_zipvoice_distill
+"""
+
+import argparse
+import copy
+import logging
+import os
+import random
+from pathlib import Path
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import optim
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+from checkpoint import load_checkpoint, save_checkpoint
+from lhotse.cut import Cut, CutSet
+from lhotse.utils import fix_random_seed
+from model import get_distill_model, get_model
+from optim import FixedLRScheduler, ScaledAdam
+from tokenizer import TokenizerEmilia, TokenizerLibriTTS
+from torch import Tensor
+from torch.amp import GradScaler, autocast
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.optim import Optimizer
+from torch.utils.tensorboard import SummaryWriter
+from train_flow import add_model_arguments, get_params
+from tts_datamodule import TtsDataModule
+from utils import (
+    condition_time_mask,
+    get_adjusted_batch_count,
+    prepare_input,
+    set_batch_count,
+)
+
+from icefall import diagnostics
+from icefall.checkpoint import (
+    remove_checkpoints,
+    save_checkpoint_with_global_batch_idx,
+    update_averaged_model,
+)
+from icefall.dist import cleanup_dist, setup_dist
+from icefall.hooks import register_inf_check_hooks
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    get_parameter_groups_with_lrs,
+    make_pad_mask,
+    setup_logger,
+    str2bool,
+)
+
+LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--world-size",
+        type=int,
+        default=1,
+        help="Number of GPUs for DDP training.",
+    )
+
+    parser.add_argument(
+        "--master-port",
+        type=int,
+        default=12354,
+        help="Master port to use for DDP training.",
+    )
+
+    parser.add_argument(
+        "--tensorboard",
+        type=str2bool,
+        default=True,
+        help="Should various information be logged in tensorboard.",
+    )
+
+    parser.add_argument(
+        "--num-epochs",
+        type=int,
+        default=1,
+        help="Number of epochs to train.",
+    )
+
+    parser.add_argument(
+        "--num-updates",
+        type=int,
+        default=0,
+        help="Number of updates to train, will ignore num_epochs if > 0.",
+    )
+
+    parser.add_argument(
+        "--start-epoch",
+        type=int,
+        default=1,
+        help="""Resume training from this epoch. It should be positive.
+        If larger than 1, it will load checkpoint from
+        exp-dir/epoch-{start_epoch-1}.pt
+        """,
+    )
+
+    parser.add_argument(
+        "--teacher-model",
+        type=str,
+        help="""Checkpoints of pre-trained teacher model""",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="zipvoice/exp_zipvoice_distill",
+        help="""The experiment dir.
+        It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--base-lr", type=float, default=0.001, help="The base learning rate."
+    )
+
+    parser.add_argument(
+        "--ref-duration",
+        type=float,
+        default=50,
+        help="Reference batch duration for purposes of adjusting batch counts for setting various "
+        "schedules inside the model",
+    )
+
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="The seed for random generators intended for reproducibility",
+    )
+
+    parser.add_argument(
+        "--print-diagnostics",
+        type=str2bool,
+        default=False,
+        help="Accumulate stats on activations, print them and exit.",
+    )
+
+    parser.add_argument(
+        "--inf-check",
+        type=str2bool,
+        default=False,
+        help="Add hooks to check for infinite module outputs and gradients.",
+    )
+
+    parser.add_argument(
+        "--save-every-n",
+        type=int,
+        default=4000,
+        help="""Save checkpoint after processing this number of batches"
+        periodically. We save checkpoint to exp-dir/ whenever
+        params.batch_idx_train % save_every_n == 0. The checkpoint filename
+        has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt'
+        Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the
+        end of each epoch where `xxx` is the epoch number counting from 1.
+        """,
+    )
+
+    parser.add_argument(
+        "--keep-last-k",
+        type=int,
+        default=30,
+        help="""Only keep this number of checkpoints on disk.
+        For instance, if it is 3, there are only 3 checkpoints
+        in the exp-dir with filenames `checkpoint-xxx.pt`.
+        It does not affect checkpoints with name `epoch-xxx.pt`.
+        """,
+    )
+
+    parser.add_argument(
+        "--average-period",
+        type=int,
+        default=200,
+        help="""Update the averaged model, namely `model_avg`, after processing
+        this number of batches. `model_avg` is a separate version of model,
+        in which each floating-point parameter is the average of all the
+        parameters from the start of training. Each time we take the average,
+        we do: `model_avg = model * (average_period / batch_idx_train) +
+            model_avg * ((batch_idx_train - average_period) / batch_idx_train)`.
+        """,
+    )
+
+    parser.add_argument(
+        "--use-fp16",
+        type=str2bool,
+        default=False,
+        help="Whether to use half precision training.",
+    )
+
+    parser.add_argument(
+        "--feat-scale",
+        type=float,
+        default=0.1,
+        help="The scale factor of fbank feature",
+    )
+
+    parser.add_argument(
+        "--ema-decay",
+        type=float,
+        default=0.9999,
+        help="The EMA decay factor of target model in distillation.",
+    )
+    parser.add_argument(
+        "--distill-stage",
+        type=str,
+        choices=["first", "second"],
+        help="The stage of distillation.",
+    )
+
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="emilia",
+        choices=["emilia", "libritts"],
+        help="The used training dataset",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def ema(new_model, ema_model, decay):
+    if isinstance(new_model, DDP):
+        new_model = new_model.module
+    if isinstance(ema_model, DDP):
+        ema_model = ema_model.module
+    new_model_dict = new_model.state_dict()
+    ema_model_dict = ema_model.state_dict()
+    for key in new_model_dict.keys():
+        ema_model_dict[key].data.copy_(
+            ema_model_dict[key].data * decay + new_model_dict[key].data * (1 - decay)
+        )
+
+
+def resume_checkpoint(
+    params: AttributeDict, model: nn.Module, model_avg: nn.Module, model_ema: nn.Module
+) -> Optional[Dict[str, Any]]:
+    """Load checkpoint from file.
+
+    If params.start_epoch is larger than 1, it will load the checkpoint from
+    `params.start_epoch - 1`.
+
+    Apart from loading state dict for `model` and `optimizer` it also updates
+    `best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
+    and `best_valid_loss` in `params`.
+
+    Args:
+      params:
+        The return value of :func:`get_params`.
+      model:
+        The training model.
+    Returns:
+      Return a dict containing previously saved training info.
+    """
+    filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
+
+    assert filename.is_file(), f"{filename} does not exist!"
+
+    saved_params = load_checkpoint(
+        filename, model=model, model_avg=model_avg, model_ema=model_ema, strict=True
+    )
+
+    if params.start_epoch > 1:
+        keys = [
+            "best_train_epoch",
+            "best_valid_epoch",
+            "batch_idx_train",
+            "best_train_loss",
+            "best_valid_loss",
+        ]
+        for k in keys:
+            params[k] = saved_params[k]
+
+    return saved_params
+
+
+def compute_fbank_loss(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    teacher_model: Union[nn.Module, DDP],
+    features: Tensor,
+    features_lens: Tensor,
+    tokens: List[List[int]],
+    is_training: bool,
+) -> Tuple[Tensor, MetricsTracker]:
+    """
+    Compute loss given the model and its inputs.
+
+    Args:
+      params:
+        Parameters for training. See :func:`get_params`.
+      model:
+        The model for training.
+      teacher_model:
+        The teacher model for distillation.
+      features:
+        The target acoustic feature.
+      features_lens:
+        The number of frames of each utterance.
+      tokens:
+        Input tokens that representing the transcripts.
+      durations:
+        Duration of each token.
+      is_training:
+        True for training. False for validation. When it is True, this
+        function enables autograd during computation; when it is False, it
+        disables autograd.
+    """
+
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+
+    batch_size, num_frames, _ = features.shape
+
+    features = torch.nn.functional.pad(
+        features, (0, 0, 0, num_frames - features.size(1))
+    )  # (B, T, F)
+    noise = torch.randn_like(features)  # (B, T, F)
+
+    # Sampling t and guidance_scale from uniform distribution
+
+    t_value = random.random()
+    t = torch.ones(batch_size, 1, 1, device=device) * t_value
+    if params.distill_stage == "first":
+        guidance_scale = torch.rand(batch_size, 1, 1, device=device) * 2
+    else:
+        guidance_scale = torch.rand(batch_size, 1, 1, device=device) * 2 + 1
+    xt = features * t + noise * (1 - t)
+    t_delta_fix = random.uniform(0.0, min(0.3, 1 - t_value))
+    t_delta_ema = random.uniform(0.0, min(0.3, 1 - t_value - t_delta_fix))
+    t_dest = t + t_delta_fix + t_delta_ema
+
+    with torch.no_grad():
+        speech_condition_mask = condition_time_mask(
+            features_lens=features_lens,
+            mask_percent=(0.7, 1.0),
+            max_len=features.size(1),
+        )
+
+        if params.distill_stage == "first":
+            teacher_x_t_mid, _ = teacher_model.sample_intermediate(
+                tokens=tokens,
+                features=features,
+                features_lens=features_lens,
+                noise=xt,
+                speech_condition_mask=speech_condition_mask,
+                t_start=t,
+                t_end=t + t_delta_fix,
+                num_step=1,
+                guidance_scale=guidance_scale,
+            )
+
+            target_x1, _ = teacher_model.sample_intermediate(
+                tokens=tokens,
+                features=features,
+                features_lens=features_lens,
+                noise=teacher_x_t_mid,
+                speech_condition_mask=speech_condition_mask,
+                t_start=t + t_delta_fix,
+                t_end=t_dest,
+                num_step=1,
+                guidance_scale=guidance_scale,
+            )
+        else:
+            teacher_x_t_mid, _ = teacher_model(
+                tokens=tokens,
+                features=features,
+                features_lens=features_lens,
+                noise=xt,
+                speech_condition_mask=speech_condition_mask,
+                t_start=t,
+                t_end=t + t_delta_fix,
+                num_step=1,
+                guidance_scale=guidance_scale,
+            )
+
+            target_x1, _ = teacher_model(
+                tokens=tokens,
+                features=features,
+                features_lens=features_lens,
+                noise=teacher_x_t_mid,
+                speech_condition_mask=speech_condition_mask,
+                t_start=t + t_delta_fix,
+                t_end=t_dest,
+                num_step=1,
+                guidance_scale=guidance_scale,
+            )
+
+    with torch.set_grad_enabled(is_training):
+
+        pred_x1, _ = model(
+            tokens=tokens,
+            features=features,
+            features_lens=features_lens,
+            noise=xt,
+            speech_condition_mask=speech_condition_mask,
+            t_start=t,
+            t_end=t_dest,
+            num_step=1,
+            guidance_scale=guidance_scale,
+        )
+        pred_v = (pred_x1 - xt) / (t_dest - t)
+
+        padding_mask = make_pad_mask(features_lens, max_len=num_frames)  # (B, T)
+        loss_mask = speech_condition_mask & (~padding_mask)
+
+        target_v = (target_x1 - xt) / (t_dest - t)
+        loss = torch.mean((pred_v[loss_mask] - target_v[loss_mask]) ** 2)
+
+        ut = features - noise  # (B, T, F)
+
+        ref_loss = torch.mean((pred_v[loss_mask] - ut[loss_mask]) ** 2)
+
+    assert loss.requires_grad == is_training
+    info = MetricsTracker()
+    num_frames = features_lens.sum().item()
+    info["frames"] = num_frames
+    info["loss"] = loss.detach().cpu().item() * num_frames
+    info["ref_loss"] = ref_loss.detach().cpu().item() * num_frames
+    return loss, info
+
+
+def train_one_epoch(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    teacher_model: Union[nn.Module, DDP],
+    tokenizer: TokenizerEmilia,
+    optimizer: Optimizer,
+    scheduler: LRSchedulerType,
+    train_dl: torch.utils.data.DataLoader,
+    valid_dl: torch.utils.data.DataLoader,
+    scaler: GradScaler,
+    model_avg: Optional[nn.Module] = None,
+    tb_writer: Optional[SummaryWriter] = None,
+    world_size: int = 1,
+    rank: int = 0,
+) -> None:
+    """Train the model for one epoch.
+
+    The training loss from the mean of all frames is saved in
+    `params.train_loss`. It runs the validation process every
+    `params.valid_interval` batches.
+
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The model for training.
+      teacher_model:
+        The model for distillation.
+      tokenizer:
+        Used to convert text to tokens.
+      optimizer:
+        The optimizer.
+      scheduler:
+        The learning rate scheduler, we call step() every epoch.
+      train_dl:
+        Dataloader for the training dataset.
+      valid_dl:
+        Dataloader for the validation dataset.
+      scaler:
+        The scaler used for mix precision training.
+      tb_writer:
+        Writer to write log messages to tensorboard.
+      world_size:
+        Number of nodes in DDP training. If it is 1, DDP is disabled.
+      rank:
+        The rank of the node in DDP training. If no DDP is used, it should
+        be set to 0.
+    """
+    model.train()
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+
+    # used to track the stats over iterations in one epoch
+    tot_loss = MetricsTracker()
+
+    saved_bad_model = False
+
+    def save_bad_model(suffix: str = ""):
+        save_checkpoint(
+            filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt",
+            model=model,
+            model_avg=model_avg,
+            model_ema=teacher_model,
+            params=params,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            sampler=train_dl.sampler,
+            scaler=scaler,
+            rank=0,
+        )
+
+    for batch_idx, batch in enumerate(train_dl):
+
+        if batch_idx % 10 == 0:
+            set_batch_count(model, get_adjusted_batch_count(params) + 100000)
+
+        if (
+            params.valid_interval is None
+            and batch_idx == 0
+            and not params.print_diagnostics
+        ) or (
+            params.valid_interval is not None
+            and params.batch_idx_train % params.valid_interval == 0
+            and not params.print_diagnostics
+        ):
+            logging.info("Computing validation loss")
+            valid_info = compute_validation_loss(
+                params=params,
+                model=model,
+                teacher_model=teacher_model,
+                tokenizer=tokenizer,
+                valid_dl=valid_dl,
+                world_size=world_size,
+            )
+            model.train()
+            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
+            logging.info(
+                f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
+            )
+            if tb_writer is not None:
+                valid_info.write_summary(
+                    tb_writer, "train/valid_", params.batch_idx_train
+                )
+
+        params.batch_idx_train += 1
+
+        batch_size = len(batch["text"])
+
+        tokens, features, features_lens = prepare_input(
+            params=params,
+            batch=batch,
+            device=device,
+            tokenizer=tokenizer,
+            return_tokens=True,
+            return_feature=True,
+        )
+
+        try:
+            with autocast("cuda", enabled=params.use_fp16):
+                loss, loss_info = compute_fbank_loss(
+                    params=params,
+                    model=model,
+                    teacher_model=teacher_model,
+                    features=features,
+                    features_lens=features_lens,
+                    tokens=tokens,
+                    is_training=True,
+                )
+
+            tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
+
+            scaler.scale(loss).backward()
+
+            scheduler.step_batch(params.batch_idx_train)
+            scaler.step(optimizer)
+            scaler.update()
+            optimizer.zero_grad()
+            if params.distill_stage == "second":
+                ema(model, teacher_model, params.ema_decay)
+        except RuntimeError as e:
+            if "out of memory" in str(e):
+                logging.info(f"out of memory error at rank {rank}")
+                # optimizer.zero_grad()
+                # duration_optimizer.zero_grad()
+                torch.cuda.empty_cache()
+                raise
+                continue
+            else:
+                logging.info(f"Caught exception : {e}.")
+                save_bad_model()
+                raise
+        except Exception as e:
+            logging.info(f"Caught exception : {e}.")
+            save_bad_model()
+            raise
+
+        if params.print_diagnostics and batch_idx == 5:
+            return
+
+        if (
+            rank == 0
+            and params.batch_idx_train > 0
+            and params.batch_idx_train % params.average_period == 0
+        ):
+            update_averaged_model(
+                params=params,
+                model_cur=model,
+                model_avg=model_avg,
+            )
+
+        if (
+            params.batch_idx_train > 0
+            and params.batch_idx_train % params.save_every_n == 0
+        ):
+            save_checkpoint_with_global_batch_idx(
+                out_dir=params.exp_dir,
+                global_batch_idx=params.batch_idx_train,
+                model=model,
+                model_avg=model_avg,
+                params=params,
+                optimizer=optimizer,
+                scheduler=scheduler,
+                sampler=train_dl.sampler,
+                scaler=scaler,
+                rank=rank,
+            )
+            remove_checkpoints(
+                out_dir=params.exp_dir,
+                topk=params.keep_last_k,
+                rank=rank,
+            )
+        if (
+            params.batch_idx_train > 0
+            and params.num_updates > 0
+            and params.batch_idx_train > params.num_updates
+        ):
+            break
+        if params.batch_idx_train % 100 == 0 and params.use_fp16:
+            # If the grad scale was less than 1, try increasing it.    The _growth_interval
+            # of the grad scaler is configurable, but we can't configure it to have different
+            # behavior depending on the current grad scale.
+            cur_grad_scale = scaler._scale.item()
+
+            if cur_grad_scale < 1024.0 or (
+                cur_grad_scale < 4096.0 and params.batch_idx_train % 400 == 0
+            ):
+                scaler.update(cur_grad_scale * 2.0)
+            if cur_grad_scale < 0.01:
+                if not saved_bad_model:
+                    save_bad_model(suffix="-first-warning")
+                    saved_bad_model = True
+                logging.warning(f"Grad scale is small: {cur_grad_scale}")
+            if cur_grad_scale < 1.0e-05:
+                save_bad_model()
+                raise RuntimeError(
+                    f"grad_scale is too small, exiting: {cur_grad_scale}"
+                )
+
+        if params.batch_idx_train % params.log_interval == 0:
+            cur_lr = max(scheduler.get_last_lr())
+            cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0
+
+            logging.info(
+                f"Epoch {params.cur_epoch}, batch {batch_idx}, "
+                f"global_batch_idx: {params.batch_idx_train}, batch size: {batch_size}, "
+                f"loss[{loss_info}], tot_loss[{tot_loss}], "
+                f"cur_lr: {cur_lr:.2e}, "
+                + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
+            )
+
+            if tb_writer is not None:
+                tb_writer.add_scalar(
+                    "train/learning_rate", cur_lr, params.batch_idx_train
+                )
+                loss_info.write_summary(
+                    tb_writer, "train/current_", params.batch_idx_train
+                )
+                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                if params.use_fp16:
+                    tb_writer.add_scalar(
+                        "train/grad_scale", cur_grad_scale, params.batch_idx_train
+                    )
+
+    loss_value = tot_loss["loss"]
+    params.train_loss = loss_value
+    if params.train_loss < params.best_train_loss:
+        params.best_train_epoch = params.cur_epoch
+        params.best_train_loss = params.train_loss
+
+
+def compute_validation_loss(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    teacher_model: Optional[nn.Module],
+    tokenizer: TokenizerEmilia,
+    valid_dl: torch.utils.data.DataLoader,
+    world_size: int = 1,
+) -> MetricsTracker:
+    """Run the validation process."""
+
+    model.eval()
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+
+    # used to summary the stats over iterations
+    tot_loss = MetricsTracker()
+
+    for batch_idx, batch in enumerate(valid_dl):
+        tokens, features, features_lens = prepare_input(
+            params=params,
+            batch=batch,
+            device=device,
+            tokenizer=tokenizer,
+            return_tokens=True,
+            return_feature=True,
+        )
+
+        loss, loss_info = compute_fbank_loss(
+            params=params,
+            model=model,
+            teacher_model=teacher_model,
+            features=features,
+            features_lens=features_lens,
+            tokens=tokens,
+            is_training=False,
+        )
+        assert loss.requires_grad is False
+        tot_loss = tot_loss + loss_info
+
+    if world_size > 1:
+        tot_loss.reduce(loss.device)
+
+    loss_value = tot_loss["loss"]
+    if loss_value < params.best_valid_loss:
+        params.best_valid_epoch = params.cur_epoch
+        params.best_valid_loss = loss_value
+
+    return tot_loss
+
+
+def run(rank, world_size, args):
+    """
+    Args:
+      rank:
+        It is a value between 0 and `world_size-1`, which is
+        passed automatically by `mp.spawn()` in :func:`main`.
+        The node with rank 0 is responsible for saving checkpoint.
+      world_size:
+        Number of GPUs for DDP training.
+      args:
+        The return value of get_parser().parse_args()
+    """
+    params = get_params()
+    params.update(vars(args))
+
+    fix_random_seed(params.seed)
+    if world_size > 1:
+        setup_dist(rank, world_size, params.master_port)
+
+    setup_logger(f"{params.exp_dir}/log/log-train")
+    os.makedirs(f"{params.exp_dir}/fbank", exist_ok=True)
+
+    logging.info("Training started")
+
+    if args.tensorboard and rank == 0:
+        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
+    else:
+        tb_writer = None
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", rank)
+    logging.info(f"Device: {device}")
+
+    if params.dataset == "emilia":
+        tokenizer = TokenizerEmilia(
+            token_file=params.token_file, token_type=params.token_type
+        )
+    elif params.dataset == "libritts":
+        tokenizer = TokenizerLibriTTS(
+            token_file=params.token_file, token_type=params.token_type
+        )
+
+    params.vocab_size = tokenizer.vocab_size
+    params.pad_id = tokenizer.pad_id
+
+    params.device = device
+
+    logging.info(params)
+
+    logging.info("About to create model")
+
+    assert params.teacher_model is not None
+    logging.info(f"Loading pre-trained model from {params.teacher_model}")
+    model = get_distill_model(params)
+    _ = load_checkpoint(
+        filename=params.teacher_model,
+        model=model,
+        strict=(params.distill_stage == "second"),
+    )
+
+    if params.distill_stage == "first":
+        teacher_model = get_model(params)
+        _ = load_checkpoint(
+            filename=params.teacher_model, model=teacher_model, strict=True
+        )
+    else:
+        teacher_model = copy.deepcopy(model)
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of parameters : {num_param}")
+
+    model_avg: Optional[nn.Module] = None
+    if rank == 0:
+        # model_avg is only used with rank 0
+        model_avg = copy.deepcopy(model).to(torch.float64)
+    assert params.start_epoch > 0, params.start_epoch
+    if params.start_epoch > 1:
+        logging.info(f"Resuming from epoch {params.start_epoch}")
+        if params.distill_stage == "first":
+            checkpoints = resume_checkpoint(
+                params=params, model=model, model_avg=model_avg
+            )
+        else:
+            checkpoints = resume_checkpoint(
+                params=params, model=model, model_avg=model_avg, model_ema=teacher_model
+            )
+
+    model = model.to(device)
+    teacher_model.to(device)
+    teacher_model.eval()
+
+    if world_size > 1:
+        logging.info("Using DDP")
+        model = DDP(model, device_ids=[rank], find_unused_parameters=True)
+
+    # only update the fm_decoder
+    num_trainable = 0
+    for name, p in model.named_parameters():
+        if "fm_decoder" in name:
+            p.requires_grad = True
+            num_trainable += p.numel()
+        else:
+            p.requires_grad = False
+
+    logging.info(
+        "A total of {} trainable parameters ({:.3f}% of the whole model)".format(
+            num_trainable, num_trainable / num_param * 100
+        )
+    )
+
+    optimizer = ScaledAdam(
+        get_parameter_groups_with_lrs(
+            model,
+            lr=params.base_lr,
+            include_names=True,
+        ),
+        lr=params.base_lr,  # should have no effect
+        clipping_scale=2.0,
+    )
+
+    scheduler = FixedLRScheduler(optimizer)
+
+    scaler = GradScaler("cuda", enabled=params.use_fp16)
+
+    if params.start_epoch > 1 and checkpoints is not None:
+        # load state_dict for optimizers
+        if "optimizer" in checkpoints:
+            logging.info("Loading optimizer state dict")
+            optimizer.load_state_dict(checkpoints["optimizer"])
+
+        # load state_dict for schedulers
+        if "scheduler" in checkpoints:
+            logging.info("Loading scheduler state dict")
+            scheduler.load_state_dict(checkpoints["scheduler"])
+
+        if "grad_scaler" in checkpoints:
+            logging.info("Loading grad scaler state dict")
+            scaler.load_state_dict(checkpoints["grad_scaler"])
+
+    if params.print_diagnostics:
+        opts = diagnostics.TensorDiagnosticOptions(
+            512
+        )  # allow 4 megabytes per sub-module
+        diagnostic = diagnostics.attach_diagnostics(model, opts)
+
+    if params.inf_check:
+        register_inf_check_hooks(model)
+
+    def remove_short_and_long_utt_emilia(c: Cut):
+        if c.duration < 1.0 or c.duration > 30.0:
+            return False
+        return True
+
+    def remove_short_and_long_utt_libritts(c: Cut):
+        if c.duration < 1.0 or c.duration > 20.0:
+            return False
+        return True
+
+    datamodule = TtsDataModule(args)
+    if params.dataset == "emilia":
+        train_cuts = CutSet.mux(
+            datamodule.train_emilia_EN_cuts(),
+            datamodule.train_emilia_ZH_cuts(),
+            weights=[46000, 49000],
+        )
+        train_cuts = train_cuts.filter(remove_short_and_long_utt_emilia)
+        dev_cuts = CutSet.mux(
+            datamodule.dev_emilia_EN_cuts(),
+            datamodule.dev_emilia_ZH_cuts(),
+            weights=[0.5, 0.5],
+        )
+    elif params.dataset == "libritts":
+        train_cuts = datamodule.train_libritts_cuts()
+        train_cuts = train_cuts.filter(remove_short_and_long_utt_libritts)
+        dev_cuts = datamodule.dev_libritts_cuts()
+
+    train_dl = datamodule.train_dataloaders(train_cuts)
+
+    valid_dl = datamodule.dev_dataloaders(dev_cuts)
+
+    for epoch in range(params.start_epoch, params.num_epochs + 1):
+        logging.info(f"Start epoch {epoch}")
+
+        scheduler.step_epoch(epoch - 1)
+        fix_random_seed(params.seed + epoch - 1)
+        train_dl.sampler.set_epoch(epoch - 1)
+
+        params.cur_epoch = epoch
+
+        if tb_writer is not None:
+            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
+
+        train_one_epoch(
+            params=params,
+            model=model,
+            model_avg=model_avg,
+            teacher_model=teacher_model,
+            tokenizer=tokenizer,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            train_dl=train_dl,
+            valid_dl=valid_dl,
+            scaler=scaler,
+            tb_writer=tb_writer,
+            world_size=world_size,
+            rank=rank,
+        )
+
+        if params.print_diagnostics:
+            diagnostic.print_diagnostics()
+            break
+
+        filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
+        save_checkpoint(
+            filename=filename,
+            params=params,
+            model=model,
+            model_avg=model_avg,
+            model_ema=teacher_model,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            sampler=train_dl.sampler,
+            scaler=scaler,
+            rank=rank,
+        )
+
+        if rank == 0:
+            if params.best_train_epoch == params.cur_epoch:
+                best_train_filename = params.exp_dir / "best-train-loss.pt"
+                copyfile(src=filename, dst=best_train_filename)
+
+            if params.best_valid_epoch == params.cur_epoch:
+                best_valid_filename = params.exp_dir / "best-valid-loss.pt"
+                copyfile(src=filename, dst=best_valid_filename)
+
+    logging.info("Done!")
+
+    if world_size > 1:
+        torch.distributed.barrier()
+        cleanup_dist()
+
+
+def main():
+    parser = get_parser()
+    TtsDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    world_size = args.world_size
+    assert world_size >= 1
+    if world_size > 1:
+        mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
+    else:
+        run(rank=0, world_size=1, args=args)
+
+
+if __name__ == "__main__":
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
+    main()
diff --git a/egs/zipvoice/zipvoice/train_flow.py b/egs/zipvoice/zipvoice/train_flow.py
new file mode 100644
index 000000000..0bf023273
--- /dev/null
+++ b/egs/zipvoice/zipvoice/train_flow.py
@@ -0,0 +1,1108 @@
+#!/usr/bin/env python3
+# Copyright         2023  Xiaomi Corp.        (authors: Wei Kang,
+#                                                       Han Zhu)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script trains a ZipVoice model with the flow-matching loss.
+
+Usage:
+
+python3 zipvoice/train_flow.py \
+        --world-size 8 \
+        --use-fp16 1 \
+        --dataset emilia \
+        --max-duration 500 \
+        --lr-hours 30000 \
+        --lr-batches 7500 \
+        --token-file "data/tokens_emilia.txt" \
+        --manifest-dir "data/fbank" \
+        --num-epochs 11 \
+        --exp-dir zipvoice/exp_zipvoice
+"""
+
+import argparse
+import copy
+import logging
+import os
+from pathlib import Path
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import optim
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+from checkpoint import load_checkpoint, save_checkpoint
+from lhotse.cut import Cut, CutSet
+from lhotse.utils import fix_random_seed
+from model import get_model
+from optim import Eden, ScaledAdam
+from tokenizer import TokenizerEmilia, TokenizerLibriTTS
+from torch import Tensor
+from torch.amp import GradScaler, autocast
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.optim import Optimizer
+from torch.utils.tensorboard import SummaryWriter
+from tts_datamodule import TtsDataModule
+from utils import get_adjusted_batch_count, prepare_input, set_batch_count
+
+from icefall import diagnostics
+from icefall.checkpoint import (
+    remove_checkpoints,
+    save_checkpoint_with_global_batch_idx,
+    update_averaged_model,
+)
+from icefall.dist import cleanup_dist, setup_dist
+from icefall.env import get_env_info
+from icefall.hooks import register_inf_check_hooks
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    get_parameter_groups_with_lrs,
+    setup_logger,
+    str2bool,
+)
+
+LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
+
+
+def add_model_arguments(parser: argparse.ArgumentParser):
+    parser.add_argument(
+        "--fm-decoder-downsampling-factor",
+        type=str,
+        default="1,2,4,2,1",
+        help="Downsampling factor for each stack of encoder layers.",
+    )
+
+    parser.add_argument(
+        "--fm-decoder-num-layers",
+        type=str,
+        default="2,2,4,4,4",
+        help="Number of zipformer encoder layers per stack, comma separated.",
+    )
+
+    parser.add_argument(
+        "--fm-decoder-cnn-module-kernel",
+        type=str,
+        default="31,15,7,15,31",
+        help="Sizes of convolutional kernels in convolution modules in each encoder stack: "
+        "a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--fm-decoder-feedforward-dim",
+        type=int,
+        default=1536,
+        help="Feedforward dimension of the zipformer encoder layers, per stack, comma separated.",
+    )
+
+    parser.add_argument(
+        "--fm-decoder-num-heads",
+        type=int,
+        default=4,
+        help="Number of attention heads in the zipformer encoder layers: a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--fm-decoder-dim",
+        type=int,
+        default=512,
+        help="Embedding dimension in encoder stacks: a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--text-encoder-downsampling-factor",
+        type=str,
+        default="1",
+        help="Downsampling factor for each stack of encoder layers.",
+    )
+
+    parser.add_argument(
+        "--text-encoder-num-layers",
+        type=str,
+        default="4",
+        help="Number of zipformer encoder layers per stack, comma separated.",
+    )
+
+    parser.add_argument(
+        "--text-encoder-feedforward-dim",
+        type=int,
+        default=512,
+        help="Feedforward dimension of the zipformer encoder layers, per stack, comma separated.",
+    )
+
+    parser.add_argument(
+        "--text-encoder-cnn-module-kernel",
+        type=str,
+        default="9",
+        help="Sizes of convolutional kernels in convolution modules in each encoder stack: "
+        "a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--text-encoder-num-heads",
+        type=int,
+        default=4,
+        help="Number of attention heads in the zipformer encoder layers: a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--text-encoder-dim",
+        type=int,
+        default=192,
+        help="Embedding dimension in encoder stacks: a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--query-head-dim",
+        type=int,
+        default=32,
+        help="Query/key dimension per head in encoder stacks: a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--value-head-dim",
+        type=int,
+        default=12,
+        help="Value dimension per head in encoder stacks: a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--pos-head-dim",
+        type=int,
+        default=4,
+        help="Positional-encoding dimension per head in encoder stacks: a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--pos-dim",
+        type=int,
+        default=48,
+        help="Positional-encoding embedding dimension",
+    )
+
+    parser.add_argument(
+        "--time-embed-dim",
+        type=int,
+        default=192,
+        help="Embedding dimension of timestamps embedding.",
+    )
+
+    parser.add_argument(
+        "--text-embed-dim",
+        type=int,
+        default=192,
+        help="Embedding dimension of text embedding.",
+    )
+
+    parser.add_argument(
+        "--token-type",
+        type=str,
+        default="phone",
+        choices=["phone", "char", "bpe"],
+        help="Input token type of TTS model, by default, "
+        "we use phone for emilia, char for libritts.",
+    )
+
+    parser.add_argument(
+        "--token-file",
+        type=str,
+        default="data/tokens_emilia.txt",
+        help="The file that contains information that maps tokens to ids,"
+        "which is a text file with '{token}\t{token_id}' per line if type is"
+        "char or phone, otherwise it is a bpe_model file.",
+    )
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--world-size",
+        type=int,
+        default=1,
+        help="Number of GPUs for DDP training.",
+    )
+
+    parser.add_argument(
+        "--master-port",
+        type=int,
+        default=12354,
+        help="Master port to use for DDP training.",
+    )
+
+    parser.add_argument(
+        "--tensorboard",
+        type=str2bool,
+        default=True,
+        help="Should various information be logged in tensorboard.",
+    )
+
+    parser.add_argument(
+        "--num-epochs",
+        type=int,
+        default=11,
+        help="Number of epochs to train.",
+    )
+
+    parser.add_argument(
+        "--start-epoch",
+        type=int,
+        default=1,
+        help="""Resume training from this epoch. It should be positive.
+        If larger than 1, it will load checkpoint from
+        exp-dir/epoch-{start_epoch-1}.pt
+        """,
+    )
+
+    parser.add_argument(
+        "--checkpoint",
+        type=str,
+        default=None,
+        help="""Checkpoints of pre-trained models, will load it if not None
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="zipvoice/exp_zipvoice",
+        help="""The experiment dir.
+        It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--base-lr", type=float, default=0.02, help="The base learning rate."
+    )
+
+    parser.add_argument(
+        "--lr-batches",
+        type=float,
+        default=7500,
+        help="""Number of steps that affects how rapidly the learning rate
+        decreases. We suggest not to change this.""",
+    )
+
+    parser.add_argument(
+        "--lr-epochs",
+        type=float,
+        default=10,
+        help="""Number of epochs that affects how rapidly the learning rate decreases.
+        """,
+    )
+
+    parser.add_argument(
+        "--lr-hours",
+        type=float,
+        default=0,
+        help="""If positive, --epoch is ignored and it specifies the number of hours 
+        that affects how rapidly the learning rate decreases.
+        """,
+    )
+
+    parser.add_argument(
+        "--ref-duration",
+        type=float,
+        default=50,
+        help="Reference batch duration for purposes of adjusting batch counts for setting various "
+        "schedules inside the model",
+    )
+
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="The seed for random generators intended for reproducibility",
+    )
+
+    parser.add_argument(
+        "--print-diagnostics",
+        type=str2bool,
+        default=False,
+        help="Accumulate stats on activations, print them and exit.",
+    )
+
+    parser.add_argument(
+        "--inf-check",
+        type=str2bool,
+        default=False,
+        help="Add hooks to check for infinite module outputs and gradients.",
+    )
+
+    parser.add_argument(
+        "--save-every-n",
+        type=int,
+        default=4000,
+        help="""Save checkpoint after processing this number of batches"
+        periodically. We save checkpoint to exp-dir/ whenever
+        params.batch_idx_train % save_every_n == 0. The checkpoint filename
+        has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt'
+        Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the
+        end of each epoch where `xxx` is the epoch number counting from 1.
+        """,
+    )
+
+    parser.add_argument(
+        "--keep-last-k",
+        type=int,
+        default=30,
+        help="""Only keep this number of checkpoints on disk.
+        For instance, if it is 3, there are only 3 checkpoints
+        in the exp-dir with filenames `checkpoint-xxx.pt`.
+        It does not affect checkpoints with name `epoch-xxx.pt`.
+        """,
+    )
+
+    parser.add_argument(
+        "--average-period",
+        type=int,
+        default=200,
+        help="""Update the averaged model, namely `model_avg`, after processing
+        this number of batches. `model_avg` is a separate version of model,
+        in which each floating-point parameter is the average of all the
+        parameters from the start of training. Each time we take the average,
+        we do: `model_avg = model * (average_period / batch_idx_train) +
+            model_avg * ((batch_idx_train - average_period) / batch_idx_train)`.
+        """,
+    )
+
+    parser.add_argument(
+        "--use-fp16",
+        type=str2bool,
+        default=True,
+        help="Whether to use half precision training.",
+    )
+
+    parser.add_argument(
+        "--feat-scale",
+        type=float,
+        default=0.1,
+        help="The scale factor of fbank feature",
+    )
+
+    parser.add_argument(
+        "--condition-drop-ratio",
+        type=float,
+        default=0.2,
+        help="The drop rate of text condition during training.",
+    )
+
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        default="emilia",
+        choices=["emilia", "libritts"],
+        help="The used training dataset",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    """Return a dict containing training parameters.
+
+    All training related parameters that are not passed from the commandline
+    are saved in the variable `params`.
+
+    Commandline options are merged into `params` after they are parsed, so
+    you can also access them via `params`.
+
+    Explanation of options saved in `params`:
+
+        - best_train_loss: Best training loss so far. It is used to select
+                           the model that has the lowest training loss. It is
+                           updated during the training.
+
+        - best_valid_loss: Best validation loss so far. It is used to select
+                           the model that has the lowest validation loss. It is
+                           updated during the training.
+
+        - best_train_epoch: It is the epoch that has the best training loss.
+
+        - best_valid_epoch: It is the epoch that has the best validation loss.
+
+        - batch_idx_train: Used to writing statistics to tensorboard. It
+                           contains number of batches trained so far across
+                           epochs.
+
+        - log_interval:  Print training loss if batch_idx % log_interval` is 0
+
+        - reset_interval: Reset statistics if batch_idx % reset_interval is 0
+
+        - valid_interval:  Run validation if batch_idx % valid_interval is 0
+
+        - sampling_rate: Sampling rate of the wavform.
+
+        - frame_shift_ms: Frame shift in milliseconds.
+
+        - feat_dim: The model input dim. It has to match the one used
+                       in computing features.
+
+        - env_info:  A dict containing information about the environment.
+
+    """
+    params = AttributeDict(
+        {
+            "best_train_loss": float("inf"),
+            "best_valid_loss": float("inf"),
+            "best_train_epoch": -1,
+            "best_valid_epoch": -1,
+            "batch_idx_train": 0,
+            "log_interval": 50,
+            "reset_interval": 200,
+            "valid_interval": 4000,
+            "sampling_rate": 24000,
+            "frame_shift_ms": 256 / 24000 * 1000,
+            "feat_dim": 100,
+            "env_info": get_env_info(),
+        }
+    )
+
+    return params
+
+
+def resume_checkpoint(
+    params: AttributeDict, model: nn.Module, model_avg: nn.Module
+) -> Optional[Dict[str, Any]]:
+    """Load checkpoint from file.
+
+    If params.start_epoch is larger than 1, it will load the checkpoint from
+    `params.start_epoch - 1`.
+
+    Apart from loading state dict for `model` it also updates
+    `best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
+    and `best_valid_loss` in `params`.
+
+    Args:
+      params:
+        The return value of :func:`get_params`.
+      model:
+        The training model.
+    Returns:
+      Return a dict containing previously saved training info.
+    """
+    if params.start_epoch > 1:
+        filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
+    else:
+        return None
+
+    logging.info(f"Resuming from file {filename}")
+    assert filename.is_file(), f"{filename} does not exist!"
+
+    saved_params = load_checkpoint(
+        filename, model=model, model_avg=model_avg, strict=True
+    )
+
+    keys = [
+        "best_train_epoch",
+        "best_valid_epoch",
+        "batch_idx_train",
+        "best_train_loss",
+        "best_valid_loss",
+    ]
+    for k in keys:
+        params[k] = saved_params[k]
+
+    return saved_params
+
+
+def compute_fbank_loss(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    features: Tensor,
+    features_lens: Tensor,
+    tokens: List[List[int]],
+    is_training: bool,
+) -> Tuple[Tensor, MetricsTracker]:
+    """
+    Compute loss given the model and its inputs.
+
+    Args:
+      params:
+        Parameters for training. See :func:`get_params`.
+      model:
+        The model for training.
+      features:
+        The target acoustic feature.
+      features_lens:
+        The number of frames of each utterance.
+      tokens:
+        Input tokens that representing the transcripts.
+      is_training:
+        True for training. False for validation. When it is True, this
+        function enables autograd during computation; when it is False, it
+        disables autograd.
+    """
+
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+
+    batch_size, num_frames, _ = features.shape
+
+    features = torch.nn.functional.pad(
+        features, (0, 0, 0, num_frames - features.size(1))
+    )  # (B, T, F)
+    noise = torch.randn_like(features)  # (B, T, F)
+
+    # Sampling t from uniform distribution
+    if is_training:
+        t = torch.rand(batch_size, 1, 1, device=device)
+    else:
+        t = (
+            (torch.arange(batch_size, device=device) / batch_size)
+            .unsqueeze(1)
+            .unsqueeze(2)
+        )
+    with torch.set_grad_enabled(is_training):
+
+        loss = model(
+            tokens=tokens,
+            features=features,
+            features_lens=features_lens,
+            noise=noise,
+            t=t,
+            condition_drop_ratio=params.condition_drop_ratio,
+        )
+
+    assert loss.requires_grad == is_training
+    info = MetricsTracker()
+    num_frames = features_lens.sum().item()
+    info["frames"] = num_frames
+    info["loss"] = loss.detach().cpu().item() * num_frames
+
+    return loss, info
+
+
+def train_one_epoch(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    tokenizer: TokenizerEmilia,
+    optimizer: Optimizer,
+    scheduler: LRSchedulerType,
+    train_dl: torch.utils.data.DataLoader,
+    valid_dl: torch.utils.data.DataLoader,
+    scaler: GradScaler,
+    model_avg: Optional[nn.Module] = None,
+    tb_writer: Optional[SummaryWriter] = None,
+    world_size: int = 1,
+    rank: int = 0,
+) -> None:
+    """Train the model for one epoch.
+
+    The training loss from the mean of all frames is saved in
+    `params.train_loss`. It runs the validation process every
+    `params.valid_interval` batches.
+
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The model for training.
+      tokenizer:
+        Used to convert text to tokens.
+      optimizer:
+        The optimizer.
+      scheduler:
+        The learning rate scheduler, we call step() every epoch.
+      train_dl:
+        Dataloader for the training dataset.
+      valid_dl:
+        Dataloader for the validation dataset.
+      scaler:
+        The scaler used for mix precision training.
+      tb_writer:
+        Writer to write log messages to tensorboard.
+      world_size:
+        Number of nodes in DDP training. If it is 1, DDP is disabled.
+      rank:
+        The rank of the node in DDP training. If no DDP is used, it should
+        be set to 0.
+    """
+    model.train()
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+
+    # used to track the stats over iterations in one epoch
+    tot_loss = MetricsTracker()
+
+    saved_bad_model = False
+
+    def save_bad_model(suffix: str = ""):
+        save_checkpoint(
+            filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt",
+            model=model,
+            model_avg=model_avg,
+            params=params,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            sampler=train_dl.sampler,
+            scaler=scaler,
+            rank=0,
+        )
+
+    for batch_idx, batch in enumerate(train_dl):
+
+        if batch_idx % 10 == 0:
+            set_batch_count(model, get_adjusted_batch_count(params))
+
+        if (
+            params.valid_interval is None
+            and batch_idx == 0
+            and not params.print_diagnostics
+        ) or (
+            params.valid_interval is not None
+            and params.batch_idx_train % params.valid_interval == 0
+            and not params.print_diagnostics
+        ):
+            logging.info("Computing validation loss")
+            valid_info = compute_validation_loss(
+                params=params,
+                model=model,
+                tokenizer=tokenizer,
+                valid_dl=valid_dl,
+                world_size=world_size,
+            )
+            model.train()
+            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
+            logging.info(
+                f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
+            )
+            if tb_writer is not None:
+                valid_info.write_summary(
+                    tb_writer, "train/valid_", params.batch_idx_train
+                )
+
+        params.batch_idx_train += 1
+
+        batch_size = len(batch["text"])
+
+        tokens, features, features_lens = prepare_input(
+            params=params,
+            batch=batch,
+            device=device,
+            tokenizer=tokenizer,
+            return_tokens=True,
+            return_feature=True,
+        )
+
+        try:
+            with autocast("cuda", enabled=params.use_fp16):
+                loss, loss_info = compute_fbank_loss(
+                    params=params,
+                    model=model,
+                    features=features,
+                    features_lens=features_lens,
+                    tokens=tokens,
+                    is_training=True,
+                )
+
+            tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
+
+            scaler.scale(loss).backward()
+
+            scheduler.step_batch(params.batch_idx_train)
+            # Use the number of hours of speech to adjust the learning rate
+            if params.lr_hours > 0:
+                scheduler.step_epoch(
+                    params.batch_idx_train
+                    * params.max_duration
+                    * params.world_size
+                    / 3600
+                )
+            scaler.step(optimizer)
+            scaler.update()
+            optimizer.zero_grad()
+        except RuntimeError as e:
+            if "out of memory" in str(e):
+                logging.info(f"out of memory error at rank {rank}")
+                # optimizer.zero_grad()
+                # duration_optimizer.zero_grad()
+                torch.cuda.empty_cache()
+                raise
+                continue
+            else:
+                logging.info(f"Caught exception : {e}.")
+                save_bad_model()
+                raise
+        except Exception as e:
+            logging.info(f"Caught exception : {e}.")
+            save_bad_model()
+            raise
+
+        if params.print_diagnostics and batch_idx == 5:
+            return
+
+        if (
+            rank == 0
+            and params.batch_idx_train > 0
+            and params.batch_idx_train % params.average_period == 0
+        ):
+            update_averaged_model(
+                params=params,
+                model_cur=model,
+                model_avg=model_avg,
+            )
+
+        if (
+            params.batch_idx_train > 0
+            and params.batch_idx_train % params.save_every_n == 0
+        ):
+            save_checkpoint_with_global_batch_idx(
+                out_dir=params.exp_dir,
+                global_batch_idx=params.batch_idx_train,
+                model=model,
+                model_avg=model_avg,
+                params=params,
+                optimizer=optimizer,
+                scheduler=scheduler,
+                sampler=train_dl.sampler,
+                scaler=scaler,
+                rank=rank,
+            )
+            remove_checkpoints(
+                out_dir=params.exp_dir,
+                topk=params.keep_last_k,
+                rank=rank,
+            )
+        if params.batch_idx_train % 100 == 0 and params.use_fp16:
+            # If the grad scale was less than 1, try increasing it.    The _growth_interval
+            # of the grad scaler is configurable, but we can't configure it to have different
+            # behavior depending on the current grad scale.
+            cur_grad_scale = scaler._scale.item()
+
+            if cur_grad_scale < 1024.0 or (
+                cur_grad_scale < 4096.0 and params.batch_idx_train % 400 == 0
+            ):
+                scaler.update(cur_grad_scale * 2.0)
+            if cur_grad_scale < 0.01:
+                if not saved_bad_model:
+                    save_bad_model(suffix="-first-warning")
+                    saved_bad_model = True
+                logging.warning(f"Grad scale is small: {cur_grad_scale}")
+            if cur_grad_scale < 1.0e-05:
+                save_bad_model()
+                raise RuntimeError(
+                    f"grad_scale is too small, exiting: {cur_grad_scale}"
+                )
+
+        if params.batch_idx_train % params.log_interval == 0:
+            cur_lr = max(scheduler.get_last_lr())
+            cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0
+
+            logging.info(
+                f"Epoch {params.cur_epoch}, batch {batch_idx}, "
+                f"global_batch_idx: {params.batch_idx_train}, batch size: {batch_size}, "
+                f"loss[{loss_info}], tot_loss[{tot_loss}], "
+                f"cur_lr: {cur_lr:.2e}, "
+                + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
+            )
+
+            if tb_writer is not None:
+                tb_writer.add_scalar(
+                    "train/learning_rate", cur_lr, params.batch_idx_train
+                )
+                loss_info.write_summary(
+                    tb_writer, "train/current_", params.batch_idx_train
+                )
+                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                if params.use_fp16:
+                    tb_writer.add_scalar(
+                        "train/grad_scale", cur_grad_scale, params.batch_idx_train
+                    )
+
+    loss_value = tot_loss["loss"]
+    params.train_loss = loss_value
+    if params.train_loss < params.best_train_loss:
+        params.best_train_epoch = params.cur_epoch
+        params.best_train_loss = params.train_loss
+
+
+def compute_validation_loss(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    tokenizer: TokenizerEmilia,
+    valid_dl: torch.utils.data.DataLoader,
+    world_size: int = 1,
+) -> MetricsTracker:
+    """Run the validation process."""
+
+    model.eval()
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+
+    # used to summary the stats over iterations
+    tot_loss = MetricsTracker()
+
+    for batch_idx, batch in enumerate(valid_dl):
+        tokens, features, features_lens = prepare_input(
+            params=params,
+            batch=batch,
+            device=device,
+            tokenizer=tokenizer,
+            return_tokens=True,
+            return_feature=True,
+        )
+
+        loss, loss_info = compute_fbank_loss(
+            params=params,
+            model=model,
+            features=features,
+            features_lens=features_lens,
+            tokens=tokens,
+            is_training=False,
+        )
+        assert loss.requires_grad is False
+        tot_loss = tot_loss + loss_info
+
+    if world_size > 1:
+        tot_loss.reduce(loss.device)
+
+    loss_value = tot_loss["loss"]
+    if loss_value < params.best_valid_loss:
+        params.best_valid_epoch = params.cur_epoch
+        params.best_valid_loss = loss_value
+
+    return tot_loss
+
+
+def run(rank, world_size, args):
+    """
+    Args:
+      rank:
+        It is a value between 0 and `world_size-1`, which is
+        passed automatically by `mp.spawn()` in :func:`main`.
+        The node with rank 0 is responsible for saving checkpoint.
+      world_size:
+        Number of GPUs for DDP training.
+      args:
+        The return value of get_parser().parse_args()
+    """
+    params = get_params()
+    params.update(vars(args))
+
+    fix_random_seed(params.seed)
+    if world_size > 1:
+        setup_dist(rank, world_size, params.master_port)
+
+    setup_logger(f"{params.exp_dir}/log/log-train")
+    os.makedirs(f"{params.exp_dir}/fbank", exist_ok=True)
+
+    logging.info("Training started")
+
+    if args.tensorboard and rank == 0:
+        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
+    else:
+        tb_writer = None
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", rank)
+    logging.info(f"Device: {device}")
+
+    if params.dataset == "emilia":
+        tokenizer = TokenizerEmilia(
+            token_file=params.token_file, token_type=params.token_type
+        )
+    elif params.dataset == "libritts":
+        tokenizer = TokenizerLibriTTS(
+            token_file=params.token_file, token_type=params.token_type
+        )
+    params.vocab_size = tokenizer.vocab_size
+    params.pad_id = tokenizer.pad_id
+
+    params.device = device
+
+    logging.info(params)
+
+    logging.info("About to create model")
+
+    model = get_model(params)
+    if params.checkpoint is not None:
+        logging.info(f"Loading pre-trained model from {params.checkpoint}")
+        _ = load_checkpoint(filename=params.checkpoint, model=model, strict=True)
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of parameters : {num_param}")
+
+    model_avg: Optional[nn.Module] = None
+    if rank == 0:
+        # model_avg is only used with rank 0
+        model_avg = copy.deepcopy(model).to(torch.float64)
+    assert params.start_epoch > 0, params.start_epoch
+    if params.start_epoch > 1:
+        checkpoints = resume_checkpoint(params=params, model=model, model_avg=model_avg)
+
+    model = model.to(device)
+    if world_size > 1:
+        logging.info("Using DDP")
+        model = DDP(model, device_ids=[rank], find_unused_parameters=True)
+
+    optimizer = ScaledAdam(
+        get_parameter_groups_with_lrs(
+            model,
+            lr=params.base_lr,
+            include_names=True,
+        ),
+        lr=params.base_lr,  # should have no effect
+        clipping_scale=2.0,
+    )
+
+    assert params.lr_hours >= 0
+    if params.lr_hours > 0:
+        scheduler = Eden(optimizer, params.lr_batches, params.lr_hours)
+    else:
+        scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs)
+
+    scaler = GradScaler("cuda", enabled=params.use_fp16)
+
+    if params.start_epoch > 1 and checkpoints is not None:
+        # load state_dict for optimizers
+        if "optimizer" in checkpoints:
+            logging.info("Loading optimizer state dict")
+            optimizer.load_state_dict(checkpoints["optimizer"])
+
+        # load state_dict for schedulers
+        if "scheduler" in checkpoints:
+            logging.info("Loading scheduler state dict")
+            scheduler.load_state_dict(checkpoints["scheduler"])
+
+        if "grad_scaler" in checkpoints:
+            logging.info("Loading grad scaler state dict")
+            scaler.load_state_dict(checkpoints["grad_scaler"])
+
+    if params.print_diagnostics:
+        opts = diagnostics.TensorDiagnosticOptions(
+            512
+        )  # allow 4 megabytes per sub-module
+        diagnostic = diagnostics.attach_diagnostics(model, opts)
+
+    if params.inf_check:
+        register_inf_check_hooks(model)
+
+    def remove_short_and_long_utt_emilia(c: Cut):
+        if c.duration < 1.0 or c.duration > 30.0:
+            return False
+        return True
+
+    def remove_short_and_long_utt_libritts(c: Cut):
+        if c.duration < 1.0 or c.duration > 20.0:
+            return False
+        return True
+
+    datamodule = TtsDataModule(args)
+    if params.dataset == "emilia":
+        train_cuts = CutSet.mux(
+            datamodule.train_emilia_EN_cuts(),
+            datamodule.train_emilia_ZH_cuts(),
+            weights=[46000, 49000],
+        )
+        train_cuts = train_cuts.filter(remove_short_and_long_utt_emilia)
+        dev_cuts = CutSet.mux(
+            datamodule.dev_emilia_EN_cuts(),
+            datamodule.dev_emilia_ZH_cuts(),
+            weights=[0.5, 0.5],
+        )
+    elif params.dataset == "libritts":
+        train_cuts = datamodule.train_libritts_cuts()
+        train_cuts = train_cuts.filter(remove_short_and_long_utt_libritts)
+        dev_cuts = datamodule.dev_libritts_cuts()
+
+    train_dl = datamodule.train_dataloaders(train_cuts)
+
+    valid_dl = datamodule.dev_dataloaders(dev_cuts)
+
+    for epoch in range(params.start_epoch, params.num_epochs + 1):
+        logging.info(f"Start epoch {epoch}")
+
+        if params.lr_hours == 0:
+            scheduler.step_epoch(epoch - 1)
+        fix_random_seed(params.seed + epoch - 1)
+        train_dl.sampler.set_epoch(epoch - 1)
+
+        params.cur_epoch = epoch
+
+        if tb_writer is not None:
+            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
+
+        train_one_epoch(
+            params=params,
+            model=model,
+            model_avg=model_avg,
+            tokenizer=tokenizer,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            train_dl=train_dl,
+            valid_dl=valid_dl,
+            scaler=scaler,
+            tb_writer=tb_writer,
+            world_size=world_size,
+            rank=rank,
+        )
+
+        if params.print_diagnostics:
+            diagnostic.print_diagnostics()
+            break
+
+        filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
+        save_checkpoint(
+            filename=filename,
+            params=params,
+            model=model,
+            model_avg=model_avg,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            sampler=train_dl.sampler,
+            scaler=scaler,
+            rank=rank,
+        )
+
+        if rank == 0:
+            if params.best_train_epoch == params.cur_epoch:
+                best_train_filename = params.exp_dir / "best-train-loss.pt"
+                copyfile(src=filename, dst=best_train_filename)
+
+            if params.best_valid_epoch == params.cur_epoch:
+                best_valid_filename = params.exp_dir / "best-valid-loss.pt"
+                copyfile(src=filename, dst=best_valid_filename)
+
+    logging.info("Done!")
+
+    if world_size > 1:
+        torch.distributed.barrier()
+        cleanup_dist()
+
+
+def main():
+    parser = get_parser()
+    TtsDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    world_size = args.world_size
+    assert world_size >= 1
+    if world_size > 1:
+        mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
+    else:
+        run(rank=0, world_size=1, args=args)
+
+
+if __name__ == "__main__":
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
+    main()
diff --git a/egs/zipvoice/zipvoice/tts_datamodule.py b/egs/zipvoice/zipvoice/tts_datamodule.py
new file mode 100644
index 000000000..972c700f7
--- /dev/null
+++ b/egs/zipvoice/zipvoice/tts_datamodule.py
@@ -0,0 +1,456 @@
+# Copyright      2021  Piotr Żelasko
+# Copyright      2022-2024  Xiaomi Corporation     (Authors: Mingshuang Luo,
+#                                                            Zengwei Yao,
+#                                                            Zengrui Jin,
+#                                                            Han Zhu,
+#                                                            Wei Kang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import logging
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Sequence, Union
+
+import torch
+from feature import TorchAudioFbank, TorchAudioFbankConfig
+from lhotse import CutSet, load_manifest_lazy, validate
+from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
+    DynamicBucketingSampler,
+    PrecomputedFeatures,
+    SimpleCutSampler,
+)
+from lhotse.dataset.collation import collate_audio
+from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
+    BatchIO,
+    OnTheFlyFeatures,
+)
+from lhotse.utils import fix_random_seed, ifnone
+from torch.utils.data import DataLoader
+
+from icefall.utils import str2bool
+
+
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
+
+
+SAMPLING_RATE = 24000
+
+
+class TtsDataModule:
+    """
+    DataModule for tts experiments.
+    It assumes there is always one train and valid dataloader,
+    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
+    and test-other).
+
+    It contains all the common data pipeline modules used in ASR
+    experiments, e.g.:
+    - dynamic batch size,
+    - bucketing samplers,
+    - cut concatenation,
+    - on-the-fly feature extraction
+
+    This class should be derived for specific corpora used in ASR tasks.
+    """
+
+    def __init__(self, args: argparse.Namespace):
+        self.args = args
+
+    @classmethod
+    def add_arguments(cls, parser: argparse.ArgumentParser):
+        group = parser.add_argument_group(
+            title="TTS data related options",
+            description="These options are used for the preparation of "
+            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
+            "effective batch sizes, sampling strategies, applied data "
+            "augmentations, etc.",
+        )
+        group.add_argument(
+            "--manifest-dir",
+            type=Path,
+            default=Path("data/fbank_emilia"),
+            help="Path to directory with train/valid/test cuts.",
+        )
+        group.add_argument(
+            "--max-duration",
+            type=int,
+            default=200.0,
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
+        )
+        group.add_argument(
+            "--bucketing-sampler",
+            type=str2bool,
+            default=True,
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
+        )
+        group.add_argument(
+            "--num-buckets",
+            type=int,
+            default=100,
+            help="The number of buckets for the DynamicBucketingSampler"
+            "(you might want to increase it for larger datasets).",
+        )
+
+        group.add_argument(
+            "--on-the-fly-feats",
+            type=str2bool,
+            default=False,
+            help="When enabled, use on-the-fly cut mixing and feature "
+            "extraction. Will drop existing precomputed feature manifests "
+            "if available.",
+        )
+        group.add_argument(
+            "--shuffle",
+            type=str2bool,
+            default=True,
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
+        )
+        group.add_argument(
+            "--drop-last",
+            type=str2bool,
+            default=True,
+            help="Whether to drop last batch. Used by sampler.",
+        )
+        group.add_argument(
+            "--return-cuts",
+            type=str2bool,
+            default=True,
+            help="When enabled, each batch will have the "
+            "field: batch['cut'] with the cuts that "
+            "were used to construct it.",
+        )
+        group.add_argument(
+            "--num-workers",
+            type=int,
+            default=8,
+            help="The number of training dataloader workers that "
+            "collect the batches.",
+        )
+
+        group.add_argument(
+            "--input-strategy",
+            type=str,
+            default="PrecomputedFeatures",
+            help="AudioSamples or PrecomputedFeatures",
+        )
+
+    def train_dataloaders(
+        self,
+        cuts_train: CutSet,
+        sampler_state_dict: Optional[Dict[str, Any]] = None,
+    ) -> DataLoader:
+        """
+        Args:
+          cuts_train:
+            CutSet for training.
+          sampler_state_dict:
+            The state dict for the training sampler.
+        """
+        logging.info("About to create train dataset")
+        train = SpeechSynthesisDataset(
+            return_text=True,
+            return_tokens=True,
+            return_spk_ids=True,
+            feature_input_strategy=eval(self.args.input_strategy)(),
+            return_cuts=self.args.return_cuts,
+        )
+
+        if self.args.on_the_fly_feats:
+            sampling_rate = SAMPLING_RATE
+            config = TorchAudioFbankConfig(
+                sampling_rate=sampling_rate,
+                n_mels=100,
+                n_fft=1024,
+                hop_length=256,
+            )
+            train = SpeechSynthesisDataset(
+                return_text=True,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=OnTheFlyFeatures(TorchAudioFbank(config)),
+                return_cuts=self.args.return_cuts,
+            )
+
+        if self.args.bucketing_sampler:
+            logging.info("Using DynamicBucketingSampler.")
+            train_sampler = DynamicBucketingSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+                num_buckets=self.args.num_buckets,
+                buffer_size=self.args.num_buckets * 2000,
+                shuffle_buffer_size=self.args.num_buckets * 5000,
+                drop_last=self.args.drop_last,
+            )
+        else:
+            logging.info("Using SimpleCutSampler.")
+            train_sampler = SimpleCutSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+            )
+        logging.info("About to create train dataloader")
+
+        if sampler_state_dict is not None:
+            logging.info("Loading sampler state dict")
+            train_sampler.load_state_dict(sampler_state_dict)
+
+        # 'seed' is derived from the current random state, which will have
+        # previously been set in the main process.
+        seed = torch.randint(0, 100000, ()).item()
+        worker_init_fn = _SeedWorkers(seed)
+
+        train_dl = DataLoader(
+            train,
+            sampler=train_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=False,
+            worker_init_fn=worker_init_fn,
+        )
+
+        return train_dl
+
+    def dev_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
+        logging.info("About to create dev dataset")
+        if self.args.on_the_fly_feats:
+            sampling_rate = SAMPLING_RATE
+            config = TorchAudioFbankConfig(
+                sampling_rate=sampling_rate,
+                n_mels=100,
+                n_fft=1024,
+                hop_length=256,
+            )
+            validate = SpeechSynthesisDataset(
+                return_text=True,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=OnTheFlyFeatures(TorchAudioFbank(config)),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            validate = SpeechSynthesisDataset(
+                return_text=True,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=eval(self.args.input_strategy)(),
+                return_cuts=self.args.return_cuts,
+            )
+        dev_sampler = DynamicBucketingSampler(
+            cuts_valid,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.info("About to create valid dataloader")
+        dev_dl = DataLoader(
+            validate,
+            sampler=dev_sampler,
+            batch_size=None,
+            num_workers=2,
+            persistent_workers=False,
+        )
+
+        return dev_dl
+
+    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
+        logging.info("About to create test dataset")
+        if self.args.on_the_fly_feats:
+            sampling_rate = SAMPLING_RATE
+            config = TorchAudioFbankConfig(
+                sampling_rate=sampling_rate,
+                n_mels=100,
+                n_fft=1024,
+                hop_length=256,
+            )
+            test = SpeechSynthesisDataset(
+                return_text=True,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=OnTheFlyFeatures(TorchAudioFbank(config)),
+                return_cuts=self.args.return_cuts,
+                return_audio=True,
+            )
+        else:
+            test = SpeechSynthesisDataset(
+                return_text=True,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=eval(self.args.input_strategy)(),
+                return_cuts=self.args.return_cuts,
+                return_audio=True,
+            )
+        test_sampler = DynamicBucketingSampler(
+            cuts,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.info("About to create test dataloader")
+        test_dl = DataLoader(
+            test,
+            batch_size=None,
+            sampler=test_sampler,
+            num_workers=self.args.num_workers,
+        )
+        return test_dl
+
+    @lru_cache()
+    def train_emilia_EN_cuts(self) -> CutSet:
+        logging.info("About to get train the EN subset")
+        return load_manifest_lazy(self.args.manifest_dir / "emilia_cuts_EN.jsonl.gz")
+
+    @lru_cache()
+    def train_emilia_ZH_cuts(self) -> CutSet:
+        logging.info("About to get train the ZH subset")
+        return load_manifest_lazy(self.args.manifest_dir / "emilia_cuts_ZH.jsonl.gz")
+
+    @lru_cache()
+    def dev_emilia_EN_cuts(self) -> CutSet:
+        logging.info("About to get dev the EN subset")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "emilia_cuts_EN-dev.jsonl.gz"
+        )
+
+    @lru_cache()
+    def dev_emilia_ZH_cuts(self) -> CutSet:
+        logging.info("About to get dev the ZH subset")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "emilia_cuts_ZH-dev.jsonl.gz"
+        )
+
+    @lru_cache()
+    def train_libritts_cuts(self) -> CutSet:
+        logging.info(
+            "About to get the shuffled train-clean-100, \
+            train-clean-360 and train-other-500 cuts"
+        )
+        return load_manifest_lazy(
+            self.args.manifest_dir / "libritts_cuts_train-all-shuf.jsonl.gz"
+        )
+
+    @lru_cache()
+    def dev_libritts_cuts(self) -> CutSet:
+        logging.info("About to get dev-clean cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "libritts_cuts_dev-clean.jsonl.gz"
+        )
+
+
+class SpeechSynthesisDataset(torch.utils.data.Dataset):
+    """
+    The PyTorch Dataset for the speech synthesis task.
+    Each item in this dataset is a dict of:
+
+    .. code-block::
+
+        {
+            'audio': (B x NumSamples) float tensor
+            'features': (B x NumFrames x NumFeatures) float tensor
+            'audio_lens': (B, ) int tensor
+            'features_lens': (B, ) int tensor
+            'text': List[str] of len B  # when return_text=True
+            'tokens': List[List[str]]  # when return_tokens=True
+            'speakers': List[str] of len B  # when return_spk_ids=True
+            'cut': List of Cuts  # when return_cuts=True
+        }
+    """
+
+    def __init__(
+        self,
+        cut_transforms: List[Callable[[CutSet], CutSet]] = None,
+        feature_input_strategy: BatchIO = PrecomputedFeatures(),
+        feature_transforms: Union[Sequence[Callable], Callable] = None,
+        return_text: bool = True,
+        return_tokens: bool = False,
+        return_spk_ids: bool = False,
+        return_cuts: bool = False,
+        return_audio: bool = False,
+    ) -> None:
+        super().__init__()
+
+        self.cut_transforms = ifnone(cut_transforms, [])
+        self.feature_input_strategy = feature_input_strategy
+
+        self.return_text = return_text
+        self.return_tokens = return_tokens
+        self.return_spk_ids = return_spk_ids
+        self.return_cuts = return_cuts
+        self.return_audio = return_audio
+
+        if feature_transforms is None:
+            feature_transforms = []
+        elif not isinstance(feature_transforms, Sequence):
+            feature_transforms = [feature_transforms]
+
+        assert all(
+            isinstance(transform, Callable) for transform in feature_transforms
+        ), "Feature transforms must be Callable"
+        self.feature_transforms = feature_transforms
+
+    def __getitem__(self, cuts: CutSet) -> Dict[str, torch.Tensor]:
+        validate_for_tts(cuts)
+
+        for transform in self.cut_transforms:
+            cuts = transform(cuts)
+
+        features, features_lens = self.feature_input_strategy(cuts)
+
+        for transform in self.feature_transforms:
+            features = transform(features)
+
+        batch = {
+            "features": features,
+            "features_lens": features_lens,
+        }
+
+        if self.return_audio:
+            audio, audio_lens = collate_audio(cuts)
+            batch["audio"] = audio
+            batch["audio_lens"] = audio_lens
+
+        if self.return_text:
+            # use normalized text
+            text = [cut.supervisions[0].normalized_text for cut in cuts]
+            batch["text"] = text
+
+        if self.return_tokens:
+            tokens = [cut.tokens for cut in cuts]
+            batch["tokens"] = tokens
+
+        if self.return_spk_ids:
+            batch["speakers"] = [cut.supervisions[0].speaker for cut in cuts]
+
+        if self.return_cuts:
+            batch["cut"] = [cut for cut in cuts]
+
+        return batch
+
+
+def validate_for_tts(cuts: CutSet) -> None:
+    validate(cuts)
+    for cut in cuts:
+        assert (
+            len(cut.supervisions) == 1
+        ), "Only the Cuts with single supervision are supported."
diff --git a/egs/zipvoice/zipvoice/utils.py b/egs/zipvoice/zipvoice/utils.py
new file mode 100644
index 000000000..4092d0ae4
--- /dev/null
+++ b/egs/zipvoice/zipvoice/utils.py
@@ -0,0 +1,219 @@
+from typing import Any, List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+
+class AttributeDict(dict):
+    def __getattr__(self, key):
+        if key in self:
+            return self[key]
+        raise AttributeError(f"No such attribute '{key}'")
+
+    def __setattr__(self, key, value):
+        self[key] = value
+
+    def __delattr__(self, key):
+        if key in self:
+            del self[key]
+            return
+        raise AttributeError(f"No such attribute '{key}'")
+
+
+def prepare_input(
+    params: AttributeDict,
+    batch: dict,
+    device: torch.device,
+    tokenizer: Optional[Any] = None,
+    return_tokens: bool = False,
+    return_feature: bool = False,
+    return_audio: bool = False,
+    return_prompt: bool = False,
+):
+    """
+    Parse the features and targets of the current batch.
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      batch:
+        It is the return value from iterating
+        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
+        for the format of the `batch`.
+      sp:
+        Used to convert text to bpe tokens.
+      device:
+        The device of Tensor.
+    """
+    return_list = []
+
+    if return_tokens:
+        assert tokenizer is not None
+
+        if params.token_type == "phone":
+            tokens = tokenizer.tokens_to_token_ids(batch["tokens"])
+        else:
+            tokens = tokenizer.texts_to_token_ids(batch["text"])
+        return_list += [tokens]
+
+    if return_feature:
+        features = batch["features"].to(device)
+        features_lens = batch["features_lens"].to(device)
+        return_list += [features * params.feat_scale, features_lens]
+
+    if return_audio:
+        return_list += [batch["audio"], batch["audio_lens"]]
+
+    if return_prompt:
+        if return_tokens:
+            if params.token_type == "phone":
+                prompt_tokens = tokenizer.tokens_to_token_ids(batch["prompt"]["tokens"])
+            else:
+                prompt_tokens = tokenizer.texts_to_token_ids(batch["prompt"]["text"])
+            return_list += [prompt_tokens]
+        if return_feature:
+            prompt_features = batch["prompt"]["features"].to(device)
+            prompt_features_lens = batch["prompt"]["features_lens"].to(device)
+            return_list += [prompt_features * params.feat_scale, prompt_features_lens]
+        if return_audio:
+            return_list += [batch["prompt"]["audio"], batch["prompt"]["audio_lens"]]
+
+    return return_list
+
+
+def prepare_avg_tokens_durations(features_lens, tokens_lens):
+    tokens_durations = []
+    for i in range(len(features_lens)):
+        utt_duration = features_lens[i]
+        avg_token_duration = utt_duration // tokens_lens[i]
+        tokens_durations.append([avg_token_duration] * tokens_lens[i])
+    return tokens_durations
+
+
+def pad_labels(y: List[List[int]], pad_id: int, device: torch.device):
+    """
+    Pad the transcripts to the same length with zeros.
+
+    Args:
+      y: the transcripts, which is a list of a list
+
+    Returns:
+      Return a Tensor of padded transcripts.
+    """
+    y = [l + [pad_id] for l in y]
+    length = max([len(l) for l in y])
+    y = [l + [pad_id] * (length - len(l)) for l in y]
+    return torch.tensor(y, dtype=torch.int64, device=device)
+
+
+def get_tokens_index(durations: List[List[int]], num_frames: int) -> torch.Tensor:
+    """
+    Gets position in the transcript for each frame, i.e. the position
+    in the symbol-sequence to look up.
+
+    Args:
+      durations:
+        Duration of each token in transcripts.
+      num_frames:
+        The maximum frame length of the current batch.
+
+    Returns:
+      Return a Tensor of shape (batch_size, num_frames)
+    """
+    durations = [x + [num_frames - sum(x)] for x in durations]
+    batch_size = len(durations)
+    ans = torch.zeros(batch_size, num_frames, dtype=torch.int64)
+    for b in range(batch_size):
+        this_dur = durations[b]
+        cur_frame = 0
+        for i, d in enumerate(this_dur):
+            ans[b, cur_frame : cur_frame + d] = i
+            cur_frame += d
+        assert cur_frame == num_frames, (cur_frame, num_frames)
+    return ans
+
+
+def to_int_tuple(s: str):
+    return tuple(map(int, s.split(",")))
+
+
+def get_adjusted_batch_count(params: AttributeDict) -> float:
+    # returns the number of batches we would have used so far if we had used the reference
+    # duration.  This is for purposes of set_batch_count().
+    return (
+        params.batch_idx_train
+        * (params.max_duration * params.world_size)
+        / params.ref_duration
+    )
+
+
+def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None:
+    if isinstance(model, DDP):
+        # get underlying nn.Module
+        model = model.module
+    for name, module in model.named_modules():
+        if hasattr(module, "batch_count"):
+            module.batch_count = batch_count
+        if hasattr(module, "name"):
+            module.name = name
+
+
+def condition_time_mask(
+    features_lens: torch.Tensor, mask_percent: Tuple[float, float], max_len: int = 0
+) -> torch.Tensor:
+    """
+    Apply Time masking.
+    Args:
+        features_lens:
+            input tensor of shape ``(B)``
+        mask_size:
+            the width size for masking.
+        max_len:
+            the maximum length of the mask.
+    Returns:
+        Return a 2-D bool tensor (B, T), where masked positions
+        are filled with `True` and non-masked positions are
+        filled with `False`.
+    """
+    mask_size = (
+        torch.zeros_like(features_lens, dtype=torch.float32).uniform_(*mask_percent)
+        * features_lens
+    ).to(torch.int64)
+    mask_starts = (
+        torch.rand_like(mask_size, dtype=torch.float32) * (features_lens - mask_size)
+    ).to(torch.int64)
+    mask_ends = mask_starts + mask_size
+    max_len = max(max_len, features_lens.max())
+    seq_range = torch.arange(0, max_len, device=features_lens.device)
+    mask = (seq_range[None, :] >= mask_starts[:, None]) & (
+        seq_range[None, :] < mask_ends[:, None]
+    )
+    return mask
+
+
+def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
+    """
+    Args:
+      lengths:
+        A 1-D tensor containing sentence lengths.
+      max_len:
+        The length of masks.
+    Returns:
+      Return a 2-D bool tensor, where masked positions
+      are filled with `True` and non-masked positions are
+      filled with `False`.
+
+    >>> lengths = torch.tensor([1, 3, 2, 5])
+    >>> make_pad_mask(lengths)
+    tensor([[False,  True,  True,  True,  True],
+            [False, False, False,  True,  True],
+            [False, False,  True,  True,  True],
+            [False, False, False, False, False]])
+    """
+    assert lengths.ndim == 1, lengths.ndim
+    max_len = max(max_len, lengths.max())
+    n = lengths.size(0)
+    seq_range = torch.arange(0, max_len, device=lengths.device)
+    expaned_lengths = seq_range.unsqueeze(0).expand(n, max_len)
+
+    return expaned_lengths >= lengths.unsqueeze(-1)
diff --git a/egs/zipvoice/zipvoice/zipformer.py b/egs/zipvoice/zipvoice/zipformer.py
new file mode 100644
index 000000000..190191cbb
--- /dev/null
+++ b/egs/zipvoice/zipvoice/zipformer.py
@@ -0,0 +1,1648 @@
+#!/usr/bin/env python3
+# Copyright    2022-2024  Xiaomi Corp.        (authors: Daniel Povey,
+#                                                       Zengwei Yao,
+#                                                       Wei Kang
+#                                                       Han Zhu)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import logging
+import math
+import random
+from typing import Optional, Tuple, Union
+
+import torch
+from scaling import (
+    Identity,  # more friendly to backward hooks than nn.Identity(), for diagnostic reasons.
+)
+from scaling import (
+    ScaledLinear,  # not as in other dirs.. just scales down initial parameter values.
+)
+from scaling import (
+    ActivationDropoutAndLinear,
+    Balancer,
+    BiasNorm,
+    Dropout2,
+    FloatLike,
+    ScheduledFloat,
+    SwooshR,
+    Whiten,
+    limit_param_value,
+    penalize_abs_values_gt,
+    softmax,
+)
+from torch import Tensor, nn
+
+
+def timestep_embedding(timesteps, dim, max_period=10000):
+    """Create sinusoidal timestep embeddings.
+
+    :param timesteps: shape of (N) or (N, T)
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an Tensor of positional embeddings. shape of (N, dim) or (T, N, dim)
+    """
+    half = dim // 2
+    freqs = torch.exp(
+        -math.log(max_period)
+        * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device)
+        / half
+    )
+
+    if timesteps.dim() == 2:
+        timesteps = timesteps.transpose(0, 1)  # (N, T) -> (T, N)
+
+    args = timesteps[..., None].float() * freqs[None]
+    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = torch.cat([embedding, torch.zeros_like(embedding[..., :1])], dim=-1)
+    return embedding
+
+
+class TTSZipformer(nn.Module):
+    """
+    Args:
+
+    Note: all "int or Tuple[int]" arguments below will be treated as lists of the same length
+    as downsampling_factor if they are single ints or one-element tuples.  The length of
+    downsampling_factor defines the number of stacks.
+
+        downsampling_factor (Tuple[int]): downsampling factor for each encoder stack.
+           Note: this is in addition to the downsampling factor of 2 that is applied in
+           the frontend (self.encoder_embed).
+        encoder_dim (Tuple[int]): embedding dimension of each of the encoder stacks, one per
+           encoder stack.
+        num_encoder_layers (int or Tuple[int])): number of encoder layers for each stack
+        query_head_dim (int or Tuple[int]): dimension of query and key per attention
+           head: per stack, if a tuple..
+        pos_head_dim (int or Tuple[int]): dimension of positional-encoding projection per
+           attention head
+        value_head_dim (int or Tuple[int]): dimension of value in each attention head
+        num_heads: (int or Tuple[int]): number of heads in the self-attention mechanism.
+              Must be at least 4.
+        feedforward_dim (int or Tuple[int]): hidden dimension in feedforward modules
+        cnn_module_kernel (int or Tuple[int])): Kernel size of convolution module
+
+        pos_dim (int): the dimension of each positional-encoding vector prior to projection,
+            e.g. 128.
+
+        dropout (float): dropout rate
+        warmup_batches (float): number of batches to warm up over; this controls
+          dropout of encoder layers.
+        use_time_embed: (bool): if True, do not take time embedding as additional input.
+        time_embed_dim: (int): the dimension of the time embedding.
+    """
+
+    def __init__(
+        self,
+        in_dim: int,
+        out_dim: int,
+        downsampling_factor: Tuple[int] = (2, 4),
+        num_encoder_layers: Union[int, Tuple[int]] = 4,
+        cnn_module_kernel: Union[int, Tuple[int]] = 31,
+        encoder_dim: int = 384,
+        query_head_dim: int = 24,
+        pos_head_dim: int = 4,
+        value_head_dim: int = 12,
+        num_heads: int = 8,
+        feedforward_dim: int = 1536,
+        pos_dim: int = 192,
+        dropout: FloatLike = None,  # see code below for default
+        warmup_batches: float = 4000.0,
+        use_time_embed: bool = True,
+        time_embed_dim: int = 192,
+        use_guidance_scale_embed: bool = False,
+        guidance_scale_embed_dim: int = 192,
+        use_conv: bool = True,
+    ) -> None:
+        super(TTSZipformer, self).__init__()
+
+        if dropout is None:
+            dropout = ScheduledFloat((0.0, 0.3), (20000.0, 0.1))
+
+        def _to_tuple(x):
+            """Converts a single int or a 1-tuple of an int to a tuple with the same length
+            as downsampling_factor"""
+            if isinstance(x, int):
+                x = (x,)
+            if len(x) == 1:
+                x = x * len(downsampling_factor)
+            else:
+                assert len(x) == len(downsampling_factor) and isinstance(x[0], int)
+            return x
+
+        def _assert_downsampling_factor(factors):
+            """assert downsampling_factor follows u-net style"""
+            assert factors[0] == 1 and factors[-1] == 1
+
+            for i in range(1, len(factors) // 2 + 1):
+                assert factors[i] == factors[i - 1] * 2
+
+            for i in range(len(factors) // 2 + 1, len(factors)):
+                assert factors[i] * 2 == factors[i - 1]
+
+        _assert_downsampling_factor(downsampling_factor)
+        self.downsampling_factor = downsampling_factor  # tuple
+        num_encoder_layers = _to_tuple(num_encoder_layers)
+        self.cnn_module_kernel = cnn_module_kernel = _to_tuple(cnn_module_kernel)
+        self.encoder_dim = encoder_dim
+        self.num_encoder_layers = num_encoder_layers
+        self.query_head_dim = query_head_dim
+        self.value_head_dim = value_head_dim
+        self.num_heads = num_heads
+
+        self.use_time_embed = use_time_embed
+        self.use_guidance_scale_embed = use_guidance_scale_embed
+
+        self.time_embed_dim = time_embed_dim
+        if self.use_time_embed:
+            assert time_embed_dim != -1
+        else:
+            time_embed_dim = -1
+        self.guidance_scale_embed_dim = guidance_scale_embed_dim
+
+        self.in_proj = nn.Linear(in_dim, encoder_dim)
+        self.out_proj = nn.Linear(encoder_dim, out_dim)
+
+        # each one will be Zipformer2Encoder or DownsampledZipformer2Encoder
+        encoders = []
+
+        num_encoders = len(downsampling_factor)
+        for i in range(num_encoders):
+            encoder_layer = Zipformer2EncoderLayer(
+                embed_dim=encoder_dim,
+                pos_dim=pos_dim,
+                num_heads=num_heads,
+                query_head_dim=query_head_dim,
+                pos_head_dim=pos_head_dim,
+                value_head_dim=value_head_dim,
+                feedforward_dim=feedforward_dim,
+                use_conv=use_conv,
+                cnn_module_kernel=cnn_module_kernel[i],
+                dropout=dropout,
+            )
+
+            # For the segment of the warmup period, we let the Conv2dSubsampling
+            # layer learn something.  Then we start to warm up the other encoders.
+            encoder = Zipformer2Encoder(
+                encoder_layer,
+                num_encoder_layers[i],
+                embed_dim=encoder_dim,
+                time_embed_dim=time_embed_dim,
+                pos_dim=pos_dim,
+                warmup_begin=warmup_batches * (i + 1) / (num_encoders + 1),
+                warmup_end=warmup_batches * (i + 2) / (num_encoders + 1),
+                final_layerdrop_rate=0.035 * (downsampling_factor[i] ** 0.5),
+            )
+
+            if downsampling_factor[i] != 1:
+                encoder = DownsampledZipformer2Encoder(
+                    encoder,
+                    dim=encoder_dim,
+                    downsample=downsampling_factor[i],
+                )
+
+            encoders.append(encoder)
+
+        self.encoders = nn.ModuleList(encoders)
+        if self.use_time_embed:
+            self.time_embed = nn.Sequential(
+                nn.Linear(time_embed_dim, time_embed_dim * 2),
+                SwooshR(),
+                nn.Linear(time_embed_dim * 2, time_embed_dim),
+            )
+        else:
+            self.time_embed = None
+
+        if self.use_guidance_scale_embed:
+            self.guidance_scale_embed = ScaledLinear(
+                guidance_scale_embed_dim, time_embed_dim, bias=False, initial_scale=0.1
+            )
+        else:
+            self.guidance_scale_embed = None
+
+    def forward(
+        self,
+        x: Tensor,
+        t: Optional[Tensor] = None,
+        padding_mask: Optional[Tensor] = None,
+        guidance_scale: Optional[Tensor] = None,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        Args:
+          x:
+            The input tensor. Its shape is (batch_size, seq_len, feature_dim).
+          t:
+            A t tensor of shape (batch_size,) or (batch_size, seq_len)
+          padding_mask:
+            The mask for padding, of shape (batch_size, seq_len); True means
+            masked position. May be None.
+        Returns:
+          Return the output embeddings. its shape is (batch_size, output_seq_len, encoder_dim)
+        """
+        x = x.permute(1, 0, 2)
+        x = self.in_proj(x)
+
+        if t is not None:
+            assert t.dim() == 1 or t.dim() == 2, t.shape
+            time_emb = timestep_embedding(t, self.time_embed_dim)
+            if guidance_scale is not None:
+                assert (
+                    guidance_scale.dim() == 1 or guidance_scale.dim() == 2
+                ), guidance_scale.shape
+                guidance_scale_emb = self.guidance_scale_embed(
+                    timestep_embedding(guidance_scale, self.guidance_scale_embed_dim)
+                )
+                time_emb = time_emb + guidance_scale_emb
+            time_emb = self.time_embed(time_emb)
+        else:
+            time_emb = None
+
+        attn_mask = None
+
+        for i, module in enumerate(self.encoders):
+            x = module(
+                x,
+                time_emb=time_emb,
+                src_key_padding_mask=padding_mask,
+                attn_mask=attn_mask,
+            )
+        x = self.out_proj(x)
+        x = x.permute(1, 0, 2)
+        return x
+
+
+def _whitening_schedule(x: float, ratio: float = 2.0) -> ScheduledFloat:
+    return ScheduledFloat((0.0, x), (20000.0, ratio * x), default=x)
+
+
+class Zipformer2EncoderLayer(nn.Module):
+    """
+    Args:
+        embed_dim: the number of expected features in the input (required).
+        nhead: the number of heads in the multiheadattention models (required).
+        feedforward_dim: the dimension of the feedforward network model (required).
+        dropout: the dropout value (default=0.1).
+        cnn_module_kernel (int): Kernel size of convolution module (default=31).
+
+    Examples::
+        >>> encoder_layer = Zipformer2EncoderLayer(embed_dim=512, nhead=8)
+        >>> src = torch.rand(10, 32, 512)
+        >>> pos_emb = torch.rand(32, 19, 512)
+        >>> out = encoder_layer(src, pos_emb)
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        pos_dim: int,
+        num_heads: int,
+        query_head_dim: int,
+        pos_head_dim: int,
+        value_head_dim: int,
+        feedforward_dim: int,
+        dropout: FloatLike = 0.1,
+        cnn_module_kernel: int = 31,
+        use_conv: bool = True,
+        attention_skip_rate: FloatLike = ScheduledFloat(
+            (0.0, 0.2), (4000.0, 0.05), (16000, 0.0), default=0
+        ),
+        conv_skip_rate: FloatLike = ScheduledFloat(
+            (0.0, 0.2), (4000.0, 0.05), (16000, 0.0), default=0
+        ),
+        const_attention_rate: FloatLike = ScheduledFloat(
+            (0.0, 0.25), (4000.0, 0.025), default=0
+        ),
+        ff2_skip_rate: FloatLike = ScheduledFloat(
+            (0.0, 0.1), (4000.0, 0.01), (50000.0, 0.0)
+        ),
+        ff3_skip_rate: FloatLike = ScheduledFloat(
+            (0.0, 0.1), (4000.0, 0.01), (50000.0, 0.0)
+        ),
+        bypass_skip_rate: FloatLike = ScheduledFloat(
+            (0.0, 0.5), (4000.0, 0.02), default=0
+        ),
+    ) -> None:
+        super(Zipformer2EncoderLayer, self).__init__()
+        self.embed_dim = embed_dim
+
+        # self.bypass implements layer skipping as well as bypass; see its default values.
+        self.bypass = BypassModule(
+            embed_dim, skip_rate=bypass_skip_rate, straight_through_rate=0
+        )
+        # bypass_mid is bypass used in the middle of the layer.
+        self.bypass_mid = BypassModule(embed_dim, straight_through_rate=0)
+
+        # skip probability for dynamic modules (meaning: anything but feedforward).
+        self.attention_skip_rate = copy.deepcopy(attention_skip_rate)
+        # an additional skip probability that applies to ConvModule to stop it from
+        # contributing too much early on.
+        self.conv_skip_rate = copy.deepcopy(conv_skip_rate)
+
+        # ff2_skip_rate is to prevent the ff2 module from having output that's too big
+        # compared to its residual.
+        self.ff2_skip_rate = copy.deepcopy(ff2_skip_rate)
+        self.ff3_skip_rate = copy.deepcopy(ff3_skip_rate)
+
+        self.const_attention_rate = copy.deepcopy(const_attention_rate)
+
+        self.self_attn_weights = RelPositionMultiheadAttentionWeights(
+            embed_dim,
+            pos_dim=pos_dim,
+            num_heads=num_heads,
+            query_head_dim=query_head_dim,
+            pos_head_dim=pos_head_dim,
+            dropout=0.0,
+        )
+
+        self.self_attn1 = SelfAttention(embed_dim, num_heads, value_head_dim)
+
+        self.self_attn2 = SelfAttention(embed_dim, num_heads, value_head_dim)
+
+        self.feed_forward1 = FeedforwardModule(
+            embed_dim, (feedforward_dim * 3) // 4, dropout
+        )
+
+        self.feed_forward2 = FeedforwardModule(embed_dim, feedforward_dim, dropout)
+
+        self.feed_forward3 = FeedforwardModule(
+            embed_dim, (feedforward_dim * 5) // 4, dropout
+        )
+
+        self.nonlin_attention = NonlinAttention(
+            embed_dim, hidden_channels=3 * embed_dim // 4
+        )
+
+        self.use_conv = use_conv
+
+        if self.use_conv:
+            self.conv_module1 = ConvolutionModule(embed_dim, cnn_module_kernel)
+
+            self.conv_module2 = ConvolutionModule(embed_dim, cnn_module_kernel)
+
+        self.norm = BiasNorm(embed_dim)
+
+        self.balancer1 = Balancer(
+            embed_dim,
+            channel_dim=-1,
+            min_positive=0.45,
+            max_positive=0.55,
+            min_abs=0.2,
+            max_abs=4.0,
+        )
+
+        # balancer for output of NonlinAttentionModule
+        self.balancer_na = Balancer(
+            embed_dim,
+            channel_dim=-1,
+            min_positive=0.3,
+            max_positive=0.7,
+            min_abs=ScheduledFloat((0.0, 0.004), (4000.0, 0.02)),
+            prob=0.05,  # out of concern for memory usage
+        )
+
+        # balancer for output of feedforward2, prevent it from staying too
+        # small.  give this a very small probability, even at the start of
+        # training, it's to fix a rare problem and it's OK to fix it slowly.
+        self.balancer_ff2 = Balancer(
+            embed_dim,
+            channel_dim=-1,
+            min_positive=0.3,
+            max_positive=0.7,
+            min_abs=ScheduledFloat((0.0, 0.0), (4000.0, 0.1), default=0.0),
+            max_abs=2.0,
+            prob=0.05,
+        )
+
+        self.balancer_ff3 = Balancer(
+            embed_dim,
+            channel_dim=-1,
+            min_positive=0.3,
+            max_positive=0.7,
+            min_abs=ScheduledFloat((0.0, 0.0), (4000.0, 0.2), default=0.0),
+            max_abs=4.0,
+            prob=0.05,
+        )
+
+        self.whiten = Whiten(
+            num_groups=1,
+            whitening_limit=_whitening_schedule(4.0, ratio=3.0),
+            prob=(0.025, 0.25),
+            grad_scale=0.01,
+        )
+
+        self.balancer2 = Balancer(
+            embed_dim,
+            channel_dim=-1,
+            min_positive=0.45,
+            max_positive=0.55,
+            min_abs=0.1,
+            max_abs=4.0,
+        )
+
+    def get_sequence_dropout_mask(
+        self, x: Tensor, dropout_rate: float
+    ) -> Optional[Tensor]:
+        if (
+            dropout_rate == 0.0
+            or not self.training
+            or torch.jit.is_scripting()
+            or torch.jit.is_tracing()
+        ):
+            return None
+        batch_size = x.shape[1]
+        mask = (torch.rand(batch_size, 1, device=x.device) > dropout_rate).to(x.dtype)
+        return mask
+
+    def sequence_dropout(self, x: Tensor, dropout_rate: float) -> Tensor:
+        """
+        Apply sequence-level dropout to x.
+        x shape: (seq_len, batch_size, embed_dim)
+        """
+        dropout_mask = self.get_sequence_dropout_mask(x, dropout_rate)
+        if dropout_mask is None:
+            return x
+        else:
+            return x * dropout_mask
+
+    def forward(
+        self,
+        src: Tensor,
+        pos_emb: Tensor,
+        time_emb: Optional[Tensor] = None,
+        attn_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        """
+        Pass the input through the encoder layer.
+        Args:
+          src: the sequence to the encoder (required): shape (seq_len, batch_size, embedding_dim).
+          pos_emb: (1, 2*seq_len-1, pos_emb_dim) or (batch_size, 2*seq_len-1, pos_emb_dim)
+          time_emb: the embedding representing the current timestep: shape  (batch_size, embedding_dim)
+            or (seq_len, batch_size, embedding_dim) .
+          attn_mask: the attention mask, of shape (batch_size, seq_len, seq_len) or (seq_len, seq_len),
+            interpreted as (batch_size, tgt_seq_len, src_seq_len) or (tgt_seq_len, src_seq_len).
+            True means masked position. May be None.
+          src_key_padding_mask:  the mask for padding, of shape (batch_size, seq_len); True means
+            masked position.  May be None.
+
+        Returns:
+           A tensor which has the same shape as src
+        """
+        src_orig = src
+
+        # dropout rate for non-feedforward submodules
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            attention_skip_rate = 0.0
+        else:
+            attention_skip_rate = (
+                float(self.attention_skip_rate) if self.training else 0.0
+            )
+
+        # attn_weights: (num_heads, batch_size, seq_len, seq_len)
+        attn_weights = self.self_attn_weights(
+            src,
+            pos_emb=pos_emb,
+            attn_mask=attn_mask,
+            key_padding_mask=src_key_padding_mask,
+        )
+        if time_emb is not None:
+
+            src = src + time_emb
+
+        src = src + self.feed_forward1(src)
+
+        self_attn_dropout_mask = self.get_sequence_dropout_mask(
+            src, attention_skip_rate
+        )
+
+        selected_attn_weights = attn_weights[0:1]
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            pass
+        elif self.training and random.random() < float(self.const_attention_rate):
+            # Make attention weights constant.  The intention is to
+            # encourage these modules to do something similar to an
+            # averaging-over-time operation.
+            # only need the mask, can just use the 1st one and expand later
+            selected_attn_weights = selected_attn_weights[0:1]
+            selected_attn_weights = (selected_attn_weights > 0.0).to(
+                selected_attn_weights.dtype
+            )
+            selected_attn_weights = selected_attn_weights * (
+                1.0 / selected_attn_weights.sum(dim=-1, keepdim=True)
+            )
+
+        na = self.balancer_na(self.nonlin_attention(src, selected_attn_weights))
+
+        src = src + (
+            na if self_attn_dropout_mask is None else na * self_attn_dropout_mask
+        )
+
+        self_attn = self.self_attn1(src, attn_weights)
+
+        src = src + (
+            self_attn
+            if self_attn_dropout_mask is None
+            else self_attn * self_attn_dropout_mask
+        )
+
+        if self.use_conv:
+            if torch.jit.is_scripting() or torch.jit.is_tracing():
+                conv_skip_rate = 0.0
+            else:
+                conv_skip_rate = float(self.conv_skip_rate) if self.training else 0.0
+
+            if time_emb is not None:
+                src = src + time_emb
+
+            src = src + self.sequence_dropout(
+                self.conv_module1(
+                    src,
+                    src_key_padding_mask=src_key_padding_mask,
+                ),
+                conv_skip_rate,
+            )
+
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            ff2_skip_rate = 0.0
+        else:
+            ff2_skip_rate = float(self.ff2_skip_rate) if self.training else 0.0
+        src = src + self.sequence_dropout(
+            self.balancer_ff2(self.feed_forward2(src)), ff2_skip_rate
+        )
+
+        # bypass in the middle of the layer.
+        src = self.bypass_mid(src_orig, src)
+
+        self_attn = self.self_attn2(src, attn_weights)
+
+        src = src + (
+            self_attn
+            if self_attn_dropout_mask is None
+            else self_attn * self_attn_dropout_mask
+        )
+
+        if self.use_conv:
+
+            if torch.jit.is_scripting() or torch.jit.is_tracing():
+                conv_skip_rate = 0.0
+            else:
+                conv_skip_rate = float(self.conv_skip_rate) if self.training else 0.0
+
+            if time_emb is not None:
+                src = src + time_emb
+
+            src = src + self.sequence_dropout(
+                self.conv_module2(
+                    src,
+                    src_key_padding_mask=src_key_padding_mask,
+                ),
+                conv_skip_rate,
+            )
+
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            ff3_skip_rate = 0.0
+        else:
+            ff3_skip_rate = float(self.ff3_skip_rate) if self.training else 0.0
+        src = src + self.sequence_dropout(
+            self.balancer_ff3(self.feed_forward3(src)), ff3_skip_rate
+        )
+
+        src = self.balancer1(src)
+        src = self.norm(src)
+
+        src = self.bypass(src_orig, src)
+
+        src = self.balancer2(src)
+        src = self.whiten(src)
+
+        return src
+
+
+class Zipformer2Encoder(nn.Module):
+    r"""Zipformer2Encoder is a stack of N encoder layers
+
+    Args:
+        encoder_layer: an instance of the Zipformer2EncoderLayer() class (required).
+        num_layers: the number of sub-encoder-layers in the encoder (required).
+        pos_dim: the dimension for the relative positional encoding
+
+    Examples::
+        >>> encoder_layer = Zipformer2EncoderLayer(embed_dim=512, nhead=8)
+        >>> zipformer_encoder = Zipformer2Encoder(encoder_layer, num_layers=6)
+        >>> src = torch.rand(10, 32, 512)
+        >>> out = zipformer_encoder(src)
+    """
+
+    def __init__(
+        self,
+        encoder_layer: nn.Module,
+        num_layers: int,
+        embed_dim: int,
+        time_embed_dim: int,
+        pos_dim: int,
+        warmup_begin: float,
+        warmup_end: float,
+        initial_layerdrop_rate: float = 0.5,
+        final_layerdrop_rate: float = 0.05,
+    ) -> None:
+        super().__init__()
+        self.encoder_pos = CompactRelPositionalEncoding(
+            pos_dim, dropout_rate=0.15, length_factor=1.0
+        )
+        if time_embed_dim != -1:
+            self.time_emb = nn.Sequential(
+                SwooshR(),
+                nn.Linear(time_embed_dim, embed_dim),
+            )
+        else:
+            self.time_emb = None
+
+        self.layers = nn.ModuleList(
+            [copy.deepcopy(encoder_layer) for i in range(num_layers)]
+        )
+        self.num_layers = num_layers
+
+        assert 0 <= warmup_begin <= warmup_end
+
+        delta = (1.0 / num_layers) * (warmup_end - warmup_begin)
+        cur_begin = warmup_begin  # interpreted as a training batch index
+        for i in range(num_layers):
+            cur_end = cur_begin + delta
+            self.layers[i].bypass.skip_rate = ScheduledFloat(
+                (cur_begin, initial_layerdrop_rate),
+                (cur_end, final_layerdrop_rate),
+                default=0.0,
+            )
+            cur_begin = cur_end
+
+    def forward(
+        self,
+        src: Tensor,
+        time_emb: Optional[Tensor] = None,
+        attn_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layers in turn.
+
+        Args:
+            src: the sequence to the encoder (required): shape (seq_len, batch_size, embedding_dim).
+              the embedding representing the current timestep: shape  (batch_size, embedding_dim)
+              or (seq_len, batch_size, embedding_dim) .
+            time_emb: the embedding representing the current timestep: shape  (batch_size, embedding_dim)
+               or (seq_len, batch_size, embedding_dim) .
+            attn_mask: the attention mask, of shape (batch_size, seq_len, seq_len) or (seq_len, seq_len),
+                 interpreted as (batch_size, tgt_seq_len, src_seq_len) or (tgt_seq_len, src_seq_len).
+                 True means masked position. May be None.
+            src_key_padding_mask:  the mask for padding, of shape (batch_size, seq_len); True means
+                 masked position.  May be None.
+
+        Returns: a Tensor with the same shape as src.
+        """
+        pos_emb = self.encoder_pos(src)
+        if self.time_emb is not None:
+            assert time_emb is not None
+            time_emb = self.time_emb(time_emb)
+        else:
+            assert time_emb is None
+
+        output = src
+
+        for i, mod in enumerate(self.layers):
+            output = mod(
+                output,
+                pos_emb,
+                time_emb=time_emb,
+                attn_mask=attn_mask,
+                src_key_padding_mask=src_key_padding_mask,
+            )
+
+        return output
+
+
+class BypassModule(nn.Module):
+    """
+    An nn.Module that implements a learnable bypass scale, and also randomized per-sequence
+    layer-skipping.  The bypass is limited during early stages of training to be close to
+    "straight-through", i.e. to not do the bypass operation much initially, in order to
+    force all the modules to learn something.
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        skip_rate: FloatLike = 0.0,
+        straight_through_rate: FloatLike = 0.0,
+        scale_min: FloatLike = ScheduledFloat((0.0, 0.9), (20000.0, 0.2), default=0),
+        scale_max: FloatLike = 1.0,
+    ):
+        super().__init__()
+        self.bypass_scale = nn.Parameter(torch.full((embed_dim,), 0.5))
+        self.skip_rate = copy.deepcopy(skip_rate)
+        self.straight_through_rate = copy.deepcopy(straight_through_rate)
+        self.scale_min = copy.deepcopy(scale_min)
+        self.scale_max = copy.deepcopy(scale_max)
+
+    def _get_bypass_scale(self, batch_size: int):
+        # returns bypass-scale of shape (num_channels,),
+        # or (batch_size, num_channels,).  This is actually the
+        # scale on the non-residual term, so 0 corresponds to bypassing
+        # this module.
+        if torch.jit.is_scripting() or torch.jit.is_tracing() or not self.training:
+            return self.bypass_scale
+        else:
+            ans = limit_param_value(
+                self.bypass_scale, min=float(self.scale_min), max=float(self.scale_max)
+            )
+            skip_rate = float(self.skip_rate)
+            if skip_rate != 0.0:
+                mask = torch.rand((batch_size, 1), device=ans.device) > skip_rate
+                ans = ans * mask
+                # now ans is of shape (batch_size, num_channels), and is zero for sequences
+                # on which we have randomly chosen to do layer-skipping.
+            straight_through_rate = float(self.straight_through_rate)
+            if straight_through_rate != 0.0:
+                mask = (
+                    torch.rand((batch_size, 1), device=ans.device)
+                    < straight_through_rate
+                )
+                ans = torch.maximum(ans, mask.to(ans.dtype))
+            return ans
+
+    def forward(self, src_orig: Tensor, src: Tensor):
+        """
+        Args: src_orig and src are both of shape (seq_len, batch_size, num_channels)
+        Returns: something with the same shape as src and src_orig
+        """
+        bypass_scale = self._get_bypass_scale(src.shape[1])
+        return src_orig + (src - src_orig) * bypass_scale
+
+
+class DownsampledZipformer2Encoder(nn.Module):
+    r"""
+    DownsampledZipformer2Encoder is a zipformer encoder evaluated at a reduced frame rate,
+    after convolutional downsampling, and then upsampled again at the output, and combined
+    with the origin input, so that the output has the same shape as the input.
+    """
+
+    def __init__(self, encoder: nn.Module, dim: int, downsample: int):
+        super(DownsampledZipformer2Encoder, self).__init__()
+        self.downsample_factor = downsample
+        self.downsample = SimpleDownsample(downsample)
+        self.num_layers = encoder.num_layers
+        self.encoder = encoder
+        self.upsample = SimpleUpsample(downsample)
+        self.out_combiner = BypassModule(dim, straight_through_rate=0)
+
+    def forward(
+        self,
+        src: Tensor,
+        time_emb: Optional[Tensor] = None,
+        attn_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Downsample, go through encoder, upsample.
+
+        Args:
+            src: the sequence to the encoder (required): shape (seq_len, batch_size, embedding_dim).
+            time_emb: the embedding representing the current timestep: shape  (batch_size, embedding_dim)
+               or (seq_len, batch_size, embedding_dim) .
+            feature_mask: something that broadcasts with src, that we'll multiply `src`
+               by at every layer: if a Tensor, likely of shape (seq_len, batch_size, embedding_dim)
+            attn_mask: the attention mask, of shape (batch_size, seq_len, seq_len) or (seq_len, seq_len),
+                 interpreted as (batch_size, tgt_seq_len, src_seq_len) or (tgt_seq_len, src_seq_len).
+                 True means masked position. May be None.
+            src_key_padding_mask:  the mask for padding, of shape (batch_size, seq_len); True means
+                 masked position.  May be None.
+
+        Returns: a Tensor with the same shape as src.
+        """
+        src_orig = src
+        src = self.downsample(src)
+        ds = self.downsample_factor
+        if time_emb is not None and time_emb.dim() == 3:
+            time_emb = time_emb[::ds]
+        if attn_mask is not None:
+            attn_mask = attn_mask[::ds, ::ds]
+        if src_key_padding_mask is not None:
+            src_key_padding_mask = src_key_padding_mask[..., ::ds]
+
+        src = self.encoder(
+            src,
+            time_emb=time_emb,
+            attn_mask=attn_mask,
+            src_key_padding_mask=src_key_padding_mask,
+        )
+        src = self.upsample(src)
+        # remove any extra frames that are not a multiple of downsample_factor
+        src = src[: src_orig.shape[0]]
+
+        return self.out_combiner(src_orig, src)
+
+
+class SimpleDownsample(torch.nn.Module):
+    """
+    Does downsampling with attention, by weighted sum.
+    """
+
+    def __init__(self, downsample: int):
+        super(SimpleDownsample, self).__init__()
+
+        self.bias = nn.Parameter(torch.zeros(downsample))
+
+        self.name = None  # will be set from training code
+
+        self.downsample = downsample
+
+    def forward(self, src: Tensor) -> Tensor:
+        """
+        x: (seq_len, batch_size, in_channels)
+        Returns a tensor of shape
+           ( (seq_len+downsample-1)//downsample, batch_size, channels)
+        """
+        (seq_len, batch_size, in_channels) = src.shape
+        ds = self.downsample
+        d_seq_len = (seq_len + ds - 1) // ds
+
+        # Pad to an exact multiple of self.downsample
+        # right-pad src, repeating the last element.
+        pad = d_seq_len * ds - seq_len
+        src_extra = src[src.shape[0] - 1 :].expand(pad, src.shape[1], src.shape[2])
+        src = torch.cat((src, src_extra), dim=0)
+        assert src.shape[0] == d_seq_len * ds
+
+        src = src.reshape(d_seq_len, ds, batch_size, in_channels)
+
+        weights = self.bias.softmax(dim=0)
+        # weights: (downsample, 1, 1)
+        weights = weights.unsqueeze(-1).unsqueeze(-1)
+
+        # ans1 is the first `in_channels` channels of the output
+        ans = (src * weights).sum(dim=1)
+
+        return ans
+
+
+class SimpleUpsample(torch.nn.Module):
+    """
+    A very simple form of upsampling that just repeats the input.
+    """
+
+    def __init__(self, upsample: int):
+        super(SimpleUpsample, self).__init__()
+        self.upsample = upsample
+
+    def forward(self, src: Tensor) -> Tensor:
+        """
+        x: (seq_len, batch_size, num_channels)
+        Returns a tensor of shape
+           ( (seq_len*upsample), batch_size, num_channels)
+        """
+        upsample = self.upsample
+        (seq_len, batch_size, num_channels) = src.shape
+        src = src.unsqueeze(1).expand(seq_len, upsample, batch_size, num_channels)
+        src = src.reshape(seq_len * upsample, batch_size, num_channels)
+        return src
+
+
+class CompactRelPositionalEncoding(torch.nn.Module):
+    """
+    Relative positional encoding module.  This version is "compact" meaning it is able to encode
+    the important information about the relative position in a relatively small number of dimensions.
+    The goal is to make it so that small differences between large relative offsets (e.g. 1000 vs. 1001)
+    make very little difference to the embedding.   Such differences were potentially important
+    when encoding absolute position, but not important when encoding relative position because there
+    is now no need to compare two large offsets with each other.
+
+    Our embedding works by projecting the interval [-infinity,infinity] to a finite interval
+    using the atan() function, before doing the Fourier transform of that fixed interval.  The
+    atan() function would compress the "long tails" too small,
+    making it hard to distinguish between different magnitudes of large offsets, so we use a logarithmic
+    function to compress large offsets to a smaller range before applying atan().
+    Scalings are chosen in such a way that the embedding can clearly distinguish individual offsets as long
+    as they are quite close to the origin, e.g. abs(offset) <= about sqrt(embedding_dim)
+
+
+    Args:
+        embed_dim: Embedding dimension.
+        dropout_rate: Dropout rate.
+        max_len: Maximum input length: just a heuristic for initialization.
+        length_factor: a heuristic scale (should be >= 1.0) which, if larger, gives
+           less weight to small differences of offset near the origin.
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        dropout_rate: FloatLike,
+        max_len: int = 1000,
+        length_factor: float = 1.0,
+    ) -> None:
+        """Construct a CompactRelPositionalEncoding object."""
+        super(CompactRelPositionalEncoding, self).__init__()
+        self.embed_dim = embed_dim
+        assert embed_dim % 2 == 0, embed_dim
+        self.dropout = Dropout2(dropout_rate)
+        self.pe = None
+        assert length_factor >= 1.0, length_factor
+        self.length_factor = length_factor
+        self.extend_pe(torch.tensor(0.0).expand(max_len))
+
+    def extend_pe(self, x: Tensor, left_context_len: int = 0) -> None:
+        """Reset the positional encodings."""
+        T = x.size(0) + left_context_len
+
+        if self.pe is not None:
+            # self.pe contains both positive and negative parts
+            # the length of self.pe is 2 * input_len - 1
+            if self.pe.size(0) >= T * 2 - 1:
+                self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+                return
+
+        # if T == 4, x would contain [ -3, -2, 1, 0, 1, 2, 3 ]
+        x = torch.arange(-(T - 1), T, device=x.device).to(torch.float32).unsqueeze(1)
+
+        freqs = 1 + torch.arange(self.embed_dim // 2, device=x.device)
+
+        # `compression_length` this is arbitrary/heuristic, if it is larger we have more resolution
+        # for small time offsets but less resolution for large time offsets.
+        compression_length = self.embed_dim**0.5
+        # x_compressed, like X, goes from -infinity to infinity as T goes from -infinity to infinity;
+        # but it does so more slowly than T for large absolute values of T.
+        # The formula is chosen so that d(x_compressed )/dx is 1 around x == 0, which
+        # is important.
+        x_compressed = (
+            compression_length
+            * x.sign()
+            * ((x.abs() + compression_length).log() - math.log(compression_length))
+        )
+
+        # if self.length_factor == 1.0, then length_scale is chosen so that the
+        # FFT can exactly separate points close to the origin (T == 0).  So this
+        # part of the formulation is not really heuristic.
+        # But empirically, for ASR at least, length_factor > 1.0 seems to work better.
+        length_scale = self.length_factor * self.embed_dim / (2.0 * math.pi)
+
+        # note for machine implementations: if atan is not available, we can use:
+        #   x.sign() * ((1 / (x.abs() + 1)) - 1)  * (-math.pi/2)
+        #  check on wolframalpha.com: plot(sign(x) *  (1 / ( abs(x) + 1) - 1 ) * -pi/2 , atan(x))
+        x_atan = (x_compressed / length_scale).atan()  # results between -pi and pi
+
+        cosines = (x_atan * freqs).cos()
+        sines = (x_atan * freqs).sin()
+
+        pe = torch.zeros(x.shape[0], self.embed_dim, device=x.device)
+        pe[:, 0::2] = cosines
+        pe[:, 1::2] = sines
+        pe[:, -1] = 1.0  # for bias.
+
+        self.pe = pe.to(dtype=x.dtype)
+
+    def forward(self, x: Tensor, left_context_len: int = 0) -> Tensor:
+        """Create positional encoding.
+
+        Args:
+            x (Tensor): Input tensor (time, batch, `*`).
+            left_context_len: (int): Length of cached left context.
+
+        Returns:
+            positional embedding, of shape (batch, left_context_len + 2*time-1, `*`).
+        """
+        self.extend_pe(x, left_context_len)
+        x_size_left = x.size(0) + left_context_len
+        # length of positive side: x.size(0) + left_context_len
+        # length of negative side: x.size(0)
+        pos_emb = self.pe[
+            self.pe.size(0) // 2
+            - x_size_left
+            + 1 : self.pe.size(0) // 2  # noqa E203
+            + x.size(0),
+            :,
+        ]
+        pos_emb = pos_emb.unsqueeze(0)
+        return self.dropout(pos_emb)
+
+
+class RelPositionMultiheadAttentionWeights(nn.Module):
+    r"""Module that computes multi-head attention weights with relative position encoding.
+    Various other modules consume the resulting attention weights: see, for example, the
+    SimpleAttention module which allows you to compute conventional attention.
+
+    This is a quite heavily modified from: "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context",
+    we have to write up the differences.
+
+
+    Args:
+           embed_dim: number of channels at the input to this module, e.g. 256
+             pos_dim: dimension of the positional encoding vectors, e.g. 128.
+           num_heads:  number of heads to compute weights for, e.g. 8
+     query_head_dim: dimension of the query (and key), per head.  e.g. 24.
+       pos_head_dim: dimension of the projected positional encoding per head, e.g. 4.
+            dropout: dropout probability for attn_output_weights. Default: 0.0.
+       pos_emb_skip_rate: probability for skipping the pos_emb part of the scores on
+                     any given call to forward(), in training time.
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        pos_dim: int,
+        num_heads: int,
+        query_head_dim: int,
+        pos_head_dim: int,
+        dropout: float = 0.0,
+        pos_emb_skip_rate: FloatLike = ScheduledFloat((0.0, 0.5), (4000.0, 0.0)),
+    ) -> None:
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.query_head_dim = query_head_dim
+        self.pos_head_dim = pos_head_dim
+        self.dropout = dropout
+        self.pos_emb_skip_rate = copy.deepcopy(pos_emb_skip_rate)
+        self.name = None  # will be overwritten in training code; for diagnostics.
+
+        key_head_dim = query_head_dim
+        in_proj_dim = (query_head_dim + key_head_dim + pos_head_dim) * num_heads
+
+        # the initial_scale is supposed to take over the "scaling" factor of
+        # head_dim ** -0.5 that has been used in previous forms of attention,
+        # dividing it between the query and key.   Note: this module is intended
+        # to be used with the ScaledAdam optimizer; with most other optimizers,
+        # it would be necessary to apply the scaling factor in the forward function.
+        self.in_proj = ScaledLinear(
+            embed_dim, in_proj_dim, bias=True, initial_scale=query_head_dim**-0.25
+        )
+
+        self.whiten_keys = Whiten(
+            num_groups=num_heads,
+            whitening_limit=_whitening_schedule(3.0),
+            prob=(0.025, 0.25),
+            grad_scale=0.025,
+        )
+
+        # add a balancer for the keys that runs with very small probability, and
+        # tries to enforce that all dimensions have mean around zero.  The
+        # weights produced by this module are invariant to adding a constant to
+        # the keys, so the derivative of the bias is mathematically zero; but
+        # due to how Adam/ScaledAdam work, it can learn a fairly large nonzero
+        # bias because the small numerical roundoff tends to have a non-random
+        # sign.  This module is intended to prevent that.  Use a very small
+        # probability; that should be sufficient to fix the problem.
+        self.balance_keys = Balancer(
+            key_head_dim * num_heads,
+            channel_dim=-1,
+            min_positive=0.4,
+            max_positive=0.6,
+            min_abs=0.0,
+            max_abs=100.0,
+            prob=0.025,
+        )
+
+        # linear transformation for positional encoding.
+        self.linear_pos = ScaledLinear(
+            pos_dim, num_heads * pos_head_dim, bias=False, initial_scale=0.05
+        )
+
+        # the following are for diagnostics only, see --print-diagnostics option
+        self.copy_pos_query = Identity()
+        self.copy_query = Identity()
+
+    def forward(
+        self,
+        x: Tensor,
+        pos_emb: Tensor,
+        key_padding_mask: Optional[Tensor] = None,
+        attn_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""
+        Args:
+            x: input of shape (seq_len, batch_size, embed_dim)
+            pos_emb: Positional embedding tensor, of shape (1, 2*seq_len - 1, pos_dim)
+            key_padding_mask: a bool tensor of shape (batch_size, seq_len).  Positions that
+               are True in this mask will be ignored as sources in the attention weighting.
+            attn_mask: mask of shape (seq_len, seq_len) or (batch_size, seq_len, seq_len),
+               interpreted as ([batch_size,] tgt_seq_len, src_seq_len)
+               saying which positions are allowed to attend to which other positions.
+        Returns:
+           a tensor of attention weights, of shape (hum_heads, batch_size, seq_len, seq_len)
+           interpreted as (hum_heads, batch_size, tgt_seq_len, src_seq_len).
+        """
+        x = self.in_proj(x)
+        query_head_dim = self.query_head_dim
+        pos_head_dim = self.pos_head_dim
+        num_heads = self.num_heads
+
+        seq_len, batch_size, _ = x.shape
+
+        query_dim = query_head_dim * num_heads
+
+        # self-attention
+        q = x[..., 0:query_dim]
+        k = x[..., query_dim : 2 * query_dim]
+        # p is the position-encoding query
+        p = x[..., 2 * query_dim :]
+        assert p.shape[-1] == num_heads * pos_head_dim, (
+            p.shape[-1],
+            num_heads,
+            pos_head_dim,
+        )
+
+        q = self.copy_query(q)  # for diagnostics only, does nothing.
+        k = self.whiten_keys(self.balance_keys(k))  # does nothing in the forward pass.
+        p = self.copy_pos_query(p)  # for diagnostics only, does nothing.
+
+        q = q.reshape(seq_len, batch_size, num_heads, query_head_dim)
+        p = p.reshape(seq_len, batch_size, num_heads, pos_head_dim)
+        k = k.reshape(seq_len, batch_size, num_heads, query_head_dim)
+
+        # time1 refers to target, time2 refers to source.
+        q = q.permute(2, 1, 0, 3)  # (head, batch, time1, query_head_dim)
+        p = p.permute(2, 1, 0, 3)  # (head, batch, time1, pos_head_dim)
+        k = k.permute(2, 1, 3, 0)  # (head, batch, d_k, time2)
+
+        attn_scores = torch.matmul(q, k)
+
+        use_pos_scores = False
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            # We can't put random.random() in the same line
+            use_pos_scores = True
+        elif not self.training or random.random() >= float(self.pos_emb_skip_rate):
+            use_pos_scores = True
+
+        if use_pos_scores:
+            pos_emb = self.linear_pos(pos_emb)
+            seq_len2 = 2 * seq_len - 1
+            pos_emb = pos_emb.reshape(-1, seq_len2, num_heads, pos_head_dim).permute(
+                2, 0, 3, 1
+            )
+            # pos shape now: (head, {1 or batch_size}, pos_dim, seq_len2)
+
+            # (head, batch, time1, pos_dim) x (head, 1, pos_dim, seq_len2) -> (head, batch, time1, seq_len2)
+            #  [where seq_len2 represents relative position.]
+            pos_scores = torch.matmul(p, pos_emb)
+            # the following .as_strided() expression converts the last axis of pos_scores from relative
+            # to absolute position.  I don't know whether I might have got the time-offsets backwards or
+            # not, but let this code define which way round it is supposed to be.
+            if torch.jit.is_tracing():
+                (num_heads, batch_size, time1, n) = pos_scores.shape
+                rows = torch.arange(start=time1 - 1, end=-1, step=-1)
+                cols = torch.arange(seq_len)
+                rows = rows.repeat(batch_size * num_heads).unsqueeze(-1)
+                indexes = rows + cols
+                pos_scores = pos_scores.reshape(-1, n)
+                pos_scores = torch.gather(pos_scores, dim=1, index=indexes)
+                pos_scores = pos_scores.reshape(num_heads, batch_size, time1, seq_len)
+            else:
+                pos_scores = pos_scores.as_strided(
+                    (num_heads, batch_size, seq_len, seq_len),
+                    (
+                        pos_scores.stride(0),
+                        pos_scores.stride(1),
+                        pos_scores.stride(2) - pos_scores.stride(3),
+                        pos_scores.stride(3),
+                    ),
+                    storage_offset=pos_scores.stride(3) * (seq_len - 1),
+                )
+
+            attn_scores = attn_scores + pos_scores
+
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            pass
+        elif self.training and random.random() < 0.1:
+            # This is a harder way of limiting the attention scores to not be
+            # too large.  It incurs a penalty if any of them has an absolute
+            # value greater than 50.0.  this should be outside the normal range
+            # of the attention scores.  We use this mechanism instead of, say,
+            # something added to the loss function involving the entropy,
+            # because once the entropy gets very small gradients through the
+            # softmax can become very small, and we'd get zero derivatives.  The
+            # choices of 1.0e-04 as the scale on the penalty makes this
+            # mechanism vulnerable to the absolute scale of the loss function,
+            # but we view this as a failsafe to avoid "implausible" parameter
+            # values rather than a regularization method that should be active
+            # under normal circumstances.
+            attn_scores = penalize_abs_values_gt(
+                attn_scores, limit=25.0, penalty=1.0e-04, name=self.name
+            )
+
+        assert attn_scores.shape == (num_heads, batch_size, seq_len, seq_len)
+
+        if attn_mask is not None:
+            assert attn_mask.dtype == torch.bool
+            # use -1000 to avoid nan's where attn_mask and key_padding_mask make
+            # all scores zero.  It's important that this be large enough that exp(-1000)
+            # is exactly zero, for reasons related to const_attention_rate, it
+            # compares the final weights with zero.
+            attn_scores = attn_scores.masked_fill(attn_mask, -1000)
+
+        if key_padding_mask is not None:
+            assert key_padding_mask.shape == (
+                batch_size,
+                seq_len,
+            ), key_padding_mask.shape
+            attn_scores = attn_scores.masked_fill(
+                key_padding_mask.unsqueeze(1),
+                -1000,
+            )
+
+        # We use our own version of softmax, defined in scaling.py, which should
+        # save a little of the memory used in backprop by, if we are in
+        # automatic mixed precision mode (amp / autocast), by only storing the
+        # half-precision output for backprop purposes.
+        attn_weights = softmax(attn_scores, dim=-1)
+
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            pass
+        elif random.random() < 0.001 and not self.training:
+            self._print_attn_entropy(attn_weights)
+
+        attn_weights = nn.functional.dropout(
+            attn_weights, p=self.dropout, training=self.training
+        )
+
+        return attn_weights
+
+    def _print_attn_entropy(self, attn_weights: Tensor):
+        # attn_weights: (num_heads, batch_size, seq_len, seq_len)
+        (num_heads, batch_size, seq_len, seq_len) = attn_weights.shape
+
+        with torch.no_grad():
+            with torch.amp.autocast("cuda", enabled=False):
+                attn_weights = attn_weights.to(torch.float32)
+                attn_weights_entropy = (
+                    -((attn_weights + 1.0e-20).log() * attn_weights)
+                    .sum(dim=-1)
+                    .mean(dim=(1, 2))
+                )
+                logging.debug(
+                    f"name={self.name}, attn_weights_entropy = {attn_weights_entropy}"
+                )
+
+
+class SelfAttention(nn.Module):
+    """
+    The simplest possible attention module.  This one works with already-computed attention
+    weights, e.g. as computed by RelPositionMultiheadAttentionWeights.
+
+    Args:
+          embed_dim: the input and output embedding dimension
+          num_heads: the number of attention heads
+          value_head_dim: the value dimension per head
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        value_head_dim: int,
+    ) -> None:
+        super().__init__()
+        self.in_proj = nn.Linear(embed_dim, num_heads * value_head_dim, bias=True)
+
+        self.out_proj = ScaledLinear(
+            num_heads * value_head_dim, embed_dim, bias=True, initial_scale=0.05
+        )
+
+        self.whiten = Whiten(
+            num_groups=1,
+            whitening_limit=_whitening_schedule(7.5, ratio=3.0),
+            prob=(0.025, 0.25),
+            grad_scale=0.01,
+        )
+
+    def forward(
+        self,
+        x: Tensor,
+        attn_weights: Tensor,
+    ) -> Tensor:
+        """
+        Args:
+          x: input tensor, of shape (seq_len, batch_size, embed_dim)
+         attn_weights: a tensor of shape (num_heads, batch_size, seq_len, seq_len),
+          with seq_len being interpreted as (tgt_seq_len, src_seq_len).  Expect
+          attn_weights.sum(dim=-1) == 1.
+        Returns:
+           a tensor with the same shape as x.
+        """
+        (seq_len, batch_size, embed_dim) = x.shape
+        num_heads = attn_weights.shape[0]
+        assert attn_weights.shape == (num_heads, batch_size, seq_len, seq_len)
+
+        x = self.in_proj(x)  # (seq_len, batch_size, num_heads * value_head_dim)
+        x = x.reshape(seq_len, batch_size, num_heads, -1).permute(2, 1, 0, 3)
+        # now x: (num_heads, batch_size, seq_len, value_head_dim)
+        value_head_dim = x.shape[-1]
+
+        # todo: see whether there is benefit in overriding matmul
+        x = torch.matmul(attn_weights, x)
+        # v: (num_heads, batch_size, seq_len, value_head_dim)
+
+        x = (
+            x.permute(2, 1, 0, 3)
+            .contiguous()
+            .view(seq_len, batch_size, num_heads * value_head_dim)
+        )
+
+        # returned value is of shape (seq_len, batch_size, embed_dim), like the input.
+        x = self.out_proj(x)
+        x = self.whiten(x)
+
+        return x
+
+
+class FeedforwardModule(nn.Module):
+    """Feedforward module in TTSZipformer model."""
+
+    def __init__(self, embed_dim: int, feedforward_dim: int, dropout: FloatLike):
+        super(FeedforwardModule, self).__init__()
+        self.in_proj = nn.Linear(embed_dim, feedforward_dim)
+
+        self.hidden_balancer = Balancer(
+            feedforward_dim,
+            channel_dim=-1,
+            min_positive=0.3,
+            max_positive=1.0,
+            min_abs=0.75,
+            max_abs=5.0,
+        )
+
+        # shared_dim=0 means we share the dropout mask along the time axis
+        self.out_proj = ActivationDropoutAndLinear(
+            feedforward_dim,
+            embed_dim,
+            activation="SwooshL",
+            dropout_p=dropout,
+            dropout_shared_dim=0,
+            bias=True,
+            initial_scale=0.1,
+        )
+
+        self.out_whiten = Whiten(
+            num_groups=1,
+            whitening_limit=_whitening_schedule(7.5),
+            prob=(0.025, 0.25),
+            grad_scale=0.01,
+        )
+
+    def forward(self, x: Tensor):
+        x = self.in_proj(x)
+        x = self.hidden_balancer(x)
+        # out_proj contains SwooshL activation, then dropout, then linear.
+        x = self.out_proj(x)
+        x = self.out_whiten(x)
+        return x
+
+
+class NonlinAttention(nn.Module):
+    """This is like the ConvolutionModule, but refactored so that we use multiplication by attention weights (borrowed
+       from the attention module) in place of actual convolution.  We also took out the second nonlinearity, the
+       one after the attention mechanism.
+
+    Args:
+        channels (int): The number of channels of conv layers.
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        hidden_channels: int,
+    ) -> None:
+        super().__init__()
+
+        self.hidden_channels = hidden_channels
+
+        self.in_proj = nn.Linear(channels, hidden_channels * 3, bias=True)
+
+        # balancer that goes before the sigmoid.  Have quite a large min_abs value, at 2.0,
+        # because we noticed that well-trained instances of this module have abs-value before the sigmoid
+        # starting from about 3, and poorly-trained instances of the module have smaller abs values
+        # before the sigmoid.
+        self.balancer = Balancer(
+            hidden_channels,
+            channel_dim=-1,
+            min_positive=ScheduledFloat((0.0, 0.25), (20000.0, 0.05)),
+            max_positive=ScheduledFloat((0.0, 0.75), (20000.0, 0.95)),
+            min_abs=0.5,
+            max_abs=5.0,
+        )
+        self.tanh = nn.Tanh()
+
+        self.identity1 = Identity()  # for diagnostics.
+        self.identity2 = Identity()  # for diagnostics.
+        self.identity3 = Identity()  # for diagnostics.
+
+        self.out_proj = ScaledLinear(
+            hidden_channels, channels, bias=True, initial_scale=0.05
+        )
+
+        self.whiten1 = Whiten(
+            num_groups=1,
+            whitening_limit=_whitening_schedule(5.0),
+            prob=(0.025, 0.25),
+            grad_scale=0.01,
+        )
+
+        self.whiten2 = Whiten(
+            num_groups=1,
+            whitening_limit=_whitening_schedule(5.0, ratio=3.0),
+            prob=(0.025, 0.25),
+            grad_scale=0.01,
+        )
+
+    def forward(
+        self,
+        x: Tensor,
+        attn_weights: Tensor,
+    ) -> Tensor:
+        """.
+        Args:
+            x: a Tensor of shape (seq_len, batch_size, num_channels)
+            attn_weights: a Tensor of shape (num_heads, batch_size, seq_len, seq_len)
+        Returns:
+            a Tensor with the same shape as x
+        """
+        x = self.in_proj(x)
+
+        (seq_len, batch_size, _) = x.shape
+        hidden_channels = self.hidden_channels
+
+        s, x, y = x.chunk(3, dim=2)
+
+        # s will go through tanh.
+
+        s = self.balancer(s)
+        s = self.tanh(s)
+
+        s = s.unsqueeze(-1).reshape(seq_len, batch_size, hidden_channels)
+        x = self.whiten1(x)
+        x = x * s
+        x = self.identity1(x)  # diagnostics only, it's the identity.
+
+        (seq_len, batch_size, embed_dim) = x.shape
+        num_heads = attn_weights.shape[0]
+        assert attn_weights.shape == (num_heads, batch_size, seq_len, seq_len)
+
+        x = x.reshape(seq_len, batch_size, num_heads, -1).permute(2, 1, 0, 3)
+        # now x: (num_heads, batch_size, seq_len, head_dim)
+        x = torch.matmul(attn_weights, x)
+        # now x: (num_heads, batch_size, seq_len, head_dim)
+        x = x.permute(2, 1, 0, 3).reshape(seq_len, batch_size, -1)
+
+        y = self.identity2(y)
+        x = x * y
+        x = self.identity3(x)
+
+        x = self.out_proj(x)
+        x = self.whiten2(x)
+        return x
+
+
+class ConvolutionModule(nn.Module):
+    """ConvolutionModule in Zipformer2 model.
+    Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/zipformer/convolution.py
+
+    Args:
+        channels (int): The number of channels of conv layers.
+        kernel_size (int): Kernerl size of conv layers.
+        bias (bool): Whether to use bias in conv layers (default=True).
+
+    """
+
+    def __init__(
+        self,
+        channels: int,
+        kernel_size: int,
+    ) -> None:
+        """Construct a ConvolutionModule object."""
+        super(ConvolutionModule, self).__init__()
+        # kernerl_size should be a odd number for 'SAME' padding
+        assert (kernel_size - 1) % 2 == 0
+
+        bottleneck_dim = channels
+
+        self.in_proj = nn.Linear(
+            channels,
+            2 * bottleneck_dim,
+        )
+        # the gradients on in_proj are a little noisy, likely to do with the
+        # sigmoid in glu.
+
+        # after in_proj we put x through a gated linear unit (nn.functional.glu).
+        # For most layers the normal rms value of channels of x seems to be in the range 1 to 4,
+        # but sometimes, for some reason, for layer 0 the rms ends up being very large,
+        # between 50 and 100 for different channels.  This will cause very peaky and
+        # sparse derivatives for the sigmoid gating function, which will tend to make
+        # the loss function not learn effectively.  (for most layers the average absolute values
+        # are in the range 0.5..9.0, and the average p(x>0), i.e. positive proportion,
+        # at the output of pointwise_conv1.output is around 0.35 to 0.45 for different
+        # layers, which likely breaks down as 0.5 for the "linear" half and
+        # 0.2 to 0.3 for the part that goes into the sigmoid.  The idea is that if we
+        # constrain the rms values to a reasonable range via a constraint of max_abs=10.0,
+        # it will be in a better position to start learning something, i.e. to latch onto
+        # the correct range.
+        self.balancer1 = Balancer(
+            bottleneck_dim,
+            channel_dim=-1,
+            min_positive=ScheduledFloat((0.0, 0.05), (8000.0, 0.025)),
+            max_positive=1.0,
+            min_abs=1.5,
+            max_abs=ScheduledFloat((0.0, 5.0), (8000.0, 10.0), default=1.0),
+        )
+
+        self.activation1 = Identity()  # for diagnostics
+
+        self.sigmoid = nn.Sigmoid()
+
+        self.activation2 = Identity()  # for diagnostics
+
+        assert kernel_size % 2 == 1
+
+        self.depthwise_conv = nn.Conv1d(
+            in_channels=bottleneck_dim,
+            out_channels=bottleneck_dim,
+            groups=bottleneck_dim,
+            kernel_size=kernel_size,
+            padding=kernel_size // 2,
+        )
+
+        self.balancer2 = Balancer(
+            bottleneck_dim,
+            channel_dim=1,
+            min_positive=ScheduledFloat((0.0, 0.1), (8000.0, 0.05)),
+            max_positive=1.0,
+            min_abs=ScheduledFloat((0.0, 0.2), (20000.0, 0.5)),
+            max_abs=10.0,
+        )
+
+        self.whiten = Whiten(
+            num_groups=1,
+            whitening_limit=_whitening_schedule(7.5),
+            prob=(0.025, 0.25),
+            grad_scale=0.01,
+        )
+
+        self.out_proj = ActivationDropoutAndLinear(
+            bottleneck_dim,
+            channels,
+            activation="SwooshR",
+            dropout_p=0.0,
+            initial_scale=0.05,
+        )
+
+    def forward(
+        self,
+        x: Tensor,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        """Compute convolution module.
+
+        Args:
+            x: Input tensor (#time, batch, channels).
+           src_key_padding_mask: the mask for the src keys per batch (optional):
+               (batch, #time), contains True in masked positions.
+
+        Returns:
+            Tensor: Output tensor (#time, batch, channels).
+
+        """
+
+        x = self.in_proj(x)  # (time, batch, 2*channels)
+
+        x, s = x.chunk(2, dim=2)
+        s = self.balancer1(s)
+        s = self.sigmoid(s)
+        x = self.activation1(x)  # identity.
+        x = x * s
+        x = self.activation2(x)  # identity
+
+        # (time, batch, channels)
+
+        # exchange the temporal dimension and the feature dimension
+        x = x.permute(1, 2, 0)  # (#batch, channels, time).
+
+        if src_key_padding_mask is not None:
+            x = x.masked_fill(src_key_padding_mask.unsqueeze(1).expand_as(x), 0.0)
+
+        x = self.depthwise_conv(x)
+
+        x = self.balancer2(x)
+        x = x.permute(2, 0, 1)  # (time, batch, channels)
+
+        x = self.whiten(x)  # (time, batch, channels)
+        x = self.out_proj(x)  # (time, batch, channels)
+
+        return x
diff --git a/egs/zipvoice/zipvoice/zipvoice_infer.py b/egs/zipvoice/zipvoice/zipvoice_infer.py
new file mode 100644
index 000000000..16cf8e039
--- /dev/null
+++ b/egs/zipvoice/zipvoice/zipvoice_infer.py
@@ -0,0 +1,645 @@
+#!/usr/bin/env python3
+# Copyright         2025  Xiaomi Corp.        (authors: Han Zhu)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script generates speech with our pre-trained ZipVoice or
+    ZipVoice-Distill models. Required models will be automatically
+    downloaded from HuggingFace.
+
+Usage:
+
+Note: If you having trouble connecting to HuggingFace,
+    try switching endpoint to mirror site:
+
+export HF_ENDPOINT=https://hf-mirror.com
+
+(1) Inference of a single sentence:
+
+python3 zipvoice/zipvoice_infer.py \
+    --model-name "zipvoice_distill" \
+    --prompt-wav prompt.wav \
+    --prompt-text "I am a prompt." \
+    --text "I am a sentence." \
+    --res-wav-path result.wav
+
+(2) Inference of a list of sentences:
+python3 zipvoice/zipvoice_infer.py \
+    --model-name "zipvoice-distill" \
+    --test-list test.tsv \
+    --res-dir results
+
+`--model-name` can be `zipvoice` or `zipvoice_distill`,
+    which are the models before and after distillation, respectively.
+
+Each line of `test.tsv` is in the format of
+    `{wav_name}\t{prompt_transcription}\t{prompt_wav}\t{text}`.
+"""
+
+import argparse
+import datetime as dt
+import os
+
+import numpy as np
+import safetensors.torch
+import torch
+import torch.nn as nn
+import torchaudio
+from feature import TorchAudioFbank, TorchAudioFbankConfig
+from huggingface_hub import hf_hub_download
+from lhotse.utils import fix_random_seed
+from model import get_distill_model, get_model
+from tokenizer import TokenizerEmilia
+from utils import AttributeDict
+from vocos import Vocos
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--model-name",
+        type=str,
+        default="zipvoice_distill",
+        choices=["zipvoice", "zipvoice_distill"],
+        help="The model used for inference",
+    )
+
+    parser.add_argument(
+        "--test-list",
+        type=str,
+        default=None,
+        help="The list of prompt speech, prompt_transcription, "
+        "and text to synthesizein the format of "
+        "'{wav_name}\t{prompt_transcription}\t{prompt_wav}\t{text}'.",
+    )
+
+    parser.add_argument(
+        "--prompt-wav",
+        type=str,
+        default=None,
+        help="The prompt wav to mimic",
+    )
+
+    parser.add_argument(
+        "--prompt-text",
+        type=str,
+        default=None,
+        help="The transcription of the prompt wav",
+    )
+
+    parser.add_argument(
+        "--text",
+        type=str,
+        default=None,
+        help="The text to synthesize",
+    )
+
+    parser.add_argument(
+        "--res-dir",
+        type=str,
+        default="results",
+        help="""
+        Path name of the generated wavs dir,
+        used when test-list is not None
+        """,
+    )
+
+    parser.add_argument(
+        "--res-wav-path",
+        type=str,
+        default="result.wav",
+        help="""
+        Path name of the generated wav path,
+        used when test-list is None
+        """,
+    )
+
+    parser.add_argument(
+        "--guidance-scale",
+        type=float,
+        default=None,
+        help="The scale of classifier-free guidance during inference.",
+    )
+
+    parser.add_argument(
+        "--num-step",
+        type=int,
+        default=None,
+        help="The number of sampling steps.",
+    )
+
+    parser.add_argument(
+        "--feat-scale",
+        type=float,
+        default=0.1,
+        help="The scale factor of fbank feature",
+    )
+
+    parser.add_argument(
+        "--speed",
+        type=float,
+        default=1.0,
+        help="Control speech speed, 1.0 means normal, >1.0 means speed up",
+    )
+
+    parser.add_argument(
+        "--t-shift",
+        type=float,
+        default=0.5,
+        help="Shift t to smaller ones if t_shift < 1.0",
+    )
+
+    parser.add_argument(
+        "--target-rms",
+        type=float,
+        default=0.1,
+        help="Target speech normalization rms value",
+    )
+
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=666,
+        help="Random seed",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def add_model_arguments(parser: argparse.ArgumentParser):
+    parser.add_argument(
+        "--fm-decoder-downsampling-factor",
+        type=str,
+        default="1,2,4,2,1",
+        help="Downsampling factor for each stack of encoder layers.",
+    )
+
+    parser.add_argument(
+        "--fm-decoder-num-layers",
+        type=str,
+        default="2,2,4,4,4",
+        help="Number of zipformer encoder layers per stack, comma separated.",
+    )
+
+    parser.add_argument(
+        "--fm-decoder-cnn-module-kernel",
+        type=str,
+        default="31,15,7,15,31",
+        help="Sizes of convolutional kernels in convolution modules "
+        "in each encoder stack: a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--fm-decoder-feedforward-dim",
+        type=int,
+        default=1536,
+        help="Feedforward dimension of the zipformer encoder layers, "
+        "per stack, comma separated.",
+    )
+
+    parser.add_argument(
+        "--fm-decoder-num-heads",
+        type=int,
+        default=4,
+        help="Number of attention heads in the zipformer encoder layers: "
+        "a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--fm-decoder-dim",
+        type=int,
+        default=512,
+        help="Embedding dimension in encoder stacks: a single int "
+        "or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--text-encoder-downsampling-factor",
+        type=str,
+        default="1",
+        help="Downsampling factor for each stack of encoder layers.",
+    )
+
+    parser.add_argument(
+        "--text-encoder-num-layers",
+        type=str,
+        default="4",
+        help="Number of zipformer encoder layers per stack, comma separated.",
+    )
+
+    parser.add_argument(
+        "--text-encoder-feedforward-dim",
+        type=int,
+        default=512,
+        help="Feedforward dimension of the zipformer encoder layers, "
+        "per stack, comma separated.",
+    )
+
+    parser.add_argument(
+        "--text-encoder-cnn-module-kernel",
+        type=str,
+        default="9",
+        help="Sizes of convolutional kernels in convolution modules in "
+        "each encoder stack: a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--text-encoder-num-heads",
+        type=int,
+        default=4,
+        help="Number of attention heads in the zipformer encoder layers: "
+        "a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--text-encoder-dim",
+        type=int,
+        default=192,
+        help="Embedding dimension in encoder stacks: "
+        "a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--query-head-dim",
+        type=int,
+        default=32,
+        help="Query/key dimension per head in encoder stacks: "
+        "a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--value-head-dim",
+        type=int,
+        default=12,
+        help="Value dimension per head in encoder stacks: "
+        "a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--pos-head-dim",
+        type=int,
+        default=4,
+        help="Positional-encoding dimension per head in encoder stacks: "
+        "a single int or comma-separated list.",
+    )
+
+    parser.add_argument(
+        "--pos-dim",
+        type=int,
+        default=48,
+        help="Positional-encoding embedding dimension",
+    )
+
+    parser.add_argument(
+        "--time-embed-dim",
+        type=int,
+        default=192,
+        help="Embedding dimension of timestamps embedding.",
+    )
+
+    parser.add_argument(
+        "--text-embed-dim",
+        type=int,
+        default=192,
+        help="Embedding dimension of text embedding.",
+    )
+
+
+def get_params() -> AttributeDict:
+    params = AttributeDict(
+        {
+            "sampling_rate": 24000,
+            "frame_shift_ms": 256 / 24000 * 1000,
+            "feat_dim": 100,
+        }
+    )
+
+    return params
+
+
+def get_vocoder():
+    vocoder = Vocos.from_pretrained("charactr/vocos-mel-24khz")
+    return vocoder
+
+
+def generate_sentence(
+    save_path: str,
+    prompt_text: str,
+    prompt_wav: str,
+    text: str,
+    model: nn.Module,
+    vocoder: nn.Module,
+    tokenizer: TokenizerEmilia,
+    feature_extractor: TorchAudioFbank,
+    device: torch.device,
+    num_step: int = 16,
+    guidance_scale: float = 1.0,
+    speed: float = 1.0,
+    t_shift: float = 0.5,
+    target_rms: float = 0.1,
+    feat_scale: float = 0.1,
+    sampling_rate: int = 24000,
+):
+    """
+    Generate waveform of a text based on a given prompt
+        waveform and its transcription.
+
+    Args:
+        save_path (str): Path to save the generated wav.
+        prompt_text (str): Transcription of the prompt wav.
+        prompt_wav (str): Path to the prompt wav file.
+        text (str): Text to be synthesized into a waveform.
+        model (nn.Module): The model used for generation.
+        vocoder (nn.Module): The vocoder used to convert features to waveforms.
+        tokenizer (TokenizerEmilia): The tokenizer used to convert text to tokens.
+        feature_extractor (TorchAudioFbank): The feature extractor used to
+            extract acoustic features.
+        device (torch.device): The device on which computations are performed.
+        num_step (int, optional): Number of steps for decoding. Defaults to 16.
+        guidance_scale (float, optional): Scale for classifier-free guidance.
+            Defaults to 1.0.
+        speed (float, optional): Speed control. Defaults to 1.0.
+        t_shift (float, optional): Time shift. Defaults to 0.5.
+        target_rms (float, optional): Target RMS for waveform normalization.
+            Defaults to 0.1.
+        feat_scale (float, optional): Scale for features.
+            Defaults to 0.1.
+        sampling_rate (int, optional): Sampling rate for the waveform.
+            Defaults to 24000.
+    Returns:
+        metrics (dict): Dictionary containing time and real-time
+            factor metrics for processing.
+    """
+    # Convert text to tokens
+    tokens = tokenizer.texts_to_token_ids([text])
+    prompt_tokens = tokenizer.texts_to_token_ids([prompt_text])
+
+    # Load and preprocess prompt wav
+    prompt_wav, prompt_sampling_rate = torchaudio.load(prompt_wav)
+    prompt_rms = torch.sqrt(torch.mean(torch.square(prompt_wav)))
+    if prompt_rms < target_rms:
+        prompt_wav = prompt_wav * target_rms / prompt_rms
+
+    if prompt_sampling_rate != sampling_rate:
+        resampler = torchaudio.transforms.Resample(
+            orig_freq=prompt_sampling_rate, new_freq=sampling_rate
+        )
+        prompt_wav = resampler(prompt_wav)
+
+    # Extract features from prompt wav
+    prompt_features = feature_extractor.extract(
+        prompt_wav, sampling_rate=sampling_rate
+    ).to(device)
+    prompt_features = prompt_features.unsqueeze(0) * feat_scale
+    prompt_features_lens = torch.tensor([prompt_features.size(1)], device=device)
+
+    # Start timing
+    start_t = dt.datetime.now()
+
+    # Generate features
+    (
+        pred_features,
+        pred_features_lens,
+        pred_prompt_features,
+        pred_prompt_features_lens,
+    ) = model.sample(
+        tokens=tokens,
+        prompt_tokens=prompt_tokens,
+        prompt_features=prompt_features,
+        prompt_features_lens=prompt_features_lens,
+        speed=speed,
+        t_shift=t_shift,
+        duration="predict",
+        num_step=num_step,
+        guidance_scale=guidance_scale,
+    )
+
+    # Postprocess predicted features
+    pred_features = pred_features.permute(0, 2, 1) / feat_scale  # (B, C, T)
+
+    # Start vocoder processing
+    start_vocoder_t = dt.datetime.now()
+    wav = vocoder.decode(pred_features).squeeze(1).clamp(-1, 1)
+
+    # Calculate processing times and real-time factors
+    t = (dt.datetime.now() - start_t).total_seconds()
+    t_no_vocoder = (start_vocoder_t - start_t).total_seconds()
+    t_vocoder = (dt.datetime.now() - start_vocoder_t).total_seconds()
+    wav_seconds = wav.shape[-1] / sampling_rate
+    rtf = t / wav_seconds
+    rtf_no_vocoder = t_no_vocoder / wav_seconds
+    rtf_vocoder = t_vocoder / wav_seconds
+    metrics = {
+        "t": t,
+        "t_no_vocoder": t_no_vocoder,
+        "t_vocoder": t_vocoder,
+        "wav_seconds": wav_seconds,
+        "rtf": rtf,
+        "rtf_no_vocoder": rtf_no_vocoder,
+        "rtf_vocoder": rtf_vocoder,
+    }
+
+    # Adjust wav volume if necessary
+    if prompt_rms < target_rms:
+        wav = wav * prompt_rms / target_rms
+    torchaudio.save(save_path, wav.cpu(), sample_rate=sampling_rate)
+
+    return metrics
+
+
+def generate(
+    res_dir: str,
+    test_list: str,
+    model: nn.Module,
+    vocoder: nn.Module,
+    tokenizer: TokenizerEmilia,
+    feature_extractor: TorchAudioFbank,
+    device: torch.device,
+    num_step: int = 16,
+    guidance_scale: float = 1.0,
+    speed: float = 1.0,
+    t_shift: float = 0.5,
+    target_rms: float = 0.1,
+    feat_scale: float = 0.1,
+    sampling_rate: int = 24000,
+):
+    total_t = []
+    total_t_no_vocoder = []
+    total_t_vocoder = []
+    total_wav_seconds = []
+
+    with open(test_list, "r") as fr:
+        lines = fr.readlines()
+
+    for i, line in enumerate(lines):
+        wav_name, prompt_text, prompt_wav, text = line.strip().split("\t")
+        save_path = f"{res_dir}/{wav_name}.wav"
+        metrics = generate_sentence(
+            save_path=save_path,
+            prompt_text=prompt_text,
+            prompt_wav=prompt_wav,
+            text=text,
+            model=model,
+            vocoder=vocoder,
+            tokenizer=tokenizer,
+            feature_extractor=feature_extractor,
+            device=device,
+            num_step=num_step,
+            guidance_scale=guidance_scale,
+            speed=speed,
+            t_shift=t_shift,
+            target_rms=target_rms,
+            feat_scale=feat_scale,
+            sampling_rate=sampling_rate,
+        )
+        print(f"[Sentence: {i}] RTF: {metrics['rtf']:.4f}")
+        total_t.append(metrics["t"])
+        total_t_no_vocoder.append(metrics["t_no_vocoder"])
+        total_t_vocoder.append(metrics["t_vocoder"])
+        total_wav_seconds.append(metrics["wav_seconds"])
+
+    print(f"Average RTF: {np.sum(total_t)/np.sum(total_wav_seconds):.4f}")
+    print(
+        f"Average RTF w/o vocoder: "
+        f"{np.sum(total_t_no_vocoder)/np.sum(total_wav_seconds):.4f}"
+    )
+    print(
+        f"Average RTF vocoder: "
+        f"{np.sum(total_t_vocoder)/np.sum(total_wav_seconds):.4f}"
+    )
+
+
+@torch.inference_mode()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+
+    params = get_params()
+    params.update(vars(args))
+
+    model_defaults = {
+        "zipvoice": {
+            "num_step": 16,
+            "guidance_scale": 1.0,
+        },
+        "zipvoice_distill": {
+            "num_step": 8,
+            "guidance_scale": 3.0,
+        },
+    }
+
+    model_specific_defaults = model_defaults.get(params.model_name, {})
+
+    for param, value in model_specific_defaults.items():
+        if getattr(params, param) == parser.get_default(param):
+            setattr(params, param, value)
+            print(f"Setting {param} to default value: {value}")
+
+    assert (params.test_list is not None) ^ (
+        (params.prompt_wav and params.prompt_text and params.text) is not None
+    ), (
+        "For inference, please provide prompts and text with either '--test-list'"
+        " or '--prompt-wav, --prompt-text and --text'."
+    )
+
+    if torch.cuda.is_available():
+        params.device = torch.device("cuda", 0)
+    else:
+        params.device = torch.device("cpu")
+
+    token_file = hf_hub_download("zhu-han/ZipVoice", filename="tokens_emilia.txt")
+
+    tokenizer = TokenizerEmilia(token_file)
+
+    params.vocab_size = tokenizer.vocab_size
+    params.pad_id = tokenizer.pad_id
+    fix_random_seed(params.seed)
+
+    if params.model_name == "zipvoice_distill":
+        model = get_distill_model(params)
+        model_ckpt = hf_hub_download(
+            "zhu-han/ZipVoice", filename="exp_zipvoice_distill/model.safetensors"
+        )
+    else:
+        model = get_model(params)
+        model_ckpt = hf_hub_download(
+            "zhu-han/ZipVoice", filename="exp_zipvoice/model.safetensors"
+        )
+
+    safetensors.torch.load_model(model, model_ckpt)
+
+    model = model.to(params.device)
+    model.eval()
+
+    vocoder = get_vocoder()
+    vocoder = vocoder.to(params.device)
+    vocoder.eval()
+
+    config = TorchAudioFbankConfig(
+        sampling_rate=params.sampling_rate,
+        n_mels=100,
+        n_fft=1024,
+        hop_length=256,
+    )
+    feature_extractor = TorchAudioFbank(config)
+
+    if params.test_list:
+        os.makedirs(params.res_dir, exist_ok=True)
+        generate(
+            res_dir=params.res_dir,
+            test_list=params.test_list,
+            model=model,
+            vocoder=vocoder,
+            tokenizer=tokenizer,
+            feature_extractor=feature_extractor,
+            device=params.device,
+            num_step=params.num_step,
+            guidance_scale=params.guidance_scale,
+            speed=params.speed,
+            t_shift=params.t_shift,
+            target_rms=params.target_rms,
+            feat_scale=params.feat_scale,
+            sampling_rate=params.sampling_rate,
+        )
+    else:
+        generate_sentence(
+            save_path=params.res_wav_path,
+            prompt_text=params.prompt_text,
+            prompt_wav=params.prompt_wav,
+            text=params.text,
+            model=model,
+            vocoder=vocoder,
+            tokenizer=tokenizer,
+            feature_extractor=feature_extractor,
+            device=params.device,
+            num_step=params.num_step,
+            guidance_scale=params.guidance_scale,
+            speed=params.speed,
+            t_shift=params.t_shift,
+            target_rms=params.target_rms,
+            feat_scale=params.feat_scale,
+            sampling_rate=params.sampling_rate,
+        )
+    print("Done")
+
+
+if __name__ == "__main__":
+    main()

From 762f965cf774bb7b9e33520d6cf700fda05ed114 Mon Sep 17 00:00:00 2001
From: Wei Kang <wkang.pku@gmail.com>
Date: Wed, 18 Jun 2025 18:38:46 +0800
Subject: [PATCH 15/22] [zipvoice] Add requirements.txt and pinyin.txt, remove
 k2 from pretrained model inference. (#1965)

* Add requirements.txt and pinyin.txt needed by zipvoice

* simplify the requirements for pretrained model inference
---
 egs/zipvoice/README.md                  |   21 +-
 egs/zipvoice/local/compute_fbank.py     |    7 +-
 egs/zipvoice/local/pinyin.txt           | 1550 +++++++++++++++++++++++
 egs/zipvoice/local/preprocess_emilia.py |    5 +-
 egs/zipvoice/requirements.txt           |   17 +
 egs/zipvoice/zipvoice/scaling.py        |   28 +-
 egs/zipvoice/zipvoice/tokenizer.py      |    8 +-
 7 files changed, 1612 insertions(+), 24 deletions(-)
 create mode 100644 egs/zipvoice/local/pinyin.txt
 create mode 100644 egs/zipvoice/requirements.txt

diff --git a/egs/zipvoice/README.md b/egs/zipvoice/README.md
index 4bca60301..0c97d7ed8 100644
--- a/egs/zipvoice/README.md
+++ b/egs/zipvoice/README.md
@@ -39,15 +39,6 @@ source venv/bin/activate
 * Install the required packages:
 
 ```bash
-# Install pytorch and k2.
-# If you want to use different versions, please refer to https://k2-fsa.org/get-started/k2/ for details.
-# For users in China mainland, please refer to https://k2-fsa.org/zh-CN/get-started/k2/
-
-pip install torch==2.5.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
-pip install k2==1.24.4.dev20250208+cuda12.1.torch2.5.1 -f https://k2-fsa.github.io/k2/cuda.html
-
-# Install other dependencies.
-pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html
 pip install -r requirements.txt
 ```
 
@@ -97,6 +88,16 @@ The following steps show how to train a model from scratch on Emilia and LibriTT
 ### 0. Install dependencies for training
 
 ```bash
+# Install pytorch and k2.
+# If you want to use different versions, please refer to https://k2-fsa.org/get-started/k2/ for details.
+# For users in China mainland, please refer to https://k2-fsa.org/zh-CN/get-started/k2/
+
+# Note: Make sure you have installed the correct version of PyTorch and k2 that matches your CUDA version.
+# For example, if want to use pytorch 2.5.1 and you are using CUDA 12.1, you can install PyTorch and k2 as follows:
+
+pip install torch==2.5.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
+pip install k2==1.24.4.dev20250208+cuda12.1.torch2.5.1 -f https://k2-fsa.github.io/k2/cuda.html
+
 pip install -r ../../requirements.txt
 ```
 
@@ -403,7 +404,7 @@ on three test sets, i.e., LibriSpeech-PC test-clean, Seed-TTS test-en and Seed-T
 
 ```bibtex
 @article{zhu-2025-zipvoice,
-      title={ZipVoice: Fast and High-Quality Zero-Shot Text-to-Speech with Flow Matching}, 
+      title={ZipVoice: Fast and High-Quality Zero-Shot Text-to-Speech with Flow Matching},
       author={Han Zhu and Wei Kang and Zengwei Yao and Liyong Guo and Fangjun Kuang and Zhaoqing Li and Weiji Zhuang and Long Lin and Daniel Povey}
       journal={arXiv preprint arXiv:2506.13053},
       year={2025},
diff --git a/egs/zipvoice/local/compute_fbank.py b/egs/zipvoice/local/compute_fbank.py
index 09a154229..0c440b995 100644
--- a/egs/zipvoice/local/compute_fbank.py
+++ b/egs/zipvoice/local/compute_fbank.py
@@ -19,11 +19,13 @@
 import argparse
 import logging
 import os
+from concurrent.futures import ProcessPoolExecutor as Pool
 from pathlib import Path
 from typing import Optional
-from concurrent.futures import ProcessPoolExecutor as Pool
 
+import lhotse
 import torch
+from feature import TorchAudioFbank, TorchAudioFbankConfig
 from lhotse import (
     CutSet,
     LilcomChunkyWriter,
@@ -31,9 +33,6 @@ from lhotse import (
     set_audio_duration_mismatch_tolerance,
 )
 
-from feature import TorchAudioFbank, TorchAudioFbankConfig
-import lhotse
-
 # Torch's multithreaded behavior needs to be disabled or
 # it wastes a lot of CPU and slow things down.
 # Do this outside of main() in case it needs to take effect
diff --git a/egs/zipvoice/local/pinyin.txt b/egs/zipvoice/local/pinyin.txt
new file mode 100644
index 000000000..cd8d14dc3
--- /dev/null
+++ b/egs/zipvoice/local/pinyin.txt
@@ -0,0 +1,1550 @@
+a
+a1
+a2
+a3
+a4
+ai1
+ai2
+ai3
+ai4
+an1
+an2
+an3
+an4
+ang1
+ang2
+ang3
+ang4
+ao1
+ao2
+ao3
+ao4
+ba
+ba1
+ba2
+ba3
+ba4
+bai
+bai1
+bai2
+bai3
+bai4
+ban
+ban1
+ban3
+ban4
+bang1
+bang3
+bang4
+bao1
+bao2
+bao3
+bao4
+bei
+bei1
+bei3
+bei4
+ben1
+ben3
+ben4
+beng
+beng1
+beng2
+beng3
+beng4
+bi1
+bi2
+bi3
+bi4
+bian
+bian1
+bian3
+bian4
+biang2
+biao1
+biao3
+biao4
+bie1
+bie2
+bie3
+bie4
+bin
+bin1
+bin3
+bin4
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diff --git a/egs/zipvoice/local/preprocess_emilia.py b/egs/zipvoice/local/preprocess_emilia.py
index bd76446aa..96a7ff228 100644
--- a/egs/zipvoice/local/preprocess_emilia.py
+++ b/egs/zipvoice/local/preprocess_emilia.py
@@ -24,15 +24,14 @@ This file reads the texts in given manifest and save the cleaned new cuts.
 """
 
 import argparse
-import logging
 import glob
+import logging
 import os
+from concurrent.futures import ProcessPoolExecutor as Pool
 from pathlib import Path
 from typing import List
 
 from lhotse import CutSet, load_manifest_lazy
-from concurrent.futures import ProcessPoolExecutor as Pool
-
 from tokenizer import (
     is_alphabet,
     is_chinese,
diff --git a/egs/zipvoice/requirements.txt b/egs/zipvoice/requirements.txt
new file mode 100644
index 000000000..cbbe860a5
--- /dev/null
+++ b/egs/zipvoice/requirements.txt
@@ -0,0 +1,17 @@
+--find-links https://k2-fsa.github.io/icefall/piper_phonemize.html
+
+torch
+torchaudio
+huggingface_hub
+lhotse
+safetensors
+vocos
+
+# Normalization
+cn2an
+inflect
+
+# Tokenization
+jieba
+piper_phonemize
+pypinyin
diff --git a/egs/zipvoice/zipvoice/scaling.py b/egs/zipvoice/zipvoice/scaling.py
index 5211e3a76..afe9ad468 100644
--- a/egs/zipvoice/zipvoice/scaling.py
+++ b/egs/zipvoice/zipvoice/scaling.py
@@ -18,9 +18,17 @@
 import logging
 import math
 import random
+import sys
 from typing import Optional, Tuple, Union
 
-import k2
+try:
+    import k2
+except Exception as ex:
+    logging.warning(
+        "k2 is not installed correctly. Swoosh functions will fallback to "
+        "pytorch implementation."
+    )
+
 import torch
 import torch.nn as nn
 from torch import Tensor
@@ -1398,7 +1406,11 @@ class SwooshLFunction(torch.autograd.Function):
 class SwooshL(torch.nn.Module):
     def forward(self, x: Tensor) -> Tensor:
         """Return Swoosh-L activation."""
-        if torch.jit.is_scripting() or torch.jit.is_tracing():
+        if (
+            torch.jit.is_scripting()
+            or torch.jit.is_tracing()
+            or "k2" not in sys.modules
+        ):
             zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
             return logaddexp(zero, x - 4.0) - 0.08 * x - 0.035
         if not x.requires_grad:
@@ -1472,7 +1484,11 @@ class SwooshRFunction(torch.autograd.Function):
 class SwooshR(torch.nn.Module):
     def forward(self, x: Tensor) -> Tensor:
         """Return Swoosh-R activation."""
-        if torch.jit.is_scripting() or torch.jit.is_tracing():
+        if (
+            torch.jit.is_scripting()
+            or torch.jit.is_tracing()
+            or "k2" not in sys.modules
+        ):
             zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
             return logaddexp(zero, x - 1.0) - 0.08 * x - 0.313261687
         if not x.requires_grad:
@@ -1636,7 +1652,11 @@ class ActivationDropoutAndLinear(torch.nn.Module):
         self.dropout_shared_dim = dropout_shared_dim
 
     def forward(self, x: Tensor):
-        if torch.jit.is_scripting() or torch.jit.is_tracing():
+        if (
+            torch.jit.is_scripting()
+            or torch.jit.is_tracing()
+            or "k2" not in sys.modules
+        ):
             if self.activation == "SwooshL":
                 x = SwooshLForward(x)
             elif self.activation == "SwooshR":
diff --git a/egs/zipvoice/zipvoice/tokenizer.py b/egs/zipvoice/zipvoice/tokenizer.py
index 87af061e6..ea25d3498 100644
--- a/egs/zipvoice/zipvoice/tokenizer.py
+++ b/egs/zipvoice/zipvoice/tokenizer.py
@@ -321,7 +321,8 @@ def tokenize_ZH(text: str) -> List[str]:
             if final != "":
                 phones.append(final)
         return phones
-    except:
+    except Exception as ex:
+        logging.warning(f"Tokenize ZH failed: {ex}")
         return []
 
 
@@ -332,7 +333,8 @@ def tokenize_EN(text: str) -> List[str]:
         tokens = phonemize_espeak(text, "en-us")
         tokens = reduce(lambda x, y: x + y, tokens)
         return tokens
-    except:
+    except Exception as ex:
+        logging.warning(f"Tokenize EN failed: {ex}")
         return []
 
 
@@ -561,7 +563,7 @@ class TokenizerLibriTTS(object):
 
 if __name__ == "__main__":
     text = "我们是5年小米人,是吗? Yes I think so! mr king, 5 years, from 2019 to 2024. 霍...啦啦啦超过90%的人咯...?!9204"
-    tokenizer = Tokenizer()
+    tokenizer = TokenizerEmilia()
     tokens = tokenizer.texts_to_tokens([text])
     print(f"tokens : {tokens}")
     tokens2 = "|".join(tokens[0])

From 3587c4b3b704b2618585971854ffbe4c435f2f09 Mon Sep 17 00:00:00 2001
From: Wei Kang <wkang.pku@gmail.com>
Date: Thu, 19 Jun 2025 12:26:01 +0800
Subject: [PATCH 16/22] Fix decoding byte bpes tokens to words. (#1966)

---
 egs/multi_zh-hans/ASR/zipformer/pretrained.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/egs/multi_zh-hans/ASR/zipformer/pretrained.py b/egs/multi_zh-hans/ASR/zipformer/pretrained.py
index c15db11f7..1b53465c0 100755
--- a/egs/multi_zh-hans/ASR/zipformer/pretrained.py
+++ b/egs/multi_zh-hans/ASR/zipformer/pretrained.py
@@ -328,9 +328,14 @@ def main():
     logging.info(msg)
 
     def token_ids_to_words(token_ids: List[int]) -> str:
-        text = ""
+        byte_list = []
         for i in token_ids:
-            text += token_table[i]
+            token = token_table[i]
+            if token.startswith("<0x") and token.endswith(">"):
+                byte_list.append(int(token[3:-1], 16))
+            else:
+                byte_list += list(token.encode("utf-8"))
+        text = bytes(byte_list).decode("utf-8", errors='ignore')
         return text.replace("▁", " ").strip()
 
     if params.method == "fast_beam_search":

From f80a2ee110c460c98767ef79b651be4769fd78a3 Mon Sep 17 00:00:00 2001
From: Wei Kang <wkang.pku@gmail.com>
Date: Thu, 19 Jun 2025 12:26:37 +0800
Subject: [PATCH 17/22] Decrease num_buckets & remove shuffle_buffer_size
 (#1955)

---
 .../ASR/tdnn_lstm_ctc/asr_datamodule.py       |   5 +-
 .../asr_datamodule.py                         |   5 +-
 .../asr_datamodule.py                         |   5 +-
 .../ASR/zipformer/asr_datamodule.py           |   7 +-
 .../KWS/zipformer/asr_datamodule.py           | 478 +-----------------
 .../ASR/tdnn_lstm_ctc/asr_datamodule.py       |   3 +-
 .../asr_datamodule.py                         |   5 +-
 7 files changed, 13 insertions(+), 495 deletions(-)
 mode change 100644 => 120000 egs/gigaspeech/KWS/zipformer/asr_datamodule.py

diff --git a/egs/aishell/ASR/tdnn_lstm_ctc/asr_datamodule.py b/egs/aishell/ASR/tdnn_lstm_ctc/asr_datamodule.py
index aacbd153d..af8f5498a 100644
--- a/egs/aishell/ASR/tdnn_lstm_ctc/asr_datamodule.py
+++ b/egs/aishell/ASR/tdnn_lstm_ctc/asr_datamodule.py
@@ -92,7 +92,7 @@ class AishellAsrDataModule:
         group.add_argument(
             "--num-buckets",
             type=int,
-            default=30,
+            default=15,
             help="The number of buckets for the DynamicBucketingSampler"
             "(you might want to increase it for larger datasets).",
         )
@@ -275,8 +275,7 @@ class AishellAsrDataModule:
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,
                 num_buckets=self.args.num_buckets,
-                buffer_size=self.args.num_buckets * 2000,
-                shuffle_buffer_size=self.args.num_buckets * 5000,
+                buffer_size=self.args.num_buckets * 5000,
                 drop_last=self.args.drop_last,
             )
         else:
diff --git a/egs/aishell2/ASR/pruned_transducer_stateless5/asr_datamodule.py b/egs/aishell2/ASR/pruned_transducer_stateless5/asr_datamodule.py
index f9cdfb621..4c564d04e 100644
--- a/egs/aishell2/ASR/pruned_transducer_stateless5/asr_datamodule.py
+++ b/egs/aishell2/ASR/pruned_transducer_stateless5/asr_datamodule.py
@@ -104,7 +104,7 @@ class AiShell2AsrDataModule:
         group.add_argument(
             "--num-buckets",
             type=int,
-            default=30,
+            default=15,
             help="The number of buckets for the DynamicBucketingSampler"
             "(you might want to increase it for larger datasets).",
         )
@@ -296,8 +296,7 @@ class AiShell2AsrDataModule:
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,
                 num_buckets=self.args.num_buckets,
-                buffer_size=self.args.num_buckets * 2000,
-                shuffle_buffer_size=self.args.num_buckets * 5000,
+                buffer_size=self.args.num_buckets * 5000,
                 drop_last=self.args.drop_last,
             )
         else:
diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py
index 40339365c..c06e8f461 100644
--- a/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py
+++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py
@@ -101,7 +101,7 @@ class GigaSpeechAsrDataModule:
         group.add_argument(
             "--num-buckets",
             type=int,
-            default=30,
+            default=15,
             help="The number of buckets for the DynamicBucketingSampler"
             "(you might want to increase it for larger datasets).",
         )
@@ -294,8 +294,7 @@ class GigaSpeechAsrDataModule:
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,
                 num_buckets=self.args.num_buckets,
-                buffer_size=self.args.num_buckets * 2000,
-                shuffle_buffer_size=self.args.num_buckets * 5000,
+                buffer_size=self.args.num_buckets * 5000,
                 drop_last=True,
             )
         else:
diff --git a/egs/gigaspeech/ASR/zipformer/asr_datamodule.py b/egs/gigaspeech/ASR/zipformer/asr_datamodule.py
index 0501461cd..892817b55 100644
--- a/egs/gigaspeech/ASR/zipformer/asr_datamodule.py
+++ b/egs/gigaspeech/ASR/zipformer/asr_datamodule.py
@@ -105,7 +105,7 @@ class GigaSpeechAsrDataModule:
         group.add_argument(
             "--num-buckets",
             type=int,
-            default=100,
+            default=15,
             help="The number of buckets for the DynamicBucketingSampler"
             "(you might want to increase it for larger datasets).",
         )
@@ -311,8 +311,7 @@ class GigaSpeechAsrDataModule:
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,
                 num_buckets=self.args.num_buckets,
-                buffer_size=self.args.num_buckets * 2000,
-                shuffle_buffer_size=self.args.num_buckets * 5000,
+                buffer_size=self.args.num_buckets * 5000,
                 drop_last=self.args.drop_last,
             )
         else:
@@ -369,7 +368,7 @@ class GigaSpeechAsrDataModule:
             cuts_valid,
             max_duration=self.args.max_duration,
             num_buckets=self.args.num_buckets,
-            buffer_size=self.args.num_buckets * 2000,
+            buffer_size=self.args.num_buckets * 5000,
             shuffle=False,
         )
         logging.info("About to create dev dataloader")
diff --git a/egs/gigaspeech/KWS/zipformer/asr_datamodule.py b/egs/gigaspeech/KWS/zipformer/asr_datamodule.py
deleted file mode 100644
index ccc602404..000000000
--- a/egs/gigaspeech/KWS/zipformer/asr_datamodule.py
+++ /dev/null
@@ -1,477 +0,0 @@
-# Copyright      2021  Piotr Żelasko
-# Copyright      2024  Xiaomi Corporation     (Author: Wei Kang)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import argparse
-import glob
-import inspect
-import logging
-import re
-from functools import lru_cache
-from pathlib import Path
-from typing import Any, Dict, Optional
-
-import lhotse
-import torch
-from lhotse import CutSet, Fbank, FbankConfig, load_manifest, load_manifest_lazy
-from lhotse.dataset import (
-    CutConcatenate,
-    CutMix,
-    DynamicBucketingSampler,
-    K2SpeechRecognitionDataset,
-    PrecomputedFeatures,
-    SimpleCutSampler,
-    SpecAugment,
-)
-from lhotse.dataset.input_strategies import AudioSamples, OnTheFlyFeatures
-from lhotse.utils import fix_random_seed
-from torch.utils.data import DataLoader
-
-from icefall.utils import str2bool
-
-
-class _SeedWorkers:
-    def __init__(self, seed: int):
-        self.seed = seed
-
-    def __call__(self, worker_id: int):
-        fix_random_seed(self.seed + worker_id)
-
-
-class GigaSpeechAsrDataModule:
-    """
-    DataModule for k2 ASR experiments.
-    It assumes there is always one train and valid dataloader,
-    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
-    and test-other).
-
-    It contains all the common data pipeline modules used in ASR
-    experiments, e.g.:
-    - dynamic batch size,
-    - bucketing samplers,
-    - cut concatenation,
-    - augmentation,
-    - on-the-fly feature extraction
-
-    This class should be derived for specific corpora used in ASR tasks.
-    """
-
-    def __init__(self, args: argparse.Namespace):
-        self.args = args
-
-    @classmethod
-    def add_arguments(cls, parser: argparse.ArgumentParser):
-        group = parser.add_argument_group(
-            title="ASR data related options",
-            description="These options are used for the preparation of "
-            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
-            "effective batch sizes, sampling strategies, applied data "
-            "augmentations, etc.",
-        )
-        group.add_argument(
-            "--manifest-dir",
-            type=Path,
-            default=Path("data/fbank"),
-            help="Path to directory with train/valid/test cuts.",
-        )
-        group.add_argument(
-            "--max-duration",
-            type=int,
-            default=200.0,
-            help="Maximum pooled recordings duration (seconds) in a "
-            "single batch. You can reduce it if it causes CUDA OOM.",
-        )
-        group.add_argument(
-            "--bucketing-sampler",
-            type=str2bool,
-            default=True,
-            help="When enabled, the batches will come from buckets of "
-            "similar duration (saves padding frames).",
-        )
-        group.add_argument(
-            "--num-buckets",
-            type=int,
-            default=30,
-            help="The number of buckets for the DynamicBucketingSampler"
-            "(you might want to increase it for larger datasets).",
-        )
-        group.add_argument(
-            "--concatenate-cuts",
-            type=str2bool,
-            default=False,
-            help="When enabled, utterances (cuts) will be concatenated "
-            "to minimize the amount of padding.",
-        )
-        group.add_argument(
-            "--duration-factor",
-            type=float,
-            default=1.0,
-            help="Determines the maximum duration of a concatenated cut "
-            "relative to the duration of the longest cut in a batch.",
-        )
-        group.add_argument(
-            "--gap",
-            type=float,
-            default=1.0,
-            help="The amount of padding (in seconds) inserted between "
-            "concatenated cuts. This padding is filled with noise when "
-            "noise augmentation is used.",
-        )
-        group.add_argument(
-            "--on-the-fly-feats",
-            type=str2bool,
-            default=False,
-            help="When enabled, use on-the-fly cut mixing and feature "
-            "extraction. Will drop existing precomputed feature manifests "
-            "if available.",
-        )
-        group.add_argument(
-            "--shuffle",
-            type=str2bool,
-            default=True,
-            help="When enabled (=default), the examples will be "
-            "shuffled for each epoch.",
-        )
-        group.add_argument(
-            "--drop-last",
-            type=str2bool,
-            default=True,
-            help="Whether to drop last batch. Used by sampler.",
-        )
-        group.add_argument(
-            "--return-cuts",
-            type=str2bool,
-            default=True,
-            help="When enabled, each batch will have the "
-            "field: batch['supervisions']['cut'] with the cuts that "
-            "were used to construct it.",
-        )
-
-        group.add_argument(
-            "--num-workers",
-            type=int,
-            default=2,
-            help="The number of training dataloader workers that "
-            "collect the batches.",
-        )
-
-        group.add_argument(
-            "--enable-spec-aug",
-            type=str2bool,
-            default=True,
-            help="When enabled, use SpecAugment for training dataset.",
-        )
-
-        group.add_argument(
-            "--spec-aug-time-warp-factor",
-            type=int,
-            default=80,
-            help="Used only when --enable-spec-aug is True. "
-            "It specifies the factor for time warping in SpecAugment. "
-            "Larger values mean more warping. "
-            "A value less than 1 means to disable time warp.",
-        )
-
-        group.add_argument(
-            "--enable-musan",
-            type=str2bool,
-            default=True,
-            help="When enabled, select noise from MUSAN and mix it"
-            "with training dataset. ",
-        )
-
-        group.add_argument(
-            "--input-strategy",
-            type=str,
-            default="PrecomputedFeatures",
-            help="AudioSamples or PrecomputedFeatures",
-        )
-
-        # GigaSpeech specific arguments
-        group.add_argument(
-            "--subset",
-            type=str,
-            default="XL",
-            help="Select the GigaSpeech subset (XS|S|M|L|XL)",
-        )
-        group.add_argument(
-            "--small-dev",
-            type=str2bool,
-            default=False,
-            help="Should we use only 1000 utterances for dev (speeds up training)",
-        )
-
-    def train_dataloaders(
-        self,
-        cuts_train: CutSet,
-        sampler_state_dict: Optional[Dict[str, Any]] = None,
-    ) -> DataLoader:
-        """
-        Args:
-          cuts_train:
-            CutSet for training.
-          sampler_state_dict:
-            The state dict for the training sampler.
-        """
-        transforms = []
-        if self.args.enable_musan:
-            logging.info("Enable MUSAN")
-            logging.info("About to get Musan cuts")
-            cuts_musan = load_manifest(self.args.manifest_dir / "musan_cuts.jsonl.gz")
-            transforms.append(
-                CutMix(cuts=cuts_musan, p=0.5, snr=(10, 20), preserve_id=True)
-            )
-        else:
-            logging.info("Disable MUSAN")
-
-        if self.args.concatenate_cuts:
-            logging.info(
-                f"Using cut concatenation with duration factor "
-                f"{self.args.duration_factor} and gap {self.args.gap}."
-            )
-            # Cut concatenation should be the first transform in the list,
-            # so that if we e.g. mix noise in, it will fill the gaps between
-            # different utterances.
-            transforms = [
-                CutConcatenate(
-                    duration_factor=self.args.duration_factor, gap=self.args.gap
-                )
-            ] + transforms
-
-        input_transforms = []
-        if self.args.enable_spec_aug:
-            logging.info("Enable SpecAugment")
-            logging.info(f"Time warp factor: {self.args.spec_aug_time_warp_factor}")
-            # Set the value of num_frame_masks according to Lhotse's version.
-            # In different Lhotse's versions, the default of num_frame_masks is
-            # different.
-            num_frame_masks = 10
-            num_frame_masks_parameter = inspect.signature(
-                SpecAugment.__init__
-            ).parameters["num_frame_masks"]
-            if num_frame_masks_parameter.default == 1:
-                num_frame_masks = 2
-            logging.info(f"Num frame mask: {num_frame_masks}")
-            input_transforms.append(
-                SpecAugment(
-                    time_warp_factor=self.args.spec_aug_time_warp_factor,
-                    num_frame_masks=num_frame_masks,
-                    features_mask_size=27,
-                    num_feature_masks=2,
-                    frames_mask_size=100,
-                )
-            )
-        else:
-            logging.info("Disable SpecAugment")
-
-        logging.info("About to create train dataset")
-        train = K2SpeechRecognitionDataset(
-            input_strategy=eval(self.args.input_strategy)(),
-            cut_transforms=transforms,
-            input_transforms=input_transforms,
-            return_cuts=self.args.return_cuts,
-        )
-
-        if self.args.on_the_fly_feats:
-            # NOTE: the PerturbSpeed transform should be added only if we
-            # remove it from data prep stage.
-            # Add on-the-fly speed perturbation; since originally it would
-            # have increased epoch size by 3, we will apply prob 2/3 and use
-            # 3x more epochs.
-            # Speed perturbation probably should come first before
-            # concatenation, but in principle the transforms order doesn't have
-            # to be strict (e.g. could be randomized)
-            # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms   # noqa
-            # Drop feats to be on the safe side.
-            train = K2SpeechRecognitionDataset(
-                cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
-                input_transforms=input_transforms,
-                return_cuts=self.args.return_cuts,
-            )
-
-        if self.args.bucketing_sampler:
-            logging.info("Using DynamicBucketingSampler.")
-            train_sampler = DynamicBucketingSampler(
-                cuts_train,
-                max_duration=self.args.max_duration,
-                shuffle=self.args.shuffle,
-                num_buckets=self.args.num_buckets,
-                drop_last=self.args.drop_last,
-                buffer_size=self.args.num_buckets * 2000,
-                shuffle_buffer_size=self.args.num_buckets * 5000,
-            )
-        else:
-            logging.info("Using SimpleCutSampler.")
-            train_sampler = SimpleCutSampler(
-                cuts_train,
-                max_duration=self.args.max_duration,
-                shuffle=self.args.shuffle,
-            )
-        logging.info("About to create train dataloader")
-
-        if sampler_state_dict is not None:
-            logging.info("Loading sampler state dict")
-            train_sampler.load_state_dict(sampler_state_dict)
-
-        # 'seed' is derived from the current random state, which will have
-        # previously been set in the main process.
-        seed = torch.randint(0, 100000, ()).item()
-        worker_init_fn = _SeedWorkers(seed)
-
-        train_dl = DataLoader(
-            train,
-            sampler=train_sampler,
-            batch_size=None,
-            num_workers=self.args.num_workers,
-            persistent_workers=False,
-            worker_init_fn=worker_init_fn,
-        )
-
-        return train_dl
-
-    def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
-        transforms = []
-        if self.args.concatenate_cuts:
-            transforms = [
-                CutConcatenate(
-                    duration_factor=self.args.duration_factor, gap=self.args.gap
-                )
-            ] + transforms
-
-        logging.info("About to create dev dataset")
-        if self.args.on_the_fly_feats:
-            validate = K2SpeechRecognitionDataset(
-                cut_transforms=transforms,
-                input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80))),
-                return_cuts=self.args.return_cuts,
-            )
-        else:
-            validate = K2SpeechRecognitionDataset(
-                cut_transforms=transforms,
-                return_cuts=self.args.return_cuts,
-            )
-        valid_sampler = DynamicBucketingSampler(
-            cuts_valid,
-            max_duration=self.args.max_duration,
-            num_buckets=self.args.num_buckets,
-            buffer_size=self.args.num_buckets * 2000,
-            shuffle=False,
-        )
-        logging.info("About to create dev dataloader")
-        valid_dl = DataLoader(
-            validate,
-            sampler=valid_sampler,
-            batch_size=None,
-            num_workers=2,
-            persistent_workers=False,
-        )
-
-        return valid_dl
-
-    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
-        logging.debug("About to create test dataset")
-        test = K2SpeechRecognitionDataset(
-            input_strategy=OnTheFlyFeatures(Fbank(FbankConfig(num_mel_bins=80)))
-            if self.args.on_the_fly_feats
-            else eval(self.args.input_strategy)(),
-            return_cuts=self.args.return_cuts,
-        )
-        sampler = DynamicBucketingSampler(
-            cuts,
-            max_duration=self.args.max_duration,
-            shuffle=False,
-        )
-        logging.debug("About to create test dataloader")
-        test_dl = DataLoader(
-            test,
-            batch_size=None,
-            sampler=sampler,
-            num_workers=self.args.num_workers,
-        )
-        return test_dl
-
-    @lru_cache()
-    def train_cuts(self) -> CutSet:
-        logging.info(f"About to get train {self.args.subset} cuts")
-        if self.args.subset == "XL":
-            filenames = glob.glob(
-                f"{self.args.manifest_dir}/XL_split/gigaspeech_cuts_XL.*.jsonl.gz"
-            )
-            pattern = re.compile(r"gigaspeech_cuts_XL.([0-9]+).jsonl.gz")
-            idx_filenames = ((int(pattern.search(f).group(1)), f) for f in filenames)
-            idx_filenames = sorted(idx_filenames, key=lambda x: x[0])
-            sorted_filenames = [f[1] for f in idx_filenames]
-            logging.info(
-                f"Loading GigaSpeech {len(sorted_filenames)} splits in lazy mode"
-            )
-
-            cuts_train = lhotse.combine(
-                lhotse.load_manifest_lazy(p) for p in sorted_filenames
-            )
-        else:
-            path = (
-                self.args.manifest_dir / f"gigaspeech_cuts_{self.args.subset}.jsonl.gz"
-            )
-            cuts_train = CutSet.from_jsonl_lazy(path)
-        return cuts_train
-
-    @lru_cache()
-    def dev_cuts(self) -> CutSet:
-        logging.info("About to get dev cuts")
-        cuts_valid = load_manifest_lazy(
-            self.args.manifest_dir / "gigaspeech_cuts_DEV.jsonl.gz"
-        )
-        if self.args.small_dev:
-            return cuts_valid.subset(first=1000)
-        else:
-            return cuts_valid
-
-    @lru_cache()
-    def test_cuts(self) -> CutSet:
-        logging.info("About to get test cuts")
-        return load_manifest_lazy(
-            self.args.manifest_dir / "gigaspeech_cuts_TEST.jsonl.gz"
-        )
-
-    @lru_cache()
-    def fsc_train_cuts(self) -> CutSet:
-        logging.info("About to get fluent speech commands train cuts")
-        return load_manifest_lazy(
-            self.args.manifest_dir / "fluent_speech_commands_cuts_train.jsonl.gz"
-        )
-
-    @lru_cache()
-    def fsc_valid_cuts(self) -> CutSet:
-        logging.info("About to get fluent speech commands valid cuts")
-        return load_manifest_lazy(
-            self.args.manifest_dir / "fluent_speech_commands_cuts_valid.jsonl.gz"
-        )
-
-    @lru_cache()
-    def fsc_test_small_cuts(self) -> CutSet:
-        logging.info("About to get fluent speech commands small test cuts")
-        return load_manifest_lazy(
-            self.args.manifest_dir / "fluent_speech_commands_cuts_small.jsonl.gz"
-        )
-
-    @lru_cache()
-    def fsc_test_large_cuts(self) -> CutSet:
-        logging.info("About to get fluent speech commands large test cuts")
-        return load_manifest_lazy(
-            self.args.manifest_dir / "fluent_speech_commands_cuts_large.jsonl.gz"
-        )
diff --git a/egs/gigaspeech/KWS/zipformer/asr_datamodule.py b/egs/gigaspeech/KWS/zipformer/asr_datamodule.py
new file mode 120000
index 000000000..75bc3d45a
--- /dev/null
+++ b/egs/gigaspeech/KWS/zipformer/asr_datamodule.py
@@ -0,0 +1 @@
+../../ASR/zipformer/asr_datamodule.py
\ No newline at end of file
diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py
index 1b52aa8b5..283252a46 100644
--- a/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py
+++ b/egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py
@@ -311,8 +311,7 @@ class LibriSpeechAsrDataModule:
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,
                 num_buckets=self.args.num_buckets,
-                buffer_size=self.args.num_buckets * 2000,
-                shuffle_buffer_size=self.args.num_buckets * 5000,
+                buffer_size=self.args.num_buckets * 5000,
                 drop_last=self.args.drop_last,
             )
         else:
diff --git a/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py b/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py
index 8b35187b1..2cc06df71 100644
--- a/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py
+++ b/egs/wenetspeech/ASR/pruned_transducer_stateless2/asr_datamodule.py
@@ -106,7 +106,7 @@ class WenetSpeechAsrDataModule:
         group.add_argument(
             "--num-buckets",
             type=int,
-            default=30,
+            default=15,
             help="The number of buckets for the DynamicBucketingSampler"
             "(you might want to increase it for larger datasets).",
         )
@@ -292,8 +292,7 @@ class WenetSpeechAsrDataModule:
                 max_duration=self.args.max_duration,
                 shuffle=self.args.shuffle,
                 num_buckets=self.args.num_buckets,
-                buffer_size=self.args.num_buckets * 2000,
-                shuffle_buffer_size=self.args.num_buckets * 5000,
+                buffer_size=self.args.num_buckets * 5000,
                 drop_last=True,
             )
         else:

From 343b8fa2dc7d1350a410677c38fd2f100e749551 Mon Sep 17 00:00:00 2001
From: Wei Kang <wkang.pku@gmail.com>
Date: Thu, 19 Jun 2025 12:27:15 +0800
Subject: [PATCH 18/22] Using non strict match in context graph for contextual
 words (#1952)

---
 .../ASR/pruned_transducer_stateless2/beam_search.py    | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py b/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py
index 66c84b2a9..f1d16749c 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py
@@ -1347,7 +1347,10 @@ def modified_beam_search(
                         (
                             context_score,
                             new_context_state,
-                        ) = context_graph.forward_one_step(hyp.context_state, new_token)
+                            _,
+                        ) = context_graph.forward_one_step(
+                            hyp.context_state, new_token, strict_mode=False
+                        )
 
                 new_log_prob = topk_log_probs[k] + context_score
 
@@ -2853,7 +2856,10 @@ def modified_beam_search_LODR(
                         (
                             context_score,
                             new_context_state,
-                        ) = context_graph.forward_one_step(hyp.context_state, new_token)
+                            _,
+                        ) = context_graph.forward_one_step(
+                            hyp.context_state, new_token, strict_mode=False
+                        )
 
                     ys.append(new_token)
                     state_cost = hyp.state_cost.forward_one_step(new_token)

From e1cf4dbacec2c48947b16e4afd62c53407390b19 Mon Sep 17 00:00:00 2001
From: Wei Kang <wkang.pku@gmail.com>
Date: Mon, 23 Jun 2025 19:22:35 +0800
Subject: [PATCH 19/22] rm zipvoice (#1967)

---
 egs/zipvoice/README.md                        |  412 ----
 egs/zipvoice/assets/prompt-en.wav             |  Bin 675496 -> 0 bytes
 egs/zipvoice/local/compute_fbank.py           |  287 ---
 egs/zipvoice/local/evaluate_sim.py            |  508 -----
 egs/zipvoice/local/evaluate_utmos.py          |  294 ---
 egs/zipvoice/local/evaluate_wer_hubert.py     |  172 --
 egs/zipvoice/local/evaluate_wer_seedtts.py    |  181 --
 egs/zipvoice/local/feature.py                 |    1 -
 egs/zipvoice/local/pinyin.txt                 | 1550 -------------
 .../local/prepare_token_file_emilia.py        |   90 -
 .../local/prepare_token_file_libritts.py      |   31 -
 egs/zipvoice/local/preprocess_emilia.py       |  155 --
 egs/zipvoice/local/tokenizer.py               |    1 -
 egs/zipvoice/local/validate_manifest.py       |   70 -
 egs/zipvoice/requirements.txt                 |   17 -
 egs/zipvoice/scripts/evaluate.sh              |  102 -
 egs/zipvoice/scripts/prepare_emilia.sh        |  126 --
 egs/zipvoice/scripts/prepare_libritts.sh      |   97 -
 egs/zipvoice/zipvoice/checkpoint.py           |  142 --
 egs/zipvoice/zipvoice/feature.py              |  135 --
 .../zipvoice/generate_averaged_model.py       |  209 --
 egs/zipvoice/zipvoice/infer.py                |  586 -----
 egs/zipvoice/zipvoice/model.py                |  578 -----
 egs/zipvoice/zipvoice/optim.py                | 1256 -----------
 egs/zipvoice/zipvoice/scaling.py              | 1930 -----------------
 egs/zipvoice/zipvoice/solver.py               |  277 ---
 egs/zipvoice/zipvoice/tokenizer.py            |  572 -----
 egs/zipvoice/zipvoice/train_distill.py        | 1043 ---------
 egs/zipvoice/zipvoice/train_flow.py           | 1108 ----------
 egs/zipvoice/zipvoice/tts_datamodule.py       |  456 ----
 egs/zipvoice/zipvoice/utils.py                |  219 --
 egs/zipvoice/zipvoice/zipformer.py            | 1648 --------------
 egs/zipvoice/zipvoice/zipvoice_infer.py       |  645 ------
 33 files changed, 14898 deletions(-)
 delete mode 100644 egs/zipvoice/README.md
 delete mode 100644 egs/zipvoice/assets/prompt-en.wav
 delete mode 100644 egs/zipvoice/local/compute_fbank.py
 delete mode 100644 egs/zipvoice/local/evaluate_sim.py
 delete mode 100644 egs/zipvoice/local/evaluate_utmos.py
 delete mode 100644 egs/zipvoice/local/evaluate_wer_hubert.py
 delete mode 100644 egs/zipvoice/local/evaluate_wer_seedtts.py
 delete mode 120000 egs/zipvoice/local/feature.py
 delete mode 100644 egs/zipvoice/local/pinyin.txt
 delete mode 100644 egs/zipvoice/local/prepare_token_file_emilia.py
 delete mode 100644 egs/zipvoice/local/prepare_token_file_libritts.py
 delete mode 100644 egs/zipvoice/local/preprocess_emilia.py
 delete mode 120000 egs/zipvoice/local/tokenizer.py
 delete mode 100644 egs/zipvoice/local/validate_manifest.py
 delete mode 100644 egs/zipvoice/requirements.txt
 delete mode 100644 egs/zipvoice/scripts/evaluate.sh
 delete mode 100755 egs/zipvoice/scripts/prepare_emilia.sh
 delete mode 100755 egs/zipvoice/scripts/prepare_libritts.sh
 delete mode 100644 egs/zipvoice/zipvoice/checkpoint.py
 delete mode 100644 egs/zipvoice/zipvoice/feature.py
 delete mode 100644 egs/zipvoice/zipvoice/generate_averaged_model.py
 delete mode 100644 egs/zipvoice/zipvoice/infer.py
 delete mode 100644 egs/zipvoice/zipvoice/model.py
 delete mode 100644 egs/zipvoice/zipvoice/optim.py
 delete mode 100644 egs/zipvoice/zipvoice/scaling.py
 delete mode 100644 egs/zipvoice/zipvoice/solver.py
 delete mode 100644 egs/zipvoice/zipvoice/tokenizer.py
 delete mode 100644 egs/zipvoice/zipvoice/train_distill.py
 delete mode 100644 egs/zipvoice/zipvoice/train_flow.py
 delete mode 100644 egs/zipvoice/zipvoice/tts_datamodule.py
 delete mode 100644 egs/zipvoice/zipvoice/utils.py
 delete mode 100644 egs/zipvoice/zipvoice/zipformer.py
 delete mode 100644 egs/zipvoice/zipvoice/zipvoice_infer.py

diff --git a/egs/zipvoice/README.md b/egs/zipvoice/README.md
deleted file mode 100644
index 0c97d7ed8..000000000
--- a/egs/zipvoice/README.md
+++ /dev/null
@@ -1,412 +0,0 @@
-## ZipVoice: Fast and High-Quality Zero-Shot Text-to-Speech with Flow Matching
-
-
-[![arXiv](https://img.shields.io/badge/arXiv-Paper-COLOR.svg)](http://arxiv.org/abs/2506.13053)
-[![demo](https://img.shields.io/badge/GitHub-Demo%20page-orange.svg)](https://zipvoice.github.io/)
-
-
-## Overview
-ZipVoice is a high-quality zero-shot TTS model with a small model size and fast inference speed.
-#### Key features:
-
-- Small and fast: only 123M parameters.
-
-- High-quality: state-of-the-art voice cloning performance in speaker similarity, intelligibility, and naturalness.
-
-- Multi-lingual: support Chinese and English.
-
-
-## News
-**2025/06/16**: 🔥 ZipVoice is released.
-
-
-## Installation
-
-* Clone icefall repository and change to zipvoice directory:
-
-```bash
-git clone https://github.com/k2-fsa/icefall.git
-cd icefall/egs/zipvoice
-```
-
-* Create a Python virtual environment (optional but recommended):
-
-```bash
-python3 -m venv venv
-source venv/bin/activate
-```
-
-* Install the required packages:
-
-```bash
-pip install -r requirements.txt
-```
-
-## Usage
-
-To generate speech with our pre-trained ZipVoice or ZipVoice-Distill models, use the following commands (Required models will be downloaded from HuggingFace):
-
-### 1. Inference of a single sentence:
-```bash
-python3 zipvoice/zipvoice_infer.py \
-    --model-name "zipvoice_distill" \
-    --prompt-wav prompt.wav \
-    --prompt-text "I am the transcription of the prompt wav." \
-    --text "I am the text to be synthesized." \
-    --res-wav-path result.wav
-
-# Example with a pre-defined prompt wav and text
-python3 zipvoice/zipvoice_infer.py \
-    --model-name "zipvoice_distill" \
-    --prompt-wav assets/prompt-en.wav \
-    --prompt-text "Some call me nature, others call me mother nature. I've been here for over four point five billion years, twenty two thousand five hundred times longer than you." \
-    --text "Welcome to use our tts model, have fun!" \
-    --res-wav-path result.wav
-```
-
-### 2. Inference of a list of sentences:
-```bash
-python3 zipvoice/zipvoice_infer.py \
-    --model-name "zipvoice_distill" \
-    --test-list test.tsv \
-    --res-dir results/test
-```
-
-- `--model-name` can be `zipvoice` or `zipvoice_distill`, which are models before and after distillation, respectively.
-- Each line of `test.tsv` is in the format of `{wav_name}\t{prompt_transcription}\t{prompt_wav}\t{text}`.
-
-
-> **Note:**  If you having trouble connecting to HuggingFace, try:
-```bash
-export HF_ENDPOINT=https://hf-mirror.com
-```
-
-## Training Your Own Model
-
-The following steps show how to train a model from scratch on Emilia and LibriTTS datasets, respectively.
-
-### 0. Install dependencies for training
-
-```bash
-# Install pytorch and k2.
-# If you want to use different versions, please refer to https://k2-fsa.org/get-started/k2/ for details.
-# For users in China mainland, please refer to https://k2-fsa.org/zh-CN/get-started/k2/
-
-# Note: Make sure you have installed the correct version of PyTorch and k2 that matches your CUDA version.
-# For example, if want to use pytorch 2.5.1 and you are using CUDA 12.1, you can install PyTorch and k2 as follows:
-
-pip install torch==2.5.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
-pip install k2==1.24.4.dev20250208+cuda12.1.torch2.5.1 -f https://k2-fsa.github.io/k2/cuda.html
-
-pip install -r ../../requirements.txt
-```
-
-### 1. Data Preparation
-
-#### 1.1. Prepare the Emilia dataset
-
-```bash
-bash scripts/prepare_emilia.sh
-```
-
-See [scripts/prepare_emilia.sh](scripts/prepare_emilia.sh) for step by step instructions.
-
-#### 1.2 Prepare the LibriTTS dataset
-
-```bash
-bash scripts/prepare_libritts.sh
-```
-
-See [scripts/prepare_libritts.sh](scripts/prepare_libritts.sh) for step by step instructions.
-
-### 2. Training
-
-#### 2.1 Traininig on Emilia
-
-<details>
-<summary>Expand to view training steps</summary>
-
-##### 2.1.1 Train the ZipVoice model
-
-- Training:
-
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 zipvoice/train_flow.py \
-        --world-size 8 \
-        --use-fp16 1 \
-        --dataset emilia \
-        --max-duration 500 \
-        --lr-hours 30000 \
-        --lr-batches 7500 \
-        --token-file "data/tokens_emilia.txt" \
-        --manifest-dir "data/fbank" \
-        --num-epochs 11 \
-        --exp-dir zipvoice/exp_zipvoice
-```
-
--  Average the checkpoints to produce the final model:
-
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 zipvoice/generate_averaged_model.py \
-      --epoch 11 \
-      --avg 4 \
-      --distill 0 \
-      --token-file data/tokens_emilia.txt \
-      --dataset "emilia" \
-      --exp-dir ./zipvoice/exp_zipvoice
-# The generated model is zipvoice/exp_zipvoice/epoch-11-avg-4.pt
-```
-
-##### 2.1.2. Train the ZipVoice-Distill model (Optional)
-
-- The first-stage distillation:
-
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 zipvoice/train_distill.py \
-        --world-size 8 \
-        --use-fp16 1 \
-        --tensorboard 1 \
-        --dataset "emilia" \
-        --base-lr 0.0005 \
-        --max-duration 500 \
-        --token-file "data/tokens_emilia.txt" \
-        --manifest-dir "data/fbank" \
-        --teacher-model zipvoice/exp_zipvoice/epoch-11-avg-4.pt \
-        --num-updates 60000 \
-        --distill-stage "first" \
-        --exp-dir zipvoice/exp_zipvoice_distill_1stage
-```
-
-- Average checkpoints for the second-stage initialization:
-
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 zipvoice/generate_averaged_model.py \
-      --iter 60000 \
-      --avg 7 \
-      --distill 1 \
-      --token-file data/tokens_emilia.txt \
-      --dataset "emilia" \
-      --exp-dir ./zipvoice/exp_zipvoice_distill_1stage
-# The generated model is zipvoice/exp_zipvoice_distill_1stage/iter-60000-avg-7.pt
-```
-
--  The second-stage distillation:
-
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 zipvoice/train_distill.py \
-        --world-size 8 \
-        --use-fp16 1 \
-        --tensorboard 1 \
-        --dataset "emilia" \
-        --base-lr 0.0001 \
-        --max-duration 200 \
-        --token-file "data/tokens_emilia.txt" \
-        --manifest-dir "data/fbank" \
-        --teacher-model zipvoice/exp_zipvoice_distill_1stage/iter-60000-avg-7.pt \
-        --num-updates 2000 \
-        --distill-stage "second" \
-        --exp-dir zipvoice/exp_zipvoice_distill_new
-```
-</details>
-
-
-#### 2.2 Traininig on LibriTTS
-
-<details>
-<summary>Expand to view training steps</summary>
-
-##### 2.2.1 Train the ZipVoice model
-
-- Training:
-
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 zipvoice/train_flow.py \
-        --world-size 8 \
-        --use-fp16 1 \
-        --dataset libritts \
-        --max-duration 250 \
-        --lr-epochs 10 \
-        --lr-batches 7500 \
-        --token-file "data/tokens_libritts.txt" \
-        --manifest-dir "data/fbank" \
-        --num-epochs 60 \
-        --exp-dir zipvoice/exp_zipvoice_libritts
-```
-
-- Average the checkpoints to produce the final model:
-
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 zipvoice/generate_averaged_model.py \
-      --epoch 60 \
-      --avg 10 \
-      --distill 0 \
-      --token-file data/tokens_libritts.txt \
-      --dataset "libritts" \
-      --exp-dir ./zipvoice/exp_zipvoice_libritts
-# The generated model is zipvoice/exp_zipvoice_libritts/epoch-60-avg-10.pt
-```
-
-##### 2.1.2 Train the ZipVoice-Distill model (Optional)
-
-- The first-stage distillation:
-
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 zipvoice/train_distill.py \
-        --world-size 8 \
-        --use-fp16 1 \
-        --tensorboard 1 \
-        --dataset "libritts" \
-        --base-lr 0.001 \
-        --max-duration 250 \
-        --token-file "data/tokens_libritts.txt" \
-        --manifest-dir "data/fbank" \
-        --teacher-model zipvoice/exp_zipvoice_libritts/epoch-60-avg-10.pt \
-        --num-epochs 6 \
-        --distill-stage "first" \
-        --exp-dir zipvoice/exp_zipvoice_distill_1stage_libritts
-```
-
-- Average checkpoints for the second-stage initialization:
-
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 ./zipvoice/generate_averaged_model.py \
-      --epoch 6 \
-      --avg 3 \
-      --distill 1 \
-      --token-file data/tokens_libritts.txt \
-      --dataset "libritts" \
-      --exp-dir ./zipvoice/exp_zipvoice_distill_1stage_libritts
-# The generated model is zipvoice/exp_zipvoice_distill_1stage_libritts/epoch-6-avg-3.pt
-```
-
-- The second-stage distillation:
-
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 zipvoice/train_distill.py \
-        --world-size 8 \
-        --use-fp16 1 \
-        --tensorboard 1 \
-        --dataset "libritts" \
-        --base-lr 0.001 \
-        --max-duration 250 \
-        --token-file "data/tokens_libritts.txt" \
-        --manifest-dir "data/fbank" \
-        --teacher-model zipvoice/exp_zipvoice_distill_1stage_libritts/epoch-6-avg-3.pt \
-        --num-epochs 6 \
-        --distill-stage "second" \
-        --exp-dir zipvoice/exp_zipvoice_distill_libritts
-```
-
-- Average checkpoints to produce the final model:
-
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 ./zipvoice/generate_averaged_model.py \
-      --epoch 6 \
-      --avg 3 \
-      --distill 1 \
-      --token-file data/tokens_libritts.txt \
-      --dataset "libritts" \
-      --exp-dir ./zipvoice/exp_zipvoice_distill_libritts
-# The generated model is ./zipvoice/exp_zipvoice_distill_libritts/epoch-6-avg-3.pt
-```
-</details>
-
-
-### 3. Inference with the trained model
-
-#### 3.1 Inference with the model trained on Emilia
-<details>
-<summary>Expand to view inference commands.</summary>
-
-##### 3.1.1 ZipVoice model before distill:
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 zipvoice/infer.py \
-      --checkpoint zipvoice/exp_zipvoice/epoch-11-avg-4.pt \
-      --distill 0 \
-      --token-file "data/tokens_emilia.txt" \
-      --test-list test.tsv \
-      --res-dir results/test \
-      --num-step 16 \
-      --guidance-scale 1
-```
-
-##### 3.1.2 ZipVoice-Distill model before distill:
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 zipvoice/infer.py \
-      --checkpoint zipvoice/exp_zipvoice_distill/checkpoint-2000.pt \
-      --distill 1 \
-      --token-file "data/tokens_emilia.txt" \
-      --test-list test.tsv \
-      --res-dir results/test_distill \
-      --num-step 8 \
-      --guidance-scale 3
-```
-</details>
-
-
-#### 3.2 Inference with the model trained on LibriTTS
-
-<details>
-<summary>Expand to view inference commands.</summary>
-
-##### 3.2.1 ZipVoice model before distill:
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 zipvoice/infer.py \
-      --checkpoint zipvoice/exp_zipvoice_libritts/epoch-60-avg-10.pt \
-      --distill 0 \
-      --token-file "data/tokens_libritts.txt" \
-      --test-list test.tsv \
-      --res-dir results/test_libritts \
-      --num-step 8 \
-      --guidance-scale 1 \
-      --target-rms 1.0 \
-      --t-shift 0.7
-```
-
-##### 3.2.2 ZipVoice-Distill model before distill
-
-```bash
-export PYTHONPATH=../../:$PYTHONPATH
-python3 zipvoice/infer.py \
-      --checkpoint zipvoice/exp_zipvoice_distill/epoch-6-avg-3.pt \
-      --distill 1 \
-      --token-file "data/tokens_libritts.txt" \
-      --test-list test.tsv \
-      --res-dir results/test_distill_libritts \
-      --num-step 4 \
-      --guidance-scale 3 \
-      --target-rms 1.0 \
-      --t-shift 0.7
-```
-</details>
-
-### 4. Evaluation on benchmarks
-
-See [local/evaluate.sh](local/evaluate.sh) for details of objective metrics evaluation
-on three test sets, i.e., LibriSpeech-PC test-clean, Seed-TTS test-en and Seed-TTS test-zh.
-
-
-## Citation
-
-```bibtex
-@article{zhu-2025-zipvoice,
-      title={ZipVoice: Fast and High-Quality Zero-Shot Text-to-Speech with Flow Matching},
-      author={Han Zhu and Wei Kang and Zengwei Yao and Liyong Guo and Fangjun Kuang and Zhaoqing Li and Weiji Zhuang and Long Lin and Daniel Povey}
-      journal={arXiv preprint arXiv:2506.13053},
-      year={2025},
-}
-```
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diff --git a/egs/zipvoice/local/compute_fbank.py b/egs/zipvoice/local/compute_fbank.py
deleted file mode 100644
index 0c440b995..000000000
--- a/egs/zipvoice/local/compute_fbank.py
+++ /dev/null
@@ -1,287 +0,0 @@
-#!/usr/bin/env python3
-# Copyright    2025  Xiaomi Corp.        (authors: Wei Kang)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import argparse
-import logging
-import os
-from concurrent.futures import ProcessPoolExecutor as Pool
-from pathlib import Path
-from typing import Optional
-
-import lhotse
-import torch
-from feature import TorchAudioFbank, TorchAudioFbankConfig
-from lhotse import (
-    CutSet,
-    LilcomChunkyWriter,
-    load_manifest_lazy,
-    set_audio_duration_mismatch_tolerance,
-)
-
-# Torch's multithreaded behavior needs to be disabled or
-# it wastes a lot of CPU and slow things down.
-# Do this outside of main() in case it needs to take effect
-# even when we are not invoking the main (e.g. when spawning subprocesses).
-torch.set_num_threads(1)
-torch.set_num_interop_threads(1)
-
-
-def str2bool(v):
-    """Used in argparse.ArgumentParser.add_argument to indicate
-    that a type is a bool type and user can enter
-
-        - yes, true, t, y, 1, to represent True
-        - no, false, f, n, 0, to represent False
-
-    See https://stackoverflow.com/questions/15008758/parsing-boolean-values-with-argparse  # noqa
-    """
-    if isinstance(v, bool):
-        return v
-    if v.lower() in ("yes", "true", "t", "y", "1"):
-        return True
-    elif v.lower() in ("no", "false", "f", "n", "0"):
-        return False
-    else:
-        raise argparse.ArgumentTypeError("Boolean value expected.")
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--sampling-rate",
-        type=int,
-        default=24000,
-        help="The target sampling rate, the audio will be resampled to this sampling_rate.",
-    )
-
-    parser.add_argument(
-        "--frame-shift",
-        type=int,
-        default=256,
-        help="Frame shift in samples",
-    )
-
-    parser.add_argument(
-        "--frame-length",
-        type=int,
-        default=1024,
-        help="Frame length in samples",
-    )
-
-    parser.add_argument(
-        "--num-mel-bins",
-        type=int,
-        default=100,
-        help="The num of mel filters.",
-    )
-
-    parser.add_argument(
-        "--dataset",
-        type=str,
-        help="Dataset name.",
-    )
-
-    parser.add_argument(
-        "--subset",
-        type=str,
-        help="The subset of the dataset.",
-    )
-
-    parser.add_argument(
-        "--source-dir",
-        type=str,
-        default="data/manifests",
-        help="The source directory of manifest files.",
-    )
-
-    parser.add_argument(
-        "--dest-dir",
-        type=str,
-        default="data/fbank",
-        help="The destination directory of manifest files.",
-    )
-
-    parser.add_argument(
-        "--split-cuts",
-        type=str2bool,
-        default=False,
-        help="Whether to use splited cuts.",
-    )
-
-    parser.add_argument(
-        "--split-begin",
-        type=int,
-        help="Start idx of splited cuts.",
-    )
-
-    parser.add_argument(
-        "--split-end",
-        type=int,
-        help="End idx of splited cuts.",
-    )
-
-    parser.add_argument(
-        "--batch-duration",
-        type=int,
-        default=1000,
-        help="The batch duration when computing the features.",
-    )
-
-    parser.add_argument(
-        "--num-jobs", type=int, default=20, help="The number of extractor workers."
-    )
-
-    return parser.parse_args()
-
-
-def compute_fbank_split_single(params, idx):
-    lhotse.set_audio_duration_mismatch_tolerance(0.1)  # for emilia
-    src_dir = Path(params.source_dir)
-    output_dir = Path(params.dest_dir)
-    num_mel_bins = params.num_mel_bins
-
-    if not src_dir.exists():
-        logging.error(f"{src_dir} not exists")
-        return
-
-    if not output_dir.exists():
-        output_dir.mkdir(parents=True, exist_ok=True)
-
-    num_digits = 8
-
-    config = TorchAudioFbankConfig(
-        sampling_rate=params.sampling_rate,
-        n_mels=params.num_mel_bins,
-        n_fft=params.frame_length,
-        hop_length=params.frame_shift,
-    )
-    extractor = TorchAudioFbank(config)
-
-    prefix = params.dataset
-    subset = params.subset
-    suffix = "jsonl.gz"
-
-    idx = f"{idx}".zfill(num_digits)
-    cuts_filename = f"{prefix}_cuts_{subset}.{idx}.{suffix}"
-
-    if (src_dir / cuts_filename).is_file():
-        logging.info(f"Loading manifests {src_dir / cuts_filename}")
-        cut_set = load_manifest_lazy(src_dir / cuts_filename)
-    else:
-        logging.warning(f"Raw {cuts_filename} not exists, skipping")
-        return
-
-    cut_set = cut_set.resample(params.sampling_rate)
-
-    if (output_dir / cuts_filename).is_file():
-        logging.info(f"{cuts_filename} already exists - skipping.")
-        return
-
-    logging.info(f"Processing {subset}.{idx} of {prefix}")
-
-    cut_set = cut_set.compute_and_store_features_batch(
-        extractor=extractor,
-        storage_path=f"{output_dir}/{prefix}_feats_{subset}_{idx}",
-        num_workers=4,
-        batch_duration=params.batch_duration,
-        storage_type=LilcomChunkyWriter,
-        overwrite=True,
-    )
-    cut_set.to_file(output_dir / cuts_filename)
-
-
-def compute_fbank_split(params):
-    if params.split_end < params.split_begin:
-        logging.warning(
-            f"Split begin should be smaller than split end, given "
-            f"{params.split_begin} -> {params.split_end}."
-        )
-
-    with Pool(max_workers=params.num_jobs) as pool:
-        futures = [
-            pool.submit(compute_fbank_split_single, params, i)
-            for i in range(params.split_begin, params.split_end)
-        ]
-        for f in futures:
-            f.result()
-            f.done()
-
-
-def compute_fbank(params):
-    src_dir = Path(params.source_dir)
-    output_dir = Path(params.dest_dir)
-    num_jobs = params.num_jobs
-    num_mel_bins = params.num_mel_bins
-
-    prefix = params.dataset
-    subset = params.subset
-    suffix = "jsonl.gz"
-
-    cut_set_name = f"{prefix}_cuts_{subset}.{suffix}"
-
-    if (src_dir / cut_set_name).is_file():
-        logging.info(f"Loading manifests {src_dir / cut_set_name}")
-        cut_set = load_manifest_lazy(src_dir / cut_set_name)
-    else:
-        recordings = load_manifest_lazy(
-            src_dir / f"{prefix}_recordings_{subset}.{suffix}"
-        )
-        supervisions = load_manifest_lazy(
-            src_dir / f"{prefix}_supervisions_{subset}.{suffix}"
-        )
-        cut_set = CutSet.from_manifests(
-            recordings=recordings,
-            supervisions=supervisions,
-        )
-
-    cut_set = cut_set.resample(params.sampling_rate)
-
-    config = TorchAudioFbankConfig(
-        sampling_rate=params.sampling_rate,
-        n_mels=params.num_mel_bins,
-        n_fft=params.frame_length,
-        hop_length=params.frame_shift,
-    )
-    extractor = TorchAudioFbank(config)
-
-    cuts_filename = f"{prefix}_cuts_{subset}.{suffix}"
-    if (output_dir / cuts_filename).is_file():
-        logging.info(f"{prefix} {subset} already exists - skipping.")
-        return
-    logging.info(f"Processing {subset} of {prefix}")
-
-    cut_set = cut_set.compute_and_store_features(
-        extractor=extractor,
-        storage_path=f"{output_dir}/{prefix}_feats_{subset}",
-        num_jobs=num_jobs,
-        storage_type=LilcomChunkyWriter,
-    )
-    cut_set.to_file(output_dir / cuts_filename)
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    args = get_args()
-    logging.info(vars(args))
-    if args.split_cuts:
-        compute_fbank_split(params=args)
-    else:
-        compute_fbank(params=args)
diff --git a/egs/zipvoice/local/evaluate_sim.py b/egs/zipvoice/local/evaluate_sim.py
deleted file mode 100644
index df439cf2c..000000000
--- a/egs/zipvoice/local/evaluate_sim.py
+++ /dev/null
@@ -1,508 +0,0 @@
-"""
-Calculate pairwise Speaker Similarity betweeen two speech directories.
-SV model wavlm_large_finetune.pth is downloaded from
-    https://github.com/microsoft/UniSpeech/tree/main/downstreams/speaker_verification
-SSL model wavlm_large.pt is downloaded from
-    https://huggingface.co/s3prl/converted_ckpts/resolve/main/wavlm_large.pt
-"""
-import argparse
-import logging
-import os
-
-import librosa
-import numpy as np
-import soundfile as sf
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from tqdm import tqdm
-
-logging.basicConfig(level=logging.INFO)
-
-
-def get_parser():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--eval-path", type=str, help="path of the evaluated speech directory"
-    )
-    parser.add_argument(
-        "--test-list",
-        type=str,
-        help="path of the file list that contains the corresponding "
-        "relationship between the prompt and evaluated speech. "
-        "The first column is the wav name and the third column is the prompt speech",
-    )
-    parser.add_argument(
-        "--sv-model-path",
-        type=str,
-        default="model/UniSpeech/wavlm_large_finetune.pth",
-        help="path of the wavlm-based ECAPA-TDNN model",
-    )
-    parser.add_argument(
-        "--ssl-model-path",
-        type=str,
-        default="model/s3prl/wavlm_large.pt",
-        help="path of the wavlm SSL model",
-    )
-    return parser
-
-
-class SpeakerSimilarity:
-    def __init__(
-        self,
-        sv_model_path="model/UniSpeech/wavlm_large_finetune.pth",
-        ssl_model_path="model/s3prl/wavlm_large.pt",
-    ):
-        """
-        Initialize
-        """
-        self.sample_rate = 16000
-        self.channels = 1
-        self.device = (
-            torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-        )
-        logging.info("[Speaker Similarity] Using device: {}".format(self.device))
-        self.model = ECAPA_TDNN_WAVLLM(
-            feat_dim=1024,
-            channels=512,
-            emb_dim=256,
-            sr=16000,
-            ssl_model_path=ssl_model_path,
-        )
-        state_dict = torch.load(
-            sv_model_path, map_location=lambda storage, loc: storage
-        )
-        self.model.load_state_dict(state_dict["model"], strict=False)
-        self.model.to(self.device)
-        self.model.eval()
-
-    def get_embeddings(self, wav_list, dtype="float32"):
-        """
-        Get embeddings
-        """
-
-        def _load_speech_task(fname, sample_rate):
-
-            wav_data, sr = sf.read(fname, dtype=dtype)
-            if sr != sample_rate:
-                wav_data = librosa.resample(
-                    wav_data, orig_sr=sr, target_sr=self.sample_rate
-                )
-            wav_data = torch.from_numpy(wav_data)
-
-            return wav_data
-
-        embd_lst = []
-        for file_path in tqdm(wav_list):
-            speech = _load_speech_task(file_path, self.sample_rate)
-            speech = speech.to(self.device)
-            with torch.no_grad():
-                embd = self.model([speech])
-            embd_lst.append(embd)
-
-        return embd_lst
-
-    def score(
-        self,
-        eval_path,
-        test_list,
-        dtype="float32",
-    ):
-        """
-        Computes the Speaker Similarity (SIM-o) between two directories of speech files.
-
-        Parameters:
-        - eval_path (str): Path to the directory containing evaluation speech files.
-        - test_list (str): Path to the file containing the corresponding relationship
-             between prompt and evaluated speech.
-        - dtype (str, optional): Data type for loading speech. Default is "float32".
-
-        Returns:
-        - float: The Speaker Similarity (SIM-o) score between the two directories
-            of speech files.
-        """
-        prompt_wavs = []
-        eval_wavs = []
-        with open(test_list, "r") as fr:
-            lines = fr.readlines()
-            for line in lines:
-                wav_name, prompt_text, prompt_wav, text = line.strip().split("\t")
-                prompt_wavs.append(prompt_wav)
-                eval_wavs.append(os.path.join(eval_path, wav_name + ".wav"))
-        embds_prompt = self.get_embeddings(prompt_wavs, dtype=dtype)
-
-        embds_eval = self.get_embeddings(eval_wavs, dtype=dtype)
-
-        # Check if embeddings are empty
-        if len(embds_prompt) == 0:
-            logging.info("[Speaker Similarity] real set dir is empty, exiting...")
-            return -1
-        if len(embds_eval) == 0:
-            logging.info("[Speaker Similarity] eval set dir is empty, exiting...")
-            return -1
-
-        scores = []
-        for real_embd, eval_embd in zip(embds_prompt, embds_eval):
-            scores.append(
-                torch.nn.functional.cosine_similarity(real_embd, eval_embd, dim=-1)
-                .detach()
-                .cpu()
-                .numpy()
-            )
-
-        return np.mean(scores)
-
-
-# part of the code is borrowed from https://github.com/lawlict/ECAPA-TDNN
-
-""" Res2Conv1d + BatchNorm1d + ReLU
-"""
-
-
-class Res2Conv1dReluBn(nn.Module):
-    """
-    in_channels == out_channels == channels
-    """
-
-    def __init__(
-        self,
-        channels,
-        kernel_size=1,
-        stride=1,
-        padding=0,
-        dilation=1,
-        bias=True,
-        scale=4,
-    ):
-        super().__init__()
-        assert channels % scale == 0, "{} % {} != 0".format(channels, scale)
-        self.scale = scale
-        self.width = channels // scale
-        self.nums = scale if scale == 1 else scale - 1
-
-        self.convs = []
-        self.bns = []
-        for i in range(self.nums):
-            self.convs.append(
-                nn.Conv1d(
-                    self.width,
-                    self.width,
-                    kernel_size,
-                    stride,
-                    padding,
-                    dilation,
-                    bias=bias,
-                )
-            )
-            self.bns.append(nn.BatchNorm1d(self.width))
-        self.convs = nn.ModuleList(self.convs)
-        self.bns = nn.ModuleList(self.bns)
-
-    def forward(self, x):
-        out = []
-        spx = torch.split(x, self.width, 1)
-        for i in range(self.nums):
-            if i == 0:
-                sp = spx[i]
-            else:
-                sp = sp + spx[i]
-            # Order: conv -> relu -> bn
-            sp = self.convs[i](sp)
-            sp = self.bns[i](F.relu(sp))
-            out.append(sp)
-        if self.scale != 1:
-            out.append(spx[self.nums])
-        out = torch.cat(out, dim=1)
-
-        return out
-
-
-""" Conv1d + BatchNorm1d + ReLU
-"""
-
-
-class Conv1dReluBn(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size=1,
-        stride=1,
-        padding=0,
-        dilation=1,
-        bias=True,
-    ):
-        super().__init__()
-        self.conv = nn.Conv1d(
-            in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias
-        )
-        self.bn = nn.BatchNorm1d(out_channels)
-
-    def forward(self, x):
-        return self.bn(F.relu(self.conv(x)))
-
-
-""" The SE connection of 1D case.
-"""
-
-
-class SE_Connect(nn.Module):
-    def __init__(self, channels, se_bottleneck_dim=128):
-        super().__init__()
-        self.linear1 = nn.Linear(channels, se_bottleneck_dim)
-        self.linear2 = nn.Linear(se_bottleneck_dim, channels)
-
-    def forward(self, x):
-        out = x.mean(dim=2)
-        out = F.relu(self.linear1(out))
-        out = torch.sigmoid(self.linear2(out))
-        out = x * out.unsqueeze(2)
-
-        return out
-
-
-""" SE-Res2Block of the ECAPA-TDNN architecture.
-"""
-
-
-# def SE_Res2Block(channels, kernel_size, stride, padding, dilation, scale):
-#     return nn.Sequential(
-#         Conv1dReluBn(channels, 512, kernel_size=1, stride=1, padding=0),
-#         Res2Conv1dReluBn(512, kernel_size, stride, padding, dilation, scale=scale),
-#         Conv1dReluBn(512, channels, kernel_size=1, stride=1, padding=0),
-#         SE_Connect(channels)
-#     )
-
-
-class SE_Res2Block(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride,
-        padding,
-        dilation,
-        scale,
-        se_bottleneck_dim,
-    ):
-        super().__init__()
-        self.Conv1dReluBn1 = Conv1dReluBn(
-            in_channels, out_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.Res2Conv1dReluBn = Res2Conv1dReluBn(
-            out_channels, kernel_size, stride, padding, dilation, scale=scale
-        )
-        self.Conv1dReluBn2 = Conv1dReluBn(
-            out_channels, out_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.SE_Connect = SE_Connect(out_channels, se_bottleneck_dim)
-
-        self.shortcut = None
-        if in_channels != out_channels:
-            self.shortcut = nn.Conv1d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=1,
-            )
-
-    def forward(self, x):
-        residual = x
-        if self.shortcut:
-            residual = self.shortcut(x)
-
-        x = self.Conv1dReluBn1(x)
-        x = self.Res2Conv1dReluBn(x)
-        x = self.Conv1dReluBn2(x)
-        x = self.SE_Connect(x)
-
-        return x + residual
-
-
-""" Attentive weighted mean and standard deviation pooling.
-"""
-
-
-class AttentiveStatsPool(nn.Module):
-    def __init__(self, in_dim, attention_channels=128, global_context_att=False):
-        super().__init__()
-        self.global_context_att = global_context_att
-
-        # Use Conv1d with stride == 1 rather than Linear,
-        #  then we don't need to transpose inputs.
-        if global_context_att:
-            self.linear1 = nn.Conv1d(
-                in_dim * 3, attention_channels, kernel_size=1
-            )  # equals W and b in the paper
-        else:
-            self.linear1 = nn.Conv1d(
-                in_dim, attention_channels, kernel_size=1
-            )  # equals W and b in the paper
-        self.linear2 = nn.Conv1d(
-            attention_channels, in_dim, kernel_size=1
-        )  # equals V and k in the paper
-
-    def forward(self, x):
-
-        if self.global_context_att:
-            context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
-            context_std = torch.sqrt(
-                torch.var(x, dim=-1, keepdim=True) + 1e-10
-            ).expand_as(x)
-            x_in = torch.cat((x, context_mean, context_std), dim=1)
-        else:
-            x_in = x
-
-        # DON'T use ReLU here! In experiments, I find ReLU hard to converge.
-        alpha = torch.tanh(self.linear1(x_in))
-        # alpha = F.relu(self.linear1(x_in))
-        alpha = torch.softmax(self.linear2(alpha), dim=2)
-        mean = torch.sum(alpha * x, dim=2)
-        residuals = torch.sum(alpha * (x**2), dim=2) - mean**2
-        std = torch.sqrt(residuals.clamp(min=1e-9))
-        return torch.cat([mean, std], dim=1)
-
-
-class ECAPA_TDNN_WAVLLM(nn.Module):
-    def __init__(
-        self,
-        feat_dim=80,
-        channels=512,
-        emb_dim=192,
-        global_context_att=False,
-        sr=16000,
-        ssl_model_path=None,
-    ):
-        super().__init__()
-        self.sr = sr
-
-        if ssl_model_path is None:
-            self.feature_extract = torch.hub.load("s3prl/s3prl", "wavlm_large")
-        else:
-            self.feature_extract = torch.hub.load(
-                os.path.dirname(ssl_model_path),
-                "wavlm_local",
-                source="local",
-                ckpt=ssl_model_path,
-            )
-
-        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
-            self.feature_extract.model.encoder.layers[23].self_attn, "fp32_attention"
-        ):
-            self.feature_extract.model.encoder.layers[
-                23
-            ].self_attn.fp32_attention = False
-        if len(self.feature_extract.model.encoder.layers) == 24 and hasattr(
-            self.feature_extract.model.encoder.layers[11].self_attn, "fp32_attention"
-        ):
-            self.feature_extract.model.encoder.layers[
-                11
-            ].self_attn.fp32_attention = False
-
-        self.feat_num = self.get_feat_num()
-        self.feature_weight = nn.Parameter(torch.zeros(self.feat_num))
-
-        self.instance_norm = nn.InstanceNorm1d(feat_dim)
-        # self.channels = [channels] * 4 + [channels * 3]
-        self.channels = [channels] * 4 + [1536]
-
-        self.layer1 = Conv1dReluBn(feat_dim, self.channels[0], kernel_size=5, padding=2)
-        self.layer2 = SE_Res2Block(
-            self.channels[0],
-            self.channels[1],
-            kernel_size=3,
-            stride=1,
-            padding=2,
-            dilation=2,
-            scale=8,
-            se_bottleneck_dim=128,
-        )
-        self.layer3 = SE_Res2Block(
-            self.channels[1],
-            self.channels[2],
-            kernel_size=3,
-            stride=1,
-            padding=3,
-            dilation=3,
-            scale=8,
-            se_bottleneck_dim=128,
-        )
-        self.layer4 = SE_Res2Block(
-            self.channels[2],
-            self.channels[3],
-            kernel_size=3,
-            stride=1,
-            padding=4,
-            dilation=4,
-            scale=8,
-            se_bottleneck_dim=128,
-        )
-
-        # self.conv = nn.Conv1d(self.channels[-1], self.channels[-1], kernel_size=1)
-        cat_channels = channels * 3
-        self.conv = nn.Conv1d(cat_channels, self.channels[-1], kernel_size=1)
-        self.pooling = AttentiveStatsPool(
-            self.channels[-1],
-            attention_channels=128,
-            global_context_att=global_context_att,
-        )
-        self.bn = nn.BatchNorm1d(self.channels[-1] * 2)
-        self.linear = nn.Linear(self.channels[-1] * 2, emb_dim)
-
-    def get_feat_num(self):
-        self.feature_extract.eval()
-        wav = [torch.randn(self.sr).to(next(self.feature_extract.parameters()).device)]
-        with torch.no_grad():
-            features = self.feature_extract(wav)
-        select_feature = features["hidden_states"]
-        if isinstance(select_feature, (list, tuple)):
-            return len(select_feature)
-        else:
-            return 1
-
-    def get_feat(self, x):
-        with torch.no_grad():
-            x = self.feature_extract([sample for sample in x])
-
-        x = x["hidden_states"]
-        if isinstance(x, (list, tuple)):
-            x = torch.stack(x, dim=0)
-        else:
-            x = x.unsqueeze(0)
-        norm_weights = (
-            F.softmax(self.feature_weight, dim=-1)
-            .unsqueeze(-1)
-            .unsqueeze(-1)
-            .unsqueeze(-1)
-        )
-        x = (norm_weights * x).sum(dim=0)
-        x = torch.transpose(x, 1, 2) + 1e-6
-
-        x = self.instance_norm(x)
-        return x
-
-    def forward(self, x):
-        x = self.get_feat(x)
-
-        out1 = self.layer1(x)
-        out2 = self.layer2(out1)
-        out3 = self.layer3(out2)
-        out4 = self.layer4(out3)
-
-        out = torch.cat([out2, out3, out4], dim=1)
-        out = F.relu(self.conv(out))
-        out = self.bn(self.pooling(out))
-        out = self.linear(out)
-
-        return out
-
-
-if __name__ == "__main__":
-    parser = get_parser()
-    args = parser.parse_args()
-    SIM = SpeakerSimilarity(
-        sv_model_path=args.sv_model_path, ssl_model_path=args.ssl_model_path
-    )
-    score = SIM.score(args.eval_path, args.test_list)
-    logging.info(f"SIM-o score: {score:.3f}")
diff --git a/egs/zipvoice/local/evaluate_utmos.py b/egs/zipvoice/local/evaluate_utmos.py
deleted file mode 100644
index 369e139c1..000000000
--- a/egs/zipvoice/local/evaluate_utmos.py
+++ /dev/null
@@ -1,294 +0,0 @@
-"""
-Calculate UTMOS score with automatic Mean Opinion Score (MOS) prediction system
-adapted from https://huggingface.co/spaces/sarulab-speech/UTMOS-demo
-
-# Download model checkpoints
-wget https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/epoch%3D3-step%3D7459.ckpt -P model/huggingface/utmos/utmos.pt
-wget https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/wav2vec_small.pt -P model/huggingface/utmos/wav2vec_small.pt
-"""
-
-import argparse
-import logging
-import os
-
-import fairseq
-import librosa
-import numpy as np
-import pytorch_lightning as pl
-import soundfile as sf
-import torch
-import torch.nn as nn
-from tqdm import tqdm
-
-logging.basicConfig(level=logging.INFO)
-
-
-def get_parser():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--wav-path", type=str, help="path of the evaluated speech directory"
-    )
-    parser.add_argument(
-        "--utmos-model-path",
-        type=str,
-        default="model/huggingface/utmos/utmos.pt",
-        help="path of the UTMOS model",
-    )
-    parser.add_argument(
-        "--ssl-model-path",
-        type=str,
-        default="model/huggingface/utmos/wav2vec_small.pt",
-        help="path of the wav2vec SSL model",
-    )
-    return parser
-
-
-class UTMOSScore:
-    """Predicting score for each audio clip."""
-
-    def __init__(self, utmos_model_path, ssl_model_path):
-        self.sample_rate = 16000
-        self.device = (
-            torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-        )
-        self.model = (
-            BaselineLightningModule.load_from_checkpoint(
-                utmos_model_path, ssl_model_path=ssl_model_path
-            )
-            .eval()
-            .to(self.device)
-        )
-
-    def score(self, wavs: torch.Tensor) -> torch.Tensor:
-        """
-        Args:
-            wavs: waveforms to be evaluated. When len(wavs) == 1 or 2,
-                the model processes the input as a single audio clip. The model
-                performs batch processing when len(wavs) == 3.
-        """
-        if len(wavs.shape) == 1:
-            out_wavs = wavs.unsqueeze(0).unsqueeze(0)
-        elif len(wavs.shape) == 2:
-            out_wavs = wavs.unsqueeze(0)
-        elif len(wavs.shape) == 3:
-            out_wavs = wavs
-        else:
-            raise ValueError("Dimension of input tensor needs to be <= 3.")
-        bs = out_wavs.shape[0]
-        batch = {
-            "wav": out_wavs,
-            "domains": torch.zeros(bs, dtype=torch.int).to(self.device),
-            "judge_id": torch.ones(bs, dtype=torch.int).to(self.device) * 288,
-        }
-        with torch.no_grad():
-            output = self.model(batch)
-
-        return output.mean(dim=1).squeeze(1).cpu().detach() * 2 + 3
-
-    def score_dir(self, dir, dtype="float32"):
-        def _load_speech_task(fname, sample_rate):
-
-            wav_data, sr = sf.read(fname, dtype=dtype)
-            if sr != sample_rate:
-                wav_data = librosa.resample(
-                    wav_data, orig_sr=sr, target_sr=self.sample_rate
-                )
-            wav_data = torch.from_numpy(wav_data)
-
-            return wav_data
-
-        score_lst = []
-        for fname in tqdm(os.listdir(dir)):
-            speech = _load_speech_task(os.path.join(dir, fname), self.sample_rate)
-            speech = speech.to(self.device)
-            with torch.no_grad():
-                score = self.score(speech)
-            score_lst.append(score.item())
-        return np.mean(score_lst)
-
-
-def load_ssl_model(ckpt_path="wav2vec_small.pt"):
-    SSL_OUT_DIM = 768
-    model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task(
-        [ckpt_path]
-    )
-    ssl_model = model[0]
-    ssl_model.remove_pretraining_modules()
-    return SSL_model(ssl_model, SSL_OUT_DIM)
-
-
-class BaselineLightningModule(pl.LightningModule):
-    def __init__(self, ssl_model_path):
-        super().__init__()
-        self.construct_model(ssl_model_path)
-        self.save_hyperparameters()
-
-    def construct_model(self, ssl_model_path):
-        self.feature_extractors = nn.ModuleList(
-            [
-                load_ssl_model(ckpt_path=ssl_model_path),
-                DomainEmbedding(3, 128),
-            ]
-        )
-        output_dim = sum(
-            [
-                feature_extractor.get_output_dim()
-                for feature_extractor in self.feature_extractors
-            ]
-        )
-        output_layers = [
-            LDConditioner(judge_dim=128, num_judges=3000, input_dim=output_dim)
-        ]
-        output_dim = output_layers[-1].get_output_dim()
-        output_layers.append(
-            Projection(
-                hidden_dim=2048,
-                activation=torch.nn.ReLU(),
-                range_clipping=False,
-                input_dim=output_dim,
-            )
-        )
-
-        self.output_layers = nn.ModuleList(output_layers)
-
-    def forward(self, inputs):
-        outputs = {}
-        for feature_extractor in self.feature_extractors:
-            outputs.update(feature_extractor(inputs))
-        x = outputs
-        for output_layer in self.output_layers:
-            x = output_layer(x, inputs)
-        return x
-
-
-class SSL_model(nn.Module):
-    def __init__(self, ssl_model, ssl_out_dim) -> None:
-        super(SSL_model, self).__init__()
-        self.ssl_model, self.ssl_out_dim = ssl_model, ssl_out_dim
-
-    def forward(self, batch):
-        wav = batch["wav"]
-        wav = wav.squeeze(1)  # [batches, wav_len]
-        res = self.ssl_model(wav, mask=False, features_only=True)
-        x = res["x"]
-        return {"ssl-feature": x}
-
-    def get_output_dim(self):
-        return self.ssl_out_dim
-
-
-class DomainEmbedding(nn.Module):
-    def __init__(self, n_domains, domain_dim) -> None:
-        super().__init__()
-        self.embedding = nn.Embedding(n_domains, domain_dim)
-        self.output_dim = domain_dim
-
-    def forward(self, batch):
-        return {"domain-feature": self.embedding(batch["domains"])}
-
-    def get_output_dim(self):
-        return self.output_dim
-
-
-class LDConditioner(nn.Module):
-    """
-    Conditions ssl output by listener embedding
-    """
-
-    def __init__(self, input_dim, judge_dim, num_judges=None):
-        super().__init__()
-        self.input_dim = input_dim
-        self.judge_dim = judge_dim
-        self.num_judges = num_judges
-        assert num_judges != None
-        self.judge_embedding = nn.Embedding(num_judges, self.judge_dim)
-        # concat [self.output_layer, phoneme features]
-
-        self.decoder_rnn = nn.LSTM(
-            input_size=self.input_dim + self.judge_dim,
-            hidden_size=512,
-            num_layers=1,
-            batch_first=True,
-            bidirectional=True,
-        )  # linear?
-        self.out_dim = self.decoder_rnn.hidden_size * 2
-
-    def get_output_dim(self):
-        return self.out_dim
-
-    def forward(self, x, batch):
-        judge_ids = batch["judge_id"]
-        if "phoneme-feature" in x.keys():
-            concatenated_feature = torch.cat(
-                (
-                    x["ssl-feature"],
-                    x["phoneme-feature"]
-                    .unsqueeze(1)
-                    .expand(-1, x["ssl-feature"].size(1), -1),
-                ),
-                dim=2,
-            )
-        else:
-            concatenated_feature = x["ssl-feature"]
-        if "domain-feature" in x.keys():
-            concatenated_feature = torch.cat(
-                (
-                    concatenated_feature,
-                    x["domain-feature"]
-                    .unsqueeze(1)
-                    .expand(-1, concatenated_feature.size(1), -1),
-                ),
-                dim=2,
-            )
-        if judge_ids != None:
-            concatenated_feature = torch.cat(
-                (
-                    concatenated_feature,
-                    self.judge_embedding(judge_ids)
-                    .unsqueeze(1)
-                    .expand(-1, concatenated_feature.size(1), -1),
-                ),
-                dim=2,
-            )
-            decoder_output, (h, c) = self.decoder_rnn(concatenated_feature)
-        return decoder_output
-
-
-class Projection(nn.Module):
-    def __init__(self, input_dim, hidden_dim, activation, range_clipping=False):
-        super(Projection, self).__init__()
-        self.range_clipping = range_clipping
-        output_dim = 1
-        if range_clipping:
-            self.proj = nn.Tanh()
-
-        self.net = nn.Sequential(
-            nn.Linear(input_dim, hidden_dim),
-            activation,
-            nn.Dropout(0.3),
-            nn.Linear(hidden_dim, output_dim),
-        )
-        self.output_dim = output_dim
-
-    def forward(self, x, batch):
-        output = self.net(x)
-
-        # range clipping
-        if self.range_clipping:
-            return self.proj(output) * 2.0 + 3
-        else:
-            return output
-
-    def get_output_dim(self):
-        return self.output_dim
-
-
-if __name__ == "__main__":
-    parser = get_parser()
-    args = parser.parse_args()
-    UTMOS = UTMOSScore(
-        utmos_model_path=args.utmos_model_path, ssl_model_path=args.ssl_model_path
-    )
-    score = UTMOS.score_dir(args.wav_path)
-    logging.info(f"UTMOS score: {score:.2f}")
diff --git a/egs/zipvoice/local/evaluate_wer_hubert.py b/egs/zipvoice/local/evaluate_wer_hubert.py
deleted file mode 100644
index d30346e67..000000000
--- a/egs/zipvoice/local/evaluate_wer_hubert.py
+++ /dev/null
@@ -1,172 +0,0 @@
-"""
-Calculate WER with Hubert models.
-"""
-import argparse
-import os
-import re
-from pathlib import Path
-
-import librosa
-import numpy as np
-import soundfile as sf
-import torch
-from jiwer import compute_measures
-from tqdm import tqdm
-from transformers import pipeline
-
-
-def get_parser():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--wav-path", type=str, help="path of the speech directory")
-    parser.add_argument(
-        "--decode-path",
-        type=str,
-        default=None,
-        help="path of the output file of WER information",
-    )
-    parser.add_argument(
-        "--model-path",
-        type=str,
-        default=None,
-        help="path of the local hubert model, e.g., model/huggingface/hubert-large-ls960-ft",
-    )
-    parser.add_argument(
-        "--test-list",
-        type=str,
-        default="test.tsv",
-        help="path of the transcript tsv file, where the first column "
-        "is the wav name and the last column is the transcript",
-    )
-    parser.add_argument(
-        "--batch-size", type=int, default=16, help="decoding batch size"
-    )
-    return parser
-
-
-def post_process(text: str):
-    text = text.replace("‘", "'")
-    text = text.replace("’", "'")
-    text = re.sub(r"[^a-zA-Z0-9']", " ", text.lower())
-    text = re.sub(r"\s+", " ", text)
-    text = text.strip()
-    return text
-
-
-def process_one(hypo, truth):
-    truth = post_process(truth)
-    hypo = post_process(hypo)
-
-    measures = compute_measures(truth, hypo)
-    word_num = len(truth.split(" "))
-    wer = measures["wer"]
-    subs = measures["substitutions"]
-    dele = measures["deletions"]
-    inse = measures["insertions"]
-    return (truth, hypo, wer, subs, dele, inse, word_num)
-
-
-class SpeechEvalDataset(torch.utils.data.Dataset):
-    def __init__(self, wav_path: str, test_list: str):
-        super().__init__()
-        self.wav_name = []
-        self.wav_paths = []
-        self.transcripts = []
-        with Path(test_list).open("r", encoding="utf8") as f:
-            meta = [item.split("\t") for item in f.read().rstrip().split("\n")]
-        for item in meta:
-            self.wav_name.append(item[0])
-            self.wav_paths.append(Path(wav_path, item[0] + ".wav"))
-            self.transcripts.append(item[-1])
-
-    def __len__(self):
-        return len(self.wav_paths)
-
-    def __getitem__(self, index: int):
-        wav, sampling_rate = sf.read(self.wav_paths[index])
-        item = {
-            "array": librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000),
-            "sampling_rate": 16000,
-            "reference": self.transcripts[index],
-            "wav_name": self.wav_name[index],
-        }
-        return item
-
-
-def main(test_list, wav_path, model_path, decode_path, batch_size, device):
-
-    if model_path is not None:
-        pipe = pipeline(
-            "automatic-speech-recognition",
-            model=model_path,
-            device=device,
-            tokenizer=model_path,
-        )
-    else:
-        pipe = pipeline(
-            "automatic-speech-recognition",
-            model="facebook/hubert-large-ls960-ft",
-            device=device,
-        )
-
-    dataset = SpeechEvalDataset(wav_path, test_list)
-
-    bar = tqdm(
-        pipe(
-            dataset,
-            generate_kwargs={"language": "english", "task": "transcribe"},
-            batch_size=batch_size,
-        ),
-        total=len(dataset),
-    )
-
-    wers = []
-    inses = []
-    deles = []
-    subses = []
-    word_nums = 0
-    if decode_path:
-        decode_dir = os.path.dirname(decode_path)
-        if not os.path.exists(decode_dir):
-            os.makedirs(decode_dir)
-        fout = open(decode_path, "w")
-    for out in bar:
-        wav_name = out["wav_name"][0]
-        transcription = post_process(out["text"].strip())
-        text_ref = post_process(out["reference"][0].strip())
-        truth, hypo, wer, subs, dele, inse, word_num = process_one(
-            transcription, text_ref
-        )
-        if decode_path:
-            fout.write(f"{wav_name}\t{wer}\t{truth}\t{hypo}\t{inse}\t{dele}\t{subs}\n")
-        wers.append(float(wer))
-        inses.append(float(inse))
-        deles.append(float(dele))
-        subses.append(float(subs))
-        word_nums += word_num
-
-    wer = round((np.sum(subses) + np.sum(deles) + np.sum(inses)) / word_nums * 100, 3)
-    subs = round(np.mean(subses) * 100, 3)
-    dele = round(np.mean(deles) * 100, 3)
-    inse = round(np.mean(inses) * 100, 3)
-    print(f"WER: {wer}%\n")
-    if decode_path:
-        fout.write(f"WER: {wer}%\n")
-        fout.flush()
-
-
-if __name__ == "__main__":
-    parser = get_parser()
-    args = parser.parse_args()
-    if torch.cuda.is_available():
-        device = torch.device("cuda", 0)
-    else:
-        device = torch.device("cpu")
-    main(
-        args.test_list,
-        args.wav_path,
-        args.model_path,
-        args.decode_path,
-        args.batch_size,
-        device,
-    )
diff --git a/egs/zipvoice/local/evaluate_wer_seedtts.py b/egs/zipvoice/local/evaluate_wer_seedtts.py
deleted file mode 100644
index f7e256387..000000000
--- a/egs/zipvoice/local/evaluate_wer_seedtts.py
+++ /dev/null
@@ -1,181 +0,0 @@
-"""
-Calculate WER with Whisper-large-v3 or Paraformer models, 
-following Seed-TTS https://github.com/BytedanceSpeech/seed-tts-eval
-"""
-
-import argparse
-import os
-import string
-
-import numpy as np
-import scipy
-import soundfile as sf
-import torch
-import zhconv
-from funasr import AutoModel
-from jiwer import compute_measures
-from tqdm import tqdm
-from transformers import WhisperForConditionalGeneration, WhisperProcessor
-from zhon.hanzi import punctuation
-
-
-def get_parser():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument("--wav-path", type=str, help="path of the speech directory")
-    parser.add_argument(
-        "--decode-path",
-        type=str,
-        default=None,
-        help="path of the output file of WER information",
-    )
-    parser.add_argument(
-        "--model-path",
-        type=str,
-        default=None,
-        help="path of the local whisper and paraformer model, "
-        "e.g., whisper: model/huggingface/whisper-large-v3/, "
-        "paraformer: model/huggingface/paraformer-zh/",
-    )
-    parser.add_argument(
-        "--test-list",
-        type=str,
-        default="test.tsv",
-        help="path of the transcript tsv file, where the first column "
-        "is the wav name and the last column is the transcript",
-    )
-    parser.add_argument("--lang", type=str, help="decoded language, zh or en")
-    return parser
-
-
-def load_en_model(model_path):
-    if model_path is None:
-        model_path = "openai/whisper-large-v3"
-    processor = WhisperProcessor.from_pretrained(model_path)
-    model = WhisperForConditionalGeneration.from_pretrained(model_path)
-    return processor, model
-
-
-def load_zh_model(model_path):
-    if model_path is None:
-        model_path = "paraformer-zh"
-    model = AutoModel(model=model_path)
-    return model
-
-
-def process_one(hypo, truth, lang):
-    punctuation_all = punctuation + string.punctuation
-    for x in punctuation_all:
-        if x == "'":
-            continue
-        truth = truth.replace(x, "")
-        hypo = hypo.replace(x, "")
-
-    truth = truth.replace("  ", " ")
-    hypo = hypo.replace("  ", " ")
-
-    if lang == "zh":
-        truth = " ".join([x for x in truth])
-        hypo = " ".join([x for x in hypo])
-    elif lang == "en":
-        truth = truth.lower()
-        hypo = hypo.lower()
-    else:
-        raise NotImplementedError
-
-    measures = compute_measures(truth, hypo)
-    word_num = len(truth.split(" "))
-    wer = measures["wer"]
-    subs = measures["substitutions"]
-    dele = measures["deletions"]
-    inse = measures["insertions"]
-    return (truth, hypo, wer, subs, dele, inse, word_num)
-
-
-def main(test_list, wav_path, model_path, decode_path, lang, device):
-    if lang == "en":
-        processor, model = load_en_model(model_path)
-        model.to(device)
-    elif lang == "zh":
-        model = load_zh_model(model_path)
-    params = []
-    for line in open(test_list).readlines():
-        line = line.strip()
-        items = line.split("\t")
-        wav_name, text_ref = items[0], items[-1]
-        file_path = os.path.join(wav_path, wav_name + ".wav")
-        assert os.path.exists(file_path), f"{file_path}"
-
-        params.append((file_path, text_ref))
-    wers = []
-    inses = []
-    deles = []
-    subses = []
-    word_nums = 0
-    if decode_path:
-        decode_dir = os.path.dirname(decode_path)
-        if not os.path.exists(decode_dir):
-            os.makedirs(decode_dir)
-        fout = open(decode_path, "w")
-    for wav_path, text_ref in tqdm(params):
-        if lang == "en":
-            wav, sr = sf.read(wav_path)
-            if sr != 16000:
-                wav = scipy.signal.resample(wav, int(len(wav) * 16000 / sr))
-            input_features = processor(
-                wav, sampling_rate=16000, return_tensors="pt"
-            ).input_features
-            input_features = input_features.to(device)
-            forced_decoder_ids = processor.get_decoder_prompt_ids(
-                language="english", task="transcribe"
-            )
-            predicted_ids = model.generate(
-                input_features, forced_decoder_ids=forced_decoder_ids
-            )
-            transcription = processor.batch_decode(
-                predicted_ids, skip_special_tokens=True
-            )[0]
-        elif lang == "zh":
-            res = model.generate(input=wav_path, batch_size_s=300, disable_pbar=True)
-            transcription = res[0]["text"]
-            transcription = zhconv.convert(transcription, "zh-cn")
-
-        truth, hypo, wer, subs, dele, inse, word_num = process_one(
-            transcription, text_ref, lang
-        )
-        if decode_path:
-            fout.write(f"{wav_path}\t{wer}\t{truth}\t{hypo}\t{inse}\t{dele}\t{subs}\n")
-        wers.append(float(wer))
-        inses.append(float(inse))
-        deles.append(float(dele))
-        subses.append(float(subs))
-        word_nums += word_num
-
-    wer_avg = round(np.mean(wers) * 100, 3)
-    wer = round((np.sum(subses) + np.sum(deles) + np.sum(inses)) / word_nums * 100, 3)
-    subs = round(np.mean(subses) * 100, 3)
-    dele = round(np.mean(deles) * 100, 3)
-    inse = round(np.mean(inses) * 100, 3)
-    print(f"Seed-TTS WER: {wer_avg}%\n")
-    print(f"WER: {wer}%\n")
-    if decode_path:
-        fout.write(f"SeedTTS WER: {wer_avg}%\n")
-        fout.write(f"WER: {wer}%\n")
-        fout.flush()
-
-
-if __name__ == "__main__":
-    parser = get_parser()
-    args = parser.parse_args()
-    if torch.cuda.is_available():
-        device = torch.device("cuda", 0)
-    else:
-        device = torch.device("cpu")
-    main(
-        args.test_list,
-        args.wav_path,
-        args.model_path,
-        args.decode_path,
-        args.lang,
-        device,
-    )
diff --git a/egs/zipvoice/local/feature.py b/egs/zipvoice/local/feature.py
deleted file mode 120000
index 08ef7d228..000000000
--- a/egs/zipvoice/local/feature.py
+++ /dev/null
@@ -1 +0,0 @@
-../zipvoice/feature.py
\ No newline at end of file
diff --git a/egs/zipvoice/local/pinyin.txt b/egs/zipvoice/local/pinyin.txt
deleted file mode 100644
index cd8d14dc3..000000000
--- a/egs/zipvoice/local/pinyin.txt
+++ /dev/null
@@ -1,1550 +0,0 @@
-a
-a1
-a2
-a3
-a4
-ai1
-ai2
-ai3
-ai4
-an1
-an2
-an3
-an4
-ang1
-ang2
-ang3
-ang4
-ao1
-ao2
-ao3
-ao4
-ba
-ba1
-ba2
-ba3
-ba4
-bai
-bai1
-bai2
-bai3
-bai4
-ban
-ban1
-ban3
-ban4
-bang1
-bang3
-bang4
-bao1
-bao2
-bao3
-bao4
-bei
-bei1
-bei3
-bei4
-ben1
-ben3
-ben4
-beng
-beng1
-beng2
-beng3
-beng4
-bi1
-bi2
-bi3
-bi4
-bian
-bian1
-bian3
-bian4
-biang2
-biao1
-biao3
-biao4
-bie1
-bie2
-bie3
-bie4
-bin
-bin1
-bin3
-bin4
-bing1
-bing3
-bing4
-bo
-bo1
-bo2
-bo3
-bo4
-bu1
-bu2
-bu3
-bu4
-ca1
-ca3
-ca4
-cai1
-cai2
-cai3
-cai4
-can1
-can2
-can3
-can4
-cang1
-cang2
-cang3
-cang4
-cao1
-cao2
-cao3
-cao4
-ce4
-cei4
-cen1
-cen2
-ceng1
-ceng2
-ceng4
-cha1
-cha2
-cha3
-cha4
-chai1
-chai2
-chai3
-chai4
-chan1
-chan2
-chan3
-chan4
-chang
-chang1
-chang2
-chang3
-chang4
-chao1
-chao2
-chao3
-chao4
-che1
-che2
-che3
-che4
-chen
-chen1
-chen2
-chen3
-chen4
-cheng1
-cheng2
-cheng3
-cheng4
-chi
-chi1
-chi2
-chi3
-chi4
-chong1
-chong2
-chong3
-chong4
-chou1
-chou2
-chou3
-chou4
-chu
-chu1
-chu2
-chu3
-chu4
-chua1
-chua3
-chua4
-chuai1
-chuai2
-chuai3
-chuai4
-chuan1
-chuan2
-chuan3
-chuan4
-chuang1
-chuang2
-chuang3
-chuang4
-chui1
-chui2
-chui3
-chui4
-chun1
-chun2
-chun3
-chuo1
-chuo4
-ci1
-ci2
-ci3
-ci4
-cong1
-cong2
-cong3
-cong4
-cou1
-cou2
-cou3
-cou4
-cu1
-cu2
-cu3
-cu4
-cuan1
-cuan2
-cuan4
-cui
-cui1
-cui3
-cui4
-cun1
-cun2
-cun3
-cun4
-cuo1
-cuo2
-cuo3
-cuo4
-da
-da1
-da2
-da3
-da4
-dai
-dai1
-dai3
-dai4
-dan1
-dan3
-dan4
-dang
-dang1
-dang3
-dang4
-dao1
-dao2
-dao3
-dao4
-de
-de1
-de2
-dei1
-dei3
-den4
-deng1
-deng3
-deng4
-di1
-di2
-di3
-di4
-dia3
-dian1
-dian2
-dian3
-dian4
-diao1
-diao3
-diao4
-die1
-die2
-die3
-die4
-din4
-ding1
-ding3
-ding4
-diu1
-dong1
-dong3
-dong4
-dou1
-dou3
-dou4
-du1
-du2
-du3
-du4
-duan1
-duan3
-duan4
-dui1
-dui3
-dui4
-dun1
-dun3
-dun4
-duo
-duo1
-duo2
-duo3
-duo4
-e
-e1
-e2
-e3
-e4
-ei1
-ei2
-ei3
-ei4
-en1
-en3
-en4
-eng1
-er
-er2
-er3
-er4
-fa
-fa1
-fa2
-fa3
-fa4
-fan1
-fan2
-fan3
-fan4
-fang
-fang1
-fang2
-fang3
-fang4
-fei1
-fei2
-fei3
-fei4
-fen1
-fen2
-fen3
-fen4
-feng1
-feng2
-feng3
-feng4
-fiao4
-fo2
-fou1
-fou2
-fou3
-fu
-fu1
-fu2
-fu3
-fu4
-ga1
-ga2
-ga3
-ga4
-gai1
-gai3
-gai4
-gan1
-gan3
-gan4
-gang1
-gang3
-gang4
-gao1
-gao3
-gao4
-ge1
-ge2
-ge3
-ge4
-gei3
-gen1
-gen2
-gen3
-gen4
-geng1
-geng3
-geng4
-gong
-gong1
-gong3
-gong4
-gou1
-gou3
-gou4
-gu
-gu1
-gu2
-gu3
-gu4
-gua1
-gua2
-gua3
-gua4
-guai1
-guai3
-guai4
-guan1
-guan3
-guan4
-guang
-guang1
-guang3
-guang4
-gui1
-gui3
-gui4
-gun3
-gun4
-guo
-guo1
-guo2
-guo3
-guo4
-ha1
-ha2
-ha3
-ha4
-hai
-hai1
-hai2
-hai3
-hai4
-han
-han1
-han2
-han3
-han4
-hang1
-hang2
-hang3
-hang4
-hao1
-hao2
-hao3
-hao4
-he1
-he2
-he3
-he4
-hei1
-hen1
-hen2
-hen3
-hen4
-heng1
-heng2
-heng4
-hm
-hng
-hong1
-hong2
-hong3
-hong4
-hou1
-hou2
-hou3
-hou4
-hu
-hu1
-hu2
-hu3
-hu4
-hua1
-hua2
-hua4
-huai
-huai2
-huai4
-huan1
-huan2
-huan3
-huan4
-huang
-huang1
-huang2
-huang3
-huang4
-hui
-hui1
-hui2
-hui3
-hui4
-hun1
-hun2
-hun3
-hun4
-huo
-huo1
-huo2
-huo3
-huo4
-ji1
-ji2
-ji3
-ji4
-jia
-jia1
-jia2
-jia3
-jia4
-jian
-jian1
-jian3
-jian4
-jiang
-jiang1
-jiang3
-jiang4
-jiao
-jiao1
-jiao2
-jiao3
-jiao4
-jie
-jie1
-jie2
-jie3
-jie4
-jin1
-jin3
-jin4
-jing
-jing1
-jing3
-jing4
-jiong1
-jiong3
-jiong4
-jiu
-jiu1
-jiu2
-jiu3
-jiu4
-ju
-ju1
-ju2
-ju3
-ju4
-juan1
-juan3
-juan4
-jue1
-jue2
-jue3
-jue4
-jun1
-jun3
-jun4
-ka1
-ka3
-kai1
-kai3
-kai4
-kan1
-kan3
-kan4
-kang1
-kang2
-kang3
-kang4
-kao1
-kao3
-kao4
-ke
-ke1
-ke2
-ke3
-ke4
-kei1
-ken1
-ken3
-ken4
-keng1
-keng3
-kong1
-kong3
-kong4
-kou1
-kou3
-kou4
-ku1
-ku2
-ku3
-ku4
-kua1
-kua3
-kua4
-kuai3
-kuai4
-kuan1
-kuan3
-kuang1
-kuang2
-kuang3
-kuang4
-kui1
-kui2
-kui3
-kui4
-kun
-kun1
-kun3
-kun4
-kuo4
-la
-la1
-la2
-la3
-la4
-lai2
-lai3
-lai4
-lan2
-lan3
-lan4
-lang
-lang1
-lang2
-lang3
-lang4
-lao
-lao1
-lao2
-lao3
-lao4
-le
-le1
-le4
-lei
-lei1
-lei2
-lei3
-lei4
-len4
-leng1
-leng2
-leng3
-leng4
-li
-li1
-li2
-li3
-li4
-lia3
-lian2
-lian3
-lian4
-liang
-liang2
-liang3
-liang4
-liao1
-liao2
-liao3
-liao4
-lie
-lie1
-lie2
-lie3
-lie4
-lin1
-lin2
-lin3
-lin4
-ling
-ling1
-ling2
-ling3
-ling4
-liu1
-liu2
-liu3
-liu4
-lo
-long1
-long2
-long3
-long4
-lou
-lou1
-lou2
-lou3
-lou4
-lu
-lu1
-lu2
-lu3
-lu4
-luan2
-luan3
-luan4
-lun1
-lun2
-lun3
-lun4
-luo
-luo1
-luo2
-luo3
-luo4
-lv2
-lv3
-lv4
-lve3
-lve4
-m1
-m2
-m4
-ma
-ma1
-ma2
-ma3
-ma4
-mai2
-mai3
-mai4
-man1
-man2
-man3
-man4
-mang1
-mang2
-mang3
-mang4
-mao1
-mao2
-mao3
-mao4
-me
-me1
-mei2
-mei3
-mei4
-men
-men1
-men2
-men4
-meng
-meng1
-meng2
-meng3
-meng4
-mi1
-mi2
-mi3
-mi4
-mian2
-mian3
-mian4
-miao1
-miao2
-miao3
-miao4
-mie
-mie1
-mie2
-mie4
-min
-min2
-min3
-ming
-ming2
-ming3
-ming4
-miu3
-miu4
-mo
-mo1
-mo2
-mo3
-mo4
-mou1
-mou2
-mou3
-mou4
-mu2
-mu3
-mu4
-n
-n2
-n3
-n4
-na
-na1
-na2
-na3
-na4
-nai2
-nai3
-nai4
-nan1
-nan2
-nan3
-nan4
-nang
-nang1
-nang2
-nang3
-nang4
-nao1
-nao2
-nao3
-nao4
-ne
-ne2
-ne4
-nei2
-nei3
-nei4
-nen4
-neng2
-neng3
-neng4
-ng
-ng2
-ng3
-ng4
-ni1
-ni2
-ni3
-ni4
-nia1
-nian1
-nian2
-nian3
-nian4
-niang2
-niang3
-niang4
-niao3
-niao4
-nie1
-nie2
-nie3
-nie4
-nin
-nin2
-nin3
-ning2
-ning3
-ning4
-niu1
-niu2
-niu3
-niu4
-nong2
-nong3
-nong4
-nou2
-nou3
-nou4
-nu2
-nu3
-nu4
-nuan2
-nuan3
-nuan4
-nun2
-nun4
-nuo2
-nuo3
-nuo4
-nv2
-nv3
-nv4
-nve4
-o
-o1
-o2
-o3
-o4
-ou
-ou1
-ou2
-ou3
-ou4
-pa1
-pa2
-pa3
-pa4
-pai1
-pai2
-pai3
-pai4
-pan1
-pan2
-pan3
-pan4
-pang1
-pang2
-pang3
-pang4
-pao1
-pao2
-pao3
-pao4
-pei1
-pei2
-pei3
-pei4
-pen1
-pen2
-pen3
-pen4
-peng1
-peng2
-peng3
-peng4
-pi1
-pi2
-pi3
-pi4
-pian1
-pian2
-pian3
-pian4
-piao1
-piao2
-piao3
-piao4
-pie1
-pie3
-pie4
-pin1
-pin2
-pin3
-pin4
-ping1
-ping2
-ping3
-ping4
-po
-po1
-po2
-po3
-po4
-pou1
-pou2
-pou3
-pou4
-pu
-pu1
-pu2
-pu3
-pu4
-qi
-qi1
-qi2
-qi3
-qi4
-qia1
-qia2
-qia3
-qia4
-qian
-qian1
-qian2
-qian3
-qian4
-qiang1
-qiang2
-qiang3
-qiang4
-qiao1
-qiao2
-qiao3
-qiao4
-qie1
-qie2
-qie3
-qie4
-qin1
-qin2
-qin3
-qin4
-qing
-qing1
-qing2
-qing3
-qing4
-qiong1
-qiong2
-qiong4
-qiu1
-qiu2
-qiu3
-qiu4
-qu
-qu1
-qu2
-qu3
-qu4
-quan
-quan1
-quan2
-quan3
-quan4
-que1
-que2
-que4
-qun1
-qun2
-qun3
-ran2
-ran3
-ran4
-rang1
-rang2
-rang3
-rang4
-rao2
-rao3
-rao4
-re2
-re3
-re4
-ren2
-ren3
-ren4
-reng1
-reng2
-reng4
-ri4
-rong
-rong1
-rong2
-rong3
-rong4
-rou2
-rou3
-rou4
-ru
-ru2
-ru3
-ru4
-rua2
-ruan2
-ruan3
-ruan4
-rui2
-rui3
-rui4
-run2
-run3
-run4
-ruo2
-ruo4
-sa
-sa1
-sa3
-sa4
-sai1
-sai3
-sai4
-san
-san1
-san3
-san4
-sang1
-sang3
-sang4
-sao1
-sao3
-sao4
-se1
-se4
-sen1
-sen3
-seng1
-seng4
-sha
-sha1
-sha2
-sha3
-sha4
-shai1
-shai3
-shai4
-shan1
-shan2
-shan3
-shan4
-shang
-shang1
-shang3
-shang4
-shao1
-shao2
-shao3
-shao4
-she1
-she2
-she3
-she4
-shei2
-shen1
-shen2
-shen3
-shen4
-sheng1
-sheng2
-sheng3
-sheng4
-shi
-shi1
-shi2
-shi3
-shi4
-shou
-shou1
-shou2
-shou3
-shou4
-shu1
-shu2
-shu3
-shu4
-shua1
-shua3
-shua4
-shuai1
-shuai3
-shuai4
-shuan1
-shuan4
-shuang1
-shuang3
-shuang4
-shui
-shui2
-shui3
-shui4
-shun3
-shun4
-shuo1
-shuo2
-shuo4
-si
-si1
-si2
-si3
-si4
-song1
-song2
-song3
-song4
-sou1
-sou3
-sou4
-su1
-su2
-su3
-su4
-suan1
-suan3
-suan4
-sui1
-sui2
-sui3
-sui4
-sun1
-sun3
-sun4
-suo
-suo1
-suo2
-suo3
-suo4
-ta
-ta1
-ta2
-ta3
-ta4
-tai
-tai1
-tai2
-tai3
-tai4
-tan1
-tan2
-tan3
-tan4
-tang1
-tang2
-tang3
-tang4
-tao1
-tao2
-tao3
-tao4
-te
-te4
-tei1
-teng1
-teng2
-teng4
-ti
-ti1
-ti2
-ti3
-ti4
-tian1
-tian2
-tian3
-tian4
-tiao
-tiao1
-tiao2
-tiao3
-tiao4
-tie1
-tie2
-tie3
-tie4
-ting1
-ting2
-ting3
-ting4
-tong1
-tong2
-tong3
-tong4
-tou
-tou1
-tou2
-tou3
-tou4
-tu
-tu1
-tu2
-tu3
-tu4
-tuan1
-tuan2
-tuan3
-tuan4
-tui1
-tui2
-tui3
-tui4
-tun1
-tun2
-tun3
-tun4
-tuo1
-tuo2
-tuo3
-tuo4
-wa
-wa1
-wa2
-wa3
-wa4
-wai
-wai1
-wai3
-wai4
-wan1
-wan2
-wan3
-wan4
-wang1
-wang2
-wang3
-wang4
-wei
-wei1
-wei2
-wei3
-wei4
-wen
-wen1
-wen2
-wen3
-wen4
-weng1
-weng3
-weng4
-wo1
-wo3
-wo4
-wong4
-wu
-wu1
-wu2
-wu3
-wu4
-xi1
-xi2
-xi3
-xi4
-xia1
-xia2
-xia3
-xia4
-xian
-xian1
-xian2
-xian3
-xian4
-xiang1
-xiang2
-xiang3
-xiang4
-xiao
-xiao1
-xiao2
-xiao3
-xiao4
-xie1
-xie2
-xie3
-xie4
-xin
-xin1
-xin2
-xin3
-xin4
-xing
-xing1
-xing2
-xing3
-xing4
-xiong1
-xiong2
-xiong3
-xiong4
-xiu1
-xiu2
-xiu3
-xiu4
-xu
-xu1
-xu2
-xu3
-xu4
-xuan1
-xuan2
-xuan3
-xuan4
-xue1
-xue2
-xue3
-xue4
-xun1
-xun2
-xun4
-ya
-ya1
-ya2
-ya3
-ya4
-yan1
-yan2
-yan3
-yan4
-yang
-yang1
-yang2
-yang3
-yang4
-yao1
-yao2
-yao3
-yao4
-ye
-ye1
-ye2
-ye3
-ye4
-yi
-yi1
-yi2
-yi3
-yi4
-yin
-yin1
-yin2
-yin3
-yin4
-ying1
-ying2
-ying3
-ying4
-yo
-yo1
-yong1
-yong2
-yong3
-yong4
-you
-you1
-you2
-you3
-you4
-yu
-yu1
-yu2
-yu3
-yu4
-yuan1
-yuan2
-yuan3
-yuan4
-yue1
-yue2
-yue3
-yue4
-yun
-yun1
-yun2
-yun3
-yun4
-za1
-za2
-za3
-za4
-zai1
-zai3
-zai4
-zan
-zan1
-zan2
-zan3
-zan4
-zang1
-zang3
-zang4
-zao1
-zao2
-zao3
-zao4
-ze
-ze2
-ze4
-zei2
-zen
-zen1
-zen3
-zen4
-zeng1
-zeng3
-zeng4
-zha
-zha1
-zha2
-zha3
-zha4
-zhai1
-zhai2
-zhai3
-zhai4
-zhan1
-zhan2
-zhan3
-zhan4
-zhang
-zhang1
-zhang3
-zhang4
-zhao
-zhao1
-zhao2
-zhao3
-zhao4
-zhe
-zhe1
-zhe2
-zhe3
-zhe4
-zhei4
-zhen1
-zhen2
-zhen3
-zhen4
-zheng1
-zheng3
-zheng4
-zhi
-zhi1
-zhi2
-zhi3
-zhi4
-zhong1
-zhong3
-zhong4
-zhou1
-zhou2
-zhou3
-zhou4
-zhu1
-zhu2
-zhu3
-zhu4
-zhua1
-zhua3
-zhuai1
-zhuai3
-zhuai4
-zhuan1
-zhuan2
-zhuan3
-zhuan4
-zhuang1
-zhuang3
-zhuang4
-zhui1
-zhui3
-zhui4
-zhun1
-zhun3
-zhun4
-zhuo
-zhuo1
-zhuo2
-zhuo4
-zi
-zi1
-zi2
-zi3
-zi4
-zong
-zong1
-zong3
-zong4
-zou1
-zou3
-zou4
-zu1
-zu2
-zu3
-zu4
-zuan1
-zuan3
-zuan4
-zui
-zui1
-zui2
-zui3
-zui4
-zun1
-zun2
-zun3
-zun4
-zuo
-zuo1
-zuo2
-zuo3
-zuo4
-ê1
-ê2
-ê3
-ê4
diff --git a/egs/zipvoice/local/prepare_token_file_emilia.py b/egs/zipvoice/local/prepare_token_file_emilia.py
deleted file mode 100644
index 68af8d397..000000000
--- a/egs/zipvoice/local/prepare_token_file_emilia.py
+++ /dev/null
@@ -1,90 +0,0 @@
-#!/usr/bin/env python3
-# Copyright         2024  Xiaomi Corp.        (authors: Zengwei Yao,
-#                                                       Wei Kang)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-"""
-This file generates the file that maps tokens to IDs.
-"""
-
-import argparse
-import logging
-from pathlib import Path
-from typing import List
-
-from piper_phonemize import get_espeak_map
-from pypinyin.contrib.tone_convert import to_finals_tone3, to_initials
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--tokens",
-        type=Path,
-        default=Path("data/tokens_emilia.txt"),
-        help="Path to the dict that maps the text tokens to IDs",
-    )
-
-    parser.add_argument(
-        "--pinyin",
-        type=Path,
-        default=Path("local/pinyin.txt"),
-        help="Path to the all unique pinyin",
-    )
-
-    return parser.parse_args()
-
-
-def get_pinyin_tokens(pinyin: Path) -> List[str]:
-    phones = set()
-    with open(pinyin, "r") as f:
-        for line in f:
-            x = line.strip()
-            initial = to_initials(x, strict=False)
-            # don't want to share tokens with espeak tokens, so use tone3 style
-            finals = to_finals_tone3(x, strict=False, neutral_tone_with_five=True)
-            if initial != "":
-                # don't want to share tokens with espeak tokens, so add a '0' after each initial
-                phones.add(initial + "0")
-            if finals != "":
-                phones.add(finals)
-    return sorted(phones)
-
-
-def get_token2id(args):
-    """Get a dict that maps token to IDs, and save it to the given filename."""
-    all_tokens = get_espeak_map()  # token: [token_id]
-    all_tokens = {token: token_id[0] for token, token_id in all_tokens.items()}
-    # sort by token_id
-    all_tokens = sorted(all_tokens.items(), key=lambda x: x[1])
-
-    all_pinyin = get_pinyin_tokens(args.pinyin)
-    with open(args.tokens, "w", encoding="utf-8") as f:
-        for token, token_id in all_tokens:
-            f.write(f"{token} {token_id}\n")
-        num_espeak_tokens = len(all_tokens)
-        for i, pinyin in enumerate(all_pinyin):
-            f.write(f"{pinyin} {num_espeak_tokens + i}\n")
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-    logging.basicConfig(format=formatter, level=logging.INFO)
-
-    args = get_args()
-    get_token2id(args)
diff --git a/egs/zipvoice/local/prepare_token_file_libritts.py b/egs/zipvoice/local/prepare_token_file_libritts.py
deleted file mode 100644
index 374b02613..000000000
--- a/egs/zipvoice/local/prepare_token_file_libritts.py
+++ /dev/null
@@ -1,31 +0,0 @@
-import re
-from collections import Counter
-
-from lhotse import load_manifest_lazy
-
-
-def prepare_tokens(manifest_file, token_file):
-    counter = Counter()
-    manifest = load_manifest_lazy(manifest_file)
-    for cut in manifest:
-        line = re.sub(r"\s+", " ", cut.supervisions[0].text)
-        counter.update(line)
-
-    unique_chars = set(counter.keys())
-
-    if "_" in unique_chars:
-        unique_chars.remove("_")
-
-    sorted_chars = sorted(unique_chars, key=lambda char: counter[char], reverse=True)
-
-    result = ["_"] + sorted_chars
-
-    with open(token_file, "w", encoding="utf-8") as file:
-        for index, char in enumerate(result):
-            file.write(f"{char} {index}\n")
-
-
-if __name__ == "__main__":
-    manifest_file = "data/fbank_libritts/libritts_cuts_train-all-shuf.jsonl.gz"
-    output_token_file = "data/tokens_libritts.txt"
-    prepare_tokens(manifest_file, output_token_file)
diff --git a/egs/zipvoice/local/preprocess_emilia.py b/egs/zipvoice/local/preprocess_emilia.py
deleted file mode 100644
index 96a7ff228..000000000
--- a/egs/zipvoice/local/preprocess_emilia.py
+++ /dev/null
@@ -1,155 +0,0 @@
-#!/usr/bin/env python3
-# Copyright         2024  Xiaomi Corp.        (authors: Zengwei Yao,
-#                                                       Zengrui Jin,
-#                                                       Wei Kang)
-#                   2024  Tsinghua University (authors: Zengrui Jin,)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-"""
-This file reads the texts in given manifest and save the cleaned new cuts.
-"""
-
-import argparse
-import glob
-import logging
-import os
-from concurrent.futures import ProcessPoolExecutor as Pool
-from pathlib import Path
-from typing import List
-
-from lhotse import CutSet, load_manifest_lazy
-from tokenizer import (
-    is_alphabet,
-    is_chinese,
-    is_hangul,
-    is_japanese,
-    tokenize_by_CJK_char,
-)
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "--subset",
-        type=str,
-        help="Subset of emilia, (ZH, EN, etc.)",
-    )
-
-    parser.add_argument(
-        "--jobs",
-        type=int,
-        default=20,
-        help="Number of jobs to processing.",
-    )
-
-    parser.add_argument(
-        "--source-dir",
-        type=str,
-        default="data/manifests/splits_raw",
-        help="The source directory of manifest files.",
-    )
-
-    parser.add_argument(
-        "--dest-dir",
-        type=str,
-        default="data/manifests/splits",
-        help="The destination directory of manifest files.",
-    )
-
-    return parser.parse_args()
-
-
-def preprocess_emilia(file_name: str, input_dir: Path, output_dir: Path):
-    logging.info(f"Processing {file_name}")
-    if (output_dir / file_name).is_file():
-        logging.info(f"{file_name} exists, skipping.")
-        return
-
-    def _filter_cut(cut):
-        text = cut.supervisions[0].text
-        duration = cut.supervisions[0].duration
-        chinese = []
-        english = []
-
-        # only contains chinese and space and alphabets
-        clean_chars = []
-        for x in text:
-            if is_hangul(x):
-                logging.warning(f"Delete cut with text containing Korean : {text}")
-                return False
-            if is_japanese(x):
-                logging.warning(f"Delete cut with text containing Japanese : {text}")
-                return False
-            if is_chinese(x):
-                chinese.append(x)
-                clean_chars.append(x)
-            if is_alphabet(x):
-                english.append(x)
-                clean_chars.append(x)
-            if x == " ":
-                clean_chars.append(x)
-        if len(english) + len(chinese) == 0:
-            logging.warning(f"Delete cut with text has no valid chars : {text}")
-            return False
-
-        words = tokenize_by_CJK_char("".join(clean_chars))
-        for i in range(len(words) - 10):
-            if words[i : i + 10].count(words[i]) == 10:
-                logging.warning(f"Delete cut with text with too much repeats : {text}")
-                return False
-        # word speed, 20 - 600 / minute
-        if duration < len(words) / 600 * 60 or duration > len(words) / 20 * 60:
-            logging.warning(
-                f"Delete cut with audio text mismatch, duration : {duration}s, "
-                f"words : {len(words)}, text : {text}"
-            )
-            return False
-        return True
-
-    try:
-        cut_set = load_manifest_lazy(input_dir / file_name)
-        cut_set = cut_set.filter(_filter_cut)
-        cut_set.to_file(output_dir / file_name)
-    except Exception as e:
-        logging.error(f"Manifest {file_name} failed with error: {e}")
-        os.remove(str(output_dir / file_name))
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-    logging.basicConfig(format=formatter, level=logging.INFO)
-
-    args = get_args()
-
-    input_dir = Path(args.source_dir)
-    output_dir = Path(args.dest_dir)
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    cut_files = glob.glob(f"{args.source_dir}/emilia_cuts_{args.subset}.*.jsonl.gz")
-
-    with Pool(max_workers=args.jobs) as pool:
-        futures = [
-            pool.submit(
-                preprocess_emilia, filename.split("/")[-1], input_dir, output_dir
-            )
-            for filename in cut_files
-        ]
-        for f in futures:
-            f.result()
-            f.done()
-    logging.info("Processing done.")
diff --git a/egs/zipvoice/local/tokenizer.py b/egs/zipvoice/local/tokenizer.py
deleted file mode 120000
index 024e340cc..000000000
--- a/egs/zipvoice/local/tokenizer.py
+++ /dev/null
@@ -1 +0,0 @@
-../zipvoice/tokenizer.py
\ No newline at end of file
diff --git a/egs/zipvoice/local/validate_manifest.py b/egs/zipvoice/local/validate_manifest.py
deleted file mode 100644
index 68159ae03..000000000
--- a/egs/zipvoice/local/validate_manifest.py
+++ /dev/null
@@ -1,70 +0,0 @@
-#!/usr/bin/env python3
-# Copyright    2022-2023  Xiaomi Corp.        (authors: Fangjun Kuang,
-#                                                       Zengwei Yao)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-This script checks the following assumptions of the generated manifest:
-
-- Single supervision per cut
-
-We will add more checks later if needed.
-
-Usage example:
-
-    python3 ./local/validate_manifest.py \
-            ./data/spectrogram/ljspeech_cuts_all.jsonl.gz
-
-"""
-
-import argparse
-import logging
-from pathlib import Path
-
-from lhotse import CutSet, load_manifest_lazy
-from lhotse.dataset.speech_synthesis import validate_for_tts
-
-
-def get_args():
-    parser = argparse.ArgumentParser()
-
-    parser.add_argument(
-        "manifest",
-        type=Path,
-        help="Path to the manifest file",
-    )
-
-    return parser.parse_args()
-
-
-def main():
-    args = get_args()
-
-    manifest = args.manifest
-    logging.info(f"Validating {manifest}")
-
-    assert manifest.is_file(), f"{manifest} does not exist"
-    cut_set = load_manifest_lazy(manifest)
-    assert isinstance(cut_set, CutSet), type(cut_set)
-
-    validate_for_tts(cut_set)
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-
-    main()
diff --git a/egs/zipvoice/requirements.txt b/egs/zipvoice/requirements.txt
deleted file mode 100644
index cbbe860a5..000000000
--- a/egs/zipvoice/requirements.txt
+++ /dev/null
@@ -1,17 +0,0 @@
---find-links https://k2-fsa.github.io/icefall/piper_phonemize.html
-
-torch
-torchaudio
-huggingface_hub
-lhotse
-safetensors
-vocos
-
-# Normalization
-cn2an
-inflect
-
-# Tokenization
-jieba
-piper_phonemize
-pypinyin
diff --git a/egs/zipvoice/scripts/evaluate.sh b/egs/zipvoice/scripts/evaluate.sh
deleted file mode 100644
index fbc0eed9a..000000000
--- a/egs/zipvoice/scripts/evaluate.sh
+++ /dev/null
@@ -1,102 +0,0 @@
-export CUDA_VISIBLE_DEVICES="0"
-export PYTHONWARNINGS=ignore
-export PYTHONPATH=../../:$PYTHONPATH
-
-# Uncomment this if you have trouble connecting to HuggingFace
-# export HF_ENDPOINT=https://hf-mirror.com
-
-start_stage=1
-end_stage=3
-
-# Models used for SIM-o evaluation.
-# SV model wavlm_large_finetune.pth is downloaded from https://github.com/microsoft/UniSpeech/tree/main/downstreams/speaker_verification
-# SSL model wavlm_large.pt is downloaded from https://huggingface.co/s3prl/converted_ckpts/resolve/main/wavlm_large.pt
-sv_model_path=model/UniSpeech/wavlm_large_finetune.pth
-wavlm_model_path=model/s3prl/wavlm_large.pt
-
-# Models used for UTMOS evaluation.
-# wget https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/epoch%3D3-step%3D7459.ckpt -P model/huggingface/utmos/utmos.pt
-# wget https://huggingface.co/spaces/sarulab-speech/UTMOS-demo/resolve/main/wav2vec_small.pt -P model/huggingface/utmos/wav2vec_small.pt
-utmos_model_path=model/huggingface/utmos/utmos.pt
-wav2vec_model_path=model/huggingface/utmos/wav2vec_small.pt
-
-
-if [ $start_stage -le 1 ] && [ $end_stage -ge 1 ]; then 
-
-        echo "=====Evaluate for Seed-TTS test-en======="
-        test_list=testset/test_seedtts_en.tsv
-        wav_path=results/zipvoice_seedtts_en
-
-        echo $wav_path
-        echo "-----Computing SIM-o-----"
-        python3 local/evaluate_sim.py \
-                --sv-model-path ${sv_model_path} \
-                --ssl-model-path ${wavlm_model_path} \
-                --eval-path ${wav_path} \
-                --test-list ${test_list}
-
-        echo "-----Computing WER-----"
-        python3 local/evaluate_wer_seedtts.py \
-                --test-list ${test_list} \
-                --wav-path ${wav_path} \
-                --lang "en" 
-
-        echo "-----Computing UTSMOS-----"
-        python3 local/evaluate_utmos.py \
-                --wav-path ${wav_path} \
-                --utmos-model-path ${utmos_model_path} \
-                --ssl-model-path ${wav2vec_model_path}
-
-fi
-
-if [ $start_stage -le 2 ] && [ $end_stage -ge 2 ]; then 
-        echo "=====Evaluate for Seed-TTS test-zh======="
-        test_list=testset/test_seedtts_zh.tsv
-        wav_path=results/zipvoice_seedtts_zh
-
-        echo $wav_path
-        echo "-----Computing SIM-o-----"
-        python3 local/evaluate_sim.py \
-                --sv-model-path ${sv_model_path} \
-                --ssl-model-path ${wavlm_model_path} \
-                --eval-path ${wav_path} \
-                --test-list ${test_list}
-
-        echo "-----Computing WER-----"
-        python3 local/evaluate_wer_seedtts.py \
-                --test-list ${test_list} \
-                --wav-path ${wav_path} \
-                --lang "zh" 
-
-        echo "-----Computing UTSMOS-----"
-        python3 local/evaluate_utmos.py \
-                --wav-path ${wav_path} \
-                --utmos-model-path ${utmos_model_path} \
-                --ssl-model-path ${wav2vec_model_path}
-fi
-
-if [ $start_stage -le 3 ] && [ $end_stage -ge 3 ]; then 
-        echo "=====Evaluate for Librispeech test-clean======="
-        test_list=testset/test_librispeech_pc_test_clean.tsv
-        wav_path=results/zipvoice_librispeech_test_clean
-
-        echo $wav_path
-        echo "-----Computing SIM-o-----"
-        python3 local/evaluate_sim.py \
-                --sv-model-path ${sv_model_path} \
-                --ssl-model-path ${wavlm_model_path} \
-                --eval-path ${wav_path} \
-                --test-list ${test_list}
-
-        echo "-----Computing WER-----"
-        python3 local/evaluate_wer_hubert.py \
-                --test-list ${test_list} \
-                --wav-path ${wav_path} \
-
-        echo "-----Computing UTSMOS-----"
-        python3 local/evaluate_utmos.py \
-                --wav-path ${wav_path} \
-                --utmos-model-path ${utmos_model_path} \
-                --ssl-model-path ${wav2vec_model_path}
-
-fi
\ No newline at end of file
diff --git a/egs/zipvoice/scripts/prepare_emilia.sh b/egs/zipvoice/scripts/prepare_emilia.sh
deleted file mode 100755
index bf19ed1a5..000000000
--- a/egs/zipvoice/scripts/prepare_emilia.sh
+++ /dev/null
@@ -1,126 +0,0 @@
-#!/usr/bin/env bash
-
-# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
-export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
-
-set -eou pipefail
-
-stage=0
-stop_stage=5
-sampling_rate=24000
-nj=32
-
-dl_dir=$PWD/download
-
-# All files generated by this script are saved in "data".
-# You can safely remove "data" and rerun this script to regenerate it.
-mkdir -p data
-
-log() {
-  # This function is from espnet
-  local fname=${BASH_SOURCE[1]##*/}
-  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
-}
-
-log "dl_dir: $dl_dir"
-
-if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
-  log "Stage 0: Download data"
-
-  # Your download directory should look like this:
-  #
-  #    download/Amphion___Emilia
-  #    ├── metafile.yaml
-  #    ├── raw
-  #    │   ├── DE
-  #    │   ├── EN
-  #    │   ├── FR
-  #    │   ├── JA
-  #    │   ├── KO
-  #    │   ├── openemilia_45batches.tar.gz
-  #    │   ├── openemilia_all.tar.gz
-  #    │   └── ZH
-  #    └── README.md
-
-  if [ ! -d $dl_dir/Amphion___Emilia/raw ]; then
-    log "Please refer https://openxlab.org.cn/datasets/Amphion/Emilia to download the dataset."
-    exit(-1)
-  fi
-
-fi
-
-if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
-  log "Stage 1: Prepare emilia manifests (EN and ZH only)"
-  # We assume that you have downloaded the Emilia corpus
-  # to $dl_dir/Amphion___Emilia
-  # see stage 0 for the directory structure
-  mkdir -p data/manifests
-  if [ ! -e data/manifests/.emilia.done ]; then
-    lhotse prepare emilia --lang en --num-jobs ${nj} $dl_dir/Amphion___Emilia data/manifests
-    lhotse prepare emilia --lang zh --num-jobs ${nj} $dl_dir/Amphion___Emilia data/manifests
-    touch data/manifests/.emilia.done
-  fi
-fi
-
-if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
-  log "Stage 2: Preprocess Emilia dataset, mainly for cleaning"
-  mkdir -p data/manifests/splits_raw
-  if [ ! -e data/manifests/split_raw/.emilia.split.done ]; then
-    lhotse split-lazy data/manifests/emilia_cuts_EN.jsonl.gz data/manifests/splits_raw 10000
-    lhotse split-lazy data/manifests/emilia_cuts_ZH.jsonl.gz data/manifests/splits_raw 10000
-    touch data/manifests/splits_raw/.emilia.split.done
-  fi
-
-  mkdir -p data/manifests/splits
-
-  if [ ! -e data/manifests/splits/.emilia.preprocess.done ]; then
-    python local/preprocess_emilia.py --subset EN
-    python local/preprocess_emilia.py --subset ZH
-    touch data/manifests/splits/.emilia.preprocess.done
-  fi
-
-fi
-
-if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
-  log "Stage 3: Extract Fbank for Emilia"
-  mkdir -p data/fbank/emilia_splits
-  if [ ! -e data/fbank/emilia_splits/.emilia.fbank.done ]; then
-    # You can speed up the extraction by distributing splits to multiple machines.
-    for subset in EN ZH; do
-      python local/compute_fbank.py \
-        --source-dir data/manifests/splits \
-        --dest-dir data/fbank/emilia_splits \
-        --dataset emilia \
-        --subset ${subset} \
-        --splits-cuts 1 \
-        --split-begin 0 \
-        --split-end 2000 \
-        --num-jobs ${nj}
-    done
-    touch data/fbank/emilia_splits/.emilia.fbank.done
-  fi
-
-  if [ ! -e data/fbank/emilia_cuts_EN.jsonl.gz ]; then
-    log "Combining EN fbank cuts and spliting EN dev set"
-    gunzip -c data/fbank/emilia_splits/emilia_cuts_EN.*.jsonl.gz > data/fbank/emilia_cuts_EN.jsonl
-    head -n 1500 data/fbank/emilia_cuts_EN.jsonl | gzip -c > data/fbank/emilia_cuts_EN_dev.jsonl.gz
-    sed -i '1,1500d' data/fbank/emilia_cuts_EN.jsonl
-    gzip data/fbank/emilia_cuts_EN.jsonl
-  fi
-
-  if [ ! -e data/fbank/emilia_cuts_ZH.jsonl.gz ]; then
-    log "Combining ZH fbank cuts and spliting ZH dev set"
-    gunzip -c data/fbank/emilia_splits/emilia_cuts_ZH.*.jsonl.gz > data/fbank/emilia_cuts_ZH.jsonl
-    head -n 1500 data/fbank/emilia_cuts_ZH.jsonl | gzip -c > data/fbank/emilia_cuts_ZH_dev.jsonl.gz
-    sed -i '1,1500d' data/fbank/emilia_cuts_ZH.jsonl
-    gzip data/fbank/emilia_cuts_ZH.jsonl
-  fi
-
-fi
-
-if [ $stage -le 4 ] && [ $stop_stage -ge 4 ]; then
-  log "Stage 4: Generate token file"
-  if [ ! -e data/tokens_emilia.txt ]; then
-    ./local/prepare_token_file_emilia.py --tokens data/tokens_emilia.txt
-  fi
-fi
diff --git a/egs/zipvoice/scripts/prepare_libritts.sh b/egs/zipvoice/scripts/prepare_libritts.sh
deleted file mode 100755
index 6d643145e..000000000
--- a/egs/zipvoice/scripts/prepare_libritts.sh
+++ /dev/null
@@ -1,97 +0,0 @@
-#!/usr/bin/env bash
-
-# fix segmentation fault reported in https://github.com/k2-fsa/icefall/issues/674
-export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python
-
-set -eou pipefail
-
-stage=0
-stop_stage=5
-sampling_rate=24000
-nj=20
-
-dl_dir=$PWD/download
-
-# All files generated by this script are saved in "data".
-# You can safely remove "data" and rerun this script to regenerate it.
-mkdir -p data
-
-log() {
-  # This function is from espnet
-  local fname=${BASH_SOURCE[1]##*/}
-  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
-}
-
-log "dl_dir: $dl_dir"
-
-if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
-  log "Stage 0: Download data"
-
-  # If you have pre-downloaded it to /path/to/LibriTTS,
-  # you can create a symlink
-  #
-  #   ln -sfv /path/to/LibriTTS $dl_dir/LibriTTS
-  #
-  if [ ! -d $dl_dir/LibriTTS ]; then
-    lhotse download libritts $dl_dir
-  fi
-
-fi
-
-if [ $stage -le 1 ] && [ $stop_stage -ge 1 ]; then
-  log "Stage 1: Prepare LibriTTS manifest"
-  # We assume that you have downloaded the LibriTTS corpus
-  # to $dl_dir/LibriTTS
-  mkdir -p data/manifests_libritts
-  if [ ! -e data/manifests_libritts/.libritts.done ]; then
-    lhotse prepare libritts --num-jobs ${nj} $dl_dir/LibriTTS data/manifests
-    touch data/manifests/.libritts.done
-  fi
-fi
-
-if [ $stage -le 2 ] && [ $stop_stage -ge 2 ]; then
-  log "Stage 2: Compute Fbank for LibriTTS"
-  mkdir -p data/fbank
-
-  if [ ! -e data/fbank/.libritts.done ]; then
-    for subset in train-clean-100 train-clean-360 train-other-500 dev-clean test-clean; do
-      python local/compute_fbank.py \
-        --source-dir data/manifests \
-        --dest-dir data/fbank \
-        --dataset libritts \
-        --subset ${subset} \
-        --sampling-rate $sampling_rate \
-        --num-jobs ${nj}
-    done
-    touch data/fbank/.libritts.done
-  fi
-
-  # Here we shuffle and combine the train-clean-100, train-clean-360 and
-  # train-other-500 together to form the training set.
-  if [ ! -f data/fbank/libritts_cuts_train-all-shuf.jsonl.gz ]; then
-    cat <(gunzip -c data/fbank/libritts_cuts_train-clean-100.jsonl.gz) \
-      <(gunzip -c data/fbank/libritts_cuts_train-clean-360.jsonl.gz) \
-      <(gunzip -c data/fbank/libritts_cuts_train-other-500.jsonl.gz) | \
-      shuf | gzip -c > data/fbank/libritts_cuts_train-all-shuf.jsonl.gz
-  fi
-
-  if [ ! -f data/fbank/libritts_cuts_train-clean-460.jsonl.gz ]; then
-    cat <(gunzip -c data/fbank/libritts_cuts_train-clean-100.jsonl.gz) \
-      <(gunzip -c data/fbank/libritts_cuts_train-clean-360.jsonl.gz) | \
-      shuf | gzip -c > data/fbank/libritts_cuts_train-clean-460.jsonl.gz
-  fi
-
-  if [ ! -e data/fbank/.libritts-validated.done ]; then
-    log "Validating data/fbank for LibriTTS"
-    ./local/validate_manifest.py \
-      data/fbank/libritts_cuts_train-all-shuf.jsonl.gz
-    touch data/fbank/.libritts-validated.done
-  fi
-fi
-
-if [ $stage -le 3 ] && [ $stop_stage -ge 3 ]; then
-  log "Stage 3: Generate token file"
-  if [ ! -e data/tokens_libritts.txt ]; then
-    ./local/prepare_token_file_libritts.py --tokens data/tokens_libritts.txt
-  fi
-fi
diff --git a/egs/zipvoice/zipvoice/checkpoint.py b/egs/zipvoice/zipvoice/checkpoint.py
deleted file mode 100644
index e3acd57dd..000000000
--- a/egs/zipvoice/zipvoice/checkpoint.py
+++ /dev/null
@@ -1,142 +0,0 @@
-# Copyright  2021-2022  Xiaomi Corporation  (authors: Fangjun Kuang,
-#                                                     Zengwei Yao)
-#
-# See ../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import logging
-from pathlib import Path
-from typing import Any, Dict, Optional, Union
-
-import torch
-import torch.nn as nn
-from lhotse.dataset.sampling.base import CutSampler
-from torch.cuda.amp import GradScaler
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.optim import Optimizer
-
-# use duck typing for LRScheduler since we have different possibilities, see
-# our class LRScheduler.
-LRSchedulerType = object
-
-
-def save_checkpoint(
-    filename: Path,
-    model: Union[nn.Module, DDP],
-    model_avg: Optional[nn.Module] = None,
-    model_ema: Optional[nn.Module] = None,
-    params: Optional[Dict[str, Any]] = None,
-    optimizer: Optional[Optimizer] = None,
-    scheduler: Optional[LRSchedulerType] = None,
-    scaler: Optional[GradScaler] = None,
-    sampler: Optional[CutSampler] = None,
-    rank: int = 0,
-) -> None:
-    """Save training information to a file.
-
-    Args:
-      filename:
-        The checkpoint filename.
-      model:
-        The model to be saved. We only save its `state_dict()`.
-      model_avg:
-        The stored model averaged from the start of training.
-      model_ema:
-        The EMA version of model.
-      params:
-        User defined parameters, e.g., epoch, loss.
-      optimizer:
-        The optimizer to be saved. We only save its `state_dict()`.
-      scheduler:
-        The scheduler to be saved. We only save its `state_dict()`.
-      scalar:
-        The GradScaler to be saved. We only save its `state_dict()`.
-      sampler:
-        The sampler used in the labeled training dataset. We only
-          save its `state_dict()`.
-      rank:
-        Used in DDP. We save checkpoint only for the node whose
-          rank is 0.
-    Returns:
-      Return None.
-    """
-    if rank != 0:
-        return
-
-    logging.info(f"Saving checkpoint to {filename}")
-
-    if isinstance(model, DDP):
-        model = model.module
-
-    checkpoint = {
-        "model": model.state_dict(),
-        "optimizer": optimizer.state_dict() if optimizer is not None else None,
-        "scheduler": scheduler.state_dict() if scheduler is not None else None,
-        "grad_scaler": scaler.state_dict() if scaler is not None else None,
-        "sampler": sampler.state_dict() if sampler is not None else None,
-    }
-
-    if model_avg is not None:
-        checkpoint["model_avg"] = model_avg.to(torch.float32).state_dict()
-    if model_ema is not None:
-        checkpoint["model_ema"] = model_ema.to(torch.float32).state_dict()
-
-    if params:
-        for k, v in params.items():
-            assert k not in checkpoint
-            checkpoint[k] = v
-
-    torch.save(checkpoint, filename)
-
-
-def load_checkpoint(
-    filename: Path,
-    model: Optional[nn.Module] = None,
-    model_avg: Optional[nn.Module] = None,
-    model_ema: Optional[nn.Module] = None,
-    strict: bool = False,
-) -> Dict[str, Any]:
-    logging.info(f"Loading checkpoint from {filename}")
-    checkpoint = torch.load(filename, map_location="cpu")
-
-    if model is not None:
-
-        if next(iter(checkpoint["model"])).startswith("module."):
-            logging.info("Loading checkpoint saved by DDP")
-
-            dst_state_dict = model.state_dict()
-            src_state_dict = checkpoint["model"]
-            for key in dst_state_dict.keys():
-                src_key = "{}.{}".format("module", key)
-                dst_state_dict[key] = src_state_dict.pop(src_key)
-            assert len(src_state_dict) == 0
-            model.load_state_dict(dst_state_dict, strict=strict)
-        else:
-            logging.info("Loading checkpoint")
-            model.load_state_dict(checkpoint["model"], strict=strict)
-
-        checkpoint.pop("model")
-
-    if model_avg is not None and "model_avg" in checkpoint:
-        logging.info("Loading averaged model")
-        model_avg.load_state_dict(checkpoint["model_avg"], strict=strict)
-        checkpoint.pop("model_avg")
-
-    if model_ema is not None and "model_ema" in checkpoint:
-        logging.info("Loading ema model")
-        model_ema.load_state_dict(checkpoint["model_ema"], strict=strict)
-        checkpoint.pop("model_ema")
-
-    return checkpoint
diff --git a/egs/zipvoice/zipvoice/feature.py b/egs/zipvoice/zipvoice/feature.py
deleted file mode 100644
index e7d484d10..000000000
--- a/egs/zipvoice/zipvoice/feature.py
+++ /dev/null
@@ -1,135 +0,0 @@
-#!/usr/bin/env python3
-# Copyright         2024  Xiaomi Corp.        (authors: Han Zhu)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from dataclasses import dataclass
-from typing import Union
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torchaudio
-from lhotse.features.base import FeatureExtractor, register_extractor
-from lhotse.utils import Seconds, compute_num_frames
-
-
-class MelSpectrogramFeatures(nn.Module):
-    def __init__(
-        self,
-        sampling_rate=24000,
-        n_mels=100,
-        n_fft=1024,
-        hop_length=256,
-    ):
-        super().__init__()
-
-        self.mel_spec = torchaudio.transforms.MelSpectrogram(
-            sample_rate=sampling_rate,
-            n_fft=n_fft,
-            hop_length=hop_length,
-            n_mels=n_mels,
-            center=True,
-            power=1,
-        )
-
-    def forward(self, inp):
-        assert len(inp.shape) == 2
-
-        mel = self.mel_spec(inp)
-        logmel = mel.clamp(min=1e-7).log()
-        return logmel
-
-
-@dataclass
-class TorchAudioFbankConfig:
-    sampling_rate: int
-    n_mels: int
-    n_fft: int
-    hop_length: int
-
-
-@register_extractor
-class TorchAudioFbank(FeatureExtractor):
-
-    name = "TorchAudioFbank"
-    config_type = TorchAudioFbankConfig
-
-    def __init__(self, config):
-        super().__init__(config=config)
-
-    def _feature_fn(self, sample):
-        fbank = MelSpectrogramFeatures(
-            sampling_rate=self.config.sampling_rate,
-            n_mels=self.config.n_mels,
-            n_fft=self.config.n_fft,
-            hop_length=self.config.hop_length,
-        )
-
-        return fbank(sample)
-
-    @property
-    def device(self) -> Union[str, torch.device]:
-        return self.config.device
-
-    def feature_dim(self, sampling_rate: int) -> int:
-        return self.config.n_mels
-
-    def extract(
-        self,
-        samples: Union[np.ndarray, torch.Tensor],
-        sampling_rate: int,
-    ) -> Union[np.ndarray, torch.Tensor]:
-        # Check for sampling rate compatibility.
-        expected_sr = self.config.sampling_rate
-        assert sampling_rate == expected_sr, (
-            f"Mismatched sampling rate: extractor expects {expected_sr}, "
-            f"got {sampling_rate}"
-        )
-        is_numpy = False
-        if not isinstance(samples, torch.Tensor):
-            samples = torch.from_numpy(samples)
-            is_numpy = True
-
-        if len(samples.shape) == 1:
-            samples = samples.unsqueeze(0)
-        assert samples.ndim == 2, samples.shape
-        assert samples.shape[0] == 1, samples.shape
-
-        mel = self._feature_fn(samples).squeeze().t()
-
-        assert mel.ndim == 2, mel.shape
-        assert mel.shape[1] == self.config.n_mels, mel.shape
-
-        num_frames = compute_num_frames(
-            samples.shape[1] / sampling_rate, self.frame_shift, sampling_rate
-        )
-
-        if mel.shape[0] > num_frames:
-            mel = mel[:num_frames]
-        elif mel.shape[0] < num_frames:
-            mel = mel.unsqueeze(0)
-            mel = torch.nn.functional.pad(
-                mel, (0, 0, 0, num_frames - mel.shape[1]), mode="replicate"
-            ).squeeze(0)
-
-        if is_numpy:
-            return mel.cpu().numpy()
-        else:
-            return mel
-
-    @property
-    def frame_shift(self) -> Seconds:
-        return self.config.hop_length / self.config.sampling_rate
diff --git a/egs/zipvoice/zipvoice/generate_averaged_model.py b/egs/zipvoice/zipvoice/generate_averaged_model.py
deleted file mode 100644
index e1b7ca7c6..000000000
--- a/egs/zipvoice/zipvoice/generate_averaged_model.py
+++ /dev/null
@@ -1,209 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright 2021-2022 Xiaomi Corporation
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-Usage:
-This script loads checkpoints and averages them.
-
-(1) Average ZipVoice models before distill:
-    python3 ./zipvoice/generate_averaged_model.py \
-        --epoch 11 \
-        --avg 4 \
-        --distill 0 \
-        --token-file data/tokens_emilia.txt \
-        --exp-dir ./zipvoice/exp_zipvoice
-
-    It will generate a file `epoch-11-avg-14.pt` in the given `exp_dir`.
-    You can later load it by `torch.load("epoch-11-avg-4.pt")`.
-
-(2) Average ZipVoice-Distill models (the first stage model):
-
-    python3 ./zipvoice/generate_averaged_model.py \
-        --iter 60000 \
-        --avg 7 \
-        --distill 1 \
-        --token-file data/tokens_emilia.txt \
-        --exp-dir ./zipvoice/exp_zipvoice_distill_1stage
-"""
-
-import argparse
-from pathlib import Path
-
-import torch
-from model import get_distill_model, get_model
-from tokenizer import TokenizerEmilia, TokenizerLibriTTS
-from train_flow import add_model_arguments, get_params
-
-from icefall.checkpoint import average_checkpoints_with_averaged_model, find_checkpoints
-from icefall.utils import str2bool
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--epoch",
-        type=int,
-        default=11,
-        help="""It specifies the checkpoint to use for decoding.
-        Note: Epoch counts from 1.
-        You can specify --avg to use more checkpoints for model averaging.""",
-    )
-
-    parser.add_argument(
-        "--iter",
-        type=int,
-        default=0,
-        help="""If positive, --epoch is ignored and it
-        will use the checkpoint exp_dir/checkpoint-iter.pt.
-        You can specify --avg to use more checkpoints for model averaging.
-        """,
-    )
-
-    parser.add_argument(
-        "--avg",
-        type=int,
-        default=4,
-        help="Number of checkpoints to average. Automatically select "
-        "consecutive checkpoints before the checkpoint specified by "
-        "'--epoch' or --iter",
-    )
-
-    parser.add_argument(
-        "--exp-dir",
-        type=str,
-        default="zipvoice/exp_zipvoice",
-        help="The experiment dir",
-    )
-
-    parser.add_argument(
-        "--distill",
-        type=str2bool,
-        default=False,
-        help="Whether to use distill model. ",
-    )
-
-    parser.add_argument(
-        "--dataset",
-        type=str,
-        default="emilia",
-        choices=["emilia", "libritts"],
-        help="The used training dataset for the model to inference",
-    )
-
-    add_model_arguments(parser)
-
-    return parser
-
-
-@torch.no_grad()
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-    args.exp_dir = Path(args.exp_dir)
-
-    params = get_params()
-    params.update(vars(args))
-
-    if params.dataset == "emilia":
-        tokenizer = TokenizerEmilia(
-            token_file=params.token_file, token_type=params.token_type
-        )
-    elif params.dataset == "libritts":
-        tokenizer = TokenizerLibriTTS(
-            token_file=params.token_file, token_type=params.token_type
-        )
-
-    params.vocab_size = tokenizer.vocab_size
-    params.pad_id = tokenizer.pad_id
-
-    params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
-
-    print("Script started")
-
-    params.device = torch.device("cpu")
-    print(f"Device: {params.device}")
-
-    print("About to create model")
-    if params.distill:
-        model = get_distill_model(params)
-    else:
-        model = get_model(params)
-
-    if params.iter > 0:
-        filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-            : params.avg + 1
-        ]
-        if len(filenames) == 0:
-            raise ValueError(
-                f"No checkpoints found for" f" --iter {params.iter}, --avg {params.avg}"
-            )
-        elif len(filenames) < params.avg + 1:
-            raise ValueError(
-                f"Not enough checkpoints ({len(filenames)}) found for"
-                f" --iter {params.iter}, --avg {params.avg}"
-            )
-        filename_start = filenames[-1]
-        filename_end = filenames[0]
-        print(
-            "Calculating the averaged model over iteration checkpoints"
-            f" from {filename_start} (excluded) to {filename_end}"
-        )
-        model.to(params.device)
-        model.load_state_dict(
-            average_checkpoints_with_averaged_model(
-                filename_start=filename_start,
-                filename_end=filename_end,
-                device=params.device,
-            ),
-            strict=True,
-        )
-    else:
-        assert params.avg > 0, params.avg
-        start = params.epoch - params.avg
-        assert start >= 1, start
-        filename_start = f"{params.exp_dir}/epoch-{start}.pt"
-        filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
-        print(
-            f"Calculating the averaged model over epoch range from "
-            f"{start} (excluded) to {params.epoch}"
-        )
-        model.to(params.device)
-        model.load_state_dict(
-            average_checkpoints_with_averaged_model(
-                filename_start=filename_start,
-                filename_end=filename_end,
-                device=params.device,
-            ),
-            strict=True,
-        )
-    if params.iter > 0:
-        filename = params.exp_dir / f"iter-{params.iter}-avg-{params.avg}.pt"
-    else:
-        filename = params.exp_dir / f"epoch-{params.epoch}-avg-{params.avg}.pt"
-    torch.save({"model": model.state_dict()}, filename)
-
-    num_param = sum([p.numel() for p in model.parameters()])
-    print(f"Number of model parameters: {num_param}")
-
-    print("Done!")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/egs/zipvoice/zipvoice/infer.py b/egs/zipvoice/zipvoice/infer.py
deleted file mode 100644
index 2819d3c85..000000000
--- a/egs/zipvoice/zipvoice/infer.py
+++ /dev/null
@@ -1,586 +0,0 @@
-#!/usr/bin/env python3
-# Copyright         2024  Xiaomi Corp.        (authors: Wei Kang
-#                                                       Han Zhu)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-This script loads checkpoints to generate waveforms.
-This script is supposed to be used with the model trained by yourself.
-If you want to use the pre-trained checkpoints provided by us, please refer to zipvoice_infer.py.
-
-(1) Usage with a pre-trained checkpoint:
-
-    (a) ZipVoice model before distill:
-        python3 zipvoice/infer.py \
-            --checkpoint zipvoice/exp_zipvoice/epoch-11-avg-4.pt \
-            --distill 0 \
-            --token-file "data/tokens_emilia.txt" \
-            --test-list test.tsv \
-            --res-dir results/test \
-            --num-step 16 \
-            --guidance-scale 1
-
-    (b) ZipVoice-Distill:
-        python3 zipvoice/infer.py \
-            --checkpoint zipvoice/exp_zipvoice_distill/checkpoint-2000.pt \
-            --distill 1 \
-            --token-file "data/tokens_emilia.txt" \
-            --test-list test.tsv \
-            --res-dir results/test_distill \
-            --num-step 8 \
-            --guidance-scale 3
-
-(2) Usage with a directory of checkpoints (may requires checkpoint averaging):
-
-    (a) ZipVoice model before distill:
-        python3 flow_match/infer.py \
-            --exp-dir zipvoice/exp_zipvoice \
-            --epoch 11 \
-            --avg 4 \
-            --distill 0 \
-            --token-file "data/tokens_emilia.txt" \
-            --test-list test.tsv \
-            --res-dir results \
-            --num-step 16 \
-            --guidance-scale 1
-
-    (b) ZipVoice-Distill:
-        python3 flow_match/infer.py \
-            --exp-dir zipvoice/exp_zipvoice_distill/ \
-            --iter 2000 \
-            --avg 0 \
-            --distill 1 \
-            --token-file "data/tokens_emilia.txt" \
-            --test-list test.tsv \
-            --res-dir results \
-            --num-step 8 \
-            --guidance-scale 3
-"""
-
-
-import argparse
-import datetime as dt
-import logging
-import os
-from pathlib import Path
-from typing import Optional
-
-import numpy as np
-import soundfile as sf
-import torch
-import torch.nn as nn
-import torchaudio
-from checkpoint import load_checkpoint
-from feature import TorchAudioFbank, TorchAudioFbankConfig
-from lhotse.utils import fix_random_seed
-from model import get_distill_model, get_model
-from tokenizer import TokenizerEmilia, TokenizerLibriTTS
-from train_flow import add_model_arguments, get_params
-from vocos import Vocos
-
-from icefall.checkpoint import average_checkpoints_with_averaged_model, find_checkpoints
-from icefall.utils import AttributeDict, setup_logger, str2bool
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--checkpoint",
-        type=str,
-        default=None,
-        help="The checkpoint for inference. "
-        "If it is None, will use checkpoints under exp_dir",
-    )
-
-    parser.add_argument(
-        "--exp-dir",
-        type=str,
-        default="zipvoice/exp",
-        help="The experiment dir",
-    )
-
-    parser.add_argument(
-        "--epoch",
-        type=int,
-        default=0,
-        help="""It specifies the checkpoint to use for decoding.
-        Note: Epoch counts from 1.
-        You can specify --avg to use more checkpoints for model averaging.""",
-    )
-
-    parser.add_argument(
-        "--iter",
-        type=int,
-        default=0,
-        help="""If positive, --epoch is ignored and it
-        will use the checkpoint exp_dir/checkpoint-iter.pt.
-        You can specify --avg to use more checkpoints for model averaging.
-        """,
-    )
-
-    parser.add_argument(
-        "--avg",
-        type=int,
-        default=4,
-        help="Number of checkpoints to average. Automatically select "
-        "consecutive checkpoints before the checkpoint specified by "
-        "'--epoch' or '--iter', avg=0 means no avg",
-    )
-
-    parser.add_argument(
-        "--vocoder-path",
-        type=str,
-        default=None,
-        help="The local vocos vocoder path, downloaded from huggingface, "
-        "will download the vocodoer from huggingface if it is None.",
-    )
-
-    parser.add_argument(
-        "--distill",
-        type=str2bool,
-        default=False,
-        help="Whether it is a distilled TTS model.",
-    )
-
-    parser.add_argument(
-        "--test-list",
-        type=str,
-        default=None,
-        help="The list of prompt speech, prompt_transcription, "
-        "and text to synthesize in the format of "
-        "'{wav_name}\t{prompt_transcription}\t{prompt_wav}\t{text}'.",
-    )
-
-    parser.add_argument(
-        "--res-dir",
-        type=str,
-        default="results",
-        help="Path name of the generated wavs dir",
-    )
-
-    parser.add_argument(
-        "--dataset",
-        type=str,
-        default="emilia",
-        choices=["emilia", "libritts"],
-        help="The used training dataset for the model to inference",
-    )
-
-    parser.add_argument(
-        "--guidance-scale",
-        type=float,
-        default=1.0,
-        help="The scale of classifier-free guidance during inference.",
-    )
-
-    parser.add_argument(
-        "--num-step",
-        type=int,
-        default=16,
-        help="The number of sampling steps.",
-    )
-
-    parser.add_argument(
-        "--feat-scale",
-        type=float,
-        default=0.1,
-        help="The scale factor of fbank feature",
-    )
-
-    parser.add_argument(
-        "--speed",
-        type=float,
-        default=1.0,
-        help="Control speech speed, 1.0 means normal, >1.0 means speed up",
-    )
-
-    parser.add_argument(
-        "--t-shift",
-        type=float,
-        default=0.5,
-        help="Shift t to smaller ones if t_shift < 1.0",
-    )
-
-    parser.add_argument(
-        "--target-rms",
-        type=float,
-        default=0.1,
-        help="Target speech normalization rms value",
-    )
-
-    parser.add_argument(
-        "--seed",
-        type=int,
-        default=666,
-        help="Random seed",
-    )
-
-    add_model_arguments(parser)
-
-    return parser
-
-
-def get_vocoder(vocos_local_path: Optional[str] = None):
-    if vocos_local_path:
-        vocos_local_path = "model/huggingface/vocos-mel-24khz/"
-        vocoder = Vocos.from_hparams(f"{vocos_local_path}/config.yaml")
-        state_dict = torch.load(
-            f"{vocos_local_path}/pytorch_model.bin",
-            weights_only=True,
-            map_location="cpu",
-        )
-        vocoder.load_state_dict(state_dict)
-    else:
-        vocoder = Vocos.from_pretrained("charactr/vocos-mel-24khz")
-    return vocoder
-
-
-def generate_sentence(
-    save_path: str,
-    prompt_text: str,
-    prompt_wav: str,
-    text: str,
-    model: nn.Module,
-    vocoder: nn.Module,
-    tokenizer: TokenizerEmilia,
-    feature_extractor: TorchAudioFbank,
-    device: torch.device,
-    num_step: int = 16,
-    guidance_scale: float = 1.0,
-    speed: float = 1.0,
-    t_shift: float = 0.5,
-    target_rms: float = 0.1,
-    feat_scale: float = 0.1,
-    sampling_rate: int = 24000,
-):
-    """
-    Generate waveform of a text based on a given prompt
-        waveform and its transcription.
-
-    Args:
-        save_path (str): Path to save the generated wav.
-        prompt_text (str): Transcription of the prompt wav.
-        prompt_wav (str): Path to the prompt wav file.
-        text (str): Text to be synthesized into a waveform.
-        model (nn.Module): The model used for generation.
-        vocoder (nn.Module): The vocoder used to convert features to waveforms.
-        tokenizer (TokenizerEmilia): The tokenizer used to convert text to tokens.
-        feature_extractor (TorchAudioFbank): The feature extractor used to
-            extract acoustic features.
-        device (torch.device): The device on which computations are performed.
-        num_step (int, optional): Number of steps for decoding. Defaults to 16.
-        guidance_scale (float, optional): Scale for classifier-free guidance.
-            Defaults to 1.0.
-        speed (float, optional): Speed control. Defaults to 1.0.
-        t_shift (float, optional): Time shift. Defaults to 0.5.
-        target_rms (float, optional): Target RMS for waveform normalization.
-            Defaults to 0.1.
-        feat_scale (float, optional): Scale for features.
-            Defaults to 0.1.
-        sampling_rate (int, optional): Sampling rate for the waveform.
-            Defaults to 24000.
-    Returns:
-        metrics (dict): Dictionary containing time and real-time
-            factor metrics for processing.
-    """
-    # Convert text to tokens
-    tokens = tokenizer.texts_to_token_ids([text])
-    prompt_tokens = tokenizer.texts_to_token_ids([prompt_text])
-
-    # Load and preprocess prompt wav
-    prompt_wav, prompt_sampling_rate = torchaudio.load(prompt_wav)
-    prompt_rms = torch.sqrt(torch.mean(torch.square(prompt_wav)))
-    if prompt_rms < target_rms:
-        prompt_wav = prompt_wav * target_rms / prompt_rms
-
-    if prompt_sampling_rate != sampling_rate:
-        resampler = torchaudio.transforms.Resample(
-            orig_freq=prompt_sampling_rate, new_freq=sampling_rate
-        )
-        prompt_wav = resampler(prompt_wav)
-
-    # Extract features from prompt wav
-    prompt_features = feature_extractor.extract(
-        prompt_wav, sampling_rate=sampling_rate
-    ).to(device)
-    prompt_features = prompt_features.unsqueeze(0) * feat_scale
-    prompt_features_lens = torch.tensor([prompt_features.size(1)], device=device)
-
-    # Start timing
-    start_t = dt.datetime.now()
-
-    # Generate features
-    (
-        pred_features,
-        pred_features_lens,
-        pred_prompt_features,
-        pred_prompt_features_lens,
-    ) = model.sample(
-        tokens=tokens,
-        prompt_tokens=prompt_tokens,
-        prompt_features=prompt_features,
-        prompt_features_lens=prompt_features_lens,
-        speed=speed,
-        t_shift=t_shift,
-        duration="predict",
-        num_step=num_step,
-        guidance_scale=guidance_scale,
-    )
-
-    # Postprocess predicted features
-    pred_features = pred_features.permute(0, 2, 1) / feat_scale  # (B, C, T)
-
-    # Start vocoder processing
-    start_vocoder_t = dt.datetime.now()
-    wav = vocoder.decode(pred_features).squeeze(1).clamp(-1, 1)
-
-    # Calculate processing times and real-time factors
-    t = (dt.datetime.now() - start_t).total_seconds()
-    t_no_vocoder = (start_vocoder_t - start_t).total_seconds()
-    t_vocoder = (dt.datetime.now() - start_vocoder_t).total_seconds()
-    wav_seconds = wav.shape[-1] / sampling_rate
-    rtf = t / wav_seconds
-    rtf_no_vocoder = t_no_vocoder / wav_seconds
-    rtf_vocoder = t_vocoder / wav_seconds
-    metrics = {
-        "t": t,
-        "t_no_vocoder": t_no_vocoder,
-        "t_vocoder": t_vocoder,
-        "wav_seconds": wav_seconds,
-        "rtf": rtf,
-        "rtf_no_vocoder": rtf_no_vocoder,
-        "rtf_vocoder": rtf_vocoder,
-    }
-
-    # Adjust wav volume if necessary
-    if prompt_rms < target_rms:
-        wav = wav * prompt_rms / target_rms
-    wav = wav[0].cpu().numpy()
-    sf.write(save_path, wav, sampling_rate)
-
-    return metrics
-
-
-def generate(
-    params: AttributeDict,
-    model: nn.Module,
-    vocoder: nn.Module,
-    tokenizer: TokenizerEmilia,
-):
-    total_t = []
-    total_t_no_vocoder = []
-    total_t_vocoder = []
-    total_wav_seconds = []
-
-    config = TorchAudioFbankConfig(
-        sampling_rate=params.sampling_rate,
-        n_mels=100,
-        n_fft=1024,
-        hop_length=256,
-    )
-    feature_extractor = TorchAudioFbank(config)
-
-    with open(params.test_list, "r") as fr:
-        lines = fr.readlines()
-
-    for i, line in enumerate(lines):
-        wav_name, prompt_text, prompt_wav, text = line.strip().split("\t")
-        save_path = f"{params.wav_dir}/{wav_name}.wav"
-        metrics = generate_sentence(
-            save_path=save_path,
-            prompt_text=prompt_text,
-            prompt_wav=prompt_wav,
-            text=text,
-            model=model,
-            vocoder=vocoder,
-            tokenizer=tokenizer,
-            feature_extractor=feature_extractor,
-            device=params.device,
-            num_step=params.num_step,
-            guidance_scale=params.guidance_scale,
-            speed=params.speed,
-            t_shift=params.t_shift,
-            target_rms=params.target_rms,
-            feat_scale=params.feat_scale,
-            sampling_rate=params.sampling_rate,
-        )
-        print(f"[Sentence: {i}] RTF: {metrics['rtf']:.4f}")
-        total_t.append(metrics["t"])
-        total_t_no_vocoder.append(metrics["t_no_vocoder"])
-        total_t_vocoder.append(metrics["t_vocoder"])
-        total_wav_seconds.append(metrics["wav_seconds"])
-
-    print(f"Average RTF: " f"{np.sum(total_t)/np.sum(total_wav_seconds):.4f}")
-    print(
-        f"Average RTF w/o vocoder: "
-        f"{np.sum(total_t_no_vocoder)/np.sum(total_wav_seconds):.4f}"
-    )
-    print(
-        f"Average RTF vocoder: "
-        f"{np.sum(total_t_vocoder)/np.sum(total_wav_seconds):.4f}"
-    )
-
-
-@torch.inference_mode()
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-    args.exp_dir = Path(args.exp_dir)
-
-    params = get_params()
-    params.update(vars(args))
-
-    if params.iter > 0:
-        params.suffix = (
-            f"wavs-iter-{params.iter}-avg"
-            f"-{params.avg}-step-{params.num_step}-scale-{params.guidance_scale}"
-        )
-    elif params.epoch > 0:
-        params.suffix = (
-            f"wavs-epoch-{params.epoch}-avg"
-            f"-{params.avg}-step-{params.num_step}-scale-{params.guidance_scale}"
-        )
-    else:
-        params.suffix = "wavs"
-
-    setup_logger(f"{params.res_dir}/log-infer-{params.suffix}")
-    logging.info("Decoding started")
-
-    if torch.cuda.is_available():
-        params.device = torch.device("cuda", 0)
-    else:
-        params.device = torch.device("cpu")
-
-    logging.info(f"Device: {params.device}")
-
-    if params.dataset == "emilia":
-        tokenizer = TokenizerEmilia(
-            token_file=params.token_file, token_type=params.token_type
-        )
-    elif params.dataset == "libritts":
-        tokenizer = TokenizerLibriTTS(
-            token_file=params.token_file, token_type=params.token_type
-        )
-
-    params.vocab_size = tokenizer.vocab_size
-    params.pad_id = tokenizer.pad_id
-
-    logging.info(params)
-    fix_random_seed(params.seed)
-
-    logging.info("About to create model")
-    if params.distill:
-        model = get_distill_model(params)
-    else:
-        model = get_model(params)
-
-    if params.checkpoint:
-        load_checkpoint(params.checkpoint, model, strict=True)
-    else:
-        if params.avg == 0:
-            if params.iter > 0:
-                load_checkpoint(
-                    f"{params.exp_dir}/checkpoint-{params.iter}.pt", model, strict=True
-                )
-            else:
-                load_checkpoint(
-                    f"{params.exp_dir}/epoch-{params.epoch}.pt", model, strict=True
-                )
-        else:
-            if params.iter > 0:
-                filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
-                    : params.avg + 1
-                ]
-                if len(filenames) == 0:
-                    raise ValueError(
-                        f"No checkpoints found for"
-                        f" --iter {params.iter}, --avg {params.avg}"
-                    )
-                elif len(filenames) < params.avg + 1:
-                    raise ValueError(
-                        f"Not enough checkpoints ({len(filenames)}) found for"
-                        f" --iter {params.iter}, --avg {params.avg}"
-                    )
-                filename_start = filenames[-1]
-                filename_end = filenames[0]
-                logging.info(
-                    "Calculating the averaged model over iteration checkpoints"
-                    f" from {filename_start} (excluded) to {filename_end}"
-                )
-                model.to(params.device)
-                model.load_state_dict(
-                    average_checkpoints_with_averaged_model(
-                        filename_start=filename_start,
-                        filename_end=filename_end,
-                        device=params.device,
-                    ),
-                    strict=True,
-                )
-            else:
-                assert params.avg > 0, params.avg
-                start = params.epoch - params.avg
-                assert start >= 1, start
-                filename_start = f"{params.exp_dir}/epoch-{start}.pt"
-                filename_end = f"{params.exp_dir}/epoch-{params.epoch}.pt"
-                logging.info(
-                    f"Calculating the averaged model over epoch range from "
-                    f"{start} (excluded) to {params.epoch}"
-                )
-                model.to(params.device)
-                model.load_state_dict(
-                    average_checkpoints_with_averaged_model(
-                        filename_start=filename_start,
-                        filename_end=filename_end,
-                        device=params.device,
-                    ),
-                    strict=True,
-                )
-
-    model = model.to(params.device)
-    model.eval()
-
-    num_param = sum([p.numel() for p in model.parameters()])
-    logging.info(f"Number of model parameters: {num_param}")
-
-    vocoder = get_vocoder(params.vocoder_path)
-    vocoder = vocoder.to(params.device)
-    vocoder.eval()
-    num_param = sum([p.numel() for p in vocoder.parameters()])
-    logging.info(f"Number of vocoder parameters: {num_param}")
-
-    params.wav_dir = f"{params.res_dir}/{params.suffix}"
-    os.makedirs(params.wav_dir, exist_ok=True)
-
-    assert (
-        params.test_list is not None
-    ), "Please provide --test-list for speech synthesize."
-    generate(
-        params=params,
-        model=model,
-        vocoder=vocoder,
-        tokenizer=tokenizer,
-    )
-
-    logging.info("Done!")
-
-
-if __name__ == "__main__":
-    torch.set_num_threads(1)
-    torch.set_num_interop_threads(1)
-    main()
diff --git a/egs/zipvoice/zipvoice/model.py b/egs/zipvoice/zipvoice/model.py
deleted file mode 100644
index 25c7973b2..000000000
--- a/egs/zipvoice/zipvoice/model.py
+++ /dev/null
@@ -1,578 +0,0 @@
-# Copyright    2024    Xiaomi Corp.        (authors:  Wei Kang
-#                                                     Han Zhu)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import List, Optional
-
-import torch
-import torch.nn as nn
-from scaling import ScheduledFloat
-from solver import EulerSolver
-from torch.nn.parallel import DistributedDataParallel as DDP
-from utils import (
-    AttributeDict,
-    condition_time_mask,
-    get_tokens_index,
-    make_pad_mask,
-    pad_labels,
-    prepare_avg_tokens_durations,
-    to_int_tuple,
-)
-from zipformer import TTSZipformer
-
-
-def get_model(params: AttributeDict) -> nn.Module:
-    """Get the normal TTS model."""
-
-    fm_decoder = get_fm_decoder_model(params)
-    text_encoder = get_text_encoder_model(params)
-
-    model = TtsModel(
-        fm_decoder=fm_decoder,
-        text_encoder=text_encoder,
-        text_embed_dim=params.text_embed_dim,
-        feat_dim=params.feat_dim,
-        vocab_size=params.vocab_size,
-        pad_id=params.pad_id,
-    )
-    return model
-
-
-def get_distill_model(params: AttributeDict) -> nn.Module:
-    """Get the distillation TTS model."""
-
-    fm_decoder = get_fm_decoder_model(params, distill=True)
-    text_encoder = get_text_encoder_model(params)
-
-    model = DistillTTSModelTrainWrapper(
-        fm_decoder=fm_decoder,
-        text_encoder=text_encoder,
-        text_embed_dim=params.text_embed_dim,
-        feat_dim=params.feat_dim,
-        vocab_size=params.vocab_size,
-        pad_id=params.pad_id,
-    )
-    return model
-
-
-def get_fm_decoder_model(params: AttributeDict, distill: bool = False) -> nn.Module:
-    """Get the Zipformer-based FM decoder model."""
-
-    encoder = TTSZipformer(
-        in_dim=params.feat_dim * 3,
-        out_dim=params.feat_dim,
-        downsampling_factor=to_int_tuple(params.fm_decoder_downsampling_factor),
-        num_encoder_layers=to_int_tuple(params.fm_decoder_num_layers),
-        cnn_module_kernel=to_int_tuple(params.fm_decoder_cnn_module_kernel),
-        encoder_dim=params.fm_decoder_dim,
-        feedforward_dim=params.fm_decoder_feedforward_dim,
-        num_heads=params.fm_decoder_num_heads,
-        query_head_dim=params.query_head_dim,
-        pos_head_dim=params.pos_head_dim,
-        value_head_dim=params.value_head_dim,
-        pos_dim=params.pos_dim,
-        dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)),
-        warmup_batches=4000.0,
-        use_time_embed=True,
-        time_embed_dim=192,
-        use_guidance_scale_embed=distill,
-    )
-    return encoder
-
-
-def get_text_encoder_model(params: AttributeDict) -> nn.Module:
-    """Get the Zipformer-based text encoder model."""
-
-    encoder = TTSZipformer(
-        in_dim=params.text_embed_dim,
-        out_dim=params.feat_dim,
-        downsampling_factor=to_int_tuple(params.text_encoder_downsampling_factor),
-        num_encoder_layers=to_int_tuple(params.text_encoder_num_layers),
-        cnn_module_kernel=to_int_tuple(params.text_encoder_cnn_module_kernel),
-        encoder_dim=params.text_encoder_dim,
-        feedforward_dim=params.text_encoder_feedforward_dim,
-        num_heads=params.text_encoder_num_heads,
-        query_head_dim=params.query_head_dim,
-        pos_head_dim=params.pos_head_dim,
-        value_head_dim=params.value_head_dim,
-        pos_dim=params.pos_dim,
-        dropout=ScheduledFloat((0.0, 0.3), (20000.0, 0.1)),
-        warmup_batches=4000.0,
-        use_time_embed=False,
-    )
-    return encoder
-
-
-class TtsModel(nn.Module):
-    """The normal TTS model."""
-
-    def __init__(
-        self,
-        fm_decoder: nn.Module,
-        text_encoder: nn.Module,
-        text_embed_dim: int,
-        feat_dim: int,
-        vocab_size: int,
-        pad_id: int = 0,
-    ):
-        """
-        Args:
-            fm_decoder: the flow-matching encoder model, inputs are the
-                input condition embeddings and noisy acoustic features,
-                outputs are better acoustic features.
-            text_encoder: the text encoder model. input are text
-                embeddings, output are contextualized text embeddings.
-            text_embed_dim: dimension of text embedding.
-            feat_dim: dimension of acoustic features.
-            vocab_size: vocabulary size.
-            pad_id: padding id.
-        """
-        super().__init__()
-
-        self.feat_dim = feat_dim
-        self.text_embed_dim = text_embed_dim
-        self.pad_id = pad_id
-
-        self.fm_decoder = fm_decoder
-
-        self.text_encoder = text_encoder
-
-        self.embed = nn.Embedding(vocab_size, text_embed_dim)
-
-        self.distill = False
-
-    def forward_fm_decoder(
-        self,
-        t: torch.Tensor,
-        xt: torch.Tensor,
-        text_condition: torch.Tensor,
-        speech_condition: torch.Tensor,
-        padding_mask: Optional[torch.Tensor] = None,
-        guidance_scale: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        """Compute velocity.
-        Args:
-            t:  A tensor of shape (N, 1, 1) or a tensor of a float,
-                in the range of (0, 1).
-            xt: the input of the current timestep, including condition
-                embeddings and noisy acoustic features.
-            text_condition: the text condition embeddings, with the
-                shape (batch, seq_len, emb_dim).
-            speech_condition: the speech condition embeddings, with the
-                shape (batch, seq_len, emb_dim).
-            padding_mask: The mask for padding, True means masked
-                position, with the shape (N, T).
-            guidance_scale: The guidance scale in classifier-free guidance,
-                which is a tensor of shape (N, 1, 1) or a tensor of a float.
-
-        Returns:
-            predicted velocity, with the shape (batch, seq_len, emb_dim).
-        """
-        assert t.dim() in (0, 3)
-        # Handle t with the shape (N, 1, 1):
-        # squeeze the last dimension if it's size is 1.
-        while t.dim() > 1 and t.size(-1) == 1:
-            t = t.squeeze(-1)
-        if guidance_scale is not None:
-            while guidance_scale.dim() > 1 and guidance_scale.size(-1) == 1:
-                guidance_scale = guidance_scale.squeeze(-1)
-        # Handle t with a single value: expand to the size of batch size.
-        if t.dim() == 0:
-            t = t.repeat(xt.shape[0])
-        if guidance_scale is not None and guidance_scale.dim() == 0:
-            guidance_scale = guidance_scale.repeat(xt.shape[0])
-
-        xt = torch.cat([xt, text_condition, speech_condition], dim=2)
-        vt = self.fm_decoder(
-            x=xt, t=t, padding_mask=padding_mask, guidance_scale=guidance_scale
-        )
-        return vt
-
-    def forward_text_embed(
-        self,
-        tokens: List[List[int]],
-    ):
-        """
-        Get the text embeddings.
-        Args:
-            tokens: a list of list of token ids.
-        Returns:
-            embed: the text embeddings, shape (batch, seq_len, emb_dim).
-            tokens_lens: the length of each token sequence, shape (batch,).
-        """
-        device = (
-            self.device if isinstance(self, DDP) else next(self.parameters()).device
-        )
-        tokens_padded = pad_labels(tokens, pad_id=self.pad_id, device=device)  # (B, S)
-        embed = self.embed(tokens_padded)  # (B, S, C)
-        tokens_lens = torch.tensor(
-            [len(token) for token in tokens], dtype=torch.int64, device=device
-        )
-        tokens_padding_mask = make_pad_mask(tokens_lens, embed.shape[1])  # (B, S)
-
-        embed = self.text_encoder(
-            x=embed, t=None, padding_mask=tokens_padding_mask
-        )  # (B, S, C)
-        return embed, tokens_lens
-
-    def forward_text_condition(
-        self,
-        embed: torch.Tensor,
-        tokens_lens: torch.Tensor,
-        features_lens: torch.Tensor,
-    ):
-        """
-        Get the text condition with the same length of the acoustic feature.
-        Args:
-            embed: the text embeddings, shape (batch, token_seq_len, emb_dim).
-            tokens_lens: the length of each token sequence, shape (batch,).
-            features_lens: the length of each acoustic feature sequence,
-                shape (batch,).
-        Returns:
-            text_condition: the text condition, shape
-                (batch, feature_seq_len, emb_dim).
-            padding_mask: the padding mask of text condition, shape
-                (batch, feature_seq_len).
-        """
-
-        num_frames = int(features_lens.max())
-
-        padding_mask = make_pad_mask(features_lens, max_len=num_frames)  # (B, T)
-
-        tokens_durations = prepare_avg_tokens_durations(features_lens, tokens_lens)
-
-        tokens_index = get_tokens_index(tokens_durations, num_frames).to(
-            embed.device
-        )  # (B, T)
-
-        text_condition = torch.gather(
-            embed,
-            dim=1,
-            index=tokens_index.unsqueeze(-1).expand(
-                embed.size(0), num_frames, embed.size(-1)
-            ),
-        )  # (B, T, F)
-        return text_condition, padding_mask
-
-    def forward_text_train(
-        self,
-        tokens: List[List[int]],
-        features_lens: torch.Tensor,
-    ):
-        """
-        Process text for training, given text tokens and real feature lengths.
-        """
-        embed, tokens_lens = self.forward_text_embed(tokens)
-        text_condition, padding_mask = self.forward_text_condition(
-            embed, tokens_lens, features_lens
-        )
-        return (
-            text_condition,
-            padding_mask,
-        )
-
-    def forward_text_inference_gt_duration(
-        self,
-        tokens: List[List[int]],
-        features_lens: torch.Tensor,
-        prompt_tokens: List[List[int]],
-        prompt_features_lens: torch.Tensor,
-    ):
-        """
-        Process text for inference, given text tokens, real feature lengths and prompts.
-        """
-        tokens = [
-            prompt_token + token for prompt_token, token in zip(prompt_tokens, tokens)
-        ]
-        features_lens = prompt_features_lens + features_lens
-        embed, tokens_lens = self.forward_text_embed(tokens)
-        text_condition, padding_mask = self.forward_text_condition(
-            embed, tokens_lens, features_lens
-        )
-        return text_condition, padding_mask
-
-    def forward_text_inference_ratio_duration(
-        self,
-        tokens: List[List[int]],
-        prompt_tokens: List[List[int]],
-        prompt_features_lens: torch.Tensor,
-        speed: float,
-    ):
-        """
-        Process text for inference, given text tokens and prompts,
-        feature lengths are predicted with the ratio of token numbers.
-        """
-        device = (
-            self.device if isinstance(self, DDP) else next(self.parameters()).device
-        )
-
-        cat_tokens = [
-            prompt_token + token for prompt_token, token in zip(prompt_tokens, tokens)
-        ]
-
-        prompt_tokens_lens = torch.tensor(
-            [len(token) for token in prompt_tokens], dtype=torch.int64, device=device
-        )
-
-        cat_embed, cat_tokens_lens = self.forward_text_embed(cat_tokens)
-
-        features_lens = torch.ceil(
-            (prompt_features_lens / prompt_tokens_lens * cat_tokens_lens / speed)
-        ).to(dtype=torch.int64)
-
-        text_condition, padding_mask = self.forward_text_condition(
-            cat_embed, cat_tokens_lens, features_lens
-        )
-        return text_condition, padding_mask
-
-    def forward(
-        self,
-        tokens: List[List[int]],
-        features: torch.Tensor,
-        features_lens: torch.Tensor,
-        noise: torch.Tensor,
-        t: torch.Tensor,
-        condition_drop_ratio: float = 0.0,
-    ) -> torch.Tensor:
-        """Forward pass of the model for training.
-        Args:
-            tokens: a list of list of token ids.
-            features: the acoustic features, with the shape (batch, seq_len, feat_dim).
-            features_lens: the length of each acoustic feature sequence, shape (batch,).
-            noise: the intitial noise, with the shape (batch, seq_len, feat_dim).
-            t: the time step, with the shape (batch, 1, 1).
-            condition_drop_ratio: the ratio of dropped text condition.
-        Returns:
-            fm_loss: the flow-matching loss.
-        """
-
-        (text_condition, padding_mask,) = self.forward_text_train(
-            tokens=tokens,
-            features_lens=features_lens,
-        )
-
-        speech_condition_mask = condition_time_mask(
-            features_lens=features_lens,
-            mask_percent=(0.7, 1.0),
-            max_len=features.size(1),
-        )
-        speech_condition = torch.where(speech_condition_mask.unsqueeze(-1), 0, features)
-
-        if condition_drop_ratio > 0.0:
-            drop_mask = (
-                torch.rand(text_condition.size(0), 1, 1).to(text_condition.device)
-                > condition_drop_ratio
-            )
-            text_condition = text_condition * drop_mask
-
-        xt = features * t + noise * (1 - t)
-        ut = features - noise  # (B, T, F)
-
-        vt = self.forward_fm_decoder(
-            t=t,
-            xt=xt,
-            text_condition=text_condition,
-            speech_condition=speech_condition,
-            padding_mask=padding_mask,
-        )
-
-        loss_mask = speech_condition_mask & (~padding_mask)
-        fm_loss = torch.mean((vt[loss_mask] - ut[loss_mask]) ** 2)
-
-        return fm_loss
-
-    def sample(
-        self,
-        tokens: List[List[int]],
-        prompt_tokens: List[List[int]],
-        prompt_features: torch.Tensor,
-        prompt_features_lens: torch.Tensor,
-        features_lens: Optional[torch.Tensor] = None,
-        speed: float = 1.0,
-        t_shift: float = 1.0,
-        duration: str = "predict",
-        num_step: int = 5,
-        guidance_scale: float = 0.5,
-    ) -> torch.Tensor:
-        """
-        Generate acoustic features, given text tokens, prompts feature
-            and prompt transcription's text tokens.
-        Args:
-            tokens: a list of list of text tokens.
-            prompt_tokens: a list of list of prompt tokens.
-            prompt_features: the prompt feature with the shape
-                (batch_size, seq_len, feat_dim).
-            prompt_features_lens: the length of each prompt feature,
-                with the shape (batch_size,).
-            features_lens: the length of the predicted eature, with the
-                shape (batch_size,). It is used only when duration is "real".
-            duration: "real" or "predict". If "real", the predicted
-                feature length is given by features_lens.
-            num_step: the number of steps to use in the ODE solver.
-            guidance_scale: the guidance scale for classifier-free guidance.
-            distill: whether to use the distillation model for sampling.
-        """
-
-        assert duration in ["real", "predict"]
-
-        if duration == "predict":
-            (
-                text_condition,
-                padding_mask,
-            ) = self.forward_text_inference_ratio_duration(
-                tokens=tokens,
-                prompt_tokens=prompt_tokens,
-                prompt_features_lens=prompt_features_lens,
-                speed=speed,
-            )
-        else:
-            assert features_lens is not None
-            text_condition, padding_mask = self.forward_text_inference_gt_duration(
-                tokens=tokens,
-                features_lens=features_lens,
-                prompt_tokens=prompt_tokens,
-                prompt_features_lens=prompt_features_lens,
-            )
-        batch_size, num_frames, _ = text_condition.shape
-
-        speech_condition = torch.nn.functional.pad(
-            prompt_features, (0, 0, 0, num_frames - prompt_features.size(1))
-        )  # (B, T, F)
-
-        # False means speech condition positions.
-        speech_condition_mask = make_pad_mask(prompt_features_lens, num_frames)
-        speech_condition = torch.where(
-            speech_condition_mask.unsqueeze(-1),
-            torch.zeros_like(speech_condition),
-            speech_condition,
-        )
-
-        x0 = torch.randn(
-            batch_size, num_frames, self.feat_dim, device=text_condition.device
-        )
-        solver = EulerSolver(self, distill=self.distill, func_name="forward_fm_decoder")
-
-        x1 = solver.sample(
-            x=x0,
-            text_condition=text_condition,
-            speech_condition=speech_condition,
-            padding_mask=padding_mask,
-            num_step=num_step,
-            guidance_scale=guidance_scale,
-            t_shift=t_shift,
-        )
-        x1_wo_prompt_lens = (~padding_mask).sum(-1) - prompt_features_lens
-        x1_prompt = torch.zeros(
-            x1.size(0), prompt_features_lens.max(), x1.size(2), device=x1.device
-        )
-        x1_wo_prompt = torch.zeros(
-            x1.size(0), x1_wo_prompt_lens.max(), x1.size(2), device=x1.device
-        )
-        for i in range(x1.size(0)):
-            x1_wo_prompt[i, : x1_wo_prompt_lens[i], :] = x1[
-                i,
-                prompt_features_lens[i] : prompt_features_lens[i]
-                + x1_wo_prompt_lens[i],
-            ]
-            x1_prompt[i, : prompt_features_lens[i], :] = x1[
-                i, : prompt_features_lens[i]
-            ]
-
-        return x1_wo_prompt, x1_wo_prompt_lens, x1_prompt, prompt_features_lens
-
-    def sample_intermediate(
-        self,
-        tokens: List[List[int]],
-        features: torch.Tensor,
-        features_lens: torch.Tensor,
-        noise: torch.Tensor,
-        speech_condition_mask: torch.Tensor,
-        t_start: torch.Tensor,
-        t_end: torch.Tensor,
-        num_step: int = 1,
-        guidance_scale: torch.Tensor = None,
-    ) -> torch.Tensor:
-        """
-        Generate acoustic features in intermediate timesteps.
-        Args:
-            tokens: List of list of token ids.
-            features: The acoustic features, with the shape (batch, seq_len, feat_dim).
-            features_lens: The length of each acoustic feature sequence,
-                with the shape (batch,).
-            noise: The initial noise, with the shape (batch, seq_len, feat_dim).
-            speech_condition_mask: The mask for speech condition, True means
-                non-condition positions, with the shape (batch, seq_len).
-            t_start: The start timestep, with the shape (batch, 1, 1).
-            t_end: The end timestep, with the shape (batch, 1, 1).
-            num_step: The number of steps for sampling.
-            guidance_scale: The scale for classifier-free guidance inference,
-                with the shape (batch, 1, 1).
-            distill: Whether to use distillation model.
-        """
-        (text_condition, padding_mask,) = self.forward_text_train(
-            tokens=tokens,
-            features_lens=features_lens,
-        )
-
-        speech_condition = torch.where(speech_condition_mask.unsqueeze(-1), 0, features)
-
-        solver = EulerSolver(self, distill=self.distill, func_name="forward_fm_decoder")
-
-        x_t_end = solver.sample(
-            x=noise,
-            text_condition=text_condition,
-            speech_condition=speech_condition,
-            padding_mask=padding_mask,
-            num_step=num_step,
-            guidance_scale=guidance_scale,
-            t_start=t_start,
-            t_end=t_end,
-        )
-        x_t_end_lens = (~padding_mask).sum(-1)
-        return x_t_end, x_t_end_lens
-
-
-class DistillTTSModelTrainWrapper(TtsModel):
-    """Wrapper for training the distilled TTS model."""
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.distill = True
-
-    def forward(
-        self,
-        tokens: List[List[int]],
-        features: torch.Tensor,
-        features_lens: torch.Tensor,
-        noise: torch.Tensor,
-        speech_condition_mask: torch.Tensor,
-        t_start: torch.Tensor,
-        t_end: torch.Tensor,
-        num_step: int = 1,
-        guidance_scale: torch.Tensor = None,
-    ) -> torch.Tensor:
-
-        return self.sample_intermediate(
-            tokens=tokens,
-            features=features,
-            features_lens=features_lens,
-            noise=noise,
-            speech_condition_mask=speech_condition_mask,
-            t_start=t_start,
-            t_end=t_end,
-            num_step=num_step,
-            guidance_scale=guidance_scale,
-        )
diff --git a/egs/zipvoice/zipvoice/optim.py b/egs/zipvoice/zipvoice/optim.py
deleted file mode 100644
index daf17556a..000000000
--- a/egs/zipvoice/zipvoice/optim.py
+++ /dev/null
@@ -1,1256 +0,0 @@
-# Copyright      2022  Xiaomi Corp.        (authors: Daniel Povey)
-#
-# See ../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import contextlib
-import logging
-import random
-from collections import defaultdict
-from typing import Dict, List, Optional, Tuple, Union
-
-import torch
-from lhotse.utils import fix_random_seed
-from torch import Tensor
-from torch.optim import Optimizer
-
-
-class BatchedOptimizer(Optimizer):
-    """
-    This class adds to class Optimizer the capability to optimize parameters in batches:
-    it will stack the parameters and their grads for you so the optimizer can work
-    on tensors with an extra leading dimension.  This is intended for speed with GPUs,
-    as it reduces the number of kernels launched in the optimizer.
-
-    Args:
-      params:
-    """
-
-    def __init__(self, params, defaults):
-        super(BatchedOptimizer, self).__init__(params, defaults)
-
-    @contextlib.contextmanager
-    def batched_params(self, param_group, group_params_names):
-        """
-        This function returns (technically, yields) a list of
-          of tuples (p, state), where
-        p is a `fake` parameter that is stacked (over axis 0) from real parameters
-        that share the same shape, and its gradient is also stacked;
-        `state` is the state corresponding to this batch of parameters
-        (it will be physically located in the "state" for one of the real
-        parameters, the last one that has any particular shape and dtype).
-
-        This function is decorated as a context manager so that it can
-        write parameters back to their "real" locations.
-
-        The idea is, instead of doing:
-        <code>
-          for p in group["params"]:
-             state = self.state[p]
-             ...
-        </code>
-        you can do:
-        <code>
-          with self.batched_params(group["params"]) as batches:
-             for p, state, p_names in batches:
-                 ...
-        </code>
-
-        Args:
-          group: a parameter group, which is a list of parameters; should be
-                one of self.param_groups.
-          group_params_names: name for each parameter in group,
-                which is List[str].
-        """
-        batches = defaultdict(
-            list
-        )  # `batches` maps from tuple (dtype_as_str,*shape) to list of nn.Parameter
-        batches_names = defaultdict(
-            list
-        )  # `batches` maps from tuple (dtype_as_str,*shape) to list of str
-
-        assert len(param_group) == len(group_params_names)
-        for p, named_p in zip(param_group, group_params_names):
-            key = (str(p.dtype), *p.shape)
-            batches[key].append(p)
-            batches_names[key].append(named_p)
-
-        batches_names_keys = list(batches_names.keys())
-        sorted_idx = sorted(
-            range(len(batches_names)), key=lambda i: batches_names_keys[i]
-        )
-        batches_names = [batches_names[batches_names_keys[idx]] for idx in sorted_idx]
-        batches = [batches[batches_names_keys[idx]] for idx in sorted_idx]
-
-        stacked_params_dict = dict()
-
-        # turn batches into a list, in deterministic order.
-        # tuples will contain tuples of (stacked_param, state, stacked_params_names),
-        # one for each batch in `batches`.
-        tuples = []
-
-        for batch, batch_names in zip(batches, batches_names):
-            p = batch[0]
-            # we arbitrarily store the state in the
-            # state corresponding to the 1st parameter in the
-            # group.  class Optimizer will take care of saving/loading state.
-            state = self.state[p]
-            p_stacked = torch.stack(batch)
-            grad = torch.stack(
-                [torch.zeros_like(p) if p.grad is None else p.grad for p in batch]
-            )
-            p_stacked.grad = grad
-            stacked_params_dict[key] = p_stacked
-            tuples.append((p_stacked, state, batch_names))
-
-        yield tuples  # <-- calling code will do the actual optimization here!
-
-        for ((stacked_params, _state, _names), batch) in zip(tuples, batches):
-            for i, p in enumerate(batch):  # batch is list of Parameter
-                p.copy_(stacked_params[i])
-
-
-def basic_step(group, p, state, grad):
-    # computes basic Adam update using beta2 (dividing by gradient stddev) only.  no momentum yet.
-    lr = group["lr"]
-    if p.numel() == p.shape[0]:
-        lr = lr * group["scalar_lr_scale"]
-    beta2 = group["betas"][1]
-    eps = group["eps"]
-    # p shape: (batch_size,) or (batch_size, 1, [1,..])
-    try:
-        exp_avg_sq = state[
-            "exp_avg_sq"
-        ]  # shape: (batch_size,) or (batch_size, 1, [1,..])
-    except KeyError:
-        exp_avg_sq = torch.zeros(*p.shape, device=p.device, dtype=torch.float)
-        state["exp_avg_sq"] = exp_avg_sq
-
-    exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
-
-    # bias_correction2 is like in Adam.
-    # slower update at the start will help stability anyway.
-    bias_correction2 = 1 - beta2 ** (state["step"] + 1)
-    if bias_correction2 < 0.99:
-        # note: not in-place.
-        exp_avg_sq = exp_avg_sq * (1.0 / bias_correction2)
-    denom = exp_avg_sq.sqrt().add_(eps)
-
-    return -lr * grad / denom
-
-
-def scaling_step(group, p, state, grad):
-    delta = basic_step(group, p, state, grad)
-    if p.numel() == p.shape[0]:
-        return delta  # there is no scaling for scalar parameters.  (p.shape[0] is the batch of parameters.)
-
-    step = state["step"]
-    size_update_period = group["size_update_period"]
-
-    try:
-        param_rms = state["param_rms"]
-        scale_grads = state["scale_grads"]
-        scale_exp_avg_sq = state["scale_exp_avg_sq"]
-    except KeyError:
-        # we know p.ndim > 1 because we'd have returned above if not, so don't worry
-        # about the speial case of dim=[] that pytorch treats inconsistently.
-        param_rms = (p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt()
-        param_rms = param_rms.to(torch.float)
-        scale_exp_avg_sq = torch.zeros_like(param_rms)
-        scale_grads = torch.zeros(
-            size_update_period, *param_rms.shape, dtype=torch.float, device=p.device
-        )
-        state["param_rms"] = param_rms
-        state["scale_grads"] = scale_grads
-        state["scale_exp_avg_sq"] = scale_exp_avg_sq
-
-    # on every step, update the gradient w.r.t. the scale of the parameter, we
-    # store these as a batch and periodically update the size (for speed only, to
-    # avoid too many operations).
-    scale_grads[step % size_update_period] = (p * grad).sum(
-        dim=list(range(1, p.ndim)), keepdim=True
-    )
-
-    # periodically recompute the value of param_rms.
-    if step % size_update_period == size_update_period - 1:
-        param_rms.copy_((p**2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt())
-
-    param_min_rms = group["param_min_rms"]
-
-    # scale the step size by param_rms.  This is the most important "scaling" part of
-    # ScaledAdam
-    delta *= param_rms.clamp(min=param_min_rms)
-
-    if step % size_update_period == size_update_period - 1 and step > 0:
-        # This block updates the size of parameter by adding a step ("delta") value in
-        # the direction of either shrinking or growing it.
-        beta2 = group["betas"][1]
-        size_lr = group["lr"] * group["scalar_lr_scale"]
-        param_max_rms = group["param_max_rms"]
-        eps = group["eps"]
-        batch_size = p.shape[0]
-        # correct beta2 for the size update period: we will have
-        # faster decay at this level.
-        beta2_corr = beta2**size_update_period
-        scale_exp_avg_sq.mul_(beta2_corr).add_(
-            (scale_grads**2).mean(dim=0),  # mean over dim `size_update_period`
-            alpha=1 - beta2_corr,
-        )  # shape is (batch_size, 1, 1, ...)
-
-        # The 1st time we reach here is when size_step == 1.
-        size_step = (step + 1) // size_update_period
-        bias_correction2 = 1 - beta2_corr**size_step
-
-        denom = scale_exp_avg_sq.sqrt() + eps
-
-        scale_step = (
-            -size_lr * (bias_correction2**0.5) * scale_grads.sum(dim=0) / denom
-        )
-
-        is_too_small = param_rms < param_min_rms
-
-        # when the param gets too small, just don't shrink it any further.
-        scale_step.masked_fill_(is_too_small, 0.0)
-
-        # The following may help prevent instability: don't allow the scale step to be too large in
-        # either direction.
-        scale_step.clamp_(min=-0.1, max=0.1)
-
-        # and ensure the parameter rms after update never exceeds param_max_rms.
-        # We have to look at the trained model for parameters at or around the
-        # param_max_rms, because sometimes they can indicate a problem with the
-        # topology or settings.
-        scale_step = torch.minimum(scale_step, (param_max_rms - param_rms) / param_rms)
-
-        delta.add_(p * scale_step)
-
-    return delta
-
-
-def momentum_step(group, p, state, grad):
-    delta = scaling_step(group, p, state, grad)
-    beta1 = group["betas"][0]
-    try:
-        stored_delta = state["delta"]
-    except KeyError:
-        stored_delta = torch.zeros(*p.shape, device=p.device, dtype=torch.float)
-        state["delta"] = stored_delta
-    stored_delta.mul_(beta1)
-    stored_delta.add_(delta, alpha=(1 - beta1))
-    # we don't bother doing the "bias correction" part of Adam for beta1 because this is just
-    # an edge effect that affects the first 10 or so batches; and the effect of not doing it
-    # is just to do a slower update for the first few batches, which will help stability.
-    return stored_delta
-
-
-class ScaledAdam(BatchedOptimizer):
-    """
-     Implements 'Scaled Adam', a variant of Adam where we scale each parameter's update
-     proportional to the norm of that parameter; and also learn the scale of the parameter,
-     in log space, subject to upper and lower limits (as if we had factored each parameter as
-     param = underlying_param * log_scale.exp())
-
-
-     Args:
-          params:  The parameters or param_groups to optimize (like other Optimizer subclasses)
-                   Unlike common optimizers, which accept model.parameters() or groups of parameters(),
-                   this optimizer could accept model.named_parameters() or groups of named_parameters().
-                   See comments of function _get_names_of_parameters for its 4 possible cases.
-              lr:  The learning rate.  We will typically use a learning rate schedule that starts
-                   at 0.03 and decreases over time, i.e. much higher than other common
-                   optimizers.
-     clipping_scale: (e.g. 2.0)
-                   A scale for gradient-clipping: if specified, the normalized gradients
-                   over the whole model will be clipped to have 2-norm equal to
-                   `clipping_scale` times the median 2-norm over the most recent period
-                   of `clipping_update_period` minibatches.  By "normalized gradients",
-                   we mean after multiplying by the rms parameter value for this tensor
-                   [for non-scalars]; this is appropriate because our update is scaled
-                   by this quantity.
-            betas: beta1,beta2 are momentum constants for regular momentum, and moving sum-sq grad.
-                   Must satisfy 0 < beta <= beta2 < 1.
-     scalar_lr_scale: A scaling factor on the learning rate, that we use to update the                   scale of each parameter tensor and scalar parameters of the mode..
-                   If each parameter were decomposed
-                   as p * p_scale.exp(), where (p**2).mean().sqrt() == 1.0, scalar_lr_scale
-                   would be a the scaling factor on the learning rate of p_scale.
-              eps:  A general-purpose epsilon to prevent division by zero
-    param_min_rms: Minimum root-mean-square value of parameter tensor, for purposes of
-                   learning the scale on the parameters (we'll constrain the rms of each non-scalar
-                   parameter tensor to be >= this value)
-    param_max_rms: Maximum root-mean-square value of parameter tensor, for purposes of
-                   learning the scale on the parameters (we'll constrain the rms of each non-scalar
-                   parameter tensor to be <= this value)
-       scalar_max: Maximum absolute value for scalar parameters (applicable if your
-                   model has any parameters with numel() == 1).
-    size_update_period: The periodicity, in steps, with which we update the size (scale)
-                   of the parameter tensor.  This is provided to save a little time
-                   in the update.
-     clipping_update_period: if clipping_scale is specified, this is the period
-    """
-
-    def __init__(
-        self,
-        params,
-        lr=3e-02,
-        clipping_scale=None,
-        betas=(0.9, 0.98),
-        scalar_lr_scale=0.1,
-        eps=1.0e-08,
-        param_min_rms=1.0e-05,
-        param_max_rms=3.0,
-        scalar_max=10.0,
-        size_update_period=4,
-        clipping_update_period=100,
-    ):
-
-        defaults = dict(
-            lr=lr,
-            clipping_scale=clipping_scale,
-            betas=betas,
-            scalar_lr_scale=scalar_lr_scale,
-            eps=eps,
-            param_min_rms=param_min_rms,
-            param_max_rms=param_max_rms,
-            scalar_max=scalar_max,
-            size_update_period=size_update_period,
-            clipping_update_period=clipping_update_period,
-        )
-
-        # If params only contains parameters or group of parameters,
-        # i.e when parameter names are not given,
-        # this flag will be set to False in funciton _get_names_of_parameters.
-        self.show_dominant_parameters = True
-        param_groups, parameters_names = self._get_names_of_parameters(params)
-        super(ScaledAdam, self).__init__(param_groups, defaults)
-        assert len(self.param_groups) == len(parameters_names)
-        self.parameters_names = parameters_names
-
-    def _get_names_of_parameters(
-        self, params_or_named_params
-    ) -> Tuple[List[Dict], List[List[str]]]:
-        """
-        Args:
-          params_or_named_params: according to the way ScaledAdam is initialized in train.py,
-            this argument could be one of following 4 cases,
-            case 1, a generator of parameter, e.g.:
-              optimizer = ScaledAdam(model.parameters(), lr=params.base_lr, clipping_scale=3.0)
-
-            case 2, a list of parameter groups with different config, e.g.:
-              model_param_groups = [
-                      {'params': model.encoder.parameters(), 'lr': 0.05},
-                      {'params': model.decoder.parameters(), 'lr': 0.01},
-                      {'params': model.joiner.parameters(), 'lr': 0.03},
-                      ]
-              optimizer = ScaledAdam(model_param_groups, lr=params.base_lr, clipping_scale=3.0)
-
-            case 3, a generator of named_parameter, e.g.:
-              optimizer = ScaledAdam(model.named_parameters(), lr=params.base_lr, clipping_scale=3.0)
-
-            case 4, a list of named_parameter groups with different config, e.g.:
-              model_named_param_groups = [
-                      {'named_params': model.encoder.named_parameters(), 'lr': 0.05},
-                      {'named_params': model.decoder.named_parameters(), 'lr': 0.01},
-                      {'named_params': model.joiner.named_parameters(), 'lr': 0.03},
-                      ]
-              optimizer = ScaledAdam(model_named_param_groups, lr=params.base_lr, clipping_scale=3.0)
-
-          For case 1 and case 2, input params is used to initialize the underlying torch.optimizer.
-          For case 3 and case 4, firstly, names and params are extracted from input named_params,
-            then, these extracted params are used to initialize the underlying torch.optimizer,
-            and these extracted names are mainly used by function
-            `_show_gradient_dominating_parameter`
-
-        Returns:
-          Returns a tuple containing 2 elements:
-            - `param_groups` with type List[Dict], each Dict element is a parameter group.
-              An example of `param_groups` could be:
-              [
-                  {'params': `one iterable of Parameter`, 'lr': 0.05},
-                  {'params': `another iterable of Parameter`, 'lr': 0.08},
-                  {'params': `a third iterable of Parameter`, 'lr': 0.1},
-              ]
-            - `param_gruops_names` with type List[List[str]],
-               each `List[str]` is for a group['params'] in param_groups,
-               and each `str` is the name of a parameter.
-               A dummy name "foo" is related to each parameter,
-               if input are params without names, i.e. case 1 or case 2.
-        """
-        # variable naming convention in this function:
-        #   p is short for param.
-        #   np is short for named_param.
-        #   p_or_np is short for param_or_named_param.
-        #   cur is short for current.
-        #   group is a dict, e.g. {'params': iterable of parameter, 'lr': 0.05, other fields}.
-        #   groups is a List[group]
-
-        iterable_or_groups = list(params_or_named_params)
-        if len(iterable_or_groups) == 0:
-            raise ValueError("optimizer got an empty parameter list")
-
-        # The first value of returned tuple.  A list of dicts containing at
-        # least 'params' as a key.
-        param_groups = []
-
-        # The second value of returned tuple,
-        # a List[List[str]], each sub-List is for a group.
-        param_groups_names = []
-
-        if not isinstance(iterable_or_groups[0], dict):
-            # case 1 or case 3,
-            # the input is an iterable of parameter or named parameter.
-            param_iterable_cur_group = []
-            param_names_cur_group = []
-            for p_or_np in iterable_or_groups:
-                if isinstance(p_or_np, tuple):
-                    # case 3
-                    name, param = p_or_np
-                else:
-                    # case 1
-                    assert isinstance(p_or_np, torch.Tensor)
-                    param = p_or_np
-                    # Assign a dummy name as a placeholder
-                    name = "foo"
-                    self.show_dominant_parameters = False
-                param_iterable_cur_group.append(param)
-                param_names_cur_group.append(name)
-            param_groups.append({"params": param_iterable_cur_group})
-            param_groups_names.append(param_names_cur_group)
-        else:
-            # case 2 or case 4
-            # the input is groups of parameter or named parameter.
-            for cur_group in iterable_or_groups:
-                if "named_params" in cur_group:
-                    name_list = [x[0] for x in cur_group["named_params"]]
-                    p_list = [x[1] for x in cur_group["named_params"]]
-                    del cur_group["named_params"]
-                    cur_group["params"] = p_list
-                else:
-                    assert "params" in cur_group
-                    name_list = ["foo" for _ in cur_group["params"]]
-                param_groups.append(cur_group)
-                param_groups_names.append(name_list)
-
-        return param_groups, param_groups_names
-
-    def __setstate__(self, state):
-        super(ScaledAdam, self).__setstate__(state)
-
-    @torch.no_grad()
-    def step(self, closure=None):
-        """Performs a single optimization step.
-
-        Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
-        """
-        loss = None
-        if closure is not None:
-            with torch.enable_grad():
-                loss = closure()
-
-        batch = True
-
-        for group, group_params_names in zip(self.param_groups, self.parameters_names):
-
-            with self.batched_params(group["params"], group_params_names) as batches:
-
-                # batches is list of pairs (stacked_param, state).  stacked_param is like
-                # a regular parameter, and will have a .grad, but the 1st dim corresponds to
-                # a stacking dim, it is not a real dim.
-
-                if (
-                    len(batches[0][1]) == 0
-                ):  # if len(first state) == 0: not yet initialized
-                    clipping_scale = 1
-                else:
-                    clipping_scale = self._get_clipping_scale(group, batches)
-
-                for p, state, _ in batches:
-                    # Perform optimization step.
-                    # grad is not going to be None, we handled that when creating the batches.
-                    grad = p.grad
-                    if grad.is_sparse:
-                        raise RuntimeError(
-                            "ScaledAdam optimizer does not support sparse gradients"
-                        )
-
-                    try:
-                        cur_step = state["step"]
-                    except KeyError:
-                        state["step"] = 0
-                        cur_step = 0
-
-                    grad = (
-                        p.grad if clipping_scale == 1.0 else p.grad.mul_(clipping_scale)
-                    )
-                    p += momentum_step(group, p.detach(), state, grad)
-
-                    if p.numel() == p.shape[0]:  # scalar parameter
-                        scalar_max = group["scalar_max"]
-                        p.clamp_(min=-scalar_max, max=scalar_max)
-
-                    state["step"] = cur_step + 1
-
-        return loss
-
-    def _get_clipping_scale(
-        self, group: dict, tuples: List[Tuple[Tensor, dict, List[str]]]
-    ) -> float:
-        """
-        Returns a scalar factor <= 1.0 that dictates gradient clipping, i.e. we will scale the gradients
-        by this amount before applying the rest of the update.
-
-        Args:
-           group: the parameter group, an item in self.param_groups
-           tuples: a list of tuples of (param, state, param_names)
-                where param is a batched set of parameters,
-                with a .grad (1st dim is batch dim)
-                and state is the state-dict where optimization parameters are kept.
-                param_names is a List[str] while each str is name for a parameter
-                in batched set of parameters "param".
-        """
-        assert len(tuples) >= 1
-        clipping_scale = group["clipping_scale"]
-        (first_p, first_state, _) = tuples[0]
-        step = first_state["step"]
-        if clipping_scale is None or step == 0:
-            # no clipping.  return early on step == 0 because the other
-            # parameters' state won't have been initialized yet.
-            return 1.0
-        clipping_update_period = group["clipping_update_period"]
-        scalar_lr_scale = group["scalar_lr_scale"]
-
-        tot_sumsq = torch.tensor(0.0, device=first_p.device)
-        for (p, state, param_names) in tuples:
-            grad = p.grad
-            if grad.is_sparse:
-                raise RuntimeError(
-                    "ScaledAdam optimizer does not support sparse gradients"
-                )
-            if p.numel() == p.shape[0]:  # a batch of scalars
-                tot_sumsq += (grad**2).sum() * (
-                    scalar_lr_scale**2
-                )  # sum() to change shape [1] to []
-            else:
-                tot_sumsq += ((grad * state["param_rms"]) ** 2).sum()
-
-        tot_norm = tot_sumsq.sqrt()
-        if "model_norms" not in first_state:
-            first_state["model_norms"] = torch.zeros(
-                clipping_update_period, device=p.device
-            )
-        first_state["model_norms"][step % clipping_update_period] = tot_norm
-
-        irregular_estimate_steps = [
-            i for i in [10, 20, 40] if i < clipping_update_period
-        ]
-        if step % clipping_update_period == 0 or step in irregular_estimate_steps:
-            # Print some stats.
-            # We don't reach here if step == 0 because we would have returned
-            # above.
-            sorted_norms = first_state["model_norms"].sort()[0].to("cpu")
-            if step in irregular_estimate_steps:
-                sorted_norms = sorted_norms[-step:]
-            num_norms = sorted_norms.numel()
-            quartiles = []
-            for n in range(0, 5):
-                index = min(num_norms - 1, (num_norms // 4) * n)
-                quartiles.append(sorted_norms[index].item())
-
-            median = quartiles[2]
-            if median - median != 0:
-                raise RuntimeError("Too many grads were not finite")
-            threshold = clipping_scale * median
-            if step in irregular_estimate_steps:
-                # use larger thresholds on first few steps of estimating threshold,
-                # as norm may be changing rapidly.
-                threshold = threshold * 2.0
-            first_state["model_norm_threshold"] = threshold
-            percent_clipped = (
-                first_state["num_clipped"] * 100.0 / num_norms
-                if "num_clipped" in first_state
-                else 0.0
-            )
-            first_state["num_clipped"] = 0
-            quartiles = " ".join(["%.3e" % x for x in quartiles])
-            logging.warning(
-                f"Clipping_scale={clipping_scale}, grad-norm quartiles {quartiles}, "
-                f"threshold={threshold:.3e}, percent-clipped={percent_clipped:.1f}"
-            )
-
-        try:
-            model_norm_threshold = first_state["model_norm_threshold"]
-        except KeyError:
-            return 1.0  # threshold has not yet been set.
-
-        ans = min(1.0, (model_norm_threshold / (tot_norm + 1.0e-20)).item())
-        if ans != ans:  # e.g. ans is nan
-            ans = 0.0
-        if ans < 1.0:
-            first_state["num_clipped"] += 1
-        if ans < 0.5:
-            logging.warning(
-                f"Scaling gradients by {ans}, model_norm_threshold={model_norm_threshold}"
-            )
-            if self.show_dominant_parameters:
-                assert p.shape[0] == len(param_names)
-                self._show_gradient_dominating_parameter(
-                    tuples, tot_sumsq, group["scalar_lr_scale"]
-                )
-                self._show_param_with_unusual_grad(tuples)
-
-        if ans == 0.0:
-            for (p, state, param_names) in tuples:
-                p.grad.zero_()  # get rid of infinity()
-
-        return ans
-
-    def _show_param_with_unusual_grad(
-        self,
-        tuples: List[Tuple[Tensor, dict, List[str]]],
-    ):
-        """
-        Print information about parameter which has the largest ratio of grad-on-this-batch
-        divided by normal grad size.
-           tuples: a list of tuples of (param, state, param_names)
-                where param is a batched set of parameters,
-                with a .grad (1st dim is batch dim)
-                and state is the state-dict where optimization parameters are kept.
-                param_names is a List[str] while each str is name for a parameter
-                in batched set of parameters "param".
-        """
-        largest_ratio = 0.0
-        largest_name = ""
-        # ratios_names is a list of 3-tuples: (grad_ratio, param_name, tensor)
-        ratios_names = []
-        for (p, state, batch_param_names) in tuples:
-            dims = list(range(1, p.ndim))
-
-            def mean(x):
-                # workaround for bad interface of torch's "mean" for when dims is the empty list.
-                if len(dims) > 0:
-                    return x.mean(dim=dims)
-                else:
-                    return x
-
-            grad_ratio = (
-                (mean(p.grad**2) / state["exp_avg_sq"].mean(dim=dims))
-                .sqrt()
-                .to("cpu")
-            )
-
-            ratios_names += zip(
-                grad_ratio.tolist(), batch_param_names, p.grad.unbind(dim=0)
-            )
-
-        ratios_names = sorted(ratios_names, reverse=True)
-        ratios_names = ratios_names[:10]
-        ratios_names = [
-            (ratio, name, largest_index(tensor))
-            for (ratio, name, tensor) in ratios_names
-        ]
-
-        logging.debug(
-            f"Parameters with most larger-than-usual grads, with ratios, are: {ratios_names}"
-        )
-
-    def _show_gradient_dominating_parameter(
-        self,
-        tuples: List[Tuple[Tensor, dict, List[str]]],
-        tot_sumsq: Tensor,
-        scalar_lr_scale: float,
-    ):
-        """
-        Show information of parameter which dominates tot_sumsq.
-
-        Args:
-           tuples: a list of tuples of (param, state, param_names)
-                where param is a batched set of parameters,
-                with a .grad (1st dim is batch dim)
-                and state is the state-dict where optimization parameters are kept.
-                param_names is a List[str] while each str is name for a parameter
-                in batched set of parameters "param".
-            tot_sumsq: sumsq of all parameters. Though it's could be calculated
-                from tuples, we still pass it to save some time.
-        """
-        all_sumsq_orig = {}
-        for (p, state, batch_param_names) in tuples:
-            # p is a stacked batch parameters.
-            batch_grad = p.grad
-            if p.numel() == p.shape[0]:  # a batch of scalars
-                # Dummy values used by following `zip` statement.
-                batch_rms_orig = torch.full(
-                    p.shape, scalar_lr_scale, device=batch_grad.device
-                )
-            else:
-                batch_rms_orig = state["param_rms"]
-            batch_sumsq_orig = (batch_grad * batch_rms_orig) ** 2
-            if batch_grad.ndim > 1:
-                # need to guard it with if-statement because sum() sums over
-                # all dims if dim == ().
-                batch_sumsq_orig = batch_sumsq_orig.sum(
-                    dim=list(range(1, batch_grad.ndim))
-                )
-            for name, sumsq_orig, rms, grad in zip(
-                batch_param_names, batch_sumsq_orig, batch_rms_orig, batch_grad
-            ):
-
-                proportion_orig = sumsq_orig / tot_sumsq
-                all_sumsq_orig[name] = (proportion_orig, sumsq_orig, rms, grad)
-
-        sorted_by_proportion = {
-            k: v
-            for k, v in sorted(
-                all_sumsq_orig.items(), key=lambda item: item[1][0], reverse=True
-            )
-        }
-        dominant_param_name = next(iter(sorted_by_proportion))
-        (
-            dominant_proportion,
-            dominant_sumsq,
-            dominant_rms,
-            dominant_grad,
-        ) = sorted_by_proportion[dominant_param_name]
-        logging.debug(
-            f"Parameter dominating tot_sumsq {dominant_param_name}"
-            f" with proportion {dominant_proportion:.2f},"
-            f" where dominant_sumsq=(grad_sumsq*orig_rms_sq)"
-            f"={dominant_sumsq:.3e},"
-            f" grad_sumsq={(dominant_grad**2).sum():.3e},"
-            f" orig_rms_sq={(dominant_rms**2).item():.3e}"
-        )
-
-
-def largest_index(x: Tensor):
-    x = x.contiguous()
-    argmax = x.abs().argmax().item()
-    return [(argmax // x.stride(i)) % x.size(i) for i in range(x.ndim)]
-
-
-class LRScheduler(object):
-    """
-    Base-class for learning rate schedulers where the learning-rate depends on both the
-    batch and the epoch.
-    """
-
-    def __init__(self, optimizer: Optimizer, verbose: bool = False):
-        # Attach optimizer
-        if not isinstance(optimizer, Optimizer):
-            raise TypeError("{} is not an Optimizer".format(type(optimizer).__name__))
-        self.optimizer = optimizer
-        self.verbose = verbose
-
-        for group in optimizer.param_groups:
-            group.setdefault("base_lr", group["lr"])
-
-        self.base_lrs = [group["base_lr"] for group in optimizer.param_groups]
-
-        self.epoch = 0
-        self.batch = 0
-
-    def state_dict(self):
-        """Returns the state of the scheduler as a :class:`dict`.
-
-        It contains an entry for every variable in self.__dict__ which
-        is not the optimizer.
-        """
-        return {
-            # the user might try to override the base_lr, so don't include this in the state.
-            # previously they were included.
-            # "base_lrs": self.base_lrs,
-            "epoch": self.epoch,
-            "batch": self.batch,
-        }
-
-    def load_state_dict(self, state_dict):
-        """Loads the schedulers state.
-
-        Args:
-            state_dict (dict): scheduler state. Should be an object returned
-                from a call to :meth:`state_dict`.
-        """
-        # the things with base_lrs are a work-around for a previous problem
-        # where base_lrs were written with the state dict.
-        base_lrs = self.base_lrs
-        self.__dict__.update(state_dict)
-        self.base_lrs = base_lrs
-
-    def get_last_lr(self) -> List[float]:
-        """Return last computed learning rate by current scheduler.  Will be a list of float."""
-        return self._last_lr
-
-    def get_lr(self):
-        # Compute list of learning rates from self.epoch and self.batch and
-        # self.base_lrs; this must be overloaded by the user.
-        # e.g. return [some_formula(self.batch, self.epoch, base_lr) for base_lr in self.base_lrs ]
-        raise NotImplementedError
-
-    def step_batch(self, batch: Optional[int] = None) -> None:
-        # Step the batch index, or just set it.  If `batch` is specified, it
-        # must be the batch index from the start of training, i.e. summed over
-        # all epochs.
-        # You can call this in any order; if you don't provide 'batch', it should
-        # of course be called once per batch.
-        if batch is not None:
-            self.batch = batch
-        else:
-            self.batch = self.batch + 1
-        self._set_lrs()
-
-    def step_epoch(self, epoch: Optional[int] = None):
-        # Step the epoch index, or just set it.  If you provide the 'epoch' arg,
-        # you should call this at the start of the epoch; if you don't provide the 'epoch'
-        # arg, you should call it at the end of the epoch.
-        if epoch is not None:
-            self.epoch = epoch
-        else:
-            self.epoch = self.epoch + 1
-        self._set_lrs()
-
-    def _set_lrs(self):
-        values = self.get_lr()
-        assert len(values) == len(self.optimizer.param_groups)
-
-        for i, data in enumerate(zip(self.optimizer.param_groups, values)):
-            param_group, lr = data
-            param_group["lr"] = lr
-            self.print_lr(self.verbose, i, lr)
-        self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
-
-    def print_lr(self, is_verbose, group, lr):
-        """Display the current learning rate."""
-        if is_verbose:
-            logging.warning(
-                f"Epoch={self.epoch}, batch={self.batch}: adjusting learning rate"
-                f" of group {group} to {lr:.4e}."
-            )
-
-
-class Eden(LRScheduler):
-    """
-    Eden scheduler.
-    The basic formula (before warmup) is:
-      lr = base_lr * (((batch**2 + lr_batches**2) / lr_batches**2) ** -0.25 *
-                     (((epoch**2 + lr_epochs**2) / lr_epochs**2) ** -0.25)) * warmup
-    where `warmup` increases from linearly 0.5 to 1 over `warmup_batches` batches
-    and then stays constant at 1.
-
-    If you don't have the concept of epochs, or one epoch takes a very long time,
-    you can replace the notion of 'epoch' with some measure of the amount of data
-    processed, e.g. hours of data or frames of data, with 'lr_epochs' being set to
-    some measure representing "quite a lot of data": say, one fifth or one third
-    of an entire training run, but it doesn't matter much.  You could also use
-    Eden2 which has only the notion of batches.
-
-    We suggest base_lr = 0.04 (passed to optimizer) if used with ScaledAdam
-
-    Args:
-        optimizer: the optimizer to change the learning rates on
-        lr_batches: the number of batches after which we start significantly
-              decreasing the learning rate, suggest 5000.
-        lr_epochs: the number of epochs after which we start significantly
-              decreasing the learning rate, suggest 6 if you plan to do e.g.
-              20 to 40 epochs, but may need smaller number if dataset is huge
-              and you will do few epochs.
-    """
-
-    def __init__(
-        self,
-        optimizer: Optimizer,
-        lr_batches: Union[int, float],
-        lr_epochs: Union[int, float],
-        warmup_batches: Union[int, float] = 500.0,
-        warmup_start: float = 0.5,
-        verbose: bool = False,
-    ):
-        super(Eden, self).__init__(optimizer, verbose)
-        self.lr_batches = lr_batches
-        self.lr_epochs = lr_epochs
-        self.warmup_batches = warmup_batches
-
-        assert 0.0 <= warmup_start <= 1.0, warmup_start
-        self.warmup_start = warmup_start
-
-    def get_lr(self):
-        factor = (
-            (self.batch**2 + self.lr_batches**2) / self.lr_batches**2
-        ) ** -0.25 * (
-            ((self.epoch**2 + self.lr_epochs**2) / self.lr_epochs**2) ** -0.25
-        )
-        warmup_factor = (
-            1.0
-            if self.batch >= self.warmup_batches
-            else self.warmup_start
-            + (1.0 - self.warmup_start) * (self.batch / self.warmup_batches)
-            # else 0.5 + 0.5 * (self.batch / self.warmup_batches)
-        )
-
-        return [x * factor * warmup_factor for x in self.base_lrs]
-
-
-class Eden2(LRScheduler):
-    """
-    Eden2 scheduler, simpler than Eden because it does not use the notion of epoch,
-    only batches.
-
-    The basic formula (before warmup) is:
-      lr = base_lr * ((batch**2 + lr_batches**2) / lr_batches**2) ** -0.5) * warmup
-
-    where `warmup` increases from linearly 0.5 to 1 over `warmup_batches` batches
-    and then stays constant at 1.
-
-
-     E.g. suggest base_lr = 0.04 (passed to optimizer) if used with ScaledAdam
-
-    Args:
-        optimizer: the optimizer to change the learning rates on
-        lr_batches: the number of batches after which we start significantly
-              decreasing the learning rate, suggest 5000.
-    """
-
-    def __init__(
-        self,
-        optimizer: Optimizer,
-        lr_batches: Union[int, float],
-        warmup_batches: Union[int, float] = 500.0,
-        warmup_start: float = 0.5,
-        verbose: bool = False,
-    ):
-        super().__init__(optimizer, verbose)
-        self.lr_batches = lr_batches
-        self.warmup_batches = warmup_batches
-
-        assert 0.0 <= warmup_start <= 1.0, warmup_start
-        self.warmup_start = warmup_start
-
-    def get_lr(self):
-        factor = (
-            (self.batch**2 + self.lr_batches**2) / self.lr_batches**2
-        ) ** -0.5
-        warmup_factor = (
-            1.0
-            if self.batch >= self.warmup_batches
-            else self.warmup_start
-            + (1.0 - self.warmup_start) * (self.batch / self.warmup_batches)
-            # else 0.5 + 0.5 * (self.batch / self.warmup_batches)
-        )
-
-        return [x * factor * warmup_factor for x in self.base_lrs]
-
-
-class FixedLRScheduler(LRScheduler):
-    """
-    Fixed learning rate scheduler.
-
-    Args:
-        optimizer: the optimizer to change the learning rates on
-    """
-
-    def __init__(
-        self,
-        optimizer: Optimizer,
-        verbose: bool = False,
-    ):
-        super(FixedLRScheduler, self).__init__(optimizer, verbose)
-
-    def get_lr(self):
-
-        return [x for x in self.base_lrs]
-
-
-def _test_eden():
-    m = torch.nn.Linear(100, 100)
-    optim = ScaledAdam(m.parameters(), lr=0.03)
-
-    scheduler = Eden(optim, lr_batches=100, lr_epochs=2, verbose=True)
-
-    for epoch in range(10):
-        scheduler.step_epoch(epoch)  # sets epoch to `epoch`
-
-        for step in range(20):
-            x = torch.randn(200, 100).detach()
-            x.requires_grad = True
-            y = m(x)
-            dy = torch.randn(200, 100).detach()
-            f = (y * dy).sum()
-            f.backward()
-
-            optim.step()
-            scheduler.step_batch()
-            optim.zero_grad()
-
-    logging.info(f"last lr = {scheduler.get_last_lr()}")
-    logging.info(f"state dict = {scheduler.state_dict()}")
-
-
-# This is included mostly as a baseline for ScaledAdam.
-class Eve(Optimizer):
-    """
-    Implements Eve algorithm.  This is a modified version of AdamW with a special
-    way of setting the weight-decay / shrinkage-factor, which is designed to make the
-    rms of the parameters approach a particular target_rms (default: 0.1).  This is
-    for use with networks with 'scaled' versions of modules (see scaling.py), which
-    will be close to invariant to the absolute scale on the parameter matrix.
-
-    The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
-    The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
-    Eve is unpublished so far.
-
-    Arguments:
-        params (iterable): iterable of parameters to optimize or dicts defining
-            parameter groups
-        lr (float, optional): learning rate (default: 1e-3)
-        betas (Tuple[float, float], optional): coefficients used for computing
-            running averages of gradient and its square (default: (0.9, 0.999))
-        eps (float, optional): term added to the denominator to improve
-            numerical stability (default: 1e-8)
-        weight_decay (float, optional): weight decay coefficient (default: 3e-4;
-            this value means that the weight would decay significantly after
-            about 3k minibatches.  Is not multiplied by learning rate, but
-            is conditional on RMS-value of parameter being > target_rms.
-        target_rms (float, optional): target root-mean-square value of
-           parameters, if they fall below this we will stop applying weight decay.
-
-
-    .. _Adam: A Method for Stochastic Optimization:
-        https://arxiv.org/abs/1412.6980
-    .. _Decoupled Weight Decay Regularization:
-        https://arxiv.org/abs/1711.05101
-    .. _On the Convergence of Adam and Beyond:
-        https://openreview.net/forum?id=ryQu7f-RZ
-    """
-
-    def __init__(
-        self,
-        params,
-        lr=1e-3,
-        betas=(0.9, 0.98),
-        eps=1e-8,
-        weight_decay=1e-3,
-        target_rms=0.1,
-    ):
-        if not 0.0 <= lr:
-            raise ValueError("Invalid learning rate: {}".format(lr))
-        if not 0.0 <= eps:
-            raise ValueError("Invalid epsilon value: {}".format(eps))
-        if not 0.0 <= betas[0] < 1.0:
-            raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
-        if not 0.0 <= betas[1] < 1.0:
-            raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
-        if not 0 <= weight_decay <= 0.1:
-            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
-        if not 0 < target_rms <= 10.0:
-            raise ValueError("Invalid target_rms value: {}".format(target_rms))
-        defaults = dict(
-            lr=lr,
-            betas=betas,
-            eps=eps,
-            weight_decay=weight_decay,
-            target_rms=target_rms,
-        )
-        super(Eve, self).__init__(params, defaults)
-
-    def __setstate__(self, state):
-        super(Eve, self).__setstate__(state)
-
-    @torch.no_grad()
-    def step(self, closure=None):
-        """Performs a single optimization step.
-
-        Arguments:
-            closure (callable, optional): A closure that reevaluates the model
-                and returns the loss.
-        """
-        loss = None
-        if closure is not None:
-            with torch.enable_grad():
-                loss = closure()
-
-        for group in self.param_groups:
-            for p in group["params"]:
-                if p.grad is None:
-                    continue
-
-                # Perform optimization step
-                grad = p.grad
-                if grad.is_sparse:
-                    raise RuntimeError("AdamW does not support sparse gradients")
-
-                state = self.state[p]
-
-                # State initialization
-                if len(state) == 0:
-                    state["step"] = 0
-                    # Exponential moving average of gradient values
-                    state["exp_avg"] = torch.zeros_like(
-                        p, memory_format=torch.preserve_format
-                    )
-                    # Exponential moving average of squared gradient values
-                    state["exp_avg_sq"] = torch.zeros_like(
-                        p, memory_format=torch.preserve_format
-                    )
-
-                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
-
-                beta1, beta2 = group["betas"]
-
-                state["step"] += 1
-                bias_correction1 = 1 - beta1 ** state["step"]
-                bias_correction2 = 1 - beta2 ** state["step"]
-
-                # Decay the first and second moment running average coefficient
-                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
-                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
-                denom = (exp_avg_sq.sqrt() * (bias_correction2**-0.5)).add_(
-                    group["eps"]
-                )
-
-                step_size = group["lr"] / bias_correction1
-                target_rms = group["target_rms"]
-                weight_decay = group["weight_decay"]
-
-                if p.numel() > 1:
-                    # avoid applying this weight-decay on "scaling factors"
-                    # (which are scalar).
-                    is_above_target_rms = p.norm() > (target_rms * (p.numel() ** 0.5))
-                    p.mul_(1 - (weight_decay * is_above_target_rms))
-
-                p.addcdiv_(exp_avg, denom, value=-step_size)
-
-                if random.random() < 0.0005:
-                    step = (exp_avg / denom) * step_size
-                    logging.info(
-                        f"Delta rms = {(step**2).mean().item()}, shape = {step.shape}"
-                    )
-
-        return loss
-
-
-def _test_scaled_adam(hidden_dim: int):
-    import timeit
-
-    from scaling import ScaledLinear
-
-    E = 100
-    B = 4
-    T = 2
-    logging.info("in test_eve_cain")
-    # device = torch.device('cuda')
-    device = torch.device("cpu")
-    dtype = torch.float32
-
-    fix_random_seed(42)
-    # these input_magnitudes and output_magnitudes are to test that
-    # Abel is working as we expect and is able to adjust scales of
-    # different dims differently.
-    input_magnitudes = (1.0 * torch.randn(E, dtype=dtype, device=device)).exp()
-    output_magnitudes = (1.0 * torch.randn(E, dtype=dtype, device=device)).exp()
-
-    for iter in [1, 0]:
-        fix_random_seed(42)
-        Linear = torch.nn.Linear if iter == 0 else ScaledLinear
-
-        m = torch.nn.Sequential(
-            Linear(E, hidden_dim),
-            torch.nn.PReLU(),
-            Linear(hidden_dim, hidden_dim),
-            torch.nn.PReLU(),
-            Linear(hidden_dim, E),
-        ).to(device)
-
-        train_pairs = [
-            (
-                100.0
-                * torch.randn(B, T, E, device=device, dtype=dtype)
-                * input_magnitudes,
-                torch.randn(B, T, E, device=device, dtype=dtype) * output_magnitudes,
-            )
-            for _ in range(20)
-        ]
-
-        if iter == 0:
-            optim = Eve(m.parameters(), lr=0.003)
-        elif iter == 1:
-            optim = ScaledAdam(m.named_parameters(), lr=0.03, clipping_scale=2.0)
-        scheduler = Eden(optim, lr_batches=200, lr_epochs=5, verbose=False)
-
-        start = timeit.default_timer()
-        avg_loss = 0.0
-        for epoch in range(180):
-            scheduler.step_epoch()
-            # if epoch == 100 and iter in [2,3]:
-            #    optim.reset_speedup()  # check it doesn't crash.
-
-            # if epoch == 130:
-            #    opts = diagnostics.TensorDiagnosticOptions(
-            #        512
-            #    )  # allow 4 megabytes per sub-module
-            #    diagnostic = diagnostics.attach_diagnostics(m, opts)
-
-            for n, (x, y) in enumerate(train_pairs):
-                y_out = m(x)
-                loss = ((y_out - y) ** 2).mean() * 100.0
-                if epoch == 0 and n == 0:
-                    avg_loss = loss.item()
-                else:
-                    avg_loss = 0.98 * avg_loss + 0.02 * loss.item()
-                if n == 0 and epoch % 5 == 0:
-                    # norm1 = '%.2e' % (m[0].weight**2).mean().sqrt().item()
-                    # norm1b = '%.2e' % (m[0].bias**2).mean().sqrt().item()
-                    # norm2 = '%.2e' % (m[2].weight**2).mean().sqrt().item()
-                    # norm2b = '%.2e' % (m[2].bias**2).mean().sqrt().item()
-                    # scale1 = '%.2e' % (m[0].weight_scale.exp().item())
-                    # scale1b = '%.2e' % (m[0].bias_scale.exp().item())
-                    # scale2 = '%.2e' % (m[2].weight_scale.exp().item())
-                    # scale2b = '%.2e' % (m[2].bias_scale.exp().item())
-                    lr = scheduler.get_last_lr()[0]
-                    logging.info(
-                        f"Iter {iter}, epoch {epoch}, batch {n}, avg_loss {avg_loss:.4g}, lr={lr:.4e}"
-                    )  # , norms={norm1,norm1b,norm2,norm2b}") # scales={scale1,scale1b,scale2,scale2b}
-                loss.log().backward()
-                optim.step()
-                optim.zero_grad()
-                scheduler.step_batch()
-
-        # diagnostic.print_diagnostics()
-
-        stop = timeit.default_timer()
-        logging.info(f"Iter={iter}, Time taken: {stop - start}")
-
-        logging.info(f"last lr = {scheduler.get_last_lr()}")
-        # logging.info("state dict = ", scheduler.state_dict())
-        # logging.info("optim state_dict = ", optim.state_dict())
-        logging.info(f"input_magnitudes = {input_magnitudes}")
-        logging.info(f"output_magnitudes = {output_magnitudes}")
-
-
-if __name__ == "__main__":
-    torch.set_num_threads(1)
-    torch.set_num_interop_threads(1)
-    logging.getLogger().setLevel(logging.INFO)
-    import subprocess
-
-    s = subprocess.check_output(
-        "git status -uno .; git log -1; git diff HEAD .", shell=True
-    )
-    logging.info(s)
-    import sys
-
-    if len(sys.argv) > 1:
-        hidden_dim = int(sys.argv[1])
-    else:
-        hidden_dim = 200
-
-    _test_scaled_adam(hidden_dim)
-    _test_eden()
diff --git a/egs/zipvoice/zipvoice/scaling.py b/egs/zipvoice/zipvoice/scaling.py
deleted file mode 100644
index afe9ad468..000000000
--- a/egs/zipvoice/zipvoice/scaling.py
+++ /dev/null
@@ -1,1930 +0,0 @@
-# Copyright    2022-2023  Xiaomi Corp.        (authors: Daniel Povey)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import logging
-import math
-import random
-import sys
-from typing import Optional, Tuple, Union
-
-try:
-    import k2
-except Exception as ex:
-    logging.warning(
-        "k2 is not installed correctly. Swoosh functions will fallback to "
-        "pytorch implementation."
-    )
-
-import torch
-import torch.nn as nn
-from torch import Tensor
-
-custom_bwd = lambda func: torch.amp.custom_bwd(func, device_type="cuda")
-custom_fwd = lambda func: torch.amp.custom_fwd(func, device_type="cuda")
-
-
-def logaddexp_onnx(x: Tensor, y: Tensor) -> Tensor:
-    max_value = torch.max(x, y)
-    diff = torch.abs(x - y)
-    return max_value + torch.log1p(torch.exp(-diff))
-
-
-# RuntimeError: Exporting the operator logaddexp to ONNX opset version
-# 14 is not supported. Please feel free to request support or submit
-# a pull request on PyTorch GitHub.
-#
-# The following function is to solve the above error when exporting
-# models to ONNX via torch.jit.trace()
-def logaddexp(x: Tensor, y: Tensor) -> Tensor:
-    # Caution(fangjun): Put torch.jit.is_scripting() before
-    # torch.onnx.is_in_onnx_export();
-    # otherwise, it will cause errors for torch.jit.script().
-    #
-    # torch.logaddexp() works for both torch.jit.script() and
-    # torch.jit.trace() but it causes errors for ONNX export.
-    #
-    if torch.jit.is_scripting():
-        # Note: We cannot use torch.jit.is_tracing() here as it also
-        # matches torch.onnx.export().
-        return torch.logaddexp(x, y)
-    elif torch.onnx.is_in_onnx_export():
-        return logaddexp_onnx(x, y)
-    else:
-        # for torch.jit.trace()
-        return torch.logaddexp(x, y)
-
-
-class PiecewiseLinear(object):
-    """
-    Piecewise linear function, from float to float, specified as nonempty list of (x,y) pairs with
-    the x values in order.  x values <[initial x] or >[final x] are map to [initial y], [final y]
-    respectively.
-    """
-
-    def __init__(self, *args):
-        assert len(args) >= 1, len(args)
-        if len(args) == 1 and isinstance(args[0], PiecewiseLinear):
-            self.pairs = list(args[0].pairs)
-        else:
-            self.pairs = [(float(x), float(y)) for x, y in args]
-        for x, y in self.pairs:
-            assert isinstance(x, (float, int)), type(x)
-            assert isinstance(y, (float, int)), type(y)
-
-        for i in range(len(self.pairs) - 1):
-            assert self.pairs[i + 1][0] > self.pairs[i][0], (
-                i,
-                self.pairs[i],
-                self.pairs[i + 1],
-            )
-
-    def __str__(self):
-        # e.g. 'PiecewiseLinear((0., 10.), (100., 0.))'
-        return f"PiecewiseLinear({str(self.pairs)[1:-1]})"
-
-    def __call__(self, x):
-        if x <= self.pairs[0][0]:
-            return self.pairs[0][1]
-        elif x >= self.pairs[-1][0]:
-            return self.pairs[-1][1]
-        else:
-            cur_x, cur_y = self.pairs[0]
-            for i in range(1, len(self.pairs)):
-                next_x, next_y = self.pairs[i]
-                if x >= cur_x and x <= next_x:
-                    return cur_y + (next_y - cur_y) * (x - cur_x) / (next_x - cur_x)
-                cur_x, cur_y = next_x, next_y
-            assert False
-
-    def __mul__(self, alpha):
-        return PiecewiseLinear(*[(x, y * alpha) for x, y in self.pairs])
-
-    def __add__(self, x):
-        if isinstance(x, (float, int)):
-            return PiecewiseLinear(*[(p[0], p[1] + x) for p in self.pairs])
-        s, x = self.get_common_basis(x)
-        return PiecewiseLinear(
-            *[(sp[0], sp[1] + xp[1]) for sp, xp in zip(s.pairs, x.pairs)]
-        )
-
-    def max(self, x):
-        if isinstance(x, (float, int)):
-            x = PiecewiseLinear((0, x))
-        s, x = self.get_common_basis(x, include_crossings=True)
-        return PiecewiseLinear(
-            *[(sp[0], max(sp[1], xp[1])) for sp, xp in zip(s.pairs, x.pairs)]
-        )
-
-    def min(self, x):
-        if isinstance(x, float) or isinstance(x, int):
-            x = PiecewiseLinear((0, x))
-        s, x = self.get_common_basis(x, include_crossings=True)
-        return PiecewiseLinear(
-            *[(sp[0], min(sp[1], xp[1])) for sp, xp in zip(s.pairs, x.pairs)]
-        )
-
-    def __eq__(self, other):
-        return self.pairs == other.pairs
-
-    def get_common_basis(self, p: "PiecewiseLinear", include_crossings: bool = False):
-        """
-        Returns (self_mod, p_mod) which are equivalent piecewise linear
-        functions to self and p, but with the same x values.
-
-          p: the other piecewise linear function
-          include_crossings: if true, include in the x values positions
-              where the functions indicate by this and p crosss.
-        """
-        assert isinstance(p, PiecewiseLinear), type(p)
-
-        # get sorted x-values without repetition.
-        x_vals = sorted(set([x for x, _ in self.pairs] + [x for x, _ in p.pairs]))
-        y_vals1 = [self(x) for x in x_vals]
-        y_vals2 = [p(x) for x in x_vals]
-
-        if include_crossings:
-            extra_x_vals = []
-            for i in range(len(x_vals) - 1):
-                if (y_vals1[i] > y_vals2[i]) != (y_vals1[i + 1] > y_vals2[i + 1]):
-                    # if the two lines in this subsegment potentially cross each other..
-                    diff_cur = abs(y_vals1[i] - y_vals2[i])
-                    diff_next = abs(y_vals1[i + 1] - y_vals2[i + 1])
-                    # `pos`, between 0 and 1, gives the relative x position,
-                    # with 0 being x_vals[i] and 1 being x_vals[i+1].
-                    pos = diff_cur / (diff_cur + diff_next)
-                    extra_x_val = x_vals[i] + pos * (x_vals[i + 1] - x_vals[i])
-                    extra_x_vals.append(extra_x_val)
-            if len(extra_x_vals) > 0:
-                x_vals = sorted(set(x_vals + extra_x_vals))
-        y_vals1 = [self(x) for x in x_vals]
-        y_vals2 = [p(x) for x in x_vals]
-        return (
-            PiecewiseLinear(*zip(x_vals, y_vals1)),
-            PiecewiseLinear(*zip(x_vals, y_vals2)),
-        )
-
-
-class ScheduledFloat(torch.nn.Module):
-    """
-    This object is a torch.nn.Module only because we want it to show up in [top_level module].modules();
-    it does not have a working forward() function.  You are supposed to cast it to float, as
-    in, float(parent_module.whatever), and use it as something like a dropout prob.
-
-    It is a floating point value whose value changes depending on the batch count of the
-    training loop.  It is a piecewise linear function where you specify the (x,y) pairs
-    in sorted order on x; x corresponds to the batch index.  For batch-index values before the
-    first x or after the last x, we just use the first or last y value.
-
-    Example:
-       self.dropout = ScheduledFloat((0.0, 0.2), (4000.0, 0.0), default=0.0)
-
-    `default` is used when self.batch_count is not set or not in training mode or in
-     torch.jit scripting mode.
-    """
-
-    def __init__(self, *args, default: float = 0.0):
-        super().__init__()
-        # self.batch_count and self.name will be written to in the training loop.
-        self.batch_count = None
-        self.name = None
-        self.default = default
-        self.schedule = PiecewiseLinear(*args)
-
-    def extra_repr(self) -> str:
-        return (
-            f"batch_count={self.batch_count}, schedule={str(self.schedule.pairs[1:-1])}"
-        )
-
-    def __float__(self):
-        batch_count = self.batch_count
-        if (
-            batch_count is None
-            or not self.training
-            or torch.jit.is_scripting()
-            or torch.jit.is_tracing()
-        ):
-            return float(self.default)
-        else:
-            ans = self.schedule(self.batch_count)
-            if random.random() < 0.0002:
-                logging.debug(
-                    f"ScheduledFloat: name={self.name}, batch_count={self.batch_count}, ans={ans}"
-                )
-            return ans
-
-    def __add__(self, x):
-        if isinstance(x, float) or isinstance(x, int):
-            return ScheduledFloat(self.schedule + x, default=self.default)
-        else:
-            return ScheduledFloat(
-                self.schedule + x.schedule, default=self.default + x.default
-            )
-
-    def max(self, x):
-        if isinstance(x, float) or isinstance(x, int):
-            return ScheduledFloat(self.schedule.max(x), default=self.default)
-        else:
-            return ScheduledFloat(
-                self.schedule.max(x.schedule), default=max(self.default, x.default)
-            )
-
-
-FloatLike = Union[float, ScheduledFloat]
-
-
-def random_cast_to_half(x: Tensor, min_abs: float = 5.0e-06) -> Tensor:
-    """
-    A randomized way of casting a floating point value to half precision.
-    """
-    if x.dtype == torch.float16:
-        return x
-    x_abs = x.abs()
-    is_too_small = x_abs < min_abs
-    # for elements where is_too_small is true, random_val will contain +-min_abs with
-    # probability (x.abs() / min_abs), and 0.0 otherwise.  [so this preserves expectations,
-    # for those elements].
-    random_val = min_abs * x.sign() * (torch.rand_like(x) * min_abs < x_abs)
-    return torch.where(is_too_small, random_val, x).to(torch.float16)
-
-
-class CutoffEstimator:
-    """
-    Estimates cutoffs of an arbitrary numerical quantity such that a specified
-    proportion of items will be above the cutoff on average.
-
-      p is the proportion of items that should be above the cutoff.
-    """
-
-    def __init__(self, p: float):
-        self.p = p
-        # total count of items
-        self.count = 0
-        # total count of items that were above the cutoff
-        self.count_above = 0
-        # initial cutoff value
-        self.cutoff = 0
-
-    def __call__(self, x: float) -> bool:
-        """
-        Returns true if x is above the cutoff.
-        """
-        ans = x > self.cutoff
-        self.count += 1
-        if ans:
-            self.count_above += 1
-        cur_p = self.count_above / self.count
-        delta_p = cur_p - self.p
-        if (delta_p > 0) == ans:
-            q = abs(delta_p)
-            self.cutoff = x * q + self.cutoff * (1 - q)
-        return ans
-
-
-class SoftmaxFunction(torch.autograd.Function):
-    """
-    Tries to handle half-precision derivatives in a randomized way that should
-    be more accurate for training than the default behavior.
-    """
-
-    @staticmethod
-    def forward(ctx, x: Tensor, dim: int):
-        ans = x.softmax(dim=dim)
-        # if x dtype is float16, x.softmax() returns a float32 because
-        # (presumably) that op does not support float16, and autocast
-        # is enabled.
-        if torch.is_autocast_enabled():
-            ans = ans.to(torch.float16)
-        ctx.save_for_backward(ans)
-        ctx.x_dtype = x.dtype
-        ctx.dim = dim
-        return ans
-
-    @staticmethod
-    def backward(ctx, ans_grad: Tensor):
-        (ans,) = ctx.saved_tensors
-        with torch.amp.autocast("cuda", enabled=False):
-            ans_grad = ans_grad.to(torch.float32)
-            ans = ans.to(torch.float32)
-            x_grad = ans_grad * ans
-            x_grad = x_grad - ans * x_grad.sum(dim=ctx.dim, keepdim=True)
-            return x_grad, None
-
-
-def softmax(x: Tensor, dim: int):
-    if not x.requires_grad or torch.jit.is_scripting() or torch.jit.is_tracing():
-        return x.softmax(dim=dim)
-
-    return SoftmaxFunction.apply(x, dim)
-
-
-class MaxEigLimiterFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        x: Tensor,
-        coeffs: Tensor,
-        direction: Tensor,
-        channel_dim: int,
-        grad_scale: float,
-    ) -> Tensor:
-        ctx.channel_dim = channel_dim
-        ctx.grad_scale = grad_scale
-        ctx.save_for_backward(x.detach(), coeffs.detach(), direction.detach())
-        return x
-
-    @staticmethod
-    def backward(ctx, x_grad, *args):
-        with torch.enable_grad():
-            (x_orig, coeffs, new_direction) = ctx.saved_tensors
-            x_orig.requires_grad = True
-            num_channels = x_orig.shape[ctx.channel_dim]
-            x = x_orig.transpose(ctx.channel_dim, -1).reshape(-1, num_channels)
-            new_direction.requires_grad = False
-            x = x - x.mean(dim=0)
-            x_var = (x**2).mean()
-            x_residual = x - coeffs * new_direction
-            x_residual_var = (x_residual**2).mean()
-            # `variance_proportion` is the proportion of the variance accounted for
-            # by the top eigen-direction.  This is to be minimized.
-            variance_proportion = (x_var - x_residual_var) / (x_var + 1.0e-20)
-            variance_proportion.backward()
-        x_orig_grad = x_orig.grad
-        x_extra_grad = (
-            x_orig.grad
-            * ctx.grad_scale
-            * x_grad.norm()
-            / (x_orig_grad.norm() + 1.0e-20)
-        )
-        return x_grad + x_extra_grad.detach(), None, None, None, None
-
-
-class BiasNormFunction(torch.autograd.Function):
-    # This computes:
-    #   scales = (torch.mean((x - bias) ** 2, keepdim=True)) ** -0.5 * log_scale.exp()
-    #   return x * scales
-    # (after unsqueezing the bias), but it does it in a memory-efficient way so that
-    # it can just store the returned value (chances are, this will also be needed for
-    # some other reason, related to the next operation, so we can save memory).
-    @staticmethod
-    def forward(
-        ctx,
-        x: Tensor,
-        bias: Tensor,
-        log_scale: Tensor,
-        channel_dim: int,
-        store_output_for_backprop: bool,
-    ) -> Tensor:
-        assert bias.ndim == 1
-        if channel_dim < 0:
-            channel_dim = channel_dim + x.ndim
-        ctx.store_output_for_backprop = store_output_for_backprop
-        ctx.channel_dim = channel_dim
-        for _ in range(channel_dim + 1, x.ndim):
-            bias = bias.unsqueeze(-1)
-        scales = (
-            torch.mean((x - bias) ** 2, dim=channel_dim, keepdim=True) ** -0.5
-        ) * log_scale.exp()
-        ans = x * scales
-        ctx.save_for_backward(
-            ans.detach() if store_output_for_backprop else x,
-            scales.detach(),
-            bias.detach(),
-            log_scale.detach(),
-        )
-        return ans
-
-    @staticmethod
-    def backward(ctx, ans_grad: Tensor) -> Tensor:
-        ans_or_x, scales, bias, log_scale = ctx.saved_tensors
-        if ctx.store_output_for_backprop:
-            x = ans_or_x / scales
-        else:
-            x = ans_or_x
-        x = x.detach()
-        x.requires_grad = True
-        bias.requires_grad = True
-        log_scale.requires_grad = True
-        with torch.enable_grad():
-            # recompute scales from x, bias and log_scale.
-            scales = (
-                torch.mean((x - bias) ** 2, dim=ctx.channel_dim, keepdim=True) ** -0.5
-            ) * log_scale.exp()
-            ans = x * scales
-            ans.backward(gradient=ans_grad)
-        return x.grad, bias.grad.flatten(), log_scale.grad, None, None
-
-
-class BiasNorm(torch.nn.Module):
-    """
-    This is intended to be a simpler, and hopefully cheaper, replacement for
-    LayerNorm.  The observation this is based on, is that Transformer-type
-    networks, especially with pre-norm, sometimes seem to set one of the
-    feature dimensions to a large constant value (e.g. 50), which "defeats"
-    the LayerNorm because the output magnitude is then not strongly dependent
-    on the other (useful) features.  Presumably the weight and bias of the
-    LayerNorm are required to allow it to do this.
-
-    Instead, we give the BiasNorm a trainable bias that it can use when
-    computing the scale for normalization.  We also give it a (scalar)
-    trainable scale on the output.
-
-
-    Args:
-       num_channels: the number of channels, e.g. 512.
-       channel_dim: the axis/dimension corresponding to the channel,
-         interpreted as an offset from the input's ndim if negative.
-         This is NOT the num_channels; it should typically be one of
-         {-2, -1, 0, 1, 2, 3}.
-      log_scale: the initial log-scale that we multiply the output by; this
-         is learnable.
-      log_scale_min: FloatLike, minimum allowed value of log_scale
-      log_scale_max: FloatLike, maximum allowed value of log_scale
-      store_output_for_backprop: only possibly affects memory use; recommend
-         to set to True if you think the output of this module is more likely
-         than the input of this module to be required to be stored for the
-         backprop.
-    """
-
-    def __init__(
-        self,
-        num_channels: int,
-        channel_dim: int = -1,  # CAUTION: see documentation.
-        log_scale: float = 1.0,
-        log_scale_min: float = -1.5,
-        log_scale_max: float = 1.5,
-        store_output_for_backprop: bool = False,
-    ) -> None:
-        super(BiasNorm, self).__init__()
-        self.num_channels = num_channels
-        self.channel_dim = channel_dim
-        self.log_scale = nn.Parameter(torch.tensor(log_scale))
-        self.bias = nn.Parameter(torch.zeros(num_channels))
-
-        self.log_scale_min = log_scale_min
-        self.log_scale_max = log_scale_max
-
-        self.store_output_for_backprop = store_output_for_backprop
-
-    def forward(self, x: Tensor) -> Tensor:
-        assert x.shape[self.channel_dim] == self.num_channels
-
-        if torch.jit.is_scripting() or torch.jit.is_tracing():
-            channel_dim = self.channel_dim
-            if channel_dim < 0:
-                channel_dim += x.ndim
-            bias = self.bias
-            for _ in range(channel_dim + 1, x.ndim):
-                bias = bias.unsqueeze(-1)
-            scales = (
-                torch.mean((x - bias) ** 2, dim=channel_dim, keepdim=True) ** -0.5
-            ) * self.log_scale.exp()
-            return x * scales
-
-        log_scale = limit_param_value(
-            self.log_scale,
-            min=float(self.log_scale_min),
-            max=float(self.log_scale_max),
-            training=self.training,
-        )
-
-        return BiasNormFunction.apply(
-            x, self.bias, log_scale, self.channel_dim, self.store_output_for_backprop
-        )
-
-
-def ScaledLinear(*args, initial_scale: float = 1.0, **kwargs) -> nn.Linear:
-    """
-    Behaves like a constructor of a modified version of nn.Linear
-    that gives an easy way to set the default initial parameter scale.
-
-    Args:
-        Accepts the standard args and kwargs that nn.Linear accepts
-        e.g. in_features, out_features, bias=False.
-
-        initial_scale: you can override this if you want to increase
-           or decrease the initial magnitude of the module's output
-           (affects the initialization of weight_scale and bias_scale).
-           Another option, if you want to do something like this, is
-           to re-initialize the parameters.
-    """
-    ans = nn.Linear(*args, **kwargs)
-    with torch.no_grad():
-        ans.weight[:] *= initial_scale
-        if ans.bias is not None:
-            torch.nn.init.uniform_(ans.bias, -0.1 * initial_scale, 0.1 * initial_scale)
-    return ans
-
-
-def ScaledConv1d(*args, initial_scale: float = 1.0, **kwargs) -> nn.Conv1d:
-    """
-    Behaves like a constructor of a modified version of nn.Conv1d
-    that gives an easy way to set the default initial parameter scale.
-
-    Args:
-        Accepts the standard args and kwargs that nn.Linear accepts
-        e.g. in_features, out_features, bias=False.
-
-        initial_scale: you can override this if you want to increase
-           or decrease the initial magnitude of the module's output
-           (affects the initialization of weight_scale and bias_scale).
-           Another option, if you want to do something like this, is
-           to re-initialize the parameters.
-    """
-    ans = nn.Conv1d(*args, **kwargs)
-    with torch.no_grad():
-        ans.weight[:] *= initial_scale
-        if ans.bias is not None:
-            torch.nn.init.uniform_(ans.bias, -0.1 * initial_scale, 0.1 * initial_scale)
-    return ans
-
-
-def ScaledConv2d(*args, initial_scale: float = 1.0, **kwargs) -> nn.Conv2d:
-    """
-    Behaves like a constructor of a modified version of nn.Conv2d
-    that gives an easy way to set the default initial parameter scale.
-
-    Args:
-        Accepts the standard args and kwargs that nn.Linear accepts
-        e.g. in_features, out_features, bias=False, but:
-    NO PADDING-RELATED ARGS.
-
-        initial_scale: you can override this if you want to increase
-           or decrease the initial magnitude of the module's output
-           (affects the initialization of weight_scale and bias_scale).
-           Another option, if you want to do something like this, is
-           to re-initialize the parameters.
-    """
-    ans = nn.Conv2d(*args, **kwargs)
-    with torch.no_grad():
-        ans.weight[:] *= initial_scale
-        if ans.bias is not None:
-            torch.nn.init.uniform_(ans.bias, -0.1 * initial_scale, 0.1 * initial_scale)
-    return ans
-
-
-class ChunkCausalDepthwiseConv1d(torch.nn.Module):
-    """
-    Behaves like a depthwise 1d convolution, except that it is causal in
-    a chunkwise way, as if we had a block-triangular attention mask.
-    The chunk size is provided at test time (it should probably be
-    kept in sync with the attention mask).
-
-    This has a little more than twice the parameters of a conventional
-    depthwise conv1d module: we implement it by having one
-    depthwise convolution, of half the width, that is causal (via
-    right-padding); and one depthwise convolution that is applied only
-    within chunks, that we multiply by a scaling factor which depends
-    on the position within the chunk.
-
-    Args:
-        Accepts the standard args and kwargs that nn.Linear accepts
-        e.g. in_features, out_features, bias=False.
-
-        initial_scale: you can override this if you want to increase
-           or decrease the initial magnitude of the module's output
-           (affects the initialization of weight_scale and bias_scale).
-           Another option, if you want to do something like this, is
-           to re-initialize the parameters.
-    """
-
-    def __init__(
-        self,
-        channels: int,
-        kernel_size: int,
-        initial_scale: float = 1.0,
-        bias: bool = True,
-    ):
-        super().__init__()
-        assert kernel_size % 2 == 1
-
-        half_kernel_size = (kernel_size + 1) // 2
-        # will pad manually, on one side.
-        self.causal_conv = nn.Conv1d(
-            in_channels=channels,
-            out_channels=channels,
-            groups=channels,
-            kernel_size=half_kernel_size,
-            padding=0,
-            bias=True,
-        )
-
-        self.chunkwise_conv = nn.Conv1d(
-            in_channels=channels,
-            out_channels=channels,
-            groups=channels,
-            kernel_size=kernel_size,
-            padding=kernel_size // 2,
-            bias=bias,
-        )
-
-        # first row is correction factors added to the scale near the left edge of the chunk,
-        # second row is correction factors added to the scale near the right edge of the chunk,
-        # both of these are added to a default scale of 1.0.
-        self.chunkwise_conv_scale = nn.Parameter(torch.zeros(2, channels, kernel_size))
-        self.kernel_size = kernel_size
-
-        with torch.no_grad():
-            self.causal_conv.weight[:] *= initial_scale
-            self.chunkwise_conv.weight[:] *= initial_scale
-            if bias:
-                torch.nn.init.uniform_(
-                    self.causal_conv.bias, -0.1 * initial_scale, 0.1 * initial_scale
-                )
-
-    def forward(self, x: Tensor, chunk_size: int = -1) -> Tensor:
-        """
-             Forward function.  Args:
-               x: a Tensor of shape (batch_size, channels, seq_len)
-        chunk_size: the chunk size, in frames; does not have to divide seq_len exactly.
-        """
-        (batch_size, num_channels, seq_len) = x.shape
-
-        # half_kernel_size = self.kernel_size + 1 // 2
-        # left_pad is half_kernel_size - 1 where half_kernel_size is the size used
-        # in the causal conv.  It's the amount by which we must pad on the left,
-        # to make the convolution causal.
-        left_pad = self.kernel_size // 2
-
-        if chunk_size < 0 or chunk_size > seq_len:
-            chunk_size = seq_len
-        right_pad = -seq_len % chunk_size
-
-        x = torch.nn.functional.pad(x, (left_pad, right_pad))
-
-        x_causal = self.causal_conv(x[..., : left_pad + seq_len])
-        assert x_causal.shape == (batch_size, num_channels, seq_len)
-
-        x_chunk = x[..., left_pad:]
-        num_chunks = x_chunk.shape[2] // chunk_size
-        x_chunk = x_chunk.reshape(batch_size, num_channels, num_chunks, chunk_size)
-        x_chunk = x_chunk.permute(0, 2, 1, 3).reshape(
-            batch_size * num_chunks, num_channels, chunk_size
-        )
-        x_chunk = self.chunkwise_conv(x_chunk)  # does not change shape
-
-        chunk_scale = self._get_chunk_scale(chunk_size)
-
-        x_chunk = x_chunk * chunk_scale
-        x_chunk = x_chunk.reshape(
-            batch_size, num_chunks, num_channels, chunk_size
-        ).permute(0, 2, 1, 3)
-        x_chunk = x_chunk.reshape(batch_size, num_channels, num_chunks * chunk_size)[
-            ..., :seq_len
-        ]
-
-        return x_chunk + x_causal
-
-    def _get_chunk_scale(self, chunk_size: int):
-        """Returns tensor of shape (num_channels, chunk_size) that will be used to
-        scale the output of self.chunkwise_conv."""
-        left_edge = self.chunkwise_conv_scale[0]
-        right_edge = self.chunkwise_conv_scale[1]
-        if chunk_size < self.kernel_size:
-            left_edge = left_edge[:, :chunk_size]
-            right_edge = right_edge[:, -chunk_size:]
-        else:
-            t = chunk_size - self.kernel_size
-            channels = left_edge.shape[0]
-            pad = torch.zeros(
-                channels, t, device=left_edge.device, dtype=left_edge.dtype
-            )
-            left_edge = torch.cat((left_edge, pad), dim=-1)
-            right_edge = torch.cat((pad, right_edge), dim=-1)
-        return 1.0 + (left_edge + right_edge)
-
-    def streaming_forward(
-        self,
-        x: Tensor,
-        cache: Tensor,
-    ) -> Tuple[Tensor, Tensor]:
-        """Streaming Forward function.
-
-        Args:
-            x: a Tensor of shape (batch_size, channels, seq_len)
-            cache: cached left context of shape (batch_size, channels, left_pad)
-        """
-        (batch_size, num_channels, seq_len) = x.shape
-
-        # left_pad is half_kernel_size - 1 where half_kernel_size is the size used
-        # in the causal conv.  It's the amount by which we must pad on the left,
-        # to make the convolution causal.
-        left_pad = self.kernel_size // 2
-
-        # Pad cache
-        assert cache.shape[-1] == left_pad, (cache.shape[-1], left_pad)
-        x = torch.cat([cache, x], dim=2)
-        # Update cache
-        cache = x[..., -left_pad:]
-
-        x_causal = self.causal_conv(x)
-        assert x_causal.shape == (batch_size, num_channels, seq_len)
-
-        x_chunk = x[..., left_pad:]
-        x_chunk = self.chunkwise_conv(x_chunk)  # does not change shape
-
-        chunk_scale = self._get_chunk_scale(chunk_size=seq_len)
-        x_chunk = x_chunk * chunk_scale
-
-        return x_chunk + x_causal, cache
-
-
-class BalancerFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        x: Tensor,
-        min_mean: float,
-        max_mean: float,
-        min_rms: float,
-        max_rms: float,
-        grad_scale: float,
-        channel_dim: int,
-    ) -> Tensor:
-        if channel_dim < 0:
-            channel_dim += x.ndim
-        ctx.channel_dim = channel_dim
-        ctx.save_for_backward(x)
-        ctx.config = (min_mean, max_mean, min_rms, max_rms, grad_scale, channel_dim)
-        return x
-
-    @staticmethod
-    def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None, None, None]:
-        (x,) = ctx.saved_tensors
-        (min_mean, max_mean, min_rms, max_rms, grad_scale, channel_dim) = ctx.config
-
-        try:
-            with torch.enable_grad():
-                with torch.amp.autocast("cuda", enabled=False):
-                    x = x.to(torch.float32)
-                    x = x.detach()
-                    x.requires_grad = True
-                    mean_dims = [i for i in range(x.ndim) if i != channel_dim]
-                    uncentered_var = (x**2).mean(dim=mean_dims, keepdim=True)
-                    mean = x.mean(dim=mean_dims, keepdim=True)
-                    stddev = (uncentered_var - (mean * mean)).clamp(min=1.0e-20).sqrt()
-                    rms = uncentered_var.clamp(min=1.0e-20).sqrt()
-
-                    m = mean / stddev
-                    # part of loss that relates to mean / stddev
-                    m_loss = (m - m.clamp(min=min_mean, max=max_mean)).abs()
-
-                    # put a much larger scale on the RMS-max-limit loss, so that if both it and the
-                    # m_loss are violated we fix the RMS loss first.
-                    rms_clamped = rms.clamp(min=min_rms, max=max_rms)
-                    r_loss = (rms_clamped / rms).log().abs()
-
-                    loss = m_loss + r_loss
-
-                    loss.backward(gradient=torch.ones_like(loss))
-                    loss_grad = x.grad
-                    loss_grad_rms = (
-                        (loss_grad**2)
-                        .mean(dim=mean_dims, keepdim=True)
-                        .sqrt()
-                        .clamp(min=1.0e-20)
-                    )
-
-                    loss_grad = loss_grad * (grad_scale / loss_grad_rms)
-
-                    x_grad_float = x_grad.to(torch.float32)
-                    # scale each element of loss_grad by the absolute value of the corresponding
-                    # element of x_grad, which we view as a noisy estimate of its magnitude for that
-                    # (frame and dimension).  later we can consider factored versions.
-                    x_grad_mod = x_grad_float + (x_grad_float.abs() * loss_grad)
-                    x_grad = x_grad_mod.to(x_grad.dtype)
-        except Exception as e:
-            logging.info(
-                f"Caught exception in Balancer backward: {e}, size={list(x_grad.shape)}, will continue."
-            )
-
-        return x_grad, None, None, None, None, None, None
-
-
-class Balancer(torch.nn.Module):
-    """
-    Modifies the backpropped derivatives of a function to try to encourage, for
-    each channel, that it is positive at least a proportion `threshold` of the
-    time.  It does this by multiplying negative derivative values by up to
-    (1+max_factor), and positive derivative values by up to (1-max_factor),
-    interpolated from 1 at the threshold to those extremal values when none
-    of the inputs are positive.
-
-    Args:
-           num_channels: the number of channels
-           channel_dim: the dimension/axis corresponding to the channel, e.g.
-               -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative.
-           min_positive: the minimum, per channel, of the proportion of the time
-               that (x > 0), below which we start to modify the derivatives.
-           max_positive: the maximum, per channel, of the proportion of the time
-               that (x > 0), above which we start to modify the derivatives.
-           scale_gain_factor: determines the 'gain' with which we increase the
-              change in gradient once the constraints on min_abs and max_abs
-              are violated.
-           min_abs:  the minimum average-absolute-value difference from the mean
-               value per channel, which we allow, before we start to modify
-               the derivatives to prevent this.
-           max_abs:  the maximum average-absolute-value difference from the mean
-               value per channel, which we allow, before we start to modify
-               the derivatives to prevent this.
-         prob: determines the minimum probability with which we modify the
-             gradients for the {min,max}_positive and {min,max}_abs constraints,
-             on each forward().  This is done randomly to prevent all layers
-             from doing it at the same time.
-    """
-
-    def __init__(
-        self,
-        num_channels: int,
-        channel_dim: int,
-        min_positive: FloatLike = 0.05,
-        max_positive: FloatLike = 0.95,
-        min_abs: FloatLike = 0.2,
-        max_abs: FloatLike = 100.0,
-        grad_scale: FloatLike = 0.04,
-        prob: Optional[FloatLike] = None,
-    ):
-        super().__init__()
-
-        if prob is None:
-            prob = ScheduledFloat((0.0, 0.5), (8000.0, 0.125), default=0.4)
-        self.prob = prob
-        # 5% of the time we will return and do nothing because memory usage is
-        # too high.
-        self.mem_cutoff = CutoffEstimator(0.05)
-
-        # actually self.num_channels is no longer needed except for an assertion.
-        self.num_channels = num_channels
-        self.channel_dim = channel_dim
-        self.min_positive = min_positive
-        self.max_positive = max_positive
-        self.min_abs = min_abs
-        self.max_abs = max_abs
-        self.grad_scale = grad_scale
-
-    def forward(self, x: Tensor) -> Tensor:
-        if (
-            torch.jit.is_scripting()
-            or not x.requires_grad
-            or (x.is_cuda and self.mem_cutoff(torch.cuda.memory_allocated()))
-        ):
-            return _no_op(x)
-
-        prob = float(self.prob)
-        if random.random() < prob:
-            # The following inner-functions convert from the way we historically specified
-            # these limitations, as limits on the absolute value and the proportion of positive
-            # values, to limits on the RMS value and the (mean / stddev).
-            def _abs_to_rms(x):
-                # for normally distributed data, if the expected absolute value is x, the
-                # expected rms value will be sqrt(pi/2) * x.
-                return 1.25331413732 * x
-
-            def _proportion_positive_to_mean(x):
-                def _atanh(x):
-                    eps = 1.0e-10
-                    # eps is to prevent crashes if x is exactly 0 or 1.
-                    # we'll just end up returning a fairly large value.
-                    return (math.log(1 + x + eps) - math.log(1 - x + eps)) / 2.0
-
-                def _approx_inverse_erf(x):
-                    # 1 / (sqrt(pi) * ln(2)),
-                    # see https://math.stackexchange.com/questions/321569/approximating-the-error-function-erf-by-analytical-functions
-                    # this approximation is extremely crude and gets progressively worse for
-                    # x very close to -1 or +1, but we mostly care about the "middle" region
-                    # e.g. _approx_inverse_erf(0.05) = 0.0407316414078772,
-                    # and math.erf(0.0407316414078772) = 0.045935330944660666,
-                    # which is pretty close to 0.05.
-                    return 0.8139535143 * _atanh(x)
-
-                # first convert x from the range 0..1 to the range -1..1 which the error
-                # function returns
-                x = -1 + (2 * x)
-                return _approx_inverse_erf(x)
-
-            min_mean = _proportion_positive_to_mean(float(self.min_positive))
-            max_mean = _proportion_positive_to_mean(float(self.max_positive))
-            min_rms = _abs_to_rms(float(self.min_abs))
-            max_rms = _abs_to_rms(float(self.max_abs))
-            grad_scale = float(self.grad_scale)
-
-            assert x.shape[self.channel_dim] == self.num_channels
-
-            return BalancerFunction.apply(
-                x, min_mean, max_mean, min_rms, max_rms, grad_scale, self.channel_dim
-            )
-        else:
-            return _no_op(x)
-
-
-def penalize_abs_values_gt(
-    x: Tensor, limit: float, penalty: float, name: str = None
-) -> Tensor:
-    """
-    Returns x unmodified, but in backprop will put a penalty for the excess of
-    the absolute values of elements of x over the limit "limit".  E.g. if
-    limit == 10.0, then if x has any values over 10 it will get a penalty.
-
-    Caution: the value of this penalty will be affected by grad scaling used
-    in automatic mixed precision training.  For this reasons we use this,
-    it shouldn't really matter, or may even be helpful; we just use this
-    to disallow really implausible values of scores to be given to softmax.
-
-    The name is for randomly printed debug info.
-    """
-    x_sign = x.sign()
-    over_limit = (x.abs() - limit) > 0
-    # The following is a memory efficient way to penalize the absolute values of
-    # x that's over the limit.  (The memory efficiency comes when you think
-    # about which items torch needs to cache for the autograd, and which ones it
-    # can throw away).  The numerical value of aux_loss as computed here will
-    # actually be larger than it should be, by limit * over_limit.sum(), but it
-    # has the same derivative as the real aux_loss which is penalty * (x.abs() -
-    # limit).relu().
-    aux_loss = penalty * ((x_sign * over_limit).to(torch.int8) * x)
-    # note: we don't do sum() here on aux)_loss, but it's as if we had done
-    # sum() due to how with_loss() works.
-    x = with_loss(x, aux_loss, name)
-    # you must use x for something, or this will be ineffective.
-    return x
-
-
-def _diag(x: Tensor):  # like .diag(), but works for tensors with 3 dims.
-    if x.ndim == 2:
-        return x.diag()
-    else:
-        (batch, dim, dim) = x.shape
-        x = x.reshape(batch, dim * dim)
-        x = x[:, :: dim + 1]
-        assert x.shape == (batch, dim)
-        return x
-
-
-def _whitening_metric(x: Tensor, num_groups: int):
-    """
-    Computes the "whitening metric", a value which will be 1.0 if all the eigenvalues of
-    of the centered feature covariance are the same within each group's covariance matrix
-    and also between groups.
-    Args:
-        x: a Tensor of shape (*, num_channels)
-     num_groups:  the number of groups of channels, a number >=1 that divides num_channels
-    Returns:
-        Returns a scalar Tensor that will be 1.0 if the data is "perfectly white" and
-    greater than 1.0 otherwise.
-    """
-    assert x.dtype != torch.float16
-    x = x.reshape(-1, x.shape[-1])
-    (num_frames, num_channels) = x.shape
-    assert num_channels % num_groups == 0
-    channels_per_group = num_channels // num_groups
-    x = x.reshape(num_frames, num_groups, channels_per_group).transpose(0, 1)
-    # x now has shape (num_groups, num_frames, channels_per_group)
-    # subtract the mean so we use the centered, not uncentered, covariance.
-    # My experience has been that when we "mess with the gradients" like this,
-    # it's better not do anything that tries to move the mean around, because
-    # that can easily cause instability.
-    x = x - x.mean(dim=1, keepdim=True)
-    # x_covar: (num_groups, channels_per_group, channels_per_group)
-    x_covar = torch.matmul(x.transpose(1, 2), x)
-    x_covar_mean_diag = _diag(x_covar).mean()
-    # the following expression is what we'd get if we took the matrix product
-    # of each covariance and measured the mean of its trace, i.e.
-    # the same as _diag(torch.matmul(x_covar, x_covar)).mean().
-    x_covarsq_mean_diag = (x_covar**2).sum() / (num_groups * channels_per_group)
-    # this metric will be >= 1.0; the larger it is, the less 'white' the data was.
-    metric = x_covarsq_mean_diag / (x_covar_mean_diag**2 + 1.0e-20)
-    return metric
-
-
-class WhiteningPenaltyFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x: Tensor, module: nn.Module) -> Tensor:
-        ctx.save_for_backward(x)
-        ctx.module = module
-        return x
-
-    @staticmethod
-    def backward(ctx, x_grad: Tensor):
-        (x_orig,) = ctx.saved_tensors
-        w = ctx.module
-
-        try:
-            with torch.enable_grad():
-                with torch.amp.autocast("cuda", enabled=False):
-                    x_detached = x_orig.to(torch.float32).detach()
-                    x_detached.requires_grad = True
-
-                    metric = _whitening_metric(x_detached, w.num_groups)
-
-                    if random.random() < 0.005 or __name__ == "__main__":
-                        logging.debug(
-                            f"Whitening: name={w.name}, num_groups={w.num_groups}, num_channels={x_orig.shape[-1]}, "
-                            f"metric={metric.item():.2f} vs. limit={float(w.whitening_limit)}"
-                        )
-
-                    if metric < float(w.whitening_limit):
-                        w.prob = w.min_prob
-                        return x_grad, None
-                    else:
-                        w.prob = w.max_prob
-                        metric.backward()
-                        penalty_grad = x_detached.grad
-                        scale = w.grad_scale * (
-                            x_grad.to(torch.float32).norm()
-                            / (penalty_grad.norm() + 1.0e-20)
-                        )
-                        penalty_grad = penalty_grad * scale
-                        return x_grad + penalty_grad.to(x_grad.dtype), None
-        except Exception as e:
-            logging.info(
-                f"Caught exception in Whiten backward: {e}, size={list(x_grad.shape)}, will continue."
-            )
-        return x_grad, None
-
-
-class Whiten(nn.Module):
-    def __init__(
-        self,
-        num_groups: int,
-        whitening_limit: FloatLike,
-        prob: Union[float, Tuple[float, float]],
-        grad_scale: FloatLike,
-    ):
-        """
-        Args:
-          num_groups: the number of groups to divide the channel dim into before
-            whitening.  We will attempt to make the feature covariance
-            within each group, after mean subtraction, as "white" as possible,
-            while having the same trace across all groups.
-         whitening_limit: a value greater than 1.0, that dictates how much
-           freedom we have to violate the constraints.  1.0 would mean perfectly
-           white, with exactly the same trace across groups; larger values
-           give more freedom.  E.g. 2.0.
-         prob: the probability with which we apply the gradient modification
-           (also affects the grad scale).  May be supplied as a float,
-           or as a pair (min_prob, max_prob)
-
-          grad_scale: determines the scale on the gradient term from this object,
-            relative to the rest of the gradient on the attention weights.
-            E.g. 0.02 (you may want to use smaller values than this if prob is large)
-        """
-        super(Whiten, self).__init__()
-        assert num_groups >= 1
-        assert float(whitening_limit) >= 1
-        assert grad_scale >= 0
-        self.num_groups = num_groups
-        self.whitening_limit = whitening_limit
-        self.grad_scale = grad_scale
-
-        if isinstance(prob, float):
-            prob = (prob, prob)
-        (self.min_prob, self.max_prob) = prob
-        assert 0 < self.min_prob <= self.max_prob <= 1
-        self.prob = self.max_prob
-        self.name = None  # will be set in training loop
-
-    def forward(self, x: Tensor) -> Tensor:
-        """
-        In the forward pass, this function just returns the input unmodified.
-        In the backward pass, it will modify the gradients to ensure that the
-        distribution in each group has close to (lambda times I) as the covariance
-        after mean subtraction, with the same lambda across groups.
-        For whitening_limit > 1, there will be more freedom to violate this
-        constraint.
-
-        Args:
-           x: the input of shape (*, num_channels)
-
-        Returns:
-            x, unmodified.   You should make sure
-        you use the returned value, or the graph will be freed
-        and nothing will happen in backprop.
-        """
-        grad_scale = float(self.grad_scale)
-        if not x.requires_grad or random.random() > self.prob or grad_scale == 0:
-            return _no_op(x)
-        else:
-            return WhiteningPenaltyFunction.apply(x, self)
-
-
-class WithLoss(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x: Tensor, y: Tensor, name: str):
-        ctx.y_shape = y.shape
-        if random.random() < 0.002 and name is not None:
-            loss_sum = y.sum().item()
-            logging.debug(f"WithLoss: name={name}, loss-sum={loss_sum:.3e}")
-        return x
-
-    @staticmethod
-    def backward(ctx, ans_grad: Tensor):
-        return (
-            ans_grad,
-            torch.ones(ctx.y_shape, dtype=ans_grad.dtype, device=ans_grad.device),
-            None,
-        )
-
-
-def with_loss(x, y, name):
-    # returns x but adds y.sum() to the loss function.
-    return WithLoss.apply(x, y, name)
-
-
-class ScaleGradFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x: Tensor, alpha: float) -> Tensor:
-        ctx.alpha = alpha
-        return x
-
-    @staticmethod
-    def backward(ctx, grad: Tensor):
-        return grad * ctx.alpha, None
-
-
-def scale_grad(x: Tensor, alpha: float):
-    return ScaleGradFunction.apply(x, alpha)
-
-
-class ScaleGrad(nn.Module):
-    def __init__(self, alpha: float):
-        super().__init__()
-        self.alpha = alpha
-
-    def forward(self, x: Tensor) -> Tensor:
-        if torch.jit.is_scripting() or torch.jit.is_tracing() or not self.training:
-            return x
-        return scale_grad(x, self.alpha)
-
-
-class LimitParamValue(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x: Tensor, min: float, max: float):
-        ctx.save_for_backward(x)
-        assert max >= min
-        ctx.min = min
-        ctx.max = max
-        return x
-
-    @staticmethod
-    def backward(ctx, x_grad: Tensor):
-        (x,) = ctx.saved_tensors
-        # where x < ctx.min, ensure all grads are negative (this will tend to make
-        # x more positive).
-        x_grad = x_grad * torch.where(
-            torch.logical_and(x_grad > 0, x < ctx.min), -1.0, 1.0
-        )
-        # where x > ctx.max, ensure all grads are positive (this will tend to make
-        # x more negative).
-        x_grad *= torch.where(torch.logical_and(x_grad < 0, x > ctx.max), -1.0, 1.0)
-        return x_grad, None, None
-
-
-def limit_param_value(
-    x: Tensor, min: float, max: float, prob: float = 0.6, training: bool = True
-):
-    # You apply this to (typically) an nn.Parameter during training to ensure that its
-    # (elements mostly) stays within a supplied range.  This is done by modifying the
-    # gradients in backprop.
-    # It's not necessary to do this on every batch: do it only some of the time,
-    # to save a little time.
-    if training and random.random() < prob:
-        return LimitParamValue.apply(x, min, max)
-    else:
-        return x
-
-
-def _no_op(x: Tensor) -> Tensor:
-    if torch.jit.is_scripting() or torch.jit.is_tracing():
-        return x
-    else:
-        # a no-op function that will have a node in the autograd graph,
-        # to avoid certain bugs relating to backward hooks
-        return x.chunk(1, dim=-1)[0]
-
-
-class Identity(torch.nn.Module):
-    def __init__(self):
-        super(Identity, self).__init__()
-
-    def forward(self, x):
-        return _no_op(x)
-
-
-class DoubleSwishFunction(torch.autograd.Function):
-    """
-      double_swish(x) = x * torch.sigmoid(x-1)
-
-    This is a definition, originally motivated by its close numerical
-    similarity to swish(swish(x)), where swish(x) =  x * sigmoid(x).
-
-    Memory-efficient derivative computation:
-     double_swish(x) = x * s, where s(x) = torch.sigmoid(x-1)
-     double_swish'(x) = d/dx double_swish(x) =  x * s'(x) + x' * s(x) = x * s'(x) + s(x).
-     Now, s'(x) = s(x) * (1-s(x)).
-     double_swish'(x) =  x * s'(x) + s(x).
-                      =  x * s(x) * (1-s(x)) + s(x).
-                     = double_swish(x) * (1-s(x)) + s(x)
-     ... so we just need to remember s(x) but not x itself.
-    """
-
-    @staticmethod
-    def forward(ctx, x: Tensor) -> Tensor:
-        requires_grad = x.requires_grad
-        if x.dtype == torch.float16:
-            x = x.to(torch.float32)
-
-        s = torch.sigmoid(x - 1.0)
-        y = x * s
-
-        if requires_grad:
-            deriv = y * (1 - s) + s
-
-            # notes on derivative of x * sigmoid(x - 1):
-            # https://www.wolframalpha.com/input?i=d%2Fdx+%28x+*+sigmoid%28x-1%29%29
-            # min \simeq -0.043638.  Take floor as -0.044 so it's a lower bund
-            # max \simeq 1.1990.   Take ceil to be 1.2 so it's an upper bound.
-            # the combination of "+ torch.rand_like(deriv)" and casting to torch.uint8 (which
-            # floors), should be expectation-preserving.
-            floor = -0.044
-            ceil = 1.2
-            d_scaled = (deriv - floor) * (255.0 / (ceil - floor)) + torch.rand_like(
-                deriv
-            )
-            if __name__ == "__main__":
-                # for self-testing only.
-                assert d_scaled.min() >= 0.0
-                assert d_scaled.max() < 256.0
-            d_int = d_scaled.to(torch.uint8)
-            ctx.save_for_backward(d_int)
-        if x.dtype == torch.float16 or torch.is_autocast_enabled():
-            y = y.to(torch.float16)
-        return y
-
-    @staticmethod
-    def backward(ctx, y_grad: Tensor) -> Tensor:
-        (d,) = ctx.saved_tensors
-        # the same constants as used in forward pass.
-        floor = -0.043637
-        ceil = 1.2
-
-        d = d * ((ceil - floor) / 255.0) + floor
-        return y_grad * d
-
-
-class DoubleSwish(torch.nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Return double-swish activation function which is an approximation to Swish(Swish(x)),
-        that we approximate closely with x * sigmoid(x-1).
-        """
-        if torch.jit.is_scripting() or torch.jit.is_tracing():
-            return x * torch.sigmoid(x - 1.0)
-        return DoubleSwishFunction.apply(x)
-
-
-# Dropout2 is just like normal dropout, except it supports schedules on the dropout rates.
-class Dropout2(nn.Module):
-    def __init__(self, p: FloatLike):
-        super().__init__()
-        self.p = p
-
-    def forward(self, x: Tensor) -> Tensor:
-        return torch.nn.functional.dropout(x, p=float(self.p), training=self.training)
-
-
-class MulForDropout3(torch.autograd.Function):
-    # returns (x * y * alpha) where alpha is a float and y doesn't require
-    # grad and is zero-or-one.
-    @staticmethod
-    @custom_fwd
-    def forward(ctx, x, y, alpha):
-        assert not y.requires_grad
-        ans = x * y * alpha
-        ctx.save_for_backward(ans)
-        ctx.alpha = alpha
-        return ans
-
-    @staticmethod
-    @custom_bwd
-    def backward(ctx, ans_grad):
-        (ans,) = ctx.saved_tensors
-        x_grad = ctx.alpha * ans_grad * (ans != 0)
-        return x_grad, None, None
-
-
-# Dropout3 is just like normal dropout, except it supports schedules on the dropout rates,
-# and it lets you choose one dimension to share the dropout mask over
-class Dropout3(nn.Module):
-    def __init__(self, p: FloatLike, shared_dim: int):
-        super().__init__()
-        self.p = p
-        self.shared_dim = shared_dim
-
-    def forward(self, x: Tensor) -> Tensor:
-        p = float(self.p)
-        if not self.training or p == 0:
-            return _no_op(x)
-        scale = 1.0 / (1 - p)
-        rand_shape = list(x.shape)
-        rand_shape[self.shared_dim] = 1
-        mask = torch.rand(*rand_shape, device=x.device) > p
-        ans = MulForDropout3.apply(x, mask, scale)
-        return ans
-
-
-class SwooshLFunction(torch.autograd.Function):
-    """
-    swoosh_l(x) =  log(1 + exp(x-4)) - 0.08*x - 0.035
-    """
-
-    @staticmethod
-    def forward(ctx, x: Tensor) -> Tensor:
-        requires_grad = x.requires_grad
-        if x.dtype == torch.float16:
-            x = x.to(torch.float32)
-
-        zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
-
-        coeff = -0.08
-
-        with torch.amp.autocast("cuda", enabled=False):
-            with torch.enable_grad():
-                x = x.detach()
-                x.requires_grad = True
-                y = torch.logaddexp(zero, x - 4.0) + coeff * x - 0.035
-
-                if not requires_grad:
-                    return y
-
-                y.backward(gradient=torch.ones_like(y))
-
-                grad = x.grad
-                floor = coeff
-                ceil = 1.0 + coeff + 0.005
-
-                d_scaled = (grad - floor) * (255.0 / (ceil - floor)) + torch.rand_like(
-                    grad
-                )
-                if __name__ == "__main__":
-                    # for self-testing only.
-                    assert d_scaled.min() >= 0.0
-                    assert d_scaled.max() < 256.0
-
-                d_int = d_scaled.to(torch.uint8)
-                ctx.save_for_backward(d_int)
-                if x.dtype == torch.float16 or torch.is_autocast_enabled():
-                    y = y.to(torch.float16)
-                return y
-
-    @staticmethod
-    def backward(ctx, y_grad: Tensor) -> Tensor:
-        (d,) = ctx.saved_tensors
-        # the same constants as used in forward pass.
-
-        coeff = -0.08
-        floor = coeff
-        ceil = 1.0 + coeff + 0.005
-        d = d * ((ceil - floor) / 255.0) + floor
-        return y_grad * d
-
-
-class SwooshL(torch.nn.Module):
-    def forward(self, x: Tensor) -> Tensor:
-        """Return Swoosh-L activation."""
-        if (
-            torch.jit.is_scripting()
-            or torch.jit.is_tracing()
-            or "k2" not in sys.modules
-        ):
-            zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
-            return logaddexp(zero, x - 4.0) - 0.08 * x - 0.035
-        if not x.requires_grad:
-            return k2.swoosh_l_forward(x)
-        else:
-            return k2.swoosh_l(x)
-        # return SwooshLFunction.apply(x)
-
-
-class SwooshLOnnx(torch.nn.Module):
-    def forward(self, x: Tensor) -> Tensor:
-        """Return Swoosh-L activation."""
-        zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
-        return logaddexp_onnx(zero, x - 4.0) - 0.08 * x - 0.035
-
-
-class SwooshRFunction(torch.autograd.Function):
-    """
-     swoosh_r(x) =  log(1 + exp(x-1)) - 0.08*x - 0.313261687
-
-    derivatives are between -0.08 and 0.92.
-    """
-
-    @staticmethod
-    def forward(ctx, x: Tensor) -> Tensor:
-        requires_grad = x.requires_grad
-
-        if x.dtype == torch.float16:
-            x = x.to(torch.float32)
-
-        zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
-
-        with torch.amp.autocast("cuda", enabled=False):
-            with torch.enable_grad():
-                x = x.detach()
-                x.requires_grad = True
-                y = torch.logaddexp(zero, x - 1.0) - 0.08 * x - 0.313261687
-
-                if not requires_grad:
-                    return y
-                y.backward(gradient=torch.ones_like(y))
-
-                grad = x.grad
-                floor = -0.08
-                ceil = 0.925
-
-                d_scaled = (grad - floor) * (255.0 / (ceil - floor)) + torch.rand_like(
-                    grad
-                )
-                if __name__ == "__main__":
-                    # for self-testing only.
-                    assert d_scaled.min() >= 0.0
-                    assert d_scaled.max() < 256.0
-
-                d_int = d_scaled.to(torch.uint8)
-                ctx.save_for_backward(d_int)
-                if x.dtype == torch.float16 or torch.is_autocast_enabled():
-                    y = y.to(torch.float16)
-                return y
-
-    @staticmethod
-    def backward(ctx, y_grad: Tensor) -> Tensor:
-        (d,) = ctx.saved_tensors
-        # the same constants as used in forward pass.
-        floor = -0.08
-        ceil = 0.925
-        d = d * ((ceil - floor) / 255.0) + floor
-        return y_grad * d
-
-
-class SwooshR(torch.nn.Module):
-    def forward(self, x: Tensor) -> Tensor:
-        """Return Swoosh-R activation."""
-        if (
-            torch.jit.is_scripting()
-            or torch.jit.is_tracing()
-            or "k2" not in sys.modules
-        ):
-            zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
-            return logaddexp(zero, x - 1.0) - 0.08 * x - 0.313261687
-        if not x.requires_grad:
-            return k2.swoosh_r_forward(x)
-        else:
-            return k2.swoosh_r(x)
-        # return SwooshRFunction.apply(x)
-
-
-class SwooshROnnx(torch.nn.Module):
-    def forward(self, x: Tensor) -> Tensor:
-        """Return Swoosh-R activation."""
-        zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
-        return logaddexp_onnx(zero, x - 1.0) - 0.08 * x - 0.313261687
-
-
-# simple version of SwooshL that does not redefine the backprop, used in
-# ActivationDropoutAndLinearFunction.
-def SwooshLForward(x: Tensor):
-    x_offset = x - 4.0
-    log_sum = (1.0 + x_offset.exp()).log().to(x.dtype)
-    log_sum = torch.where(log_sum == float("inf"), x_offset, log_sum)
-    return log_sum - 0.08 * x - 0.035
-
-
-# simple version of SwooshR that does not redefine the backprop, used in
-# ActivationDropoutAndLinearFunction.
-def SwooshRForward(x: Tensor):
-    x_offset = x - 1.0
-    log_sum = (1.0 + x_offset.exp()).log().to(x.dtype)
-    log_sum = torch.where(log_sum == float("inf"), x_offset, log_sum)
-    return log_sum - 0.08 * x - 0.313261687
-
-
-class ActivationDropoutAndLinearFunction(torch.autograd.Function):
-    @staticmethod
-    @custom_fwd
-    def forward(
-        ctx,
-        x: Tensor,
-        weight: Tensor,
-        bias: Optional[Tensor],
-        activation: str,
-        dropout_p: float,
-        dropout_shared_dim: Optional[int],
-    ):
-        if dropout_p != 0.0:
-            dropout_shape = list(x.shape)
-            if dropout_shared_dim is not None:
-                dropout_shape[dropout_shared_dim] = 1
-            # else it won't be very memory efficient.
-            dropout_mask = (1.0 / (1.0 - dropout_p)) * (
-                torch.rand(*dropout_shape, device=x.device, dtype=x.dtype) > dropout_p
-            )
-        else:
-            dropout_mask = None
-
-        ctx.save_for_backward(x, weight, bias, dropout_mask)
-
-        ctx.activation = activation
-
-        forward_activation_dict = {
-            "SwooshL": k2.swoosh_l_forward,
-            "SwooshR": k2.swoosh_r_forward,
-        }
-        # it will raise a KeyError if this fails.  This will be an error.  We let it
-        # propagate to the user.
-        activation_func = forward_activation_dict[activation]
-        x = activation_func(x)
-        if dropout_mask is not None:
-            x = x * dropout_mask
-        x = torch.nn.functional.linear(x, weight, bias)
-        return x
-
-    @staticmethod
-    @custom_bwd
-    def backward(ctx, ans_grad: Tensor):
-        saved = ctx.saved_tensors
-        (x, weight, bias, dropout_mask) = saved
-
-        forward_and_deriv_activation_dict = {
-            "SwooshL": k2.swoosh_l_forward_and_deriv,
-            "SwooshR": k2.swoosh_r_forward_and_deriv,
-        }
-        # the following lines a KeyError if the activation is unrecognized.
-        # This will be an error.  We let it propagate to the user.
-        func = forward_and_deriv_activation_dict[ctx.activation]
-
-        y, func_deriv = func(x)
-        if dropout_mask is not None:
-            y = y * dropout_mask
-        # now compute derivative of y w.r.t. weight and bias..
-        # y: (..., in_channels), ans_grad: (..., out_channels),
-        (out_channels, in_channels) = weight.shape
-
-        in_channels = y.shape[-1]
-        g = ans_grad.reshape(-1, out_channels)
-        weight_deriv = torch.matmul(g.t(), y.reshape(-1, in_channels))
-        y_deriv = torch.matmul(ans_grad, weight)
-        bias_deriv = None if bias is None else g.sum(dim=0)
-        x_deriv = y_deriv * func_deriv
-        if dropout_mask is not None:
-            # order versus func_deriv does not matter
-            x_deriv = x_deriv * dropout_mask
-
-        return x_deriv, weight_deriv, bias_deriv, None, None, None
-
-
-class ActivationDropoutAndLinear(torch.nn.Module):
-    """
-     This merges an activation function followed by dropout and then a nn.Linear module;
-     it does so in a memory efficient way so that it only stores the input to the whole
-     module.  If activation == SwooshL and dropout_shared_dim != None, this will be
-     equivalent to:
-       nn.Sequential(SwooshL(),
-                     Dropout3(dropout_p, shared_dim=dropout_shared_dim),
-                     ScaledLinear(in_channels, out_channels, bias=bias,
-                                  initial_scale=initial_scale))
-    If dropout_shared_dim is None, the dropout would be equivalent to
-    Dropout2(dropout_p).  Note: Dropout3 will be more memory efficient as the dropout
-    mask is smaller.
-
-     Args:
-        in_channels: number of input channels, e.g. 256
-        out_channels: number of output channels, e.g. 256
-        bias: if true, have a bias
-        activation: the activation function, for now just support SwooshL.
-        dropout_p: the dropout probability or schedule (happens after nonlinearity).
-        dropout_shared_dim: the dimension, if any, across which the dropout mask is
-             shared (e.g. the time dimension).  If None, this may be less memory
-             efficient if there are modules before this one that cache the input
-             for their backprop (e.g. Balancer or Whiten).
-    """
-
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        bias: bool = True,
-        activation: str = "SwooshL",
-        dropout_p: FloatLike = 0.0,
-        dropout_shared_dim: Optional[int] = -1,
-        initial_scale: float = 1.0,
-    ):
-        super().__init__()
-        # create a temporary module of nn.Linear that we'll steal the
-        # weights and bias from
-        l = ScaledLinear(
-            in_channels, out_channels, bias=bias, initial_scale=initial_scale
-        )
-
-        self.weight = l.weight
-        # register_parameter properly handles making it a parameter when l.bias
-        # is None. I think there is some reason for doing it this way rather
-        # than just setting it to None but I don't know what it is, maybe
-        # something to do with exporting the module..
-        self.register_parameter("bias", l.bias)
-
-        self.activation = activation
-        self.dropout_p = dropout_p
-        self.dropout_shared_dim = dropout_shared_dim
-
-    def forward(self, x: Tensor):
-        if (
-            torch.jit.is_scripting()
-            or torch.jit.is_tracing()
-            or "k2" not in sys.modules
-        ):
-            if self.activation == "SwooshL":
-                x = SwooshLForward(x)
-            elif self.activation == "SwooshR":
-                x = SwooshRForward(x)
-            else:
-                assert False, self.activation
-            return torch.nn.functional.linear(x, self.weight, self.bias)
-
-        return ActivationDropoutAndLinearFunction.apply(
-            x,
-            self.weight,
-            self.bias,
-            self.activation,
-            float(self.dropout_p),
-            self.dropout_shared_dim,
-        )
-
-
-def convert_num_channels(x: Tensor, num_channels: int) -> Tensor:
-    if num_channels <= x.shape[-1]:
-        return x[..., :num_channels]
-    else:
-        shape = list(x.shape)
-        shape[-1] = num_channels - shape[-1]
-        zeros = torch.zeros(shape, dtype=x.dtype, device=x.device)
-        return torch.cat((x, zeros), dim=-1)
-
-
-def _test_whiten():
-    for proportion in [0.1, 0.5, 10.0]:
-        logging.info(f"_test_whiten(): proportion = {proportion}")
-        x = torch.randn(100, 128)
-        direction = torch.randn(128)
-        coeffs = torch.randn(100, 1)
-        x += proportion * direction * coeffs
-
-        x.requires_grad = True
-
-        m = Whiten(
-            1, 5.0, prob=1.0, grad_scale=0.1  # num_groups  # whitening_limit,
-        )  # grad_scale
-
-        for _ in range(4):
-            y = m(x)
-
-        y_grad = torch.randn_like(x)
-        y.backward(gradient=y_grad)
-
-        if proportion < 0.2:
-            assert torch.allclose(x.grad, y_grad)
-        elif proportion > 1.0:
-            assert not torch.allclose(x.grad, y_grad)
-
-
-def _test_balancer_sign():
-    probs = torch.arange(0, 1, 0.01)
-    N = 1000
-    x = 1.0 * ((2.0 * (torch.rand(probs.numel(), N) < probs.unsqueeze(-1))) - 1.0)
-    x = x.detach()
-    x.requires_grad = True
-    m = Balancer(
-        probs.numel(),
-        channel_dim=0,
-        min_positive=0.05,
-        max_positive=0.95,
-        min_abs=0.0,
-        prob=1.0,
-    )
-
-    y_grad = torch.sign(torch.randn(probs.numel(), N))
-
-    y = m(x)
-    y.backward(gradient=y_grad)
-    print("_test_balancer_sign: x = ", x)
-    print("_test_balancer_sign: y grad = ", y_grad)
-    print("_test_balancer_sign: x grad = ", x.grad)
-
-
-def _test_balancer_magnitude():
-    magnitudes = torch.arange(0, 1, 0.01)
-    N = 1000
-    x = torch.sign(torch.randn(magnitudes.numel(), N)) * magnitudes.unsqueeze(-1)
-    x = x.detach()
-    x.requires_grad = True
-    m = Balancer(
-        magnitudes.numel(),
-        channel_dim=0,
-        min_positive=0.0,
-        max_positive=1.0,
-        min_abs=0.2,
-        max_abs=0.7,
-        prob=1.0,
-    )
-
-    y_grad = torch.sign(torch.randn(magnitudes.numel(), N))
-
-    y = m(x)
-    y.backward(gradient=y_grad)
-    print("_test_balancer_magnitude: x = ", x)
-    print("_test_balancer_magnitude: y grad = ", y_grad)
-    print("_test_balancer_magnitude: x grad = ", x.grad)
-
-
-def _test_double_swish_deriv():
-    x = torch.randn(10, 12, dtype=torch.double) * 3.0
-    x.requires_grad = True
-    m = DoubleSwish()
-
-    tol = (1.2 - (-0.043637)) / 255.0
-    torch.autograd.gradcheck(m, x, atol=tol)
-
-    # for self-test.
-    x = torch.randn(1000, 1000, dtype=torch.double) * 3.0
-    x.requires_grad = True
-    y = m(x)
-
-
-def _test_swooshl_deriv():
-    x = torch.randn(10, 12, dtype=torch.double) * 3.0
-    x.requires_grad = True
-    m = SwooshL()
-
-    tol = 1.0 / 255.0
-    torch.autograd.gradcheck(m, x, atol=tol, eps=0.01)
-
-    # for self-test.
-    x = torch.randn(1000, 1000, dtype=torch.double) * 3.0
-    x.requires_grad = True
-    y = m(x)
-
-
-def _test_swooshr_deriv():
-    x = torch.randn(10, 12, dtype=torch.double) * 3.0
-    x.requires_grad = True
-    m = SwooshR()
-
-    tol = 1.0 / 255.0
-    torch.autograd.gradcheck(m, x, atol=tol, eps=0.01)
-
-    # for self-test.
-    x = torch.randn(1000, 1000, dtype=torch.double) * 3.0
-    x.requires_grad = True
-    y = m(x)
-
-
-def _test_softmax():
-    a = torch.randn(2, 10, dtype=torch.float64)
-    b = a.clone()
-    a.requires_grad = True
-    b.requires_grad = True
-    a.softmax(dim=1)[:, 0].sum().backward()
-    print("a grad = ", a.grad)
-    softmax(b, dim=1)[:, 0].sum().backward()
-    print("b grad = ", b.grad)
-    assert torch.allclose(a.grad, b.grad)
-
-
-def _test_piecewise_linear():
-    p = PiecewiseLinear((0, 10.0))
-    for x in [-100, 0, 100]:
-        assert p(x) == 10.0
-    p = PiecewiseLinear((0, 10.0), (1, 0.0))
-    for x, y in [(-100, 10.0), (0, 10.0), (0.5, 5.0), (1, 0.0), (2, 0.0)]:
-        print("x, y = ", x, y)
-        assert p(x) == y, (x, p(x), y)
-
-    q = PiecewiseLinear((0.5, 15.0), (0.6, 1.0))
-    x_vals = [-1.0, 0.0, 0.1, 0.2, 0.5, 0.6, 0.7, 0.9, 1.0, 2.0]
-    pq = p.max(q)
-    for x in x_vals:
-        y1 = max(p(x), q(x))
-        y2 = pq(x)
-        assert abs(y1 - y2) < 0.001
-    pq = p.min(q)
-    for x in x_vals:
-        y1 = min(p(x), q(x))
-        y2 = pq(x)
-        assert abs(y1 - y2) < 0.001
-    pq = p + q
-    for x in x_vals:
-        y1 = p(x) + q(x)
-        y2 = pq(x)
-        assert abs(y1 - y2) < 0.001
-
-
-def _test_activation_dropout_and_linear():
-    in_channels = 20
-    out_channels = 30
-
-    for bias in [True, False]:
-        # actually we don't test for dropout_p != 0.0 because forward functions will give
-        # different answers.  This is because we are using the k2 implementation of
-        # swoosh_l an swoosh_r inside SwooshL() and SwooshR(), and they call randn()
-        # internally, messing up the random state.
-        for dropout_p in [0.0]:
-            for activation in ["SwooshL", "SwooshR"]:
-                m1 = nn.Sequential(
-                    SwooshL() if activation == "SwooshL" else SwooshR(),
-                    Dropout3(p=dropout_p, shared_dim=-1),
-                    ScaledLinear(
-                        in_channels, out_channels, bias=bias, initial_scale=0.5
-                    ),
-                )
-                m2 = ActivationDropoutAndLinear(
-                    in_channels,
-                    out_channels,
-                    bias=bias,
-                    initial_scale=0.5,
-                    activation=activation,
-                    dropout_p=dropout_p,
-                )
-                with torch.no_grad():
-                    m2.weight[:] = m1[2].weight
-                    if bias:
-                        m2.bias[:] = m1[2].bias
-                # make sure forward gives same result.
-                x1 = torch.randn(10, in_channels)
-                x1.requires_grad = True
-
-                # TEMP.
-                assert torch.allclose(
-                    SwooshRFunction.apply(x1), SwooshRForward(x1), atol=1.0e-03
-                )
-
-                x2 = x1.clone().detach()
-                x2.requires_grad = True
-                seed = 10
-                torch.manual_seed(seed)
-                y1 = m1(x1)
-                y_grad = torch.randn_like(y1)
-                y1.backward(gradient=y_grad)
-                torch.manual_seed(seed)
-                y2 = m2(x2)
-                y2.backward(gradient=y_grad)
-
-                print(
-                    f"bias = {bias}, dropout_p = {dropout_p}, activation = {activation}"
-                )
-                print("y1 = ", y1)
-                print("y2 = ", y2)
-                assert torch.allclose(y1, y2, atol=0.02)
-                assert torch.allclose(m1[2].weight.grad, m2.weight.grad, atol=1.0e-05)
-                if bias:
-                    assert torch.allclose(m1[2].bias.grad, m2.bias.grad, atol=1.0e-05)
-                print("x1.grad = ", x1.grad)
-                print("x2.grad = ", x2.grad)
-
-                def isclose(a, b):
-                    # return true if cosine similarity is > 0.9.
-                    return (a * b).sum() > 0.9 * (
-                        (a**2).sum() * (b**2).sum()
-                    ).sqrt()
-
-                # the SwooshL() implementation has a noisy gradient due to 1-byte
-                # storage of it.
-                assert isclose(x1.grad, x2.grad)
-
-
-if __name__ == "__main__":
-    logging.getLogger().setLevel(logging.DEBUG)
-    torch.set_num_threads(1)
-    torch.set_num_interop_threads(1)
-    _test_piecewise_linear()
-    _test_softmax()
-    _test_whiten()
-    _test_balancer_sign()
-    _test_balancer_magnitude()
-    _test_double_swish_deriv()
-    _test_swooshr_deriv()
-    _test_swooshl_deriv()
-    _test_activation_dropout_and_linear()
diff --git a/egs/zipvoice/zipvoice/solver.py b/egs/zipvoice/zipvoice/solver.py
deleted file mode 100644
index a1e316ec8..000000000
--- a/egs/zipvoice/zipvoice/solver.py
+++ /dev/null
@@ -1,277 +0,0 @@
-#!/usr/bin/env python3
-# Copyright         2024  Xiaomi Corp.        (authors: Han Zhu)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Optional, Union
-
-import torch
-
-
-class DiffusionModel(torch.nn.Module):
-    """A wrapper of diffusion models for inference.
-    Args:
-        model: The diffusion model.
-        distill: Whether it is a distillation model.
-    """
-
-    def __init__(
-        self,
-        model: torch.nn.Module,
-        distill: bool = False,
-        func_name: str = "forward_fm_decoder",
-    ):
-        super().__init__()
-        self.model = model
-        self.distill = distill
-        self.func_name = func_name
-        self.model_func = getattr(self.model, func_name)
-
-    def forward(
-        self,
-        t: torch.Tensor,
-        x: torch.Tensor,
-        text_condition: torch.Tensor,
-        speech_condition: torch.Tensor,
-        padding_mask: Optional[torch.Tensor] = None,
-        guidance_scale: Union[float, torch.Tensor] = 0.0,
-        **kwargs
-    ) -> torch.Tensor:
-        """
-        Forward function that Handles the classifier-free guidance.
-        Args:
-            t: The current timestep, a tensor of shape (batch, 1, 1) or a tensor of a single float.
-            x: The initial value, with the shape (batch, seq_len, emb_dim).
-            text_condition: The text_condition of the diffision model, with the shape (batch, seq_len, emb_dim).
-            speech_condition: The speech_condition of the diffision model, with the shape (batch, seq_len, emb_dim).
-            padding_mask: The mask for padding; True means masked position, with the shape (batch, seq_len).
-            guidance_scale: The scale of classifier-free guidance, a float or a tensor of shape (batch, 1, 1).
-        Retrun:
-            The prediction with the shape (batch, seq_len, emb_dim).
-        """
-        if not torch.is_tensor(guidance_scale):
-            guidance_scale = torch.tensor(
-                guidance_scale, dtype=t.dtype, device=t.device
-            )
-        if self.distill:
-            return self.model_func(
-                t=t,
-                xt=x,
-                text_condition=text_condition,
-                speech_condition=speech_condition,
-                padding_mask=padding_mask,
-                guidance_scale=guidance_scale,
-                **kwargs
-            )
-
-        if (guidance_scale == 0.0).all():
-            return self.model_func(
-                t=t,
-                xt=x,
-                text_condition=text_condition,
-                speech_condition=speech_condition,
-                padding_mask=padding_mask,
-                **kwargs
-            )
-        else:
-            if t.dim() != 0:
-                t = torch.cat([t] * 2, dim=0)
-
-            x = torch.cat([x] * 2, dim=0)
-            padding_mask = torch.cat([padding_mask] * 2, dim=0)
-
-            text_condition = torch.cat(
-                [torch.zeros_like(text_condition), text_condition], dim=0
-            )
-
-            if t.dim() == 0:
-                if t > 0.5:
-                    speech_condition = torch.cat(
-                        [torch.zeros_like(speech_condition), speech_condition], dim=0
-                    )
-                else:
-                    guidance_scale = guidance_scale * 2
-                    speech_condition = torch.cat(
-                        [speech_condition, speech_condition], dim=0
-                    )
-            else:
-                assert t.dim() > 0, t
-                larger_t_index = (t > 0.5).squeeze(1).squeeze(1)
-                zero_speech_condition = torch.cat(
-                    [torch.zeros_like(speech_condition), speech_condition], dim=0
-                )
-                speech_condition = torch.cat(
-                    [speech_condition, speech_condition], dim=0
-                )
-                speech_condition[larger_t_index] = zero_speech_condition[larger_t_index]
-                guidance_scale[~larger_t_index[: larger_t_index.size(0) // 2]] *= 2
-
-            data_uncond, data_cond = self.model_func(
-                t=t,
-                xt=x,
-                text_condition=text_condition,
-                speech_condition=speech_condition,
-                padding_mask=padding_mask,
-                **kwargs
-            ).chunk(2, dim=0)
-
-            res = (1 + guidance_scale) * data_cond - guidance_scale * data_uncond
-            return res
-
-
-class EulerSolver:
-    def __init__(
-        self,
-        model: torch.nn.Module,
-        distill: bool = False,
-        func_name: str = "forward_fm_decoder",
-    ):
-        """Construct a Euler Solver
-        Args:
-            model: The diffusion model.
-            distill: Whether it is distillation model.
-        """
-
-        self.model = DiffusionModel(model, distill=distill, func_name=func_name)
-
-    def sample(
-        self,
-        x: torch.Tensor,
-        text_condition: torch.Tensor,
-        speech_condition: torch.Tensor,
-        padding_mask: torch.Tensor,
-        num_step: int = 10,
-        guidance_scale: Union[float, torch.Tensor] = 0.0,
-        t_start: Union[float, torch.Tensor] = 0.0,
-        t_end: Union[float, torch.Tensor] = 1.0,
-        t_shift: float = 1.0,
-        **kwargs
-    ) -> torch.Tensor:
-        """
-        Compute the sample at time `t_end` by Euler Solver.
-        Args:
-            x: The initial value at time `t_start`, with the shape (batch, seq_len, emb_dim).
-            text_condition: The text condition of the diffision mode, with the shape (batch, seq_len, emb_dim).
-            speech_condition: The speech condition of the diffision model, with the shape (batch, seq_len, emb_dim).
-            padding_mask: The mask for padding; True means masked position, with the shape (batch, seq_len).
-            num_step: The number of ODE steps.
-            guidance_scale: The scale for classifier-free guidance, which is
-                a float or a tensor with the shape (batch, 1, 1).
-            t_start: the start timestep in the range of [0, 1],
-                which is a float or a tensor with the shape (batch, 1, 1).
-            t_end: the end time_step in the range of [0, 1],
-                which is a float or a tensor with the shape (batch, 1, 1).
-            t_shift: shift the t toward smaller numbers so that the sampling
-                will emphasize low SNR region. Should be in the range of (0, 1].
-                The shifting will be more significant when the number is smaller.
-
-        Returns:
-            The approximated solution at time `t_end`.
-        """
-        device = x.device
-
-        if torch.is_tensor(t_start) and t_start.dim() > 0:
-            timesteps = get_time_steps_batch(
-                t_start=t_start,
-                t_end=t_end,
-                num_step=num_step,
-                t_shift=t_shift,
-                device=device,
-            )
-        else:
-            timesteps = get_time_steps(
-                t_start=t_start,
-                t_end=t_end,
-                num_step=num_step,
-                t_shift=t_shift,
-                device=device,
-            )
-        for step in range(num_step):
-            v = self.model(
-                t=timesteps[step],
-                x=x,
-                text_condition=text_condition,
-                speech_condition=speech_condition,
-                padding_mask=padding_mask,
-                guidance_scale=guidance_scale,
-                **kwargs
-            )
-            x = x + v * (timesteps[step + 1] - timesteps[step])
-        return x
-
-
-def get_time_steps(
-    t_start: float = 0.0,
-    t_end: float = 1.0,
-    num_step: int = 10,
-    t_shift: float = 1.0,
-    device: torch.device = torch.device("cpu"),
-) -> torch.Tensor:
-    """Compute the intermediate time steps for sampling.
-
-    Args:
-        t_start: The starting time of the sampling (default is 0).
-        t_end: The starting time of the sampling (default is 1).
-        num_step: The number of sampling.
-        t_shift: shift the t toward smaller numbers so that the sampling
-            will emphasize low SNR region. Should be in the range of (0, 1].
-            The shifting will be more significant when the number is smaller.
-        device: A torch device.
-    Returns:
-        The time step with the shape (num_step + 1,).
-    """
-
-    timesteps = torch.linspace(t_start, t_end, num_step + 1).to(device)
-
-    timesteps = t_shift * timesteps / (1 + (t_shift - 1) * timesteps)
-
-    return timesteps
-
-
-def get_time_steps_batch(
-    t_start: torch.Tensor,
-    t_end: torch.Tensor,
-    num_step: int = 10,
-    t_shift: float = 1.0,
-    device: torch.device = torch.device("cpu"),
-) -> torch.Tensor:
-    """Compute the intermediate time steps for sampling in the batch mode.
-
-    Args:
-        t_start: The starting time of the sampling (default is 0), with the shape (batch, 1, 1).
-        t_end: The starting time of the sampling (default is 1), with the shape (batch, 1, 1).
-        num_step: The number of sampling.
-        t_shift: shift the t toward smaller numbers so that the sampling
-            will emphasize low SNR region. Should be in the range of (0, 1].
-            The shifting will be more significant when the number is smaller.
-        device: A torch device.
-    Returns:
-        The time step with the shape (num_step + 1, N, 1, 1).
-    """
-    while t_start.dim() > 1 and t_start.size(-1) == 1:
-        t_start = t_start.squeeze(-1)
-    while t_end.dim() > 1 and t_end.size(-1) == 1:
-        t_end = t_end.squeeze(-1)
-    assert t_start.dim() == t_end.dim() == 1
-
-    timesteps_shape = (num_step + 1, t_start.size(0))
-    timesteps = torch.zeros(timesteps_shape, device=device)
-
-    for i in range(t_start.size(0)):
-        timesteps[:, i] = torch.linspace(t_start[i], t_end[i], steps=num_step + 1)
-
-    timesteps = t_shift * timesteps / (1 + (t_shift - 1) * timesteps)
-
-    return timesteps.unsqueeze(-1).unsqueeze(-1)
diff --git a/egs/zipvoice/zipvoice/tokenizer.py b/egs/zipvoice/zipvoice/tokenizer.py
deleted file mode 100644
index ea25d3498..000000000
--- a/egs/zipvoice/zipvoice/tokenizer.py
+++ /dev/null
@@ -1,572 +0,0 @@
-# Copyright      2023-2024  Xiaomi Corp.        (authors: Zengwei Yao
-#                                                         Han Zhu,
-#                                                         Wei Kang)
-#
-# See ../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import logging
-import re
-import unicodedata
-from functools import reduce
-from typing import Dict, List, Optional
-
-import cn2an
-import inflect
-import jieba
-from pypinyin import Style, lazy_pinyin
-from pypinyin.contrib.tone_convert import to_finals_tone3, to_initials
-
-try:
-    from piper_phonemize import phonemize_espeak
-except Exception as ex:
-    raise RuntimeError(
-        f"{ex}\nPlease run\n"
-        "pip install piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html"
-    )
-
-_inflect = inflect.engine()
-_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
-_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
-_percent_number_re = re.compile(r"([0-9\.\,]*[0-9]+%)")
-_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
-_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
-_fraction_re = re.compile(r"([0-9]+)/([0-9]+)")
-_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
-_number_re = re.compile(r"[0-9]+")
-
-# List of (regular expression, replacement) pairs for abbreviations:
-_abbreviations = [
-    (re.compile("\\b%s\\b" % x[0], re.IGNORECASE), x[1])
-    for x in [
-        ("mrs", "misess"),
-        ("mr", "mister"),
-        ("dr", "doctor"),
-        ("st", "saint"),
-        ("co", "company"),
-        ("jr", "junior"),
-        ("maj", "major"),
-        ("gen", "general"),
-        ("drs", "doctors"),
-        ("rev", "reverend"),
-        ("lt", "lieutenant"),
-        ("hon", "honorable"),
-        ("sgt", "sergeant"),
-        ("capt", "captain"),
-        ("esq", "esquire"),
-        ("ltd", "limited"),
-        ("col", "colonel"),
-        ("ft", "fort"),
-        ("etc", "et cetera"),
-        ("btw", "by the way"),
-    ]
-]
-
-
-def intersperse(sequence, item=0):
-    result = [item] * (len(sequence) * 2 + 1)
-    result[1::2] = sequence
-    return result
-
-
-def expand_abbreviations(text):
-    for regex, replacement in _abbreviations:
-        text = re.sub(regex, replacement, text)
-    return text
-
-
-def _remove_commas(m):
-    return m.group(1).replace(",", "")
-
-
-def _expand_decimal_point(m):
-    return m.group(1).replace(".", " point ")
-
-
-def _expand_percent(m):
-    return m.group(1).replace("%", " percent ")
-
-
-def _expand_dollars(m):
-    match = m.group(1)
-    parts = match.split(".")
-    if len(parts) > 2:
-        return " " + match + " dollars "  # Unexpected format
-    dollars = int(parts[0]) if parts[0] else 0
-    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
-    if dollars and cents:
-        dollar_unit = "dollar" if dollars == 1 else "dollars"
-        cent_unit = "cent" if cents == 1 else "cents"
-        return " %s %s, %s %s " % (dollars, dollar_unit, cents, cent_unit)
-    elif dollars:
-        dollar_unit = "dollar" if dollars == 1 else "dollars"
-        return " %s %s " % (dollars, dollar_unit)
-    elif cents:
-        cent_unit = "cent" if cents == 1 else "cents"
-        return " %s %s " % (cents, cent_unit)
-    else:
-        return " zero dollars "
-
-
-def fraction_to_words(numerator, denominator):
-    if numerator == 1 and denominator == 2:
-        return " one half "
-    if numerator == 1 and denominator == 4:
-        return " one quarter "
-    if denominator == 2:
-        return " " + _inflect.number_to_words(numerator) + " halves "
-    if denominator == 4:
-        return " " + _inflect.number_to_words(numerator) + " quarters "
-    return (
-        " "
-        + _inflect.number_to_words(numerator)
-        + " "
-        + _inflect.ordinal(_inflect.number_to_words(denominator))
-        + " "
-    )
-
-
-def _expand_fraction(m):
-    numerator = int(m.group(1))
-    denominator = int(m.group(2))
-    return fraction_to_words(numerator, denominator)
-
-
-def _expand_ordinal(m):
-    return " " + _inflect.number_to_words(m.group(0)) + " "
-
-
-def _expand_number(m):
-    num = int(m.group(0))
-    if num > 1000 and num < 3000:
-        if num == 2000:
-            return " two thousand "
-        elif num > 2000 and num < 2010:
-            return " two thousand " + _inflect.number_to_words(num % 100) + " "
-        elif num % 100 == 0:
-            return " " + _inflect.number_to_words(num // 100) + " hundred "
-        else:
-            return (
-                " "
-                + _inflect.number_to_words(num, andword="", zero="oh", group=2).replace(
-                    ", ", " "
-                )
-                + " "
-            )
-    else:
-        return " " + _inflect.number_to_words(num, andword="") + " "
-
-
-# Normalize numbers pronunciation
-def normalize_numbers(text):
-    text = re.sub(_comma_number_re, _remove_commas, text)
-    text = re.sub(_pounds_re, r"\1 pounds", text)
-    text = re.sub(_dollars_re, _expand_dollars, text)
-    text = re.sub(_fraction_re, _expand_fraction, text)
-    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
-    text = re.sub(_percent_number_re, _expand_percent, text)
-    text = re.sub(_ordinal_re, _expand_ordinal, text)
-    text = re.sub(_number_re, _expand_number, text)
-    return text
-
-
-# Convert numbers to Chinese pronunciation
-def number_to_chinese(text):
-    text = cn2an.transform(text, "an2cn")
-    return text
-
-
-def map_punctuations(text):
-    text = text.replace("，", ",")
-    text = text.replace("。", ".")
-    text = text.replace("！", "!")
-    text = text.replace("？", "?")
-    text = text.replace("；", ";")
-    text = text.replace("：", ":")
-    text = text.replace("、", ",")
-    text = text.replace("‘", "'")
-    text = text.replace("“", '"')
-    text = text.replace("”", '"')
-    text = text.replace("’", "'")
-    text = text.replace("⋯", "…")
-    text = text.replace("···", "…")
-    text = text.replace("・・・", "…")
-    text = text.replace("...", "…")
-    return text
-
-
-def is_chinese(char):
-    if char >= "\u4e00" and char <= "\u9fa5":
-        return True
-    else:
-        return False
-
-
-def is_alphabet(char):
-    if (char >= "\u0041" and char <= "\u005a") or (
-        char >= "\u0061" and char <= "\u007a"
-    ):
-        return True
-    else:
-        return False
-
-
-def is_hangul(char):
-    letters = unicodedata.normalize("NFD", char)
-    return all(
-        ["\u1100" <= c <= "\u11ff" or "\u3131" <= c <= "\u318e" for c in letters]
-    )
-
-
-def is_japanese(char):
-    return any(
-        [
-            start <= char <= end
-            for start, end in [
-                ("\u3041", "\u3096"),
-                ("\u30a0", "\u30ff"),
-                ("\uff5f", "\uff9f"),
-                ("\u31f0", "\u31ff"),
-                ("\u3220", "\u3243"),
-                ("\u3280", "\u337f"),
-            ]
-        ]
-    )
-
-
-def get_segment(text: str) -> List[str]:
-    # sentence --> [ch_part, en_part, ch_part, ...]
-    # example :
-    # input : 我们是小米人,是吗? Yes I think so!霍...啦啦啦
-    # output : [('我们是小米人,是吗? ', 'zh'), ('Yes I think so!', 'en'), ('霍...啦啦啦', 'zh')]
-    segments = []
-    types = []
-    flag = 0
-    temp_seg = ""
-    temp_lang = ""
-
-    for i, ch in enumerate(text):
-        if is_chinese(ch):
-            types.append("zh")
-        elif is_alphabet(ch):
-            types.append("en")
-        else:
-            types.append("other")
-
-    assert len(types) == len(text)
-
-    for i in range(len(types)):
-        # find the first char of the seg
-        if flag == 0:
-            temp_seg += text[i]
-            temp_lang = types[i]
-            flag = 1
-        else:
-            if temp_lang == "other":
-                if types[i] == temp_lang:
-                    temp_seg += text[i]
-                else:
-                    temp_seg += text[i]
-                    temp_lang = types[i]
-            else:
-                if types[i] == temp_lang:
-                    temp_seg += text[i]
-                elif types[i] == "other":
-                    temp_seg += text[i]
-                else:
-                    segments.append((temp_seg, temp_lang))
-                    temp_seg = text[i]
-                    temp_lang = types[i]
-                    flag = 1
-
-    segments.append((temp_seg, temp_lang))
-    return segments
-
-
-def preprocess(text: str) -> str:
-    text = map_punctuations(text)
-    return text
-
-
-def tokenize_ZH(text: str) -> List[str]:
-    try:
-        text = number_to_chinese(text)
-        segs = list(jieba.cut(text))
-        full = lazy_pinyin(
-            segs, style=Style.TONE3, tone_sandhi=True, neutral_tone_with_five=True
-        )
-        phones = []
-        for x in full:
-            # valid pinyin (in tone3 style) is alphabet + 1 number in [1-5].
-            if not (x[0:-1].isalpha() and x[-1] in ("1", "2", "3", "4", "5")):
-                phones.append(x)
-                continue
-            initial = to_initials(x, strict=False)
-            # don't want to share tokens with espeak tokens, so use tone3 style
-            final = to_finals_tone3(x, strict=False, neutral_tone_with_five=True)
-            if initial != "":
-                # don't want to share tokens with espeak tokens, so add a '0' after each initial
-                phones.append(initial + "0")
-            if final != "":
-                phones.append(final)
-        return phones
-    except Exception as ex:
-        logging.warning(f"Tokenize ZH failed: {ex}")
-        return []
-
-
-def tokenize_EN(text: str) -> List[str]:
-    try:
-        text = expand_abbreviations(text)
-        text = normalize_numbers(text)
-        tokens = phonemize_espeak(text, "en-us")
-        tokens = reduce(lambda x, y: x + y, tokens)
-        return tokens
-    except Exception as ex:
-        logging.warning(f"Tokenize EN failed: {ex}")
-        return []
-
-
-class TokenizerEmilia(object):
-    def __init__(self, token_file: Optional[str] = None, token_type="phone"):
-        """
-        Args:
-          tokens: the file that contains information that maps tokens to ids,
-            which is a text file with '{token} {token_id}' per line.
-        """
-        assert (
-            token_type == "phone"
-        ), f"Only support phone tokenizer for Emilia, but get {token_type}."
-        self.has_tokens = False
-        if token_file is None:
-            logging.debug(
-                "Initialize Tokenizer without tokens file, will fail when map to ids."
-            )
-            return
-        self.token2id: Dict[str, int] = {}
-        with open(token_file, "r", encoding="utf-8") as f:
-            for line in f.readlines():
-                info = line.rstrip().split("\t")
-                token, id = info[0], int(info[1])
-                assert token not in self.token2id, token
-                self.token2id[token] = id
-        self.pad_id = self.token2id["_"]  # padding
-
-        self.vocab_size = len(self.token2id)
-        self.has_tokens = True
-
-    def texts_to_token_ids(
-        self,
-        texts: List[str],
-    ) -> List[List[int]]:
-        return self.tokens_to_token_ids(self.texts_to_tokens(texts))
-
-    def texts_to_tokens(
-        self,
-        texts: List[str],
-    ) -> List[List[str]]:
-        """
-        Args:
-          texts:
-            A list of transcripts.
-        Returns:
-          Return a list of a list of tokens [utterance][token]
-        """
-        for i in range(len(texts)):
-            # Text normalization
-            texts[i] = preprocess(texts[i])
-
-        phoneme_list = []
-        for text in texts:
-            # now only en and ch
-            segments = get_segment(text)
-            all_phoneme = []
-            for index in range(len(segments)):
-                seg = segments[index]
-                if seg[1] == "zh":
-                    phoneme = tokenize_ZH(seg[0])
-                else:
-                    if seg[1] != "en":
-                        logging.warning(
-                            f"The lang should be en, given {seg[1]}, skipping segment : {seg}"
-                        )
-                        continue
-                    phoneme = tokenize_EN(seg[0])
-                all_phoneme += phoneme
-        phoneme_list.append(all_phoneme)
-        return phoneme_list
-
-    def tokens_to_token_ids(
-        self,
-        tokens: List[List[str]],
-        intersperse_blank: bool = False,
-    ) -> List[List[int]]:
-        """
-        Args:
-          tokens_list:
-            A list of token list, each corresponding to one utterance.
-          intersperse_blank:
-            Whether to intersperse blanks in the token sequence.
-
-        Returns:
-          Return a list of token id list [utterance][token_id]
-        """
-        assert self.has_tokens, "Please initialize Tokenizer with a tokens file."
-        token_ids = []
-
-        for tks in tokens:
-            ids = []
-            for t in tks:
-                if t not in self.token2id:
-                    logging.warning(f"Skip OOV {t}")
-                    continue
-                ids.append(self.token2id[t])
-
-            if intersperse_blank:
-                ids = intersperse(ids, self.pad_id)
-
-            token_ids.append(ids)
-
-        return token_ids
-
-
-class TokenizerLibriTTS(object):
-    def __init__(self, token_file: str, token_type: str):
-        """
-        Args:
-          type: the type of tokenizer, e.g., bpe, char, phone.
-          tokens: the file that contains information that maps tokens to ids,
-            which is a text file with '{token} {token_id}' per line if type is
-            char or phone, otherwise it is a bpe_model file.
-        """
-        self.type = token_type
-        assert token_type in ["bpe", "char", "phone"]
-        # Parse token file
-
-        if token_type == "bpe":
-            import sentencepiece as spm
-
-            self.sp = spm.SentencePieceProcessor()
-            self.sp.load(token_file)
-            self.pad_id = self.sp.piece_to_id("<pad>")
-            self.vocab_size = self.sp.get_piece_size()
-        else:
-            self.token2id: Dict[str, int] = {}
-            with open(token_file, "r", encoding="utf-8") as f:
-                for line in f.readlines():
-                    info = line.rstrip().split("\t")
-                    token, id = info[0], int(info[1])
-                    assert token not in self.token2id, token
-                    self.token2id[token] = id
-            self.pad_id = self.token2id["_"]  # padding
-            self.vocab_size = len(self.token2id)
-        try:
-            from tacotron_cleaner.cleaners import custom_english_cleaners as cleaner
-        except Exception as ex:
-            raise RuntimeError(
-                f"{ex}\nPlease run\n"
-                "pip install espnet_tts_frontend`, refer to https://github.com/espnet/espnet_tts_frontend/"
-            )
-        self.cleaner = cleaner
-
-    def texts_to_token_ids(
-        self,
-        texts: List[str],
-        lang: str = "en-us",
-    ) -> List[List[int]]:
-        """
-        Args:
-          texts:
-            A list of transcripts.
-          intersperse_blank:
-            Whether to intersperse blanks in the token sequence.
-            Used when alignment is from MAS.
-          lang:
-            Language argument passed to phonemize_espeak().
-
-        Returns:
-          Return a list of token id list [utterance][token_id]
-        """
-        for i in range(len(texts)):
-            # Text normalization
-            texts[i] = self.cleaner(texts[i])
-
-        if self.type == "bpe":
-            token_ids_list = self.sp.encode(texts)
-
-        elif self.type == "phone":
-            token_ids_list = []
-            for text in texts:
-                tokens_list = phonemize_espeak(text.lower(), lang)
-                tokens = []
-                for t in tokens_list:
-                    tokens.extend(t)
-                token_ids = []
-                for t in tokens:
-                    if t not in self.token2id:
-                        logging.warning(f"Skip OOV {t}")
-                        continue
-                    token_ids.append(self.token2id[t])
-
-                token_ids_list.append(token_ids)
-        else:
-            token_ids_list = []
-            for text in texts:
-                token_ids = []
-                for t in text:
-                    if t not in self.token2id:
-                        logging.warning(f"Skip OOV {t}")
-                        continue
-                    token_ids.append(self.token2id[t])
-
-                token_ids_list.append(token_ids)
-
-        return token_ids_list
-
-    def tokens_to_token_ids(
-        self,
-        tokens_list: List[str],
-    ) -> List[List[int]]:
-        """
-        Args:
-          tokens_list:
-            A list of token list, each corresponding to one utterance.
-
-        Returns:
-          Return a list of token id list [utterance][token_id]
-        """
-        token_ids_list = []
-
-        for tokens in tokens_list:
-            token_ids = []
-            for t in tokens:
-                if t not in self.token2id:
-                    logging.warning(f"Skip OOV {t}")
-                    continue
-                token_ids.append(self.token2id[t])
-
-            token_ids_list.append(token_ids)
-
-        return token_ids_list
-
-
-if __name__ == "__main__":
-    text = "我们是5年小米人,是吗? Yes I think so! mr king, 5 years, from 2019 to 2024. 霍...啦啦啦超过90%的人咯...?!9204"
-    tokenizer = TokenizerEmilia()
-    tokens = tokenizer.texts_to_tokens([text])
-    print(f"tokens : {tokens}")
-    tokens2 = "|".join(tokens[0])
-    print(f"tokens2 : {tokens2}")
-    tokens2 = tokens2.split("|")
-    assert tokens[0] == tokens2
diff --git a/egs/zipvoice/zipvoice/train_distill.py b/egs/zipvoice/zipvoice/train_distill.py
deleted file mode 100644
index 9e52a3790..000000000
--- a/egs/zipvoice/zipvoice/train_distill.py
+++ /dev/null
@@ -1,1043 +0,0 @@
-#!/usr/bin/env python3
-# Copyright         2024  Xiaomi Corp.        (authors: Han Zhu)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-"""
-This script trains a ZipVoice-Distill model starting from a ZipVoice model.
-It has two distillation stages.
-
-Usage:
-
-(1) The first distillation stage with a fixed ZipVoice model as the teacher.
-python3 zipvoice/train_distill.py \
-        --world-size 8 \
-        --use-fp16 1 \
-        --tensorboard 1 \
-        --dataset "emilia" \
-        --base-lr 0.0005 \
-        --max-duration 500 \
-        --token-file "data/tokens_emilia.txt" \
-        --manifest-dir "data/fbank" \
-        --teacher-model zipvoice/exp_zipvoice/epoch-11-avg-4.pt \
-        --num-updates 60000 \
-        --distill-stage "first" \
-        --exp-dir zipvoice/exp_zipvoice_distill_1stage
-
-(2) The second distillation stage with a EMA model as the teacher.
-python3 zipvoice/train_distill.py \
-        --world-size 8 \
-        --use-fp16 1 \
-        --tensorboard 1 \
-        --dataset "emilia" \
-        --base-lr 0.0001 \
-        --max-duration 500 \
-        --token-file "data/tokens_emilia.txt" \
-        --manifest-dir "data/fbank" \
-        --teacher-model zipvoice/exp_zipvoice_distill_1stage/iter-60000-avg-7.pt \
-        --num-updates 2000 \
-        --distill-stage "second" \
-        --exp-dir zipvoice/exp_zipvoice_distill
-"""
-
-import argparse
-import copy
-import logging
-import os
-import random
-from pathlib import Path
-from shutil import copyfile
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import optim
-import torch
-import torch.multiprocessing as mp
-import torch.nn as nn
-from checkpoint import load_checkpoint, save_checkpoint
-from lhotse.cut import Cut, CutSet
-from lhotse.utils import fix_random_seed
-from model import get_distill_model, get_model
-from optim import FixedLRScheduler, ScaledAdam
-from tokenizer import TokenizerEmilia, TokenizerLibriTTS
-from torch import Tensor
-from torch.amp import GradScaler, autocast
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.optim import Optimizer
-from torch.utils.tensorboard import SummaryWriter
-from train_flow import add_model_arguments, get_params
-from tts_datamodule import TtsDataModule
-from utils import (
-    condition_time_mask,
-    get_adjusted_batch_count,
-    prepare_input,
-    set_batch_count,
-)
-
-from icefall import diagnostics
-from icefall.checkpoint import (
-    remove_checkpoints,
-    save_checkpoint_with_global_batch_idx,
-    update_averaged_model,
-)
-from icefall.dist import cleanup_dist, setup_dist
-from icefall.hooks import register_inf_check_hooks
-from icefall.utils import (
-    AttributeDict,
-    MetricsTracker,
-    get_parameter_groups_with_lrs,
-    make_pad_mask,
-    setup_logger,
-    str2bool,
-)
-
-LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--world-size",
-        type=int,
-        default=1,
-        help="Number of GPUs for DDP training.",
-    )
-
-    parser.add_argument(
-        "--master-port",
-        type=int,
-        default=12354,
-        help="Master port to use for DDP training.",
-    )
-
-    parser.add_argument(
-        "--tensorboard",
-        type=str2bool,
-        default=True,
-        help="Should various information be logged in tensorboard.",
-    )
-
-    parser.add_argument(
-        "--num-epochs",
-        type=int,
-        default=1,
-        help="Number of epochs to train.",
-    )
-
-    parser.add_argument(
-        "--num-updates",
-        type=int,
-        default=0,
-        help="Number of updates to train, will ignore num_epochs if > 0.",
-    )
-
-    parser.add_argument(
-        "--start-epoch",
-        type=int,
-        default=1,
-        help="""Resume training from this epoch. It should be positive.
-        If larger than 1, it will load checkpoint from
-        exp-dir/epoch-{start_epoch-1}.pt
-        """,
-    )
-
-    parser.add_argument(
-        "--teacher-model",
-        type=str,
-        help="""Checkpoints of pre-trained teacher model""",
-    )
-
-    parser.add_argument(
-        "--exp-dir",
-        type=str,
-        default="zipvoice/exp_zipvoice_distill",
-        help="""The experiment dir.
-        It specifies the directory where all training related
-        files, e.g., checkpoints, log, etc, are saved
-        """,
-    )
-
-    parser.add_argument(
-        "--base-lr", type=float, default=0.001, help="The base learning rate."
-    )
-
-    parser.add_argument(
-        "--ref-duration",
-        type=float,
-        default=50,
-        help="Reference batch duration for purposes of adjusting batch counts for setting various "
-        "schedules inside the model",
-    )
-
-    parser.add_argument(
-        "--seed",
-        type=int,
-        default=42,
-        help="The seed for random generators intended for reproducibility",
-    )
-
-    parser.add_argument(
-        "--print-diagnostics",
-        type=str2bool,
-        default=False,
-        help="Accumulate stats on activations, print them and exit.",
-    )
-
-    parser.add_argument(
-        "--inf-check",
-        type=str2bool,
-        default=False,
-        help="Add hooks to check for infinite module outputs and gradients.",
-    )
-
-    parser.add_argument(
-        "--save-every-n",
-        type=int,
-        default=4000,
-        help="""Save checkpoint after processing this number of batches"
-        periodically. We save checkpoint to exp-dir/ whenever
-        params.batch_idx_train % save_every_n == 0. The checkpoint filename
-        has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt'
-        Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the
-        end of each epoch where `xxx` is the epoch number counting from 1.
-        """,
-    )
-
-    parser.add_argument(
-        "--keep-last-k",
-        type=int,
-        default=30,
-        help="""Only keep this number of checkpoints on disk.
-        For instance, if it is 3, there are only 3 checkpoints
-        in the exp-dir with filenames `checkpoint-xxx.pt`.
-        It does not affect checkpoints with name `epoch-xxx.pt`.
-        """,
-    )
-
-    parser.add_argument(
-        "--average-period",
-        type=int,
-        default=200,
-        help="""Update the averaged model, namely `model_avg`, after processing
-        this number of batches. `model_avg` is a separate version of model,
-        in which each floating-point parameter is the average of all the
-        parameters from the start of training. Each time we take the average,
-        we do: `model_avg = model * (average_period / batch_idx_train) +
-            model_avg * ((batch_idx_train - average_period) / batch_idx_train)`.
-        """,
-    )
-
-    parser.add_argument(
-        "--use-fp16",
-        type=str2bool,
-        default=False,
-        help="Whether to use half precision training.",
-    )
-
-    parser.add_argument(
-        "--feat-scale",
-        type=float,
-        default=0.1,
-        help="The scale factor of fbank feature",
-    )
-
-    parser.add_argument(
-        "--ema-decay",
-        type=float,
-        default=0.9999,
-        help="The EMA decay factor of target model in distillation.",
-    )
-    parser.add_argument(
-        "--distill-stage",
-        type=str,
-        choices=["first", "second"],
-        help="The stage of distillation.",
-    )
-
-    parser.add_argument(
-        "--dataset",
-        type=str,
-        default="emilia",
-        choices=["emilia", "libritts"],
-        help="The used training dataset",
-    )
-
-    add_model_arguments(parser)
-
-    return parser
-
-
-def ema(new_model, ema_model, decay):
-    if isinstance(new_model, DDP):
-        new_model = new_model.module
-    if isinstance(ema_model, DDP):
-        ema_model = ema_model.module
-    new_model_dict = new_model.state_dict()
-    ema_model_dict = ema_model.state_dict()
-    for key in new_model_dict.keys():
-        ema_model_dict[key].data.copy_(
-            ema_model_dict[key].data * decay + new_model_dict[key].data * (1 - decay)
-        )
-
-
-def resume_checkpoint(
-    params: AttributeDict, model: nn.Module, model_avg: nn.Module, model_ema: nn.Module
-) -> Optional[Dict[str, Any]]:
-    """Load checkpoint from file.
-
-    If params.start_epoch is larger than 1, it will load the checkpoint from
-    `params.start_epoch - 1`.
-
-    Apart from loading state dict for `model` and `optimizer` it also updates
-    `best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
-    and `best_valid_loss` in `params`.
-
-    Args:
-      params:
-        The return value of :func:`get_params`.
-      model:
-        The training model.
-    Returns:
-      Return a dict containing previously saved training info.
-    """
-    filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
-
-    assert filename.is_file(), f"{filename} does not exist!"
-
-    saved_params = load_checkpoint(
-        filename, model=model, model_avg=model_avg, model_ema=model_ema, strict=True
-    )
-
-    if params.start_epoch > 1:
-        keys = [
-            "best_train_epoch",
-            "best_valid_epoch",
-            "batch_idx_train",
-            "best_train_loss",
-            "best_valid_loss",
-        ]
-        for k in keys:
-            params[k] = saved_params[k]
-
-    return saved_params
-
-
-def compute_fbank_loss(
-    params: AttributeDict,
-    model: Union[nn.Module, DDP],
-    teacher_model: Union[nn.Module, DDP],
-    features: Tensor,
-    features_lens: Tensor,
-    tokens: List[List[int]],
-    is_training: bool,
-) -> Tuple[Tensor, MetricsTracker]:
-    """
-    Compute loss given the model and its inputs.
-
-    Args:
-      params:
-        Parameters for training. See :func:`get_params`.
-      model:
-        The model for training.
-      teacher_model:
-        The teacher model for distillation.
-      features:
-        The target acoustic feature.
-      features_lens:
-        The number of frames of each utterance.
-      tokens:
-        Input tokens that representing the transcripts.
-      durations:
-        Duration of each token.
-      is_training:
-        True for training. False for validation. When it is True, this
-        function enables autograd during computation; when it is False, it
-        disables autograd.
-    """
-
-    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
-
-    batch_size, num_frames, _ = features.shape
-
-    features = torch.nn.functional.pad(
-        features, (0, 0, 0, num_frames - features.size(1))
-    )  # (B, T, F)
-    noise = torch.randn_like(features)  # (B, T, F)
-
-    # Sampling t and guidance_scale from uniform distribution
-
-    t_value = random.random()
-    t = torch.ones(batch_size, 1, 1, device=device) * t_value
-    if params.distill_stage == "first":
-        guidance_scale = torch.rand(batch_size, 1, 1, device=device) * 2
-    else:
-        guidance_scale = torch.rand(batch_size, 1, 1, device=device) * 2 + 1
-    xt = features * t + noise * (1 - t)
-    t_delta_fix = random.uniform(0.0, min(0.3, 1 - t_value))
-    t_delta_ema = random.uniform(0.0, min(0.3, 1 - t_value - t_delta_fix))
-    t_dest = t + t_delta_fix + t_delta_ema
-
-    with torch.no_grad():
-        speech_condition_mask = condition_time_mask(
-            features_lens=features_lens,
-            mask_percent=(0.7, 1.0),
-            max_len=features.size(1),
-        )
-
-        if params.distill_stage == "first":
-            teacher_x_t_mid, _ = teacher_model.sample_intermediate(
-                tokens=tokens,
-                features=features,
-                features_lens=features_lens,
-                noise=xt,
-                speech_condition_mask=speech_condition_mask,
-                t_start=t,
-                t_end=t + t_delta_fix,
-                num_step=1,
-                guidance_scale=guidance_scale,
-            )
-
-            target_x1, _ = teacher_model.sample_intermediate(
-                tokens=tokens,
-                features=features,
-                features_lens=features_lens,
-                noise=teacher_x_t_mid,
-                speech_condition_mask=speech_condition_mask,
-                t_start=t + t_delta_fix,
-                t_end=t_dest,
-                num_step=1,
-                guidance_scale=guidance_scale,
-            )
-        else:
-            teacher_x_t_mid, _ = teacher_model(
-                tokens=tokens,
-                features=features,
-                features_lens=features_lens,
-                noise=xt,
-                speech_condition_mask=speech_condition_mask,
-                t_start=t,
-                t_end=t + t_delta_fix,
-                num_step=1,
-                guidance_scale=guidance_scale,
-            )
-
-            target_x1, _ = teacher_model(
-                tokens=tokens,
-                features=features,
-                features_lens=features_lens,
-                noise=teacher_x_t_mid,
-                speech_condition_mask=speech_condition_mask,
-                t_start=t + t_delta_fix,
-                t_end=t_dest,
-                num_step=1,
-                guidance_scale=guidance_scale,
-            )
-
-    with torch.set_grad_enabled(is_training):
-
-        pred_x1, _ = model(
-            tokens=tokens,
-            features=features,
-            features_lens=features_lens,
-            noise=xt,
-            speech_condition_mask=speech_condition_mask,
-            t_start=t,
-            t_end=t_dest,
-            num_step=1,
-            guidance_scale=guidance_scale,
-        )
-        pred_v = (pred_x1 - xt) / (t_dest - t)
-
-        padding_mask = make_pad_mask(features_lens, max_len=num_frames)  # (B, T)
-        loss_mask = speech_condition_mask & (~padding_mask)
-
-        target_v = (target_x1 - xt) / (t_dest - t)
-        loss = torch.mean((pred_v[loss_mask] - target_v[loss_mask]) ** 2)
-
-        ut = features - noise  # (B, T, F)
-
-        ref_loss = torch.mean((pred_v[loss_mask] - ut[loss_mask]) ** 2)
-
-    assert loss.requires_grad == is_training
-    info = MetricsTracker()
-    num_frames = features_lens.sum().item()
-    info["frames"] = num_frames
-    info["loss"] = loss.detach().cpu().item() * num_frames
-    info["ref_loss"] = ref_loss.detach().cpu().item() * num_frames
-    return loss, info
-
-
-def train_one_epoch(
-    params: AttributeDict,
-    model: Union[nn.Module, DDP],
-    teacher_model: Union[nn.Module, DDP],
-    tokenizer: TokenizerEmilia,
-    optimizer: Optimizer,
-    scheduler: LRSchedulerType,
-    train_dl: torch.utils.data.DataLoader,
-    valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
-    model_avg: Optional[nn.Module] = None,
-    tb_writer: Optional[SummaryWriter] = None,
-    world_size: int = 1,
-    rank: int = 0,
-) -> None:
-    """Train the model for one epoch.
-
-    The training loss from the mean of all frames is saved in
-    `params.train_loss`. It runs the validation process every
-    `params.valid_interval` batches.
-
-    Args:
-      params:
-        It is returned by :func:`get_params`.
-      model:
-        The model for training.
-      teacher_model:
-        The model for distillation.
-      tokenizer:
-        Used to convert text to tokens.
-      optimizer:
-        The optimizer.
-      scheduler:
-        The learning rate scheduler, we call step() every epoch.
-      train_dl:
-        Dataloader for the training dataset.
-      valid_dl:
-        Dataloader for the validation dataset.
-      scaler:
-        The scaler used for mix precision training.
-      tb_writer:
-        Writer to write log messages to tensorboard.
-      world_size:
-        Number of nodes in DDP training. If it is 1, DDP is disabled.
-      rank:
-        The rank of the node in DDP training. If no DDP is used, it should
-        be set to 0.
-    """
-    model.train()
-    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
-
-    # used to track the stats over iterations in one epoch
-    tot_loss = MetricsTracker()
-
-    saved_bad_model = False
-
-    def save_bad_model(suffix: str = ""):
-        save_checkpoint(
-            filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt",
-            model=model,
-            model_avg=model_avg,
-            model_ema=teacher_model,
-            params=params,
-            optimizer=optimizer,
-            scheduler=scheduler,
-            sampler=train_dl.sampler,
-            scaler=scaler,
-            rank=0,
-        )
-
-    for batch_idx, batch in enumerate(train_dl):
-
-        if batch_idx % 10 == 0:
-            set_batch_count(model, get_adjusted_batch_count(params) + 100000)
-
-        if (
-            params.valid_interval is None
-            and batch_idx == 0
-            and not params.print_diagnostics
-        ) or (
-            params.valid_interval is not None
-            and params.batch_idx_train % params.valid_interval == 0
-            and not params.print_diagnostics
-        ):
-            logging.info("Computing validation loss")
-            valid_info = compute_validation_loss(
-                params=params,
-                model=model,
-                teacher_model=teacher_model,
-                tokenizer=tokenizer,
-                valid_dl=valid_dl,
-                world_size=world_size,
-            )
-            model.train()
-            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
-            logging.info(
-                f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
-            )
-            if tb_writer is not None:
-                valid_info.write_summary(
-                    tb_writer, "train/valid_", params.batch_idx_train
-                )
-
-        params.batch_idx_train += 1
-
-        batch_size = len(batch["text"])
-
-        tokens, features, features_lens = prepare_input(
-            params=params,
-            batch=batch,
-            device=device,
-            tokenizer=tokenizer,
-            return_tokens=True,
-            return_feature=True,
-        )
-
-        try:
-            with autocast("cuda", enabled=params.use_fp16):
-                loss, loss_info = compute_fbank_loss(
-                    params=params,
-                    model=model,
-                    teacher_model=teacher_model,
-                    features=features,
-                    features_lens=features_lens,
-                    tokens=tokens,
-                    is_training=True,
-                )
-
-            tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
-
-            scaler.scale(loss).backward()
-
-            scheduler.step_batch(params.batch_idx_train)
-            scaler.step(optimizer)
-            scaler.update()
-            optimizer.zero_grad()
-            if params.distill_stage == "second":
-                ema(model, teacher_model, params.ema_decay)
-        except RuntimeError as e:
-            if "out of memory" in str(e):
-                logging.info(f"out of memory error at rank {rank}")
-                # optimizer.zero_grad()
-                # duration_optimizer.zero_grad()
-                torch.cuda.empty_cache()
-                raise
-                continue
-            else:
-                logging.info(f"Caught exception : {e}.")
-                save_bad_model()
-                raise
-        except Exception as e:
-            logging.info(f"Caught exception : {e}.")
-            save_bad_model()
-            raise
-
-        if params.print_diagnostics and batch_idx == 5:
-            return
-
-        if (
-            rank == 0
-            and params.batch_idx_train > 0
-            and params.batch_idx_train % params.average_period == 0
-        ):
-            update_averaged_model(
-                params=params,
-                model_cur=model,
-                model_avg=model_avg,
-            )
-
-        if (
-            params.batch_idx_train > 0
-            and params.batch_idx_train % params.save_every_n == 0
-        ):
-            save_checkpoint_with_global_batch_idx(
-                out_dir=params.exp_dir,
-                global_batch_idx=params.batch_idx_train,
-                model=model,
-                model_avg=model_avg,
-                params=params,
-                optimizer=optimizer,
-                scheduler=scheduler,
-                sampler=train_dl.sampler,
-                scaler=scaler,
-                rank=rank,
-            )
-            remove_checkpoints(
-                out_dir=params.exp_dir,
-                topk=params.keep_last_k,
-                rank=rank,
-            )
-        if (
-            params.batch_idx_train > 0
-            and params.num_updates > 0
-            and params.batch_idx_train > params.num_updates
-        ):
-            break
-        if params.batch_idx_train % 100 == 0 and params.use_fp16:
-            # If the grad scale was less than 1, try increasing it.    The _growth_interval
-            # of the grad scaler is configurable, but we can't configure it to have different
-            # behavior depending on the current grad scale.
-            cur_grad_scale = scaler._scale.item()
-
-            if cur_grad_scale < 1024.0 or (
-                cur_grad_scale < 4096.0 and params.batch_idx_train % 400 == 0
-            ):
-                scaler.update(cur_grad_scale * 2.0)
-            if cur_grad_scale < 0.01:
-                if not saved_bad_model:
-                    save_bad_model(suffix="-first-warning")
-                    saved_bad_model = True
-                logging.warning(f"Grad scale is small: {cur_grad_scale}")
-            if cur_grad_scale < 1.0e-05:
-                save_bad_model()
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
-
-        if params.batch_idx_train % params.log_interval == 0:
-            cur_lr = max(scheduler.get_last_lr())
-            cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0
-
-            logging.info(
-                f"Epoch {params.cur_epoch}, batch {batch_idx}, "
-                f"global_batch_idx: {params.batch_idx_train}, batch size: {batch_size}, "
-                f"loss[{loss_info}], tot_loss[{tot_loss}], "
-                f"cur_lr: {cur_lr:.2e}, "
-                + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
-            )
-
-            if tb_writer is not None:
-                tb_writer.add_scalar(
-                    "train/learning_rate", cur_lr, params.batch_idx_train
-                )
-                loss_info.write_summary(
-                    tb_writer, "train/current_", params.batch_idx_train
-                )
-                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
-                if params.use_fp16:
-                    tb_writer.add_scalar(
-                        "train/grad_scale", cur_grad_scale, params.batch_idx_train
-                    )
-
-    loss_value = tot_loss["loss"]
-    params.train_loss = loss_value
-    if params.train_loss < params.best_train_loss:
-        params.best_train_epoch = params.cur_epoch
-        params.best_train_loss = params.train_loss
-
-
-def compute_validation_loss(
-    params: AttributeDict,
-    model: Union[nn.Module, DDP],
-    teacher_model: Optional[nn.Module],
-    tokenizer: TokenizerEmilia,
-    valid_dl: torch.utils.data.DataLoader,
-    world_size: int = 1,
-) -> MetricsTracker:
-    """Run the validation process."""
-
-    model.eval()
-    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
-
-    # used to summary the stats over iterations
-    tot_loss = MetricsTracker()
-
-    for batch_idx, batch in enumerate(valid_dl):
-        tokens, features, features_lens = prepare_input(
-            params=params,
-            batch=batch,
-            device=device,
-            tokenizer=tokenizer,
-            return_tokens=True,
-            return_feature=True,
-        )
-
-        loss, loss_info = compute_fbank_loss(
-            params=params,
-            model=model,
-            teacher_model=teacher_model,
-            features=features,
-            features_lens=features_lens,
-            tokens=tokens,
-            is_training=False,
-        )
-        assert loss.requires_grad is False
-        tot_loss = tot_loss + loss_info
-
-    if world_size > 1:
-        tot_loss.reduce(loss.device)
-
-    loss_value = tot_loss["loss"]
-    if loss_value < params.best_valid_loss:
-        params.best_valid_epoch = params.cur_epoch
-        params.best_valid_loss = loss_value
-
-    return tot_loss
-
-
-def run(rank, world_size, args):
-    """
-    Args:
-      rank:
-        It is a value between 0 and `world_size-1`, which is
-        passed automatically by `mp.spawn()` in :func:`main`.
-        The node with rank 0 is responsible for saving checkpoint.
-      world_size:
-        Number of GPUs for DDP training.
-      args:
-        The return value of get_parser().parse_args()
-    """
-    params = get_params()
-    params.update(vars(args))
-
-    fix_random_seed(params.seed)
-    if world_size > 1:
-        setup_dist(rank, world_size, params.master_port)
-
-    setup_logger(f"{params.exp_dir}/log/log-train")
-    os.makedirs(f"{params.exp_dir}/fbank", exist_ok=True)
-
-    logging.info("Training started")
-
-    if args.tensorboard and rank == 0:
-        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
-    else:
-        tb_writer = None
-
-    device = torch.device("cpu")
-    if torch.cuda.is_available():
-        device = torch.device("cuda", rank)
-    logging.info(f"Device: {device}")
-
-    if params.dataset == "emilia":
-        tokenizer = TokenizerEmilia(
-            token_file=params.token_file, token_type=params.token_type
-        )
-    elif params.dataset == "libritts":
-        tokenizer = TokenizerLibriTTS(
-            token_file=params.token_file, token_type=params.token_type
-        )
-
-    params.vocab_size = tokenizer.vocab_size
-    params.pad_id = tokenizer.pad_id
-
-    params.device = device
-
-    logging.info(params)
-
-    logging.info("About to create model")
-
-    assert params.teacher_model is not None
-    logging.info(f"Loading pre-trained model from {params.teacher_model}")
-    model = get_distill_model(params)
-    _ = load_checkpoint(
-        filename=params.teacher_model,
-        model=model,
-        strict=(params.distill_stage == "second"),
-    )
-
-    if params.distill_stage == "first":
-        teacher_model = get_model(params)
-        _ = load_checkpoint(
-            filename=params.teacher_model, model=teacher_model, strict=True
-        )
-    else:
-        teacher_model = copy.deepcopy(model)
-
-    num_param = sum([p.numel() for p in model.parameters()])
-    logging.info(f"Number of parameters : {num_param}")
-
-    model_avg: Optional[nn.Module] = None
-    if rank == 0:
-        # model_avg is only used with rank 0
-        model_avg = copy.deepcopy(model).to(torch.float64)
-    assert params.start_epoch > 0, params.start_epoch
-    if params.start_epoch > 1:
-        logging.info(f"Resuming from epoch {params.start_epoch}")
-        if params.distill_stage == "first":
-            checkpoints = resume_checkpoint(
-                params=params, model=model, model_avg=model_avg
-            )
-        else:
-            checkpoints = resume_checkpoint(
-                params=params, model=model, model_avg=model_avg, model_ema=teacher_model
-            )
-
-    model = model.to(device)
-    teacher_model.to(device)
-    teacher_model.eval()
-
-    if world_size > 1:
-        logging.info("Using DDP")
-        model = DDP(model, device_ids=[rank], find_unused_parameters=True)
-
-    # only update the fm_decoder
-    num_trainable = 0
-    for name, p in model.named_parameters():
-        if "fm_decoder" in name:
-            p.requires_grad = True
-            num_trainable += p.numel()
-        else:
-            p.requires_grad = False
-
-    logging.info(
-        "A total of {} trainable parameters ({:.3f}% of the whole model)".format(
-            num_trainable, num_trainable / num_param * 100
-        )
-    )
-
-    optimizer = ScaledAdam(
-        get_parameter_groups_with_lrs(
-            model,
-            lr=params.base_lr,
-            include_names=True,
-        ),
-        lr=params.base_lr,  # should have no effect
-        clipping_scale=2.0,
-    )
-
-    scheduler = FixedLRScheduler(optimizer)
-
-    scaler = GradScaler("cuda", enabled=params.use_fp16)
-
-    if params.start_epoch > 1 and checkpoints is not None:
-        # load state_dict for optimizers
-        if "optimizer" in checkpoints:
-            logging.info("Loading optimizer state dict")
-            optimizer.load_state_dict(checkpoints["optimizer"])
-
-        # load state_dict for schedulers
-        if "scheduler" in checkpoints:
-            logging.info("Loading scheduler state dict")
-            scheduler.load_state_dict(checkpoints["scheduler"])
-
-        if "grad_scaler" in checkpoints:
-            logging.info("Loading grad scaler state dict")
-            scaler.load_state_dict(checkpoints["grad_scaler"])
-
-    if params.print_diagnostics:
-        opts = diagnostics.TensorDiagnosticOptions(
-            512
-        )  # allow 4 megabytes per sub-module
-        diagnostic = diagnostics.attach_diagnostics(model, opts)
-
-    if params.inf_check:
-        register_inf_check_hooks(model)
-
-    def remove_short_and_long_utt_emilia(c: Cut):
-        if c.duration < 1.0 or c.duration > 30.0:
-            return False
-        return True
-
-    def remove_short_and_long_utt_libritts(c: Cut):
-        if c.duration < 1.0 or c.duration > 20.0:
-            return False
-        return True
-
-    datamodule = TtsDataModule(args)
-    if params.dataset == "emilia":
-        train_cuts = CutSet.mux(
-            datamodule.train_emilia_EN_cuts(),
-            datamodule.train_emilia_ZH_cuts(),
-            weights=[46000, 49000],
-        )
-        train_cuts = train_cuts.filter(remove_short_and_long_utt_emilia)
-        dev_cuts = CutSet.mux(
-            datamodule.dev_emilia_EN_cuts(),
-            datamodule.dev_emilia_ZH_cuts(),
-            weights=[0.5, 0.5],
-        )
-    elif params.dataset == "libritts":
-        train_cuts = datamodule.train_libritts_cuts()
-        train_cuts = train_cuts.filter(remove_short_and_long_utt_libritts)
-        dev_cuts = datamodule.dev_libritts_cuts()
-
-    train_dl = datamodule.train_dataloaders(train_cuts)
-
-    valid_dl = datamodule.dev_dataloaders(dev_cuts)
-
-    for epoch in range(params.start_epoch, params.num_epochs + 1):
-        logging.info(f"Start epoch {epoch}")
-
-        scheduler.step_epoch(epoch - 1)
-        fix_random_seed(params.seed + epoch - 1)
-        train_dl.sampler.set_epoch(epoch - 1)
-
-        params.cur_epoch = epoch
-
-        if tb_writer is not None:
-            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
-
-        train_one_epoch(
-            params=params,
-            model=model,
-            model_avg=model_avg,
-            teacher_model=teacher_model,
-            tokenizer=tokenizer,
-            optimizer=optimizer,
-            scheduler=scheduler,
-            train_dl=train_dl,
-            valid_dl=valid_dl,
-            scaler=scaler,
-            tb_writer=tb_writer,
-            world_size=world_size,
-            rank=rank,
-        )
-
-        if params.print_diagnostics:
-            diagnostic.print_diagnostics()
-            break
-
-        filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
-        save_checkpoint(
-            filename=filename,
-            params=params,
-            model=model,
-            model_avg=model_avg,
-            model_ema=teacher_model,
-            optimizer=optimizer,
-            scheduler=scheduler,
-            sampler=train_dl.sampler,
-            scaler=scaler,
-            rank=rank,
-        )
-
-        if rank == 0:
-            if params.best_train_epoch == params.cur_epoch:
-                best_train_filename = params.exp_dir / "best-train-loss.pt"
-                copyfile(src=filename, dst=best_train_filename)
-
-            if params.best_valid_epoch == params.cur_epoch:
-                best_valid_filename = params.exp_dir / "best-valid-loss.pt"
-                copyfile(src=filename, dst=best_valid_filename)
-
-    logging.info("Done!")
-
-    if world_size > 1:
-        torch.distributed.barrier()
-        cleanup_dist()
-
-
-def main():
-    parser = get_parser()
-    TtsDataModule.add_arguments(parser)
-    args = parser.parse_args()
-    args.exp_dir = Path(args.exp_dir)
-
-    world_size = args.world_size
-    assert world_size >= 1
-    if world_size > 1:
-        mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
-    else:
-        run(rank=0, world_size=1, args=args)
-
-
-if __name__ == "__main__":
-    torch.set_num_threads(1)
-    torch.set_num_interop_threads(1)
-    main()
diff --git a/egs/zipvoice/zipvoice/train_flow.py b/egs/zipvoice/zipvoice/train_flow.py
deleted file mode 100644
index 0bf023273..000000000
--- a/egs/zipvoice/zipvoice/train_flow.py
+++ /dev/null
@@ -1,1108 +0,0 @@
-#!/usr/bin/env python3
-# Copyright         2023  Xiaomi Corp.        (authors: Wei Kang,
-#                                                       Han Zhu)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-This script trains a ZipVoice model with the flow-matching loss.
-
-Usage:
-
-python3 zipvoice/train_flow.py \
-        --world-size 8 \
-        --use-fp16 1 \
-        --dataset emilia \
-        --max-duration 500 \
-        --lr-hours 30000 \
-        --lr-batches 7500 \
-        --token-file "data/tokens_emilia.txt" \
-        --manifest-dir "data/fbank" \
-        --num-epochs 11 \
-        --exp-dir zipvoice/exp_zipvoice
-"""
-
-import argparse
-import copy
-import logging
-import os
-from pathlib import Path
-from shutil import copyfile
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import optim
-import torch
-import torch.multiprocessing as mp
-import torch.nn as nn
-from checkpoint import load_checkpoint, save_checkpoint
-from lhotse.cut import Cut, CutSet
-from lhotse.utils import fix_random_seed
-from model import get_model
-from optim import Eden, ScaledAdam
-from tokenizer import TokenizerEmilia, TokenizerLibriTTS
-from torch import Tensor
-from torch.amp import GradScaler, autocast
-from torch.nn.parallel import DistributedDataParallel as DDP
-from torch.optim import Optimizer
-from torch.utils.tensorboard import SummaryWriter
-from tts_datamodule import TtsDataModule
-from utils import get_adjusted_batch_count, prepare_input, set_batch_count
-
-from icefall import diagnostics
-from icefall.checkpoint import (
-    remove_checkpoints,
-    save_checkpoint_with_global_batch_idx,
-    update_averaged_model,
-)
-from icefall.dist import cleanup_dist, setup_dist
-from icefall.env import get_env_info
-from icefall.hooks import register_inf_check_hooks
-from icefall.utils import (
-    AttributeDict,
-    MetricsTracker,
-    get_parameter_groups_with_lrs,
-    setup_logger,
-    str2bool,
-)
-
-LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
-
-
-def add_model_arguments(parser: argparse.ArgumentParser):
-    parser.add_argument(
-        "--fm-decoder-downsampling-factor",
-        type=str,
-        default="1,2,4,2,1",
-        help="Downsampling factor for each stack of encoder layers.",
-    )
-
-    parser.add_argument(
-        "--fm-decoder-num-layers",
-        type=str,
-        default="2,2,4,4,4",
-        help="Number of zipformer encoder layers per stack, comma separated.",
-    )
-
-    parser.add_argument(
-        "--fm-decoder-cnn-module-kernel",
-        type=str,
-        default="31,15,7,15,31",
-        help="Sizes of convolutional kernels in convolution modules in each encoder stack: "
-        "a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--fm-decoder-feedforward-dim",
-        type=int,
-        default=1536,
-        help="Feedforward dimension of the zipformer encoder layers, per stack, comma separated.",
-    )
-
-    parser.add_argument(
-        "--fm-decoder-num-heads",
-        type=int,
-        default=4,
-        help="Number of attention heads in the zipformer encoder layers: a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--fm-decoder-dim",
-        type=int,
-        default=512,
-        help="Embedding dimension in encoder stacks: a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--text-encoder-downsampling-factor",
-        type=str,
-        default="1",
-        help="Downsampling factor for each stack of encoder layers.",
-    )
-
-    parser.add_argument(
-        "--text-encoder-num-layers",
-        type=str,
-        default="4",
-        help="Number of zipformer encoder layers per stack, comma separated.",
-    )
-
-    parser.add_argument(
-        "--text-encoder-feedforward-dim",
-        type=int,
-        default=512,
-        help="Feedforward dimension of the zipformer encoder layers, per stack, comma separated.",
-    )
-
-    parser.add_argument(
-        "--text-encoder-cnn-module-kernel",
-        type=str,
-        default="9",
-        help="Sizes of convolutional kernels in convolution modules in each encoder stack: "
-        "a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--text-encoder-num-heads",
-        type=int,
-        default=4,
-        help="Number of attention heads in the zipformer encoder layers: a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--text-encoder-dim",
-        type=int,
-        default=192,
-        help="Embedding dimension in encoder stacks: a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--query-head-dim",
-        type=int,
-        default=32,
-        help="Query/key dimension per head in encoder stacks: a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--value-head-dim",
-        type=int,
-        default=12,
-        help="Value dimension per head in encoder stacks: a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--pos-head-dim",
-        type=int,
-        default=4,
-        help="Positional-encoding dimension per head in encoder stacks: a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--pos-dim",
-        type=int,
-        default=48,
-        help="Positional-encoding embedding dimension",
-    )
-
-    parser.add_argument(
-        "--time-embed-dim",
-        type=int,
-        default=192,
-        help="Embedding dimension of timestamps embedding.",
-    )
-
-    parser.add_argument(
-        "--text-embed-dim",
-        type=int,
-        default=192,
-        help="Embedding dimension of text embedding.",
-    )
-
-    parser.add_argument(
-        "--token-type",
-        type=str,
-        default="phone",
-        choices=["phone", "char", "bpe"],
-        help="Input token type of TTS model, by default, "
-        "we use phone for emilia, char for libritts.",
-    )
-
-    parser.add_argument(
-        "--token-file",
-        type=str,
-        default="data/tokens_emilia.txt",
-        help="The file that contains information that maps tokens to ids,"
-        "which is a text file with '{token}\t{token_id}' per line if type is"
-        "char or phone, otherwise it is a bpe_model file.",
-    )
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--world-size",
-        type=int,
-        default=1,
-        help="Number of GPUs for DDP training.",
-    )
-
-    parser.add_argument(
-        "--master-port",
-        type=int,
-        default=12354,
-        help="Master port to use for DDP training.",
-    )
-
-    parser.add_argument(
-        "--tensorboard",
-        type=str2bool,
-        default=True,
-        help="Should various information be logged in tensorboard.",
-    )
-
-    parser.add_argument(
-        "--num-epochs",
-        type=int,
-        default=11,
-        help="Number of epochs to train.",
-    )
-
-    parser.add_argument(
-        "--start-epoch",
-        type=int,
-        default=1,
-        help="""Resume training from this epoch. It should be positive.
-        If larger than 1, it will load checkpoint from
-        exp-dir/epoch-{start_epoch-1}.pt
-        """,
-    )
-
-    parser.add_argument(
-        "--checkpoint",
-        type=str,
-        default=None,
-        help="""Checkpoints of pre-trained models, will load it if not None
-        """,
-    )
-
-    parser.add_argument(
-        "--exp-dir",
-        type=str,
-        default="zipvoice/exp_zipvoice",
-        help="""The experiment dir.
-        It specifies the directory where all training related
-        files, e.g., checkpoints, log, etc, are saved
-        """,
-    )
-
-    parser.add_argument(
-        "--base-lr", type=float, default=0.02, help="The base learning rate."
-    )
-
-    parser.add_argument(
-        "--lr-batches",
-        type=float,
-        default=7500,
-        help="""Number of steps that affects how rapidly the learning rate
-        decreases. We suggest not to change this.""",
-    )
-
-    parser.add_argument(
-        "--lr-epochs",
-        type=float,
-        default=10,
-        help="""Number of epochs that affects how rapidly the learning rate decreases.
-        """,
-    )
-
-    parser.add_argument(
-        "--lr-hours",
-        type=float,
-        default=0,
-        help="""If positive, --epoch is ignored and it specifies the number of hours 
-        that affects how rapidly the learning rate decreases.
-        """,
-    )
-
-    parser.add_argument(
-        "--ref-duration",
-        type=float,
-        default=50,
-        help="Reference batch duration for purposes of adjusting batch counts for setting various "
-        "schedules inside the model",
-    )
-
-    parser.add_argument(
-        "--seed",
-        type=int,
-        default=42,
-        help="The seed for random generators intended for reproducibility",
-    )
-
-    parser.add_argument(
-        "--print-diagnostics",
-        type=str2bool,
-        default=False,
-        help="Accumulate stats on activations, print them and exit.",
-    )
-
-    parser.add_argument(
-        "--inf-check",
-        type=str2bool,
-        default=False,
-        help="Add hooks to check for infinite module outputs and gradients.",
-    )
-
-    parser.add_argument(
-        "--save-every-n",
-        type=int,
-        default=4000,
-        help="""Save checkpoint after processing this number of batches"
-        periodically. We save checkpoint to exp-dir/ whenever
-        params.batch_idx_train % save_every_n == 0. The checkpoint filename
-        has the form: f'exp-dir/checkpoint-{params.batch_idx_train}.pt'
-        Note: It also saves checkpoint to `exp-dir/epoch-xxx.pt` at the
-        end of each epoch where `xxx` is the epoch number counting from 1.
-        """,
-    )
-
-    parser.add_argument(
-        "--keep-last-k",
-        type=int,
-        default=30,
-        help="""Only keep this number of checkpoints on disk.
-        For instance, if it is 3, there are only 3 checkpoints
-        in the exp-dir with filenames `checkpoint-xxx.pt`.
-        It does not affect checkpoints with name `epoch-xxx.pt`.
-        """,
-    )
-
-    parser.add_argument(
-        "--average-period",
-        type=int,
-        default=200,
-        help="""Update the averaged model, namely `model_avg`, after processing
-        this number of batches. `model_avg` is a separate version of model,
-        in which each floating-point parameter is the average of all the
-        parameters from the start of training. Each time we take the average,
-        we do: `model_avg = model * (average_period / batch_idx_train) +
-            model_avg * ((batch_idx_train - average_period) / batch_idx_train)`.
-        """,
-    )
-
-    parser.add_argument(
-        "--use-fp16",
-        type=str2bool,
-        default=True,
-        help="Whether to use half precision training.",
-    )
-
-    parser.add_argument(
-        "--feat-scale",
-        type=float,
-        default=0.1,
-        help="The scale factor of fbank feature",
-    )
-
-    parser.add_argument(
-        "--condition-drop-ratio",
-        type=float,
-        default=0.2,
-        help="The drop rate of text condition during training.",
-    )
-
-    parser.add_argument(
-        "--dataset",
-        type=str,
-        default="emilia",
-        choices=["emilia", "libritts"],
-        help="The used training dataset",
-    )
-
-    add_model_arguments(parser)
-
-    return parser
-
-
-def get_params() -> AttributeDict:
-    """Return a dict containing training parameters.
-
-    All training related parameters that are not passed from the commandline
-    are saved in the variable `params`.
-
-    Commandline options are merged into `params` after they are parsed, so
-    you can also access them via `params`.
-
-    Explanation of options saved in `params`:
-
-        - best_train_loss: Best training loss so far. It is used to select
-                           the model that has the lowest training loss. It is
-                           updated during the training.
-
-        - best_valid_loss: Best validation loss so far. It is used to select
-                           the model that has the lowest validation loss. It is
-                           updated during the training.
-
-        - best_train_epoch: It is the epoch that has the best training loss.
-
-        - best_valid_epoch: It is the epoch that has the best validation loss.
-
-        - batch_idx_train: Used to writing statistics to tensorboard. It
-                           contains number of batches trained so far across
-                           epochs.
-
-        - log_interval:  Print training loss if batch_idx % log_interval` is 0
-
-        - reset_interval: Reset statistics if batch_idx % reset_interval is 0
-
-        - valid_interval:  Run validation if batch_idx % valid_interval is 0
-
-        - sampling_rate: Sampling rate of the wavform.
-
-        - frame_shift_ms: Frame shift in milliseconds.
-
-        - feat_dim: The model input dim. It has to match the one used
-                       in computing features.
-
-        - env_info:  A dict containing information about the environment.
-
-    """
-    params = AttributeDict(
-        {
-            "best_train_loss": float("inf"),
-            "best_valid_loss": float("inf"),
-            "best_train_epoch": -1,
-            "best_valid_epoch": -1,
-            "batch_idx_train": 0,
-            "log_interval": 50,
-            "reset_interval": 200,
-            "valid_interval": 4000,
-            "sampling_rate": 24000,
-            "frame_shift_ms": 256 / 24000 * 1000,
-            "feat_dim": 100,
-            "env_info": get_env_info(),
-        }
-    )
-
-    return params
-
-
-def resume_checkpoint(
-    params: AttributeDict, model: nn.Module, model_avg: nn.Module
-) -> Optional[Dict[str, Any]]:
-    """Load checkpoint from file.
-
-    If params.start_epoch is larger than 1, it will load the checkpoint from
-    `params.start_epoch - 1`.
-
-    Apart from loading state dict for `model` it also updates
-    `best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
-    and `best_valid_loss` in `params`.
-
-    Args:
-      params:
-        The return value of :func:`get_params`.
-      model:
-        The training model.
-    Returns:
-      Return a dict containing previously saved training info.
-    """
-    if params.start_epoch > 1:
-        filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
-    else:
-        return None
-
-    logging.info(f"Resuming from file {filename}")
-    assert filename.is_file(), f"{filename} does not exist!"
-
-    saved_params = load_checkpoint(
-        filename, model=model, model_avg=model_avg, strict=True
-    )
-
-    keys = [
-        "best_train_epoch",
-        "best_valid_epoch",
-        "batch_idx_train",
-        "best_train_loss",
-        "best_valid_loss",
-    ]
-    for k in keys:
-        params[k] = saved_params[k]
-
-    return saved_params
-
-
-def compute_fbank_loss(
-    params: AttributeDict,
-    model: Union[nn.Module, DDP],
-    features: Tensor,
-    features_lens: Tensor,
-    tokens: List[List[int]],
-    is_training: bool,
-) -> Tuple[Tensor, MetricsTracker]:
-    """
-    Compute loss given the model and its inputs.
-
-    Args:
-      params:
-        Parameters for training. See :func:`get_params`.
-      model:
-        The model for training.
-      features:
-        The target acoustic feature.
-      features_lens:
-        The number of frames of each utterance.
-      tokens:
-        Input tokens that representing the transcripts.
-      is_training:
-        True for training. False for validation. When it is True, this
-        function enables autograd during computation; when it is False, it
-        disables autograd.
-    """
-
-    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
-
-    batch_size, num_frames, _ = features.shape
-
-    features = torch.nn.functional.pad(
-        features, (0, 0, 0, num_frames - features.size(1))
-    )  # (B, T, F)
-    noise = torch.randn_like(features)  # (B, T, F)
-
-    # Sampling t from uniform distribution
-    if is_training:
-        t = torch.rand(batch_size, 1, 1, device=device)
-    else:
-        t = (
-            (torch.arange(batch_size, device=device) / batch_size)
-            .unsqueeze(1)
-            .unsqueeze(2)
-        )
-    with torch.set_grad_enabled(is_training):
-
-        loss = model(
-            tokens=tokens,
-            features=features,
-            features_lens=features_lens,
-            noise=noise,
-            t=t,
-            condition_drop_ratio=params.condition_drop_ratio,
-        )
-
-    assert loss.requires_grad == is_training
-    info = MetricsTracker()
-    num_frames = features_lens.sum().item()
-    info["frames"] = num_frames
-    info["loss"] = loss.detach().cpu().item() * num_frames
-
-    return loss, info
-
-
-def train_one_epoch(
-    params: AttributeDict,
-    model: Union[nn.Module, DDP],
-    tokenizer: TokenizerEmilia,
-    optimizer: Optimizer,
-    scheduler: LRSchedulerType,
-    train_dl: torch.utils.data.DataLoader,
-    valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
-    model_avg: Optional[nn.Module] = None,
-    tb_writer: Optional[SummaryWriter] = None,
-    world_size: int = 1,
-    rank: int = 0,
-) -> None:
-    """Train the model for one epoch.
-
-    The training loss from the mean of all frames is saved in
-    `params.train_loss`. It runs the validation process every
-    `params.valid_interval` batches.
-
-    Args:
-      params:
-        It is returned by :func:`get_params`.
-      model:
-        The model for training.
-      tokenizer:
-        Used to convert text to tokens.
-      optimizer:
-        The optimizer.
-      scheduler:
-        The learning rate scheduler, we call step() every epoch.
-      train_dl:
-        Dataloader for the training dataset.
-      valid_dl:
-        Dataloader for the validation dataset.
-      scaler:
-        The scaler used for mix precision training.
-      tb_writer:
-        Writer to write log messages to tensorboard.
-      world_size:
-        Number of nodes in DDP training. If it is 1, DDP is disabled.
-      rank:
-        The rank of the node in DDP training. If no DDP is used, it should
-        be set to 0.
-    """
-    model.train()
-    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
-
-    # used to track the stats over iterations in one epoch
-    tot_loss = MetricsTracker()
-
-    saved_bad_model = False
-
-    def save_bad_model(suffix: str = ""):
-        save_checkpoint(
-            filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt",
-            model=model,
-            model_avg=model_avg,
-            params=params,
-            optimizer=optimizer,
-            scheduler=scheduler,
-            sampler=train_dl.sampler,
-            scaler=scaler,
-            rank=0,
-        )
-
-    for batch_idx, batch in enumerate(train_dl):
-
-        if batch_idx % 10 == 0:
-            set_batch_count(model, get_adjusted_batch_count(params))
-
-        if (
-            params.valid_interval is None
-            and batch_idx == 0
-            and not params.print_diagnostics
-        ) or (
-            params.valid_interval is not None
-            and params.batch_idx_train % params.valid_interval == 0
-            and not params.print_diagnostics
-        ):
-            logging.info("Computing validation loss")
-            valid_info = compute_validation_loss(
-                params=params,
-                model=model,
-                tokenizer=tokenizer,
-                valid_dl=valid_dl,
-                world_size=world_size,
-            )
-            model.train()
-            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
-            logging.info(
-                f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
-            )
-            if tb_writer is not None:
-                valid_info.write_summary(
-                    tb_writer, "train/valid_", params.batch_idx_train
-                )
-
-        params.batch_idx_train += 1
-
-        batch_size = len(batch["text"])
-
-        tokens, features, features_lens = prepare_input(
-            params=params,
-            batch=batch,
-            device=device,
-            tokenizer=tokenizer,
-            return_tokens=True,
-            return_feature=True,
-        )
-
-        try:
-            with autocast("cuda", enabled=params.use_fp16):
-                loss, loss_info = compute_fbank_loss(
-                    params=params,
-                    model=model,
-                    features=features,
-                    features_lens=features_lens,
-                    tokens=tokens,
-                    is_training=True,
-                )
-
-            tot_loss = (tot_loss * (1 - 1 / params.reset_interval)) + loss_info
-
-            scaler.scale(loss).backward()
-
-            scheduler.step_batch(params.batch_idx_train)
-            # Use the number of hours of speech to adjust the learning rate
-            if params.lr_hours > 0:
-                scheduler.step_epoch(
-                    params.batch_idx_train
-                    * params.max_duration
-                    * params.world_size
-                    / 3600
-                )
-            scaler.step(optimizer)
-            scaler.update()
-            optimizer.zero_grad()
-        except RuntimeError as e:
-            if "out of memory" in str(e):
-                logging.info(f"out of memory error at rank {rank}")
-                # optimizer.zero_grad()
-                # duration_optimizer.zero_grad()
-                torch.cuda.empty_cache()
-                raise
-                continue
-            else:
-                logging.info(f"Caught exception : {e}.")
-                save_bad_model()
-                raise
-        except Exception as e:
-            logging.info(f"Caught exception : {e}.")
-            save_bad_model()
-            raise
-
-        if params.print_diagnostics and batch_idx == 5:
-            return
-
-        if (
-            rank == 0
-            and params.batch_idx_train > 0
-            and params.batch_idx_train % params.average_period == 0
-        ):
-            update_averaged_model(
-                params=params,
-                model_cur=model,
-                model_avg=model_avg,
-            )
-
-        if (
-            params.batch_idx_train > 0
-            and params.batch_idx_train % params.save_every_n == 0
-        ):
-            save_checkpoint_with_global_batch_idx(
-                out_dir=params.exp_dir,
-                global_batch_idx=params.batch_idx_train,
-                model=model,
-                model_avg=model_avg,
-                params=params,
-                optimizer=optimizer,
-                scheduler=scheduler,
-                sampler=train_dl.sampler,
-                scaler=scaler,
-                rank=rank,
-            )
-            remove_checkpoints(
-                out_dir=params.exp_dir,
-                topk=params.keep_last_k,
-                rank=rank,
-            )
-        if params.batch_idx_train % 100 == 0 and params.use_fp16:
-            # If the grad scale was less than 1, try increasing it.    The _growth_interval
-            # of the grad scaler is configurable, but we can't configure it to have different
-            # behavior depending on the current grad scale.
-            cur_grad_scale = scaler._scale.item()
-
-            if cur_grad_scale < 1024.0 or (
-                cur_grad_scale < 4096.0 and params.batch_idx_train % 400 == 0
-            ):
-                scaler.update(cur_grad_scale * 2.0)
-            if cur_grad_scale < 0.01:
-                if not saved_bad_model:
-                    save_bad_model(suffix="-first-warning")
-                    saved_bad_model = True
-                logging.warning(f"Grad scale is small: {cur_grad_scale}")
-            if cur_grad_scale < 1.0e-05:
-                save_bad_model()
-                raise RuntimeError(
-                    f"grad_scale is too small, exiting: {cur_grad_scale}"
-                )
-
-        if params.batch_idx_train % params.log_interval == 0:
-            cur_lr = max(scheduler.get_last_lr())
-            cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0
-
-            logging.info(
-                f"Epoch {params.cur_epoch}, batch {batch_idx}, "
-                f"global_batch_idx: {params.batch_idx_train}, batch size: {batch_size}, "
-                f"loss[{loss_info}], tot_loss[{tot_loss}], "
-                f"cur_lr: {cur_lr:.2e}, "
-                + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
-            )
-
-            if tb_writer is not None:
-                tb_writer.add_scalar(
-                    "train/learning_rate", cur_lr, params.batch_idx_train
-                )
-                loss_info.write_summary(
-                    tb_writer, "train/current_", params.batch_idx_train
-                )
-                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
-                if params.use_fp16:
-                    tb_writer.add_scalar(
-                        "train/grad_scale", cur_grad_scale, params.batch_idx_train
-                    )
-
-    loss_value = tot_loss["loss"]
-    params.train_loss = loss_value
-    if params.train_loss < params.best_train_loss:
-        params.best_train_epoch = params.cur_epoch
-        params.best_train_loss = params.train_loss
-
-
-def compute_validation_loss(
-    params: AttributeDict,
-    model: Union[nn.Module, DDP],
-    tokenizer: TokenizerEmilia,
-    valid_dl: torch.utils.data.DataLoader,
-    world_size: int = 1,
-) -> MetricsTracker:
-    """Run the validation process."""
-
-    model.eval()
-    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
-
-    # used to summary the stats over iterations
-    tot_loss = MetricsTracker()
-
-    for batch_idx, batch in enumerate(valid_dl):
-        tokens, features, features_lens = prepare_input(
-            params=params,
-            batch=batch,
-            device=device,
-            tokenizer=tokenizer,
-            return_tokens=True,
-            return_feature=True,
-        )
-
-        loss, loss_info = compute_fbank_loss(
-            params=params,
-            model=model,
-            features=features,
-            features_lens=features_lens,
-            tokens=tokens,
-            is_training=False,
-        )
-        assert loss.requires_grad is False
-        tot_loss = tot_loss + loss_info
-
-    if world_size > 1:
-        tot_loss.reduce(loss.device)
-
-    loss_value = tot_loss["loss"]
-    if loss_value < params.best_valid_loss:
-        params.best_valid_epoch = params.cur_epoch
-        params.best_valid_loss = loss_value
-
-    return tot_loss
-
-
-def run(rank, world_size, args):
-    """
-    Args:
-      rank:
-        It is a value between 0 and `world_size-1`, which is
-        passed automatically by `mp.spawn()` in :func:`main`.
-        The node with rank 0 is responsible for saving checkpoint.
-      world_size:
-        Number of GPUs for DDP training.
-      args:
-        The return value of get_parser().parse_args()
-    """
-    params = get_params()
-    params.update(vars(args))
-
-    fix_random_seed(params.seed)
-    if world_size > 1:
-        setup_dist(rank, world_size, params.master_port)
-
-    setup_logger(f"{params.exp_dir}/log/log-train")
-    os.makedirs(f"{params.exp_dir}/fbank", exist_ok=True)
-
-    logging.info("Training started")
-
-    if args.tensorboard and rank == 0:
-        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
-    else:
-        tb_writer = None
-
-    device = torch.device("cpu")
-    if torch.cuda.is_available():
-        device = torch.device("cuda", rank)
-    logging.info(f"Device: {device}")
-
-    if params.dataset == "emilia":
-        tokenizer = TokenizerEmilia(
-            token_file=params.token_file, token_type=params.token_type
-        )
-    elif params.dataset == "libritts":
-        tokenizer = TokenizerLibriTTS(
-            token_file=params.token_file, token_type=params.token_type
-        )
-    params.vocab_size = tokenizer.vocab_size
-    params.pad_id = tokenizer.pad_id
-
-    params.device = device
-
-    logging.info(params)
-
-    logging.info("About to create model")
-
-    model = get_model(params)
-    if params.checkpoint is not None:
-        logging.info(f"Loading pre-trained model from {params.checkpoint}")
-        _ = load_checkpoint(filename=params.checkpoint, model=model, strict=True)
-    num_param = sum([p.numel() for p in model.parameters()])
-    logging.info(f"Number of parameters : {num_param}")
-
-    model_avg: Optional[nn.Module] = None
-    if rank == 0:
-        # model_avg is only used with rank 0
-        model_avg = copy.deepcopy(model).to(torch.float64)
-    assert params.start_epoch > 0, params.start_epoch
-    if params.start_epoch > 1:
-        checkpoints = resume_checkpoint(params=params, model=model, model_avg=model_avg)
-
-    model = model.to(device)
-    if world_size > 1:
-        logging.info("Using DDP")
-        model = DDP(model, device_ids=[rank], find_unused_parameters=True)
-
-    optimizer = ScaledAdam(
-        get_parameter_groups_with_lrs(
-            model,
-            lr=params.base_lr,
-            include_names=True,
-        ),
-        lr=params.base_lr,  # should have no effect
-        clipping_scale=2.0,
-    )
-
-    assert params.lr_hours >= 0
-    if params.lr_hours > 0:
-        scheduler = Eden(optimizer, params.lr_batches, params.lr_hours)
-    else:
-        scheduler = Eden(optimizer, params.lr_batches, params.lr_epochs)
-
-    scaler = GradScaler("cuda", enabled=params.use_fp16)
-
-    if params.start_epoch > 1 and checkpoints is not None:
-        # load state_dict for optimizers
-        if "optimizer" in checkpoints:
-            logging.info("Loading optimizer state dict")
-            optimizer.load_state_dict(checkpoints["optimizer"])
-
-        # load state_dict for schedulers
-        if "scheduler" in checkpoints:
-            logging.info("Loading scheduler state dict")
-            scheduler.load_state_dict(checkpoints["scheduler"])
-
-        if "grad_scaler" in checkpoints:
-            logging.info("Loading grad scaler state dict")
-            scaler.load_state_dict(checkpoints["grad_scaler"])
-
-    if params.print_diagnostics:
-        opts = diagnostics.TensorDiagnosticOptions(
-            512
-        )  # allow 4 megabytes per sub-module
-        diagnostic = diagnostics.attach_diagnostics(model, opts)
-
-    if params.inf_check:
-        register_inf_check_hooks(model)
-
-    def remove_short_and_long_utt_emilia(c: Cut):
-        if c.duration < 1.0 or c.duration > 30.0:
-            return False
-        return True
-
-    def remove_short_and_long_utt_libritts(c: Cut):
-        if c.duration < 1.0 or c.duration > 20.0:
-            return False
-        return True
-
-    datamodule = TtsDataModule(args)
-    if params.dataset == "emilia":
-        train_cuts = CutSet.mux(
-            datamodule.train_emilia_EN_cuts(),
-            datamodule.train_emilia_ZH_cuts(),
-            weights=[46000, 49000],
-        )
-        train_cuts = train_cuts.filter(remove_short_and_long_utt_emilia)
-        dev_cuts = CutSet.mux(
-            datamodule.dev_emilia_EN_cuts(),
-            datamodule.dev_emilia_ZH_cuts(),
-            weights=[0.5, 0.5],
-        )
-    elif params.dataset == "libritts":
-        train_cuts = datamodule.train_libritts_cuts()
-        train_cuts = train_cuts.filter(remove_short_and_long_utt_libritts)
-        dev_cuts = datamodule.dev_libritts_cuts()
-
-    train_dl = datamodule.train_dataloaders(train_cuts)
-
-    valid_dl = datamodule.dev_dataloaders(dev_cuts)
-
-    for epoch in range(params.start_epoch, params.num_epochs + 1):
-        logging.info(f"Start epoch {epoch}")
-
-        if params.lr_hours == 0:
-            scheduler.step_epoch(epoch - 1)
-        fix_random_seed(params.seed + epoch - 1)
-        train_dl.sampler.set_epoch(epoch - 1)
-
-        params.cur_epoch = epoch
-
-        if tb_writer is not None:
-            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
-
-        train_one_epoch(
-            params=params,
-            model=model,
-            model_avg=model_avg,
-            tokenizer=tokenizer,
-            optimizer=optimizer,
-            scheduler=scheduler,
-            train_dl=train_dl,
-            valid_dl=valid_dl,
-            scaler=scaler,
-            tb_writer=tb_writer,
-            world_size=world_size,
-            rank=rank,
-        )
-
-        if params.print_diagnostics:
-            diagnostic.print_diagnostics()
-            break
-
-        filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
-        save_checkpoint(
-            filename=filename,
-            params=params,
-            model=model,
-            model_avg=model_avg,
-            optimizer=optimizer,
-            scheduler=scheduler,
-            sampler=train_dl.sampler,
-            scaler=scaler,
-            rank=rank,
-        )
-
-        if rank == 0:
-            if params.best_train_epoch == params.cur_epoch:
-                best_train_filename = params.exp_dir / "best-train-loss.pt"
-                copyfile(src=filename, dst=best_train_filename)
-
-            if params.best_valid_epoch == params.cur_epoch:
-                best_valid_filename = params.exp_dir / "best-valid-loss.pt"
-                copyfile(src=filename, dst=best_valid_filename)
-
-    logging.info("Done!")
-
-    if world_size > 1:
-        torch.distributed.barrier()
-        cleanup_dist()
-
-
-def main():
-    parser = get_parser()
-    TtsDataModule.add_arguments(parser)
-    args = parser.parse_args()
-    args.exp_dir = Path(args.exp_dir)
-
-    world_size = args.world_size
-    assert world_size >= 1
-    if world_size > 1:
-        mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
-    else:
-        run(rank=0, world_size=1, args=args)
-
-
-if __name__ == "__main__":
-    torch.set_num_threads(1)
-    torch.set_num_interop_threads(1)
-    main()
diff --git a/egs/zipvoice/zipvoice/tts_datamodule.py b/egs/zipvoice/zipvoice/tts_datamodule.py
deleted file mode 100644
index 972c700f7..000000000
--- a/egs/zipvoice/zipvoice/tts_datamodule.py
+++ /dev/null
@@ -1,456 +0,0 @@
-# Copyright      2021  Piotr Żelasko
-# Copyright      2022-2024  Xiaomi Corporation     (Authors: Mingshuang Luo,
-#                                                            Zengwei Yao,
-#                                                            Zengrui Jin,
-#                                                            Han Zhu,
-#                                                            Wei Kang)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import argparse
-import logging
-from functools import lru_cache
-from pathlib import Path
-from typing import Any, Callable, Dict, List, Optional, Sequence, Union
-
-import torch
-from feature import TorchAudioFbank, TorchAudioFbankConfig
-from lhotse import CutSet, load_manifest_lazy, validate
-from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
-    DynamicBucketingSampler,
-    PrecomputedFeatures,
-    SimpleCutSampler,
-)
-from lhotse.dataset.collation import collate_audio
-from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
-    BatchIO,
-    OnTheFlyFeatures,
-)
-from lhotse.utils import fix_random_seed, ifnone
-from torch.utils.data import DataLoader
-
-from icefall.utils import str2bool
-
-
-class _SeedWorkers:
-    def __init__(self, seed: int):
-        self.seed = seed
-
-    def __call__(self, worker_id: int):
-        fix_random_seed(self.seed + worker_id)
-
-
-SAMPLING_RATE = 24000
-
-
-class TtsDataModule:
-    """
-    DataModule for tts experiments.
-    It assumes there is always one train and valid dataloader,
-    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
-    and test-other).
-
-    It contains all the common data pipeline modules used in ASR
-    experiments, e.g.:
-    - dynamic batch size,
-    - bucketing samplers,
-    - cut concatenation,
-    - on-the-fly feature extraction
-
-    This class should be derived for specific corpora used in ASR tasks.
-    """
-
-    def __init__(self, args: argparse.Namespace):
-        self.args = args
-
-    @classmethod
-    def add_arguments(cls, parser: argparse.ArgumentParser):
-        group = parser.add_argument_group(
-            title="TTS data related options",
-            description="These options are used for the preparation of "
-            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
-            "effective batch sizes, sampling strategies, applied data "
-            "augmentations, etc.",
-        )
-        group.add_argument(
-            "--manifest-dir",
-            type=Path,
-            default=Path("data/fbank_emilia"),
-            help="Path to directory with train/valid/test cuts.",
-        )
-        group.add_argument(
-            "--max-duration",
-            type=int,
-            default=200.0,
-            help="Maximum pooled recordings duration (seconds) in a "
-            "single batch. You can reduce it if it causes CUDA OOM.",
-        )
-        group.add_argument(
-            "--bucketing-sampler",
-            type=str2bool,
-            default=True,
-            help="When enabled, the batches will come from buckets of "
-            "similar duration (saves padding frames).",
-        )
-        group.add_argument(
-            "--num-buckets",
-            type=int,
-            default=100,
-            help="The number of buckets for the DynamicBucketingSampler"
-            "(you might want to increase it for larger datasets).",
-        )
-
-        group.add_argument(
-            "--on-the-fly-feats",
-            type=str2bool,
-            default=False,
-            help="When enabled, use on-the-fly cut mixing and feature "
-            "extraction. Will drop existing precomputed feature manifests "
-            "if available.",
-        )
-        group.add_argument(
-            "--shuffle",
-            type=str2bool,
-            default=True,
-            help="When enabled (=default), the examples will be "
-            "shuffled for each epoch.",
-        )
-        group.add_argument(
-            "--drop-last",
-            type=str2bool,
-            default=True,
-            help="Whether to drop last batch. Used by sampler.",
-        )
-        group.add_argument(
-            "--return-cuts",
-            type=str2bool,
-            default=True,
-            help="When enabled, each batch will have the "
-            "field: batch['cut'] with the cuts that "
-            "were used to construct it.",
-        )
-        group.add_argument(
-            "--num-workers",
-            type=int,
-            default=8,
-            help="The number of training dataloader workers that "
-            "collect the batches.",
-        )
-
-        group.add_argument(
-            "--input-strategy",
-            type=str,
-            default="PrecomputedFeatures",
-            help="AudioSamples or PrecomputedFeatures",
-        )
-
-    def train_dataloaders(
-        self,
-        cuts_train: CutSet,
-        sampler_state_dict: Optional[Dict[str, Any]] = None,
-    ) -> DataLoader:
-        """
-        Args:
-          cuts_train:
-            CutSet for training.
-          sampler_state_dict:
-            The state dict for the training sampler.
-        """
-        logging.info("About to create train dataset")
-        train = SpeechSynthesisDataset(
-            return_text=True,
-            return_tokens=True,
-            return_spk_ids=True,
-            feature_input_strategy=eval(self.args.input_strategy)(),
-            return_cuts=self.args.return_cuts,
-        )
-
-        if self.args.on_the_fly_feats:
-            sampling_rate = SAMPLING_RATE
-            config = TorchAudioFbankConfig(
-                sampling_rate=sampling_rate,
-                n_mels=100,
-                n_fft=1024,
-                hop_length=256,
-            )
-            train = SpeechSynthesisDataset(
-                return_text=True,
-                return_tokens=True,
-                return_spk_ids=True,
-                feature_input_strategy=OnTheFlyFeatures(TorchAudioFbank(config)),
-                return_cuts=self.args.return_cuts,
-            )
-
-        if self.args.bucketing_sampler:
-            logging.info("Using DynamicBucketingSampler.")
-            train_sampler = DynamicBucketingSampler(
-                cuts_train,
-                max_duration=self.args.max_duration,
-                shuffle=self.args.shuffle,
-                num_buckets=self.args.num_buckets,
-                buffer_size=self.args.num_buckets * 2000,
-                shuffle_buffer_size=self.args.num_buckets * 5000,
-                drop_last=self.args.drop_last,
-            )
-        else:
-            logging.info("Using SimpleCutSampler.")
-            train_sampler = SimpleCutSampler(
-                cuts_train,
-                max_duration=self.args.max_duration,
-                shuffle=self.args.shuffle,
-            )
-        logging.info("About to create train dataloader")
-
-        if sampler_state_dict is not None:
-            logging.info("Loading sampler state dict")
-            train_sampler.load_state_dict(sampler_state_dict)
-
-        # 'seed' is derived from the current random state, which will have
-        # previously been set in the main process.
-        seed = torch.randint(0, 100000, ()).item()
-        worker_init_fn = _SeedWorkers(seed)
-
-        train_dl = DataLoader(
-            train,
-            sampler=train_sampler,
-            batch_size=None,
-            num_workers=self.args.num_workers,
-            persistent_workers=False,
-            worker_init_fn=worker_init_fn,
-        )
-
-        return train_dl
-
-    def dev_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
-        logging.info("About to create dev dataset")
-        if self.args.on_the_fly_feats:
-            sampling_rate = SAMPLING_RATE
-            config = TorchAudioFbankConfig(
-                sampling_rate=sampling_rate,
-                n_mels=100,
-                n_fft=1024,
-                hop_length=256,
-            )
-            validate = SpeechSynthesisDataset(
-                return_text=True,
-                return_tokens=True,
-                return_spk_ids=True,
-                feature_input_strategy=OnTheFlyFeatures(TorchAudioFbank(config)),
-                return_cuts=self.args.return_cuts,
-            )
-        else:
-            validate = SpeechSynthesisDataset(
-                return_text=True,
-                return_tokens=True,
-                return_spk_ids=True,
-                feature_input_strategy=eval(self.args.input_strategy)(),
-                return_cuts=self.args.return_cuts,
-            )
-        dev_sampler = DynamicBucketingSampler(
-            cuts_valid,
-            max_duration=self.args.max_duration,
-            shuffle=False,
-        )
-        logging.info("About to create valid dataloader")
-        dev_dl = DataLoader(
-            validate,
-            sampler=dev_sampler,
-            batch_size=None,
-            num_workers=2,
-            persistent_workers=False,
-        )
-
-        return dev_dl
-
-    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
-        logging.info("About to create test dataset")
-        if self.args.on_the_fly_feats:
-            sampling_rate = SAMPLING_RATE
-            config = TorchAudioFbankConfig(
-                sampling_rate=sampling_rate,
-                n_mels=100,
-                n_fft=1024,
-                hop_length=256,
-            )
-            test = SpeechSynthesisDataset(
-                return_text=True,
-                return_tokens=True,
-                return_spk_ids=True,
-                feature_input_strategy=OnTheFlyFeatures(TorchAudioFbank(config)),
-                return_cuts=self.args.return_cuts,
-                return_audio=True,
-            )
-        else:
-            test = SpeechSynthesisDataset(
-                return_text=True,
-                return_tokens=True,
-                return_spk_ids=True,
-                feature_input_strategy=eval(self.args.input_strategy)(),
-                return_cuts=self.args.return_cuts,
-                return_audio=True,
-            )
-        test_sampler = DynamicBucketingSampler(
-            cuts,
-            max_duration=self.args.max_duration,
-            shuffle=False,
-        )
-        logging.info("About to create test dataloader")
-        test_dl = DataLoader(
-            test,
-            batch_size=None,
-            sampler=test_sampler,
-            num_workers=self.args.num_workers,
-        )
-        return test_dl
-
-    @lru_cache()
-    def train_emilia_EN_cuts(self) -> CutSet:
-        logging.info("About to get train the EN subset")
-        return load_manifest_lazy(self.args.manifest_dir / "emilia_cuts_EN.jsonl.gz")
-
-    @lru_cache()
-    def train_emilia_ZH_cuts(self) -> CutSet:
-        logging.info("About to get train the ZH subset")
-        return load_manifest_lazy(self.args.manifest_dir / "emilia_cuts_ZH.jsonl.gz")
-
-    @lru_cache()
-    def dev_emilia_EN_cuts(self) -> CutSet:
-        logging.info("About to get dev the EN subset")
-        return load_manifest_lazy(
-            self.args.manifest_dir / "emilia_cuts_EN-dev.jsonl.gz"
-        )
-
-    @lru_cache()
-    def dev_emilia_ZH_cuts(self) -> CutSet:
-        logging.info("About to get dev the ZH subset")
-        return load_manifest_lazy(
-            self.args.manifest_dir / "emilia_cuts_ZH-dev.jsonl.gz"
-        )
-
-    @lru_cache()
-    def train_libritts_cuts(self) -> CutSet:
-        logging.info(
-            "About to get the shuffled train-clean-100, \
-            train-clean-360 and train-other-500 cuts"
-        )
-        return load_manifest_lazy(
-            self.args.manifest_dir / "libritts_cuts_train-all-shuf.jsonl.gz"
-        )
-
-    @lru_cache()
-    def dev_libritts_cuts(self) -> CutSet:
-        logging.info("About to get dev-clean cuts")
-        return load_manifest_lazy(
-            self.args.manifest_dir / "libritts_cuts_dev-clean.jsonl.gz"
-        )
-
-
-class SpeechSynthesisDataset(torch.utils.data.Dataset):
-    """
-    The PyTorch Dataset for the speech synthesis task.
-    Each item in this dataset is a dict of:
-
-    .. code-block::
-
-        {
-            'audio': (B x NumSamples) float tensor
-            'features': (B x NumFrames x NumFeatures) float tensor
-            'audio_lens': (B, ) int tensor
-            'features_lens': (B, ) int tensor
-            'text': List[str] of len B  # when return_text=True
-            'tokens': List[List[str]]  # when return_tokens=True
-            'speakers': List[str] of len B  # when return_spk_ids=True
-            'cut': List of Cuts  # when return_cuts=True
-        }
-    """
-
-    def __init__(
-        self,
-        cut_transforms: List[Callable[[CutSet], CutSet]] = None,
-        feature_input_strategy: BatchIO = PrecomputedFeatures(),
-        feature_transforms: Union[Sequence[Callable], Callable] = None,
-        return_text: bool = True,
-        return_tokens: bool = False,
-        return_spk_ids: bool = False,
-        return_cuts: bool = False,
-        return_audio: bool = False,
-    ) -> None:
-        super().__init__()
-
-        self.cut_transforms = ifnone(cut_transforms, [])
-        self.feature_input_strategy = feature_input_strategy
-
-        self.return_text = return_text
-        self.return_tokens = return_tokens
-        self.return_spk_ids = return_spk_ids
-        self.return_cuts = return_cuts
-        self.return_audio = return_audio
-
-        if feature_transforms is None:
-            feature_transforms = []
-        elif not isinstance(feature_transforms, Sequence):
-            feature_transforms = [feature_transforms]
-
-        assert all(
-            isinstance(transform, Callable) for transform in feature_transforms
-        ), "Feature transforms must be Callable"
-        self.feature_transforms = feature_transforms
-
-    def __getitem__(self, cuts: CutSet) -> Dict[str, torch.Tensor]:
-        validate_for_tts(cuts)
-
-        for transform in self.cut_transforms:
-            cuts = transform(cuts)
-
-        features, features_lens = self.feature_input_strategy(cuts)
-
-        for transform in self.feature_transforms:
-            features = transform(features)
-
-        batch = {
-            "features": features,
-            "features_lens": features_lens,
-        }
-
-        if self.return_audio:
-            audio, audio_lens = collate_audio(cuts)
-            batch["audio"] = audio
-            batch["audio_lens"] = audio_lens
-
-        if self.return_text:
-            # use normalized text
-            text = [cut.supervisions[0].normalized_text for cut in cuts]
-            batch["text"] = text
-
-        if self.return_tokens:
-            tokens = [cut.tokens for cut in cuts]
-            batch["tokens"] = tokens
-
-        if self.return_spk_ids:
-            batch["speakers"] = [cut.supervisions[0].speaker for cut in cuts]
-
-        if self.return_cuts:
-            batch["cut"] = [cut for cut in cuts]
-
-        return batch
-
-
-def validate_for_tts(cuts: CutSet) -> None:
-    validate(cuts)
-    for cut in cuts:
-        assert (
-            len(cut.supervisions) == 1
-        ), "Only the Cuts with single supervision are supported."
diff --git a/egs/zipvoice/zipvoice/utils.py b/egs/zipvoice/zipvoice/utils.py
deleted file mode 100644
index 4092d0ae4..000000000
--- a/egs/zipvoice/zipvoice/utils.py
+++ /dev/null
@@ -1,219 +0,0 @@
-from typing import Any, List, Optional, Tuple, Union
-
-import torch
-from torch import nn
-from torch.nn.parallel import DistributedDataParallel as DDP
-
-
-class AttributeDict(dict):
-    def __getattr__(self, key):
-        if key in self:
-            return self[key]
-        raise AttributeError(f"No such attribute '{key}'")
-
-    def __setattr__(self, key, value):
-        self[key] = value
-
-    def __delattr__(self, key):
-        if key in self:
-            del self[key]
-            return
-        raise AttributeError(f"No such attribute '{key}'")
-
-
-def prepare_input(
-    params: AttributeDict,
-    batch: dict,
-    device: torch.device,
-    tokenizer: Optional[Any] = None,
-    return_tokens: bool = False,
-    return_feature: bool = False,
-    return_audio: bool = False,
-    return_prompt: bool = False,
-):
-    """
-    Parse the features and targets of the current batch.
-    Args:
-      params:
-        It is returned by :func:`get_params`.
-      batch:
-        It is the return value from iterating
-        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
-        for the format of the `batch`.
-      sp:
-        Used to convert text to bpe tokens.
-      device:
-        The device of Tensor.
-    """
-    return_list = []
-
-    if return_tokens:
-        assert tokenizer is not None
-
-        if params.token_type == "phone":
-            tokens = tokenizer.tokens_to_token_ids(batch["tokens"])
-        else:
-            tokens = tokenizer.texts_to_token_ids(batch["text"])
-        return_list += [tokens]
-
-    if return_feature:
-        features = batch["features"].to(device)
-        features_lens = batch["features_lens"].to(device)
-        return_list += [features * params.feat_scale, features_lens]
-
-    if return_audio:
-        return_list += [batch["audio"], batch["audio_lens"]]
-
-    if return_prompt:
-        if return_tokens:
-            if params.token_type == "phone":
-                prompt_tokens = tokenizer.tokens_to_token_ids(batch["prompt"]["tokens"])
-            else:
-                prompt_tokens = tokenizer.texts_to_token_ids(batch["prompt"]["text"])
-            return_list += [prompt_tokens]
-        if return_feature:
-            prompt_features = batch["prompt"]["features"].to(device)
-            prompt_features_lens = batch["prompt"]["features_lens"].to(device)
-            return_list += [prompt_features * params.feat_scale, prompt_features_lens]
-        if return_audio:
-            return_list += [batch["prompt"]["audio"], batch["prompt"]["audio_lens"]]
-
-    return return_list
-
-
-def prepare_avg_tokens_durations(features_lens, tokens_lens):
-    tokens_durations = []
-    for i in range(len(features_lens)):
-        utt_duration = features_lens[i]
-        avg_token_duration = utt_duration // tokens_lens[i]
-        tokens_durations.append([avg_token_duration] * tokens_lens[i])
-    return tokens_durations
-
-
-def pad_labels(y: List[List[int]], pad_id: int, device: torch.device):
-    """
-    Pad the transcripts to the same length with zeros.
-
-    Args:
-      y: the transcripts, which is a list of a list
-
-    Returns:
-      Return a Tensor of padded transcripts.
-    """
-    y = [l + [pad_id] for l in y]
-    length = max([len(l) for l in y])
-    y = [l + [pad_id] * (length - len(l)) for l in y]
-    return torch.tensor(y, dtype=torch.int64, device=device)
-
-
-def get_tokens_index(durations: List[List[int]], num_frames: int) -> torch.Tensor:
-    """
-    Gets position in the transcript for each frame, i.e. the position
-    in the symbol-sequence to look up.
-
-    Args:
-      durations:
-        Duration of each token in transcripts.
-      num_frames:
-        The maximum frame length of the current batch.
-
-    Returns:
-      Return a Tensor of shape (batch_size, num_frames)
-    """
-    durations = [x + [num_frames - sum(x)] for x in durations]
-    batch_size = len(durations)
-    ans = torch.zeros(batch_size, num_frames, dtype=torch.int64)
-    for b in range(batch_size):
-        this_dur = durations[b]
-        cur_frame = 0
-        for i, d in enumerate(this_dur):
-            ans[b, cur_frame : cur_frame + d] = i
-            cur_frame += d
-        assert cur_frame == num_frames, (cur_frame, num_frames)
-    return ans
-
-
-def to_int_tuple(s: str):
-    return tuple(map(int, s.split(",")))
-
-
-def get_adjusted_batch_count(params: AttributeDict) -> float:
-    # returns the number of batches we would have used so far if we had used the reference
-    # duration.  This is for purposes of set_batch_count().
-    return (
-        params.batch_idx_train
-        * (params.max_duration * params.world_size)
-        / params.ref_duration
-    )
-
-
-def set_batch_count(model: Union[nn.Module, DDP], batch_count: float) -> None:
-    if isinstance(model, DDP):
-        # get underlying nn.Module
-        model = model.module
-    for name, module in model.named_modules():
-        if hasattr(module, "batch_count"):
-            module.batch_count = batch_count
-        if hasattr(module, "name"):
-            module.name = name
-
-
-def condition_time_mask(
-    features_lens: torch.Tensor, mask_percent: Tuple[float, float], max_len: int = 0
-) -> torch.Tensor:
-    """
-    Apply Time masking.
-    Args:
-        features_lens:
-            input tensor of shape ``(B)``
-        mask_size:
-            the width size for masking.
-        max_len:
-            the maximum length of the mask.
-    Returns:
-        Return a 2-D bool tensor (B, T), where masked positions
-        are filled with `True` and non-masked positions are
-        filled with `False`.
-    """
-    mask_size = (
-        torch.zeros_like(features_lens, dtype=torch.float32).uniform_(*mask_percent)
-        * features_lens
-    ).to(torch.int64)
-    mask_starts = (
-        torch.rand_like(mask_size, dtype=torch.float32) * (features_lens - mask_size)
-    ).to(torch.int64)
-    mask_ends = mask_starts + mask_size
-    max_len = max(max_len, features_lens.max())
-    seq_range = torch.arange(0, max_len, device=features_lens.device)
-    mask = (seq_range[None, :] >= mask_starts[:, None]) & (
-        seq_range[None, :] < mask_ends[:, None]
-    )
-    return mask
-
-
-def make_pad_mask(lengths: torch.Tensor, max_len: int = 0) -> torch.Tensor:
-    """
-    Args:
-      lengths:
-        A 1-D tensor containing sentence lengths.
-      max_len:
-        The length of masks.
-    Returns:
-      Return a 2-D bool tensor, where masked positions
-      are filled with `True` and non-masked positions are
-      filled with `False`.
-
-    >>> lengths = torch.tensor([1, 3, 2, 5])
-    >>> make_pad_mask(lengths)
-    tensor([[False,  True,  True,  True,  True],
-            [False, False, False,  True,  True],
-            [False, False,  True,  True,  True],
-            [False, False, False, False, False]])
-    """
-    assert lengths.ndim == 1, lengths.ndim
-    max_len = max(max_len, lengths.max())
-    n = lengths.size(0)
-    seq_range = torch.arange(0, max_len, device=lengths.device)
-    expaned_lengths = seq_range.unsqueeze(0).expand(n, max_len)
-
-    return expaned_lengths >= lengths.unsqueeze(-1)
diff --git a/egs/zipvoice/zipvoice/zipformer.py b/egs/zipvoice/zipvoice/zipformer.py
deleted file mode 100644
index 190191cbb..000000000
--- a/egs/zipvoice/zipvoice/zipformer.py
+++ /dev/null
@@ -1,1648 +0,0 @@
-#!/usr/bin/env python3
-# Copyright    2022-2024  Xiaomi Corp.        (authors: Daniel Povey,
-#                                                       Zengwei Yao,
-#                                                       Wei Kang
-#                                                       Han Zhu)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import copy
-import logging
-import math
-import random
-from typing import Optional, Tuple, Union
-
-import torch
-from scaling import (
-    Identity,  # more friendly to backward hooks than nn.Identity(), for diagnostic reasons.
-)
-from scaling import (
-    ScaledLinear,  # not as in other dirs.. just scales down initial parameter values.
-)
-from scaling import (
-    ActivationDropoutAndLinear,
-    Balancer,
-    BiasNorm,
-    Dropout2,
-    FloatLike,
-    ScheduledFloat,
-    SwooshR,
-    Whiten,
-    limit_param_value,
-    penalize_abs_values_gt,
-    softmax,
-)
-from torch import Tensor, nn
-
-
-def timestep_embedding(timesteps, dim, max_period=10000):
-    """Create sinusoidal timestep embeddings.
-
-    :param timesteps: shape of (N) or (N, T)
-    :param dim: the dimension of the output.
-    :param max_period: controls the minimum frequency of the embeddings.
-    :return: an Tensor of positional embeddings. shape of (N, dim) or (T, N, dim)
-    """
-    half = dim // 2
-    freqs = torch.exp(
-        -math.log(max_period)
-        * torch.arange(start=0, end=half, dtype=torch.float32, device=timesteps.device)
-        / half
-    )
-
-    if timesteps.dim() == 2:
-        timesteps = timesteps.transpose(0, 1)  # (N, T) -> (T, N)
-
-    args = timesteps[..., None].float() * freqs[None]
-    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-    if dim % 2:
-        embedding = torch.cat([embedding, torch.zeros_like(embedding[..., :1])], dim=-1)
-    return embedding
-
-
-class TTSZipformer(nn.Module):
-    """
-    Args:
-
-    Note: all "int or Tuple[int]" arguments below will be treated as lists of the same length
-    as downsampling_factor if they are single ints or one-element tuples.  The length of
-    downsampling_factor defines the number of stacks.
-
-        downsampling_factor (Tuple[int]): downsampling factor for each encoder stack.
-           Note: this is in addition to the downsampling factor of 2 that is applied in
-           the frontend (self.encoder_embed).
-        encoder_dim (Tuple[int]): embedding dimension of each of the encoder stacks, one per
-           encoder stack.
-        num_encoder_layers (int or Tuple[int])): number of encoder layers for each stack
-        query_head_dim (int or Tuple[int]): dimension of query and key per attention
-           head: per stack, if a tuple..
-        pos_head_dim (int or Tuple[int]): dimension of positional-encoding projection per
-           attention head
-        value_head_dim (int or Tuple[int]): dimension of value in each attention head
-        num_heads: (int or Tuple[int]): number of heads in the self-attention mechanism.
-              Must be at least 4.
-        feedforward_dim (int or Tuple[int]): hidden dimension in feedforward modules
-        cnn_module_kernel (int or Tuple[int])): Kernel size of convolution module
-
-        pos_dim (int): the dimension of each positional-encoding vector prior to projection,
-            e.g. 128.
-
-        dropout (float): dropout rate
-        warmup_batches (float): number of batches to warm up over; this controls
-          dropout of encoder layers.
-        use_time_embed: (bool): if True, do not take time embedding as additional input.
-        time_embed_dim: (int): the dimension of the time embedding.
-    """
-
-    def __init__(
-        self,
-        in_dim: int,
-        out_dim: int,
-        downsampling_factor: Tuple[int] = (2, 4),
-        num_encoder_layers: Union[int, Tuple[int]] = 4,
-        cnn_module_kernel: Union[int, Tuple[int]] = 31,
-        encoder_dim: int = 384,
-        query_head_dim: int = 24,
-        pos_head_dim: int = 4,
-        value_head_dim: int = 12,
-        num_heads: int = 8,
-        feedforward_dim: int = 1536,
-        pos_dim: int = 192,
-        dropout: FloatLike = None,  # see code below for default
-        warmup_batches: float = 4000.0,
-        use_time_embed: bool = True,
-        time_embed_dim: int = 192,
-        use_guidance_scale_embed: bool = False,
-        guidance_scale_embed_dim: int = 192,
-        use_conv: bool = True,
-    ) -> None:
-        super(TTSZipformer, self).__init__()
-
-        if dropout is None:
-            dropout = ScheduledFloat((0.0, 0.3), (20000.0, 0.1))
-
-        def _to_tuple(x):
-            """Converts a single int or a 1-tuple of an int to a tuple with the same length
-            as downsampling_factor"""
-            if isinstance(x, int):
-                x = (x,)
-            if len(x) == 1:
-                x = x * len(downsampling_factor)
-            else:
-                assert len(x) == len(downsampling_factor) and isinstance(x[0], int)
-            return x
-
-        def _assert_downsampling_factor(factors):
-            """assert downsampling_factor follows u-net style"""
-            assert factors[0] == 1 and factors[-1] == 1
-
-            for i in range(1, len(factors) // 2 + 1):
-                assert factors[i] == factors[i - 1] * 2
-
-            for i in range(len(factors) // 2 + 1, len(factors)):
-                assert factors[i] * 2 == factors[i - 1]
-
-        _assert_downsampling_factor(downsampling_factor)
-        self.downsampling_factor = downsampling_factor  # tuple
-        num_encoder_layers = _to_tuple(num_encoder_layers)
-        self.cnn_module_kernel = cnn_module_kernel = _to_tuple(cnn_module_kernel)
-        self.encoder_dim = encoder_dim
-        self.num_encoder_layers = num_encoder_layers
-        self.query_head_dim = query_head_dim
-        self.value_head_dim = value_head_dim
-        self.num_heads = num_heads
-
-        self.use_time_embed = use_time_embed
-        self.use_guidance_scale_embed = use_guidance_scale_embed
-
-        self.time_embed_dim = time_embed_dim
-        if self.use_time_embed:
-            assert time_embed_dim != -1
-        else:
-            time_embed_dim = -1
-        self.guidance_scale_embed_dim = guidance_scale_embed_dim
-
-        self.in_proj = nn.Linear(in_dim, encoder_dim)
-        self.out_proj = nn.Linear(encoder_dim, out_dim)
-
-        # each one will be Zipformer2Encoder or DownsampledZipformer2Encoder
-        encoders = []
-
-        num_encoders = len(downsampling_factor)
-        for i in range(num_encoders):
-            encoder_layer = Zipformer2EncoderLayer(
-                embed_dim=encoder_dim,
-                pos_dim=pos_dim,
-                num_heads=num_heads,
-                query_head_dim=query_head_dim,
-                pos_head_dim=pos_head_dim,
-                value_head_dim=value_head_dim,
-                feedforward_dim=feedforward_dim,
-                use_conv=use_conv,
-                cnn_module_kernel=cnn_module_kernel[i],
-                dropout=dropout,
-            )
-
-            # For the segment of the warmup period, we let the Conv2dSubsampling
-            # layer learn something.  Then we start to warm up the other encoders.
-            encoder = Zipformer2Encoder(
-                encoder_layer,
-                num_encoder_layers[i],
-                embed_dim=encoder_dim,
-                time_embed_dim=time_embed_dim,
-                pos_dim=pos_dim,
-                warmup_begin=warmup_batches * (i + 1) / (num_encoders + 1),
-                warmup_end=warmup_batches * (i + 2) / (num_encoders + 1),
-                final_layerdrop_rate=0.035 * (downsampling_factor[i] ** 0.5),
-            )
-
-            if downsampling_factor[i] != 1:
-                encoder = DownsampledZipformer2Encoder(
-                    encoder,
-                    dim=encoder_dim,
-                    downsample=downsampling_factor[i],
-                )
-
-            encoders.append(encoder)
-
-        self.encoders = nn.ModuleList(encoders)
-        if self.use_time_embed:
-            self.time_embed = nn.Sequential(
-                nn.Linear(time_embed_dim, time_embed_dim * 2),
-                SwooshR(),
-                nn.Linear(time_embed_dim * 2, time_embed_dim),
-            )
-        else:
-            self.time_embed = None
-
-        if self.use_guidance_scale_embed:
-            self.guidance_scale_embed = ScaledLinear(
-                guidance_scale_embed_dim, time_embed_dim, bias=False, initial_scale=0.1
-            )
-        else:
-            self.guidance_scale_embed = None
-
-    def forward(
-        self,
-        x: Tensor,
-        t: Optional[Tensor] = None,
-        padding_mask: Optional[Tensor] = None,
-        guidance_scale: Optional[Tensor] = None,
-    ) -> Tuple[Tensor, Tensor]:
-        """
-        Args:
-          x:
-            The input tensor. Its shape is (batch_size, seq_len, feature_dim).
-          t:
-            A t tensor of shape (batch_size,) or (batch_size, seq_len)
-          padding_mask:
-            The mask for padding, of shape (batch_size, seq_len); True means
-            masked position. May be None.
-        Returns:
-          Return the output embeddings. its shape is (batch_size, output_seq_len, encoder_dim)
-        """
-        x = x.permute(1, 0, 2)
-        x = self.in_proj(x)
-
-        if t is not None:
-            assert t.dim() == 1 or t.dim() == 2, t.shape
-            time_emb = timestep_embedding(t, self.time_embed_dim)
-            if guidance_scale is not None:
-                assert (
-                    guidance_scale.dim() == 1 or guidance_scale.dim() == 2
-                ), guidance_scale.shape
-                guidance_scale_emb = self.guidance_scale_embed(
-                    timestep_embedding(guidance_scale, self.guidance_scale_embed_dim)
-                )
-                time_emb = time_emb + guidance_scale_emb
-            time_emb = self.time_embed(time_emb)
-        else:
-            time_emb = None
-
-        attn_mask = None
-
-        for i, module in enumerate(self.encoders):
-            x = module(
-                x,
-                time_emb=time_emb,
-                src_key_padding_mask=padding_mask,
-                attn_mask=attn_mask,
-            )
-        x = self.out_proj(x)
-        x = x.permute(1, 0, 2)
-        return x
-
-
-def _whitening_schedule(x: float, ratio: float = 2.0) -> ScheduledFloat:
-    return ScheduledFloat((0.0, x), (20000.0, ratio * x), default=x)
-
-
-class Zipformer2EncoderLayer(nn.Module):
-    """
-    Args:
-        embed_dim: the number of expected features in the input (required).
-        nhead: the number of heads in the multiheadattention models (required).
-        feedforward_dim: the dimension of the feedforward network model (required).
-        dropout: the dropout value (default=0.1).
-        cnn_module_kernel (int): Kernel size of convolution module (default=31).
-
-    Examples::
-        >>> encoder_layer = Zipformer2EncoderLayer(embed_dim=512, nhead=8)
-        >>> src = torch.rand(10, 32, 512)
-        >>> pos_emb = torch.rand(32, 19, 512)
-        >>> out = encoder_layer(src, pos_emb)
-    """
-
-    def __init__(
-        self,
-        embed_dim: int,
-        pos_dim: int,
-        num_heads: int,
-        query_head_dim: int,
-        pos_head_dim: int,
-        value_head_dim: int,
-        feedforward_dim: int,
-        dropout: FloatLike = 0.1,
-        cnn_module_kernel: int = 31,
-        use_conv: bool = True,
-        attention_skip_rate: FloatLike = ScheduledFloat(
-            (0.0, 0.2), (4000.0, 0.05), (16000, 0.0), default=0
-        ),
-        conv_skip_rate: FloatLike = ScheduledFloat(
-            (0.0, 0.2), (4000.0, 0.05), (16000, 0.0), default=0
-        ),
-        const_attention_rate: FloatLike = ScheduledFloat(
-            (0.0, 0.25), (4000.0, 0.025), default=0
-        ),
-        ff2_skip_rate: FloatLike = ScheduledFloat(
-            (0.0, 0.1), (4000.0, 0.01), (50000.0, 0.0)
-        ),
-        ff3_skip_rate: FloatLike = ScheduledFloat(
-            (0.0, 0.1), (4000.0, 0.01), (50000.0, 0.0)
-        ),
-        bypass_skip_rate: FloatLike = ScheduledFloat(
-            (0.0, 0.5), (4000.0, 0.02), default=0
-        ),
-    ) -> None:
-        super(Zipformer2EncoderLayer, self).__init__()
-        self.embed_dim = embed_dim
-
-        # self.bypass implements layer skipping as well as bypass; see its default values.
-        self.bypass = BypassModule(
-            embed_dim, skip_rate=bypass_skip_rate, straight_through_rate=0
-        )
-        # bypass_mid is bypass used in the middle of the layer.
-        self.bypass_mid = BypassModule(embed_dim, straight_through_rate=0)
-
-        # skip probability for dynamic modules (meaning: anything but feedforward).
-        self.attention_skip_rate = copy.deepcopy(attention_skip_rate)
-        # an additional skip probability that applies to ConvModule to stop it from
-        # contributing too much early on.
-        self.conv_skip_rate = copy.deepcopy(conv_skip_rate)
-
-        # ff2_skip_rate is to prevent the ff2 module from having output that's too big
-        # compared to its residual.
-        self.ff2_skip_rate = copy.deepcopy(ff2_skip_rate)
-        self.ff3_skip_rate = copy.deepcopy(ff3_skip_rate)
-
-        self.const_attention_rate = copy.deepcopy(const_attention_rate)
-
-        self.self_attn_weights = RelPositionMultiheadAttentionWeights(
-            embed_dim,
-            pos_dim=pos_dim,
-            num_heads=num_heads,
-            query_head_dim=query_head_dim,
-            pos_head_dim=pos_head_dim,
-            dropout=0.0,
-        )
-
-        self.self_attn1 = SelfAttention(embed_dim, num_heads, value_head_dim)
-
-        self.self_attn2 = SelfAttention(embed_dim, num_heads, value_head_dim)
-
-        self.feed_forward1 = FeedforwardModule(
-            embed_dim, (feedforward_dim * 3) // 4, dropout
-        )
-
-        self.feed_forward2 = FeedforwardModule(embed_dim, feedforward_dim, dropout)
-
-        self.feed_forward3 = FeedforwardModule(
-            embed_dim, (feedforward_dim * 5) // 4, dropout
-        )
-
-        self.nonlin_attention = NonlinAttention(
-            embed_dim, hidden_channels=3 * embed_dim // 4
-        )
-
-        self.use_conv = use_conv
-
-        if self.use_conv:
-            self.conv_module1 = ConvolutionModule(embed_dim, cnn_module_kernel)
-
-            self.conv_module2 = ConvolutionModule(embed_dim, cnn_module_kernel)
-
-        self.norm = BiasNorm(embed_dim)
-
-        self.balancer1 = Balancer(
-            embed_dim,
-            channel_dim=-1,
-            min_positive=0.45,
-            max_positive=0.55,
-            min_abs=0.2,
-            max_abs=4.0,
-        )
-
-        # balancer for output of NonlinAttentionModule
-        self.balancer_na = Balancer(
-            embed_dim,
-            channel_dim=-1,
-            min_positive=0.3,
-            max_positive=0.7,
-            min_abs=ScheduledFloat((0.0, 0.004), (4000.0, 0.02)),
-            prob=0.05,  # out of concern for memory usage
-        )
-
-        # balancer for output of feedforward2, prevent it from staying too
-        # small.  give this a very small probability, even at the start of
-        # training, it's to fix a rare problem and it's OK to fix it slowly.
-        self.balancer_ff2 = Balancer(
-            embed_dim,
-            channel_dim=-1,
-            min_positive=0.3,
-            max_positive=0.7,
-            min_abs=ScheduledFloat((0.0, 0.0), (4000.0, 0.1), default=0.0),
-            max_abs=2.0,
-            prob=0.05,
-        )
-
-        self.balancer_ff3 = Balancer(
-            embed_dim,
-            channel_dim=-1,
-            min_positive=0.3,
-            max_positive=0.7,
-            min_abs=ScheduledFloat((0.0, 0.0), (4000.0, 0.2), default=0.0),
-            max_abs=4.0,
-            prob=0.05,
-        )
-
-        self.whiten = Whiten(
-            num_groups=1,
-            whitening_limit=_whitening_schedule(4.0, ratio=3.0),
-            prob=(0.025, 0.25),
-            grad_scale=0.01,
-        )
-
-        self.balancer2 = Balancer(
-            embed_dim,
-            channel_dim=-1,
-            min_positive=0.45,
-            max_positive=0.55,
-            min_abs=0.1,
-            max_abs=4.0,
-        )
-
-    def get_sequence_dropout_mask(
-        self, x: Tensor, dropout_rate: float
-    ) -> Optional[Tensor]:
-        if (
-            dropout_rate == 0.0
-            or not self.training
-            or torch.jit.is_scripting()
-            or torch.jit.is_tracing()
-        ):
-            return None
-        batch_size = x.shape[1]
-        mask = (torch.rand(batch_size, 1, device=x.device) > dropout_rate).to(x.dtype)
-        return mask
-
-    def sequence_dropout(self, x: Tensor, dropout_rate: float) -> Tensor:
-        """
-        Apply sequence-level dropout to x.
-        x shape: (seq_len, batch_size, embed_dim)
-        """
-        dropout_mask = self.get_sequence_dropout_mask(x, dropout_rate)
-        if dropout_mask is None:
-            return x
-        else:
-            return x * dropout_mask
-
-    def forward(
-        self,
-        src: Tensor,
-        pos_emb: Tensor,
-        time_emb: Optional[Tensor] = None,
-        attn_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """
-        Pass the input through the encoder layer.
-        Args:
-          src: the sequence to the encoder (required): shape (seq_len, batch_size, embedding_dim).
-          pos_emb: (1, 2*seq_len-1, pos_emb_dim) or (batch_size, 2*seq_len-1, pos_emb_dim)
-          time_emb: the embedding representing the current timestep: shape  (batch_size, embedding_dim)
-            or (seq_len, batch_size, embedding_dim) .
-          attn_mask: the attention mask, of shape (batch_size, seq_len, seq_len) or (seq_len, seq_len),
-            interpreted as (batch_size, tgt_seq_len, src_seq_len) or (tgt_seq_len, src_seq_len).
-            True means masked position. May be None.
-          src_key_padding_mask:  the mask for padding, of shape (batch_size, seq_len); True means
-            masked position.  May be None.
-
-        Returns:
-           A tensor which has the same shape as src
-        """
-        src_orig = src
-
-        # dropout rate for non-feedforward submodules
-        if torch.jit.is_scripting() or torch.jit.is_tracing():
-            attention_skip_rate = 0.0
-        else:
-            attention_skip_rate = (
-                float(self.attention_skip_rate) if self.training else 0.0
-            )
-
-        # attn_weights: (num_heads, batch_size, seq_len, seq_len)
-        attn_weights = self.self_attn_weights(
-            src,
-            pos_emb=pos_emb,
-            attn_mask=attn_mask,
-            key_padding_mask=src_key_padding_mask,
-        )
-        if time_emb is not None:
-
-            src = src + time_emb
-
-        src = src + self.feed_forward1(src)
-
-        self_attn_dropout_mask = self.get_sequence_dropout_mask(
-            src, attention_skip_rate
-        )
-
-        selected_attn_weights = attn_weights[0:1]
-        if torch.jit.is_scripting() or torch.jit.is_tracing():
-            pass
-        elif self.training and random.random() < float(self.const_attention_rate):
-            # Make attention weights constant.  The intention is to
-            # encourage these modules to do something similar to an
-            # averaging-over-time operation.
-            # only need the mask, can just use the 1st one and expand later
-            selected_attn_weights = selected_attn_weights[0:1]
-            selected_attn_weights = (selected_attn_weights > 0.0).to(
-                selected_attn_weights.dtype
-            )
-            selected_attn_weights = selected_attn_weights * (
-                1.0 / selected_attn_weights.sum(dim=-1, keepdim=True)
-            )
-
-        na = self.balancer_na(self.nonlin_attention(src, selected_attn_weights))
-
-        src = src + (
-            na if self_attn_dropout_mask is None else na * self_attn_dropout_mask
-        )
-
-        self_attn = self.self_attn1(src, attn_weights)
-
-        src = src + (
-            self_attn
-            if self_attn_dropout_mask is None
-            else self_attn * self_attn_dropout_mask
-        )
-
-        if self.use_conv:
-            if torch.jit.is_scripting() or torch.jit.is_tracing():
-                conv_skip_rate = 0.0
-            else:
-                conv_skip_rate = float(self.conv_skip_rate) if self.training else 0.0
-
-            if time_emb is not None:
-                src = src + time_emb
-
-            src = src + self.sequence_dropout(
-                self.conv_module1(
-                    src,
-                    src_key_padding_mask=src_key_padding_mask,
-                ),
-                conv_skip_rate,
-            )
-
-        if torch.jit.is_scripting() or torch.jit.is_tracing():
-            ff2_skip_rate = 0.0
-        else:
-            ff2_skip_rate = float(self.ff2_skip_rate) if self.training else 0.0
-        src = src + self.sequence_dropout(
-            self.balancer_ff2(self.feed_forward2(src)), ff2_skip_rate
-        )
-
-        # bypass in the middle of the layer.
-        src = self.bypass_mid(src_orig, src)
-
-        self_attn = self.self_attn2(src, attn_weights)
-
-        src = src + (
-            self_attn
-            if self_attn_dropout_mask is None
-            else self_attn * self_attn_dropout_mask
-        )
-
-        if self.use_conv:
-
-            if torch.jit.is_scripting() or torch.jit.is_tracing():
-                conv_skip_rate = 0.0
-            else:
-                conv_skip_rate = float(self.conv_skip_rate) if self.training else 0.0
-
-            if time_emb is not None:
-                src = src + time_emb
-
-            src = src + self.sequence_dropout(
-                self.conv_module2(
-                    src,
-                    src_key_padding_mask=src_key_padding_mask,
-                ),
-                conv_skip_rate,
-            )
-
-        if torch.jit.is_scripting() or torch.jit.is_tracing():
-            ff3_skip_rate = 0.0
-        else:
-            ff3_skip_rate = float(self.ff3_skip_rate) if self.training else 0.0
-        src = src + self.sequence_dropout(
-            self.balancer_ff3(self.feed_forward3(src)), ff3_skip_rate
-        )
-
-        src = self.balancer1(src)
-        src = self.norm(src)
-
-        src = self.bypass(src_orig, src)
-
-        src = self.balancer2(src)
-        src = self.whiten(src)
-
-        return src
-
-
-class Zipformer2Encoder(nn.Module):
-    r"""Zipformer2Encoder is a stack of N encoder layers
-
-    Args:
-        encoder_layer: an instance of the Zipformer2EncoderLayer() class (required).
-        num_layers: the number of sub-encoder-layers in the encoder (required).
-        pos_dim: the dimension for the relative positional encoding
-
-    Examples::
-        >>> encoder_layer = Zipformer2EncoderLayer(embed_dim=512, nhead=8)
-        >>> zipformer_encoder = Zipformer2Encoder(encoder_layer, num_layers=6)
-        >>> src = torch.rand(10, 32, 512)
-        >>> out = zipformer_encoder(src)
-    """
-
-    def __init__(
-        self,
-        encoder_layer: nn.Module,
-        num_layers: int,
-        embed_dim: int,
-        time_embed_dim: int,
-        pos_dim: int,
-        warmup_begin: float,
-        warmup_end: float,
-        initial_layerdrop_rate: float = 0.5,
-        final_layerdrop_rate: float = 0.05,
-    ) -> None:
-        super().__init__()
-        self.encoder_pos = CompactRelPositionalEncoding(
-            pos_dim, dropout_rate=0.15, length_factor=1.0
-        )
-        if time_embed_dim != -1:
-            self.time_emb = nn.Sequential(
-                SwooshR(),
-                nn.Linear(time_embed_dim, embed_dim),
-            )
-        else:
-            self.time_emb = None
-
-        self.layers = nn.ModuleList(
-            [copy.deepcopy(encoder_layer) for i in range(num_layers)]
-        )
-        self.num_layers = num_layers
-
-        assert 0 <= warmup_begin <= warmup_end
-
-        delta = (1.0 / num_layers) * (warmup_end - warmup_begin)
-        cur_begin = warmup_begin  # interpreted as a training batch index
-        for i in range(num_layers):
-            cur_end = cur_begin + delta
-            self.layers[i].bypass.skip_rate = ScheduledFloat(
-                (cur_begin, initial_layerdrop_rate),
-                (cur_end, final_layerdrop_rate),
-                default=0.0,
-            )
-            cur_begin = cur_end
-
-    def forward(
-        self,
-        src: Tensor,
-        time_emb: Optional[Tensor] = None,
-        attn_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        r"""Pass the input through the encoder layers in turn.
-
-        Args:
-            src: the sequence to the encoder (required): shape (seq_len, batch_size, embedding_dim).
-              the embedding representing the current timestep: shape  (batch_size, embedding_dim)
-              or (seq_len, batch_size, embedding_dim) .
-            time_emb: the embedding representing the current timestep: shape  (batch_size, embedding_dim)
-               or (seq_len, batch_size, embedding_dim) .
-            attn_mask: the attention mask, of shape (batch_size, seq_len, seq_len) or (seq_len, seq_len),
-                 interpreted as (batch_size, tgt_seq_len, src_seq_len) or (tgt_seq_len, src_seq_len).
-                 True means masked position. May be None.
-            src_key_padding_mask:  the mask for padding, of shape (batch_size, seq_len); True means
-                 masked position.  May be None.
-
-        Returns: a Tensor with the same shape as src.
-        """
-        pos_emb = self.encoder_pos(src)
-        if self.time_emb is not None:
-            assert time_emb is not None
-            time_emb = self.time_emb(time_emb)
-        else:
-            assert time_emb is None
-
-        output = src
-
-        for i, mod in enumerate(self.layers):
-            output = mod(
-                output,
-                pos_emb,
-                time_emb=time_emb,
-                attn_mask=attn_mask,
-                src_key_padding_mask=src_key_padding_mask,
-            )
-
-        return output
-
-
-class BypassModule(nn.Module):
-    """
-    An nn.Module that implements a learnable bypass scale, and also randomized per-sequence
-    layer-skipping.  The bypass is limited during early stages of training to be close to
-    "straight-through", i.e. to not do the bypass operation much initially, in order to
-    force all the modules to learn something.
-    """
-
-    def __init__(
-        self,
-        embed_dim: int,
-        skip_rate: FloatLike = 0.0,
-        straight_through_rate: FloatLike = 0.0,
-        scale_min: FloatLike = ScheduledFloat((0.0, 0.9), (20000.0, 0.2), default=0),
-        scale_max: FloatLike = 1.0,
-    ):
-        super().__init__()
-        self.bypass_scale = nn.Parameter(torch.full((embed_dim,), 0.5))
-        self.skip_rate = copy.deepcopy(skip_rate)
-        self.straight_through_rate = copy.deepcopy(straight_through_rate)
-        self.scale_min = copy.deepcopy(scale_min)
-        self.scale_max = copy.deepcopy(scale_max)
-
-    def _get_bypass_scale(self, batch_size: int):
-        # returns bypass-scale of shape (num_channels,),
-        # or (batch_size, num_channels,).  This is actually the
-        # scale on the non-residual term, so 0 corresponds to bypassing
-        # this module.
-        if torch.jit.is_scripting() or torch.jit.is_tracing() or not self.training:
-            return self.bypass_scale
-        else:
-            ans = limit_param_value(
-                self.bypass_scale, min=float(self.scale_min), max=float(self.scale_max)
-            )
-            skip_rate = float(self.skip_rate)
-            if skip_rate != 0.0:
-                mask = torch.rand((batch_size, 1), device=ans.device) > skip_rate
-                ans = ans * mask
-                # now ans is of shape (batch_size, num_channels), and is zero for sequences
-                # on which we have randomly chosen to do layer-skipping.
-            straight_through_rate = float(self.straight_through_rate)
-            if straight_through_rate != 0.0:
-                mask = (
-                    torch.rand((batch_size, 1), device=ans.device)
-                    < straight_through_rate
-                )
-                ans = torch.maximum(ans, mask.to(ans.dtype))
-            return ans
-
-    def forward(self, src_orig: Tensor, src: Tensor):
-        """
-        Args: src_orig and src are both of shape (seq_len, batch_size, num_channels)
-        Returns: something with the same shape as src and src_orig
-        """
-        bypass_scale = self._get_bypass_scale(src.shape[1])
-        return src_orig + (src - src_orig) * bypass_scale
-
-
-class DownsampledZipformer2Encoder(nn.Module):
-    r"""
-    DownsampledZipformer2Encoder is a zipformer encoder evaluated at a reduced frame rate,
-    after convolutional downsampling, and then upsampled again at the output, and combined
-    with the origin input, so that the output has the same shape as the input.
-    """
-
-    def __init__(self, encoder: nn.Module, dim: int, downsample: int):
-        super(DownsampledZipformer2Encoder, self).__init__()
-        self.downsample_factor = downsample
-        self.downsample = SimpleDownsample(downsample)
-        self.num_layers = encoder.num_layers
-        self.encoder = encoder
-        self.upsample = SimpleUpsample(downsample)
-        self.out_combiner = BypassModule(dim, straight_through_rate=0)
-
-    def forward(
-        self,
-        src: Tensor,
-        time_emb: Optional[Tensor] = None,
-        attn_mask: Optional[Tensor] = None,
-        src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        r"""Downsample, go through encoder, upsample.
-
-        Args:
-            src: the sequence to the encoder (required): shape (seq_len, batch_size, embedding_dim).
-            time_emb: the embedding representing the current timestep: shape  (batch_size, embedding_dim)
-               or (seq_len, batch_size, embedding_dim) .
-            feature_mask: something that broadcasts with src, that we'll multiply `src`
-               by at every layer: if a Tensor, likely of shape (seq_len, batch_size, embedding_dim)
-            attn_mask: the attention mask, of shape (batch_size, seq_len, seq_len) or (seq_len, seq_len),
-                 interpreted as (batch_size, tgt_seq_len, src_seq_len) or (tgt_seq_len, src_seq_len).
-                 True means masked position. May be None.
-            src_key_padding_mask:  the mask for padding, of shape (batch_size, seq_len); True means
-                 masked position.  May be None.
-
-        Returns: a Tensor with the same shape as src.
-        """
-        src_orig = src
-        src = self.downsample(src)
-        ds = self.downsample_factor
-        if time_emb is not None and time_emb.dim() == 3:
-            time_emb = time_emb[::ds]
-        if attn_mask is not None:
-            attn_mask = attn_mask[::ds, ::ds]
-        if src_key_padding_mask is not None:
-            src_key_padding_mask = src_key_padding_mask[..., ::ds]
-
-        src = self.encoder(
-            src,
-            time_emb=time_emb,
-            attn_mask=attn_mask,
-            src_key_padding_mask=src_key_padding_mask,
-        )
-        src = self.upsample(src)
-        # remove any extra frames that are not a multiple of downsample_factor
-        src = src[: src_orig.shape[0]]
-
-        return self.out_combiner(src_orig, src)
-
-
-class SimpleDownsample(torch.nn.Module):
-    """
-    Does downsampling with attention, by weighted sum.
-    """
-
-    def __init__(self, downsample: int):
-        super(SimpleDownsample, self).__init__()
-
-        self.bias = nn.Parameter(torch.zeros(downsample))
-
-        self.name = None  # will be set from training code
-
-        self.downsample = downsample
-
-    def forward(self, src: Tensor) -> Tensor:
-        """
-        x: (seq_len, batch_size, in_channels)
-        Returns a tensor of shape
-           ( (seq_len+downsample-1)//downsample, batch_size, channels)
-        """
-        (seq_len, batch_size, in_channels) = src.shape
-        ds = self.downsample
-        d_seq_len = (seq_len + ds - 1) // ds
-
-        # Pad to an exact multiple of self.downsample
-        # right-pad src, repeating the last element.
-        pad = d_seq_len * ds - seq_len
-        src_extra = src[src.shape[0] - 1 :].expand(pad, src.shape[1], src.shape[2])
-        src = torch.cat((src, src_extra), dim=0)
-        assert src.shape[0] == d_seq_len * ds
-
-        src = src.reshape(d_seq_len, ds, batch_size, in_channels)
-
-        weights = self.bias.softmax(dim=0)
-        # weights: (downsample, 1, 1)
-        weights = weights.unsqueeze(-1).unsqueeze(-1)
-
-        # ans1 is the first `in_channels` channels of the output
-        ans = (src * weights).sum(dim=1)
-
-        return ans
-
-
-class SimpleUpsample(torch.nn.Module):
-    """
-    A very simple form of upsampling that just repeats the input.
-    """
-
-    def __init__(self, upsample: int):
-        super(SimpleUpsample, self).__init__()
-        self.upsample = upsample
-
-    def forward(self, src: Tensor) -> Tensor:
-        """
-        x: (seq_len, batch_size, num_channels)
-        Returns a tensor of shape
-           ( (seq_len*upsample), batch_size, num_channels)
-        """
-        upsample = self.upsample
-        (seq_len, batch_size, num_channels) = src.shape
-        src = src.unsqueeze(1).expand(seq_len, upsample, batch_size, num_channels)
-        src = src.reshape(seq_len * upsample, batch_size, num_channels)
-        return src
-
-
-class CompactRelPositionalEncoding(torch.nn.Module):
-    """
-    Relative positional encoding module.  This version is "compact" meaning it is able to encode
-    the important information about the relative position in a relatively small number of dimensions.
-    The goal is to make it so that small differences between large relative offsets (e.g. 1000 vs. 1001)
-    make very little difference to the embedding.   Such differences were potentially important
-    when encoding absolute position, but not important when encoding relative position because there
-    is now no need to compare two large offsets with each other.
-
-    Our embedding works by projecting the interval [-infinity,infinity] to a finite interval
-    using the atan() function, before doing the Fourier transform of that fixed interval.  The
-    atan() function would compress the "long tails" too small,
-    making it hard to distinguish between different magnitudes of large offsets, so we use a logarithmic
-    function to compress large offsets to a smaller range before applying atan().
-    Scalings are chosen in such a way that the embedding can clearly distinguish individual offsets as long
-    as they are quite close to the origin, e.g. abs(offset) <= about sqrt(embedding_dim)
-
-
-    Args:
-        embed_dim: Embedding dimension.
-        dropout_rate: Dropout rate.
-        max_len: Maximum input length: just a heuristic for initialization.
-        length_factor: a heuristic scale (should be >= 1.0) which, if larger, gives
-           less weight to small differences of offset near the origin.
-    """
-
-    def __init__(
-        self,
-        embed_dim: int,
-        dropout_rate: FloatLike,
-        max_len: int = 1000,
-        length_factor: float = 1.0,
-    ) -> None:
-        """Construct a CompactRelPositionalEncoding object."""
-        super(CompactRelPositionalEncoding, self).__init__()
-        self.embed_dim = embed_dim
-        assert embed_dim % 2 == 0, embed_dim
-        self.dropout = Dropout2(dropout_rate)
-        self.pe = None
-        assert length_factor >= 1.0, length_factor
-        self.length_factor = length_factor
-        self.extend_pe(torch.tensor(0.0).expand(max_len))
-
-    def extend_pe(self, x: Tensor, left_context_len: int = 0) -> None:
-        """Reset the positional encodings."""
-        T = x.size(0) + left_context_len
-
-        if self.pe is not None:
-            # self.pe contains both positive and negative parts
-            # the length of self.pe is 2 * input_len - 1
-            if self.pe.size(0) >= T * 2 - 1:
-                self.pe = self.pe.to(dtype=x.dtype, device=x.device)
-                return
-
-        # if T == 4, x would contain [ -3, -2, 1, 0, 1, 2, 3 ]
-        x = torch.arange(-(T - 1), T, device=x.device).to(torch.float32).unsqueeze(1)
-
-        freqs = 1 + torch.arange(self.embed_dim // 2, device=x.device)
-
-        # `compression_length` this is arbitrary/heuristic, if it is larger we have more resolution
-        # for small time offsets but less resolution for large time offsets.
-        compression_length = self.embed_dim**0.5
-        # x_compressed, like X, goes from -infinity to infinity as T goes from -infinity to infinity;
-        # but it does so more slowly than T for large absolute values of T.
-        # The formula is chosen so that d(x_compressed )/dx is 1 around x == 0, which
-        # is important.
-        x_compressed = (
-            compression_length
-            * x.sign()
-            * ((x.abs() + compression_length).log() - math.log(compression_length))
-        )
-
-        # if self.length_factor == 1.0, then length_scale is chosen so that the
-        # FFT can exactly separate points close to the origin (T == 0).  So this
-        # part of the formulation is not really heuristic.
-        # But empirically, for ASR at least, length_factor > 1.0 seems to work better.
-        length_scale = self.length_factor * self.embed_dim / (2.0 * math.pi)
-
-        # note for machine implementations: if atan is not available, we can use:
-        #   x.sign() * ((1 / (x.abs() + 1)) - 1)  * (-math.pi/2)
-        #  check on wolframalpha.com: plot(sign(x) *  (1 / ( abs(x) + 1) - 1 ) * -pi/2 , atan(x))
-        x_atan = (x_compressed / length_scale).atan()  # results between -pi and pi
-
-        cosines = (x_atan * freqs).cos()
-        sines = (x_atan * freqs).sin()
-
-        pe = torch.zeros(x.shape[0], self.embed_dim, device=x.device)
-        pe[:, 0::2] = cosines
-        pe[:, 1::2] = sines
-        pe[:, -1] = 1.0  # for bias.
-
-        self.pe = pe.to(dtype=x.dtype)
-
-    def forward(self, x: Tensor, left_context_len: int = 0) -> Tensor:
-        """Create positional encoding.
-
-        Args:
-            x (Tensor): Input tensor (time, batch, `*`).
-            left_context_len: (int): Length of cached left context.
-
-        Returns:
-            positional embedding, of shape (batch, left_context_len + 2*time-1, `*`).
-        """
-        self.extend_pe(x, left_context_len)
-        x_size_left = x.size(0) + left_context_len
-        # length of positive side: x.size(0) + left_context_len
-        # length of negative side: x.size(0)
-        pos_emb = self.pe[
-            self.pe.size(0) // 2
-            - x_size_left
-            + 1 : self.pe.size(0) // 2  # noqa E203
-            + x.size(0),
-            :,
-        ]
-        pos_emb = pos_emb.unsqueeze(0)
-        return self.dropout(pos_emb)
-
-
-class RelPositionMultiheadAttentionWeights(nn.Module):
-    r"""Module that computes multi-head attention weights with relative position encoding.
-    Various other modules consume the resulting attention weights: see, for example, the
-    SimpleAttention module which allows you to compute conventional attention.
-
-    This is a quite heavily modified from: "Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context",
-    we have to write up the differences.
-
-
-    Args:
-           embed_dim: number of channels at the input to this module, e.g. 256
-             pos_dim: dimension of the positional encoding vectors, e.g. 128.
-           num_heads:  number of heads to compute weights for, e.g. 8
-     query_head_dim: dimension of the query (and key), per head.  e.g. 24.
-       pos_head_dim: dimension of the projected positional encoding per head, e.g. 4.
-            dropout: dropout probability for attn_output_weights. Default: 0.0.
-       pos_emb_skip_rate: probability for skipping the pos_emb part of the scores on
-                     any given call to forward(), in training time.
-    """
-
-    def __init__(
-        self,
-        embed_dim: int,
-        pos_dim: int,
-        num_heads: int,
-        query_head_dim: int,
-        pos_head_dim: int,
-        dropout: float = 0.0,
-        pos_emb_skip_rate: FloatLike = ScheduledFloat((0.0, 0.5), (4000.0, 0.0)),
-    ) -> None:
-        super().__init__()
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.query_head_dim = query_head_dim
-        self.pos_head_dim = pos_head_dim
-        self.dropout = dropout
-        self.pos_emb_skip_rate = copy.deepcopy(pos_emb_skip_rate)
-        self.name = None  # will be overwritten in training code; for diagnostics.
-
-        key_head_dim = query_head_dim
-        in_proj_dim = (query_head_dim + key_head_dim + pos_head_dim) * num_heads
-
-        # the initial_scale is supposed to take over the "scaling" factor of
-        # head_dim ** -0.5 that has been used in previous forms of attention,
-        # dividing it between the query and key.   Note: this module is intended
-        # to be used with the ScaledAdam optimizer; with most other optimizers,
-        # it would be necessary to apply the scaling factor in the forward function.
-        self.in_proj = ScaledLinear(
-            embed_dim, in_proj_dim, bias=True, initial_scale=query_head_dim**-0.25
-        )
-
-        self.whiten_keys = Whiten(
-            num_groups=num_heads,
-            whitening_limit=_whitening_schedule(3.0),
-            prob=(0.025, 0.25),
-            grad_scale=0.025,
-        )
-
-        # add a balancer for the keys that runs with very small probability, and
-        # tries to enforce that all dimensions have mean around zero.  The
-        # weights produced by this module are invariant to adding a constant to
-        # the keys, so the derivative of the bias is mathematically zero; but
-        # due to how Adam/ScaledAdam work, it can learn a fairly large nonzero
-        # bias because the small numerical roundoff tends to have a non-random
-        # sign.  This module is intended to prevent that.  Use a very small
-        # probability; that should be sufficient to fix the problem.
-        self.balance_keys = Balancer(
-            key_head_dim * num_heads,
-            channel_dim=-1,
-            min_positive=0.4,
-            max_positive=0.6,
-            min_abs=0.0,
-            max_abs=100.0,
-            prob=0.025,
-        )
-
-        # linear transformation for positional encoding.
-        self.linear_pos = ScaledLinear(
-            pos_dim, num_heads * pos_head_dim, bias=False, initial_scale=0.05
-        )
-
-        # the following are for diagnostics only, see --print-diagnostics option
-        self.copy_pos_query = Identity()
-        self.copy_query = Identity()
-
-    def forward(
-        self,
-        x: Tensor,
-        pos_emb: Tensor,
-        key_padding_mask: Optional[Tensor] = None,
-        attn_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        r"""
-        Args:
-            x: input of shape (seq_len, batch_size, embed_dim)
-            pos_emb: Positional embedding tensor, of shape (1, 2*seq_len - 1, pos_dim)
-            key_padding_mask: a bool tensor of shape (batch_size, seq_len).  Positions that
-               are True in this mask will be ignored as sources in the attention weighting.
-            attn_mask: mask of shape (seq_len, seq_len) or (batch_size, seq_len, seq_len),
-               interpreted as ([batch_size,] tgt_seq_len, src_seq_len)
-               saying which positions are allowed to attend to which other positions.
-        Returns:
-           a tensor of attention weights, of shape (hum_heads, batch_size, seq_len, seq_len)
-           interpreted as (hum_heads, batch_size, tgt_seq_len, src_seq_len).
-        """
-        x = self.in_proj(x)
-        query_head_dim = self.query_head_dim
-        pos_head_dim = self.pos_head_dim
-        num_heads = self.num_heads
-
-        seq_len, batch_size, _ = x.shape
-
-        query_dim = query_head_dim * num_heads
-
-        # self-attention
-        q = x[..., 0:query_dim]
-        k = x[..., query_dim : 2 * query_dim]
-        # p is the position-encoding query
-        p = x[..., 2 * query_dim :]
-        assert p.shape[-1] == num_heads * pos_head_dim, (
-            p.shape[-1],
-            num_heads,
-            pos_head_dim,
-        )
-
-        q = self.copy_query(q)  # for diagnostics only, does nothing.
-        k = self.whiten_keys(self.balance_keys(k))  # does nothing in the forward pass.
-        p = self.copy_pos_query(p)  # for diagnostics only, does nothing.
-
-        q = q.reshape(seq_len, batch_size, num_heads, query_head_dim)
-        p = p.reshape(seq_len, batch_size, num_heads, pos_head_dim)
-        k = k.reshape(seq_len, batch_size, num_heads, query_head_dim)
-
-        # time1 refers to target, time2 refers to source.
-        q = q.permute(2, 1, 0, 3)  # (head, batch, time1, query_head_dim)
-        p = p.permute(2, 1, 0, 3)  # (head, batch, time1, pos_head_dim)
-        k = k.permute(2, 1, 3, 0)  # (head, batch, d_k, time2)
-
-        attn_scores = torch.matmul(q, k)
-
-        use_pos_scores = False
-        if torch.jit.is_scripting() or torch.jit.is_tracing():
-            # We can't put random.random() in the same line
-            use_pos_scores = True
-        elif not self.training or random.random() >= float(self.pos_emb_skip_rate):
-            use_pos_scores = True
-
-        if use_pos_scores:
-            pos_emb = self.linear_pos(pos_emb)
-            seq_len2 = 2 * seq_len - 1
-            pos_emb = pos_emb.reshape(-1, seq_len2, num_heads, pos_head_dim).permute(
-                2, 0, 3, 1
-            )
-            # pos shape now: (head, {1 or batch_size}, pos_dim, seq_len2)
-
-            # (head, batch, time1, pos_dim) x (head, 1, pos_dim, seq_len2) -> (head, batch, time1, seq_len2)
-            #  [where seq_len2 represents relative position.]
-            pos_scores = torch.matmul(p, pos_emb)
-            # the following .as_strided() expression converts the last axis of pos_scores from relative
-            # to absolute position.  I don't know whether I might have got the time-offsets backwards or
-            # not, but let this code define which way round it is supposed to be.
-            if torch.jit.is_tracing():
-                (num_heads, batch_size, time1, n) = pos_scores.shape
-                rows = torch.arange(start=time1 - 1, end=-1, step=-1)
-                cols = torch.arange(seq_len)
-                rows = rows.repeat(batch_size * num_heads).unsqueeze(-1)
-                indexes = rows + cols
-                pos_scores = pos_scores.reshape(-1, n)
-                pos_scores = torch.gather(pos_scores, dim=1, index=indexes)
-                pos_scores = pos_scores.reshape(num_heads, batch_size, time1, seq_len)
-            else:
-                pos_scores = pos_scores.as_strided(
-                    (num_heads, batch_size, seq_len, seq_len),
-                    (
-                        pos_scores.stride(0),
-                        pos_scores.stride(1),
-                        pos_scores.stride(2) - pos_scores.stride(3),
-                        pos_scores.stride(3),
-                    ),
-                    storage_offset=pos_scores.stride(3) * (seq_len - 1),
-                )
-
-            attn_scores = attn_scores + pos_scores
-
-        if torch.jit.is_scripting() or torch.jit.is_tracing():
-            pass
-        elif self.training and random.random() < 0.1:
-            # This is a harder way of limiting the attention scores to not be
-            # too large.  It incurs a penalty if any of them has an absolute
-            # value greater than 50.0.  this should be outside the normal range
-            # of the attention scores.  We use this mechanism instead of, say,
-            # something added to the loss function involving the entropy,
-            # because once the entropy gets very small gradients through the
-            # softmax can become very small, and we'd get zero derivatives.  The
-            # choices of 1.0e-04 as the scale on the penalty makes this
-            # mechanism vulnerable to the absolute scale of the loss function,
-            # but we view this as a failsafe to avoid "implausible" parameter
-            # values rather than a regularization method that should be active
-            # under normal circumstances.
-            attn_scores = penalize_abs_values_gt(
-                attn_scores, limit=25.0, penalty=1.0e-04, name=self.name
-            )
-
-        assert attn_scores.shape == (num_heads, batch_size, seq_len, seq_len)
-
-        if attn_mask is not None:
-            assert attn_mask.dtype == torch.bool
-            # use -1000 to avoid nan's where attn_mask and key_padding_mask make
-            # all scores zero.  It's important that this be large enough that exp(-1000)
-            # is exactly zero, for reasons related to const_attention_rate, it
-            # compares the final weights with zero.
-            attn_scores = attn_scores.masked_fill(attn_mask, -1000)
-
-        if key_padding_mask is not None:
-            assert key_padding_mask.shape == (
-                batch_size,
-                seq_len,
-            ), key_padding_mask.shape
-            attn_scores = attn_scores.masked_fill(
-                key_padding_mask.unsqueeze(1),
-                -1000,
-            )
-
-        # We use our own version of softmax, defined in scaling.py, which should
-        # save a little of the memory used in backprop by, if we are in
-        # automatic mixed precision mode (amp / autocast), by only storing the
-        # half-precision output for backprop purposes.
-        attn_weights = softmax(attn_scores, dim=-1)
-
-        if torch.jit.is_scripting() or torch.jit.is_tracing():
-            pass
-        elif random.random() < 0.001 and not self.training:
-            self._print_attn_entropy(attn_weights)
-
-        attn_weights = nn.functional.dropout(
-            attn_weights, p=self.dropout, training=self.training
-        )
-
-        return attn_weights
-
-    def _print_attn_entropy(self, attn_weights: Tensor):
-        # attn_weights: (num_heads, batch_size, seq_len, seq_len)
-        (num_heads, batch_size, seq_len, seq_len) = attn_weights.shape
-
-        with torch.no_grad():
-            with torch.amp.autocast("cuda", enabled=False):
-                attn_weights = attn_weights.to(torch.float32)
-                attn_weights_entropy = (
-                    -((attn_weights + 1.0e-20).log() * attn_weights)
-                    .sum(dim=-1)
-                    .mean(dim=(1, 2))
-                )
-                logging.debug(
-                    f"name={self.name}, attn_weights_entropy = {attn_weights_entropy}"
-                )
-
-
-class SelfAttention(nn.Module):
-    """
-    The simplest possible attention module.  This one works with already-computed attention
-    weights, e.g. as computed by RelPositionMultiheadAttentionWeights.
-
-    Args:
-          embed_dim: the input and output embedding dimension
-          num_heads: the number of attention heads
-          value_head_dim: the value dimension per head
-    """
-
-    def __init__(
-        self,
-        embed_dim: int,
-        num_heads: int,
-        value_head_dim: int,
-    ) -> None:
-        super().__init__()
-        self.in_proj = nn.Linear(embed_dim, num_heads * value_head_dim, bias=True)
-
-        self.out_proj = ScaledLinear(
-            num_heads * value_head_dim, embed_dim, bias=True, initial_scale=0.05
-        )
-
-        self.whiten = Whiten(
-            num_groups=1,
-            whitening_limit=_whitening_schedule(7.5, ratio=3.0),
-            prob=(0.025, 0.25),
-            grad_scale=0.01,
-        )
-
-    def forward(
-        self,
-        x: Tensor,
-        attn_weights: Tensor,
-    ) -> Tensor:
-        """
-        Args:
-          x: input tensor, of shape (seq_len, batch_size, embed_dim)
-         attn_weights: a tensor of shape (num_heads, batch_size, seq_len, seq_len),
-          with seq_len being interpreted as (tgt_seq_len, src_seq_len).  Expect
-          attn_weights.sum(dim=-1) == 1.
-        Returns:
-           a tensor with the same shape as x.
-        """
-        (seq_len, batch_size, embed_dim) = x.shape
-        num_heads = attn_weights.shape[0]
-        assert attn_weights.shape == (num_heads, batch_size, seq_len, seq_len)
-
-        x = self.in_proj(x)  # (seq_len, batch_size, num_heads * value_head_dim)
-        x = x.reshape(seq_len, batch_size, num_heads, -1).permute(2, 1, 0, 3)
-        # now x: (num_heads, batch_size, seq_len, value_head_dim)
-        value_head_dim = x.shape[-1]
-
-        # todo: see whether there is benefit in overriding matmul
-        x = torch.matmul(attn_weights, x)
-        # v: (num_heads, batch_size, seq_len, value_head_dim)
-
-        x = (
-            x.permute(2, 1, 0, 3)
-            .contiguous()
-            .view(seq_len, batch_size, num_heads * value_head_dim)
-        )
-
-        # returned value is of shape (seq_len, batch_size, embed_dim), like the input.
-        x = self.out_proj(x)
-        x = self.whiten(x)
-
-        return x
-
-
-class FeedforwardModule(nn.Module):
-    """Feedforward module in TTSZipformer model."""
-
-    def __init__(self, embed_dim: int, feedforward_dim: int, dropout: FloatLike):
-        super(FeedforwardModule, self).__init__()
-        self.in_proj = nn.Linear(embed_dim, feedforward_dim)
-
-        self.hidden_balancer = Balancer(
-            feedforward_dim,
-            channel_dim=-1,
-            min_positive=0.3,
-            max_positive=1.0,
-            min_abs=0.75,
-            max_abs=5.0,
-        )
-
-        # shared_dim=0 means we share the dropout mask along the time axis
-        self.out_proj = ActivationDropoutAndLinear(
-            feedforward_dim,
-            embed_dim,
-            activation="SwooshL",
-            dropout_p=dropout,
-            dropout_shared_dim=0,
-            bias=True,
-            initial_scale=0.1,
-        )
-
-        self.out_whiten = Whiten(
-            num_groups=1,
-            whitening_limit=_whitening_schedule(7.5),
-            prob=(0.025, 0.25),
-            grad_scale=0.01,
-        )
-
-    def forward(self, x: Tensor):
-        x = self.in_proj(x)
-        x = self.hidden_balancer(x)
-        # out_proj contains SwooshL activation, then dropout, then linear.
-        x = self.out_proj(x)
-        x = self.out_whiten(x)
-        return x
-
-
-class NonlinAttention(nn.Module):
-    """This is like the ConvolutionModule, but refactored so that we use multiplication by attention weights (borrowed
-       from the attention module) in place of actual convolution.  We also took out the second nonlinearity, the
-       one after the attention mechanism.
-
-    Args:
-        channels (int): The number of channels of conv layers.
-    """
-
-    def __init__(
-        self,
-        channels: int,
-        hidden_channels: int,
-    ) -> None:
-        super().__init__()
-
-        self.hidden_channels = hidden_channels
-
-        self.in_proj = nn.Linear(channels, hidden_channels * 3, bias=True)
-
-        # balancer that goes before the sigmoid.  Have quite a large min_abs value, at 2.0,
-        # because we noticed that well-trained instances of this module have abs-value before the sigmoid
-        # starting from about 3, and poorly-trained instances of the module have smaller abs values
-        # before the sigmoid.
-        self.balancer = Balancer(
-            hidden_channels,
-            channel_dim=-1,
-            min_positive=ScheduledFloat((0.0, 0.25), (20000.0, 0.05)),
-            max_positive=ScheduledFloat((0.0, 0.75), (20000.0, 0.95)),
-            min_abs=0.5,
-            max_abs=5.0,
-        )
-        self.tanh = nn.Tanh()
-
-        self.identity1 = Identity()  # for diagnostics.
-        self.identity2 = Identity()  # for diagnostics.
-        self.identity3 = Identity()  # for diagnostics.
-
-        self.out_proj = ScaledLinear(
-            hidden_channels, channels, bias=True, initial_scale=0.05
-        )
-
-        self.whiten1 = Whiten(
-            num_groups=1,
-            whitening_limit=_whitening_schedule(5.0),
-            prob=(0.025, 0.25),
-            grad_scale=0.01,
-        )
-
-        self.whiten2 = Whiten(
-            num_groups=1,
-            whitening_limit=_whitening_schedule(5.0, ratio=3.0),
-            prob=(0.025, 0.25),
-            grad_scale=0.01,
-        )
-
-    def forward(
-        self,
-        x: Tensor,
-        attn_weights: Tensor,
-    ) -> Tensor:
-        """.
-        Args:
-            x: a Tensor of shape (seq_len, batch_size, num_channels)
-            attn_weights: a Tensor of shape (num_heads, batch_size, seq_len, seq_len)
-        Returns:
-            a Tensor with the same shape as x
-        """
-        x = self.in_proj(x)
-
-        (seq_len, batch_size, _) = x.shape
-        hidden_channels = self.hidden_channels
-
-        s, x, y = x.chunk(3, dim=2)
-
-        # s will go through tanh.
-
-        s = self.balancer(s)
-        s = self.tanh(s)
-
-        s = s.unsqueeze(-1).reshape(seq_len, batch_size, hidden_channels)
-        x = self.whiten1(x)
-        x = x * s
-        x = self.identity1(x)  # diagnostics only, it's the identity.
-
-        (seq_len, batch_size, embed_dim) = x.shape
-        num_heads = attn_weights.shape[0]
-        assert attn_weights.shape == (num_heads, batch_size, seq_len, seq_len)
-
-        x = x.reshape(seq_len, batch_size, num_heads, -1).permute(2, 1, 0, 3)
-        # now x: (num_heads, batch_size, seq_len, head_dim)
-        x = torch.matmul(attn_weights, x)
-        # now x: (num_heads, batch_size, seq_len, head_dim)
-        x = x.permute(2, 1, 0, 3).reshape(seq_len, batch_size, -1)
-
-        y = self.identity2(y)
-        x = x * y
-        x = self.identity3(x)
-
-        x = self.out_proj(x)
-        x = self.whiten2(x)
-        return x
-
-
-class ConvolutionModule(nn.Module):
-    """ConvolutionModule in Zipformer2 model.
-    Modified from https://github.com/espnet/espnet/blob/master/espnet/nets/pytorch_backend/zipformer/convolution.py
-
-    Args:
-        channels (int): The number of channels of conv layers.
-        kernel_size (int): Kernerl size of conv layers.
-        bias (bool): Whether to use bias in conv layers (default=True).
-
-    """
-
-    def __init__(
-        self,
-        channels: int,
-        kernel_size: int,
-    ) -> None:
-        """Construct a ConvolutionModule object."""
-        super(ConvolutionModule, self).__init__()
-        # kernerl_size should be a odd number for 'SAME' padding
-        assert (kernel_size - 1) % 2 == 0
-
-        bottleneck_dim = channels
-
-        self.in_proj = nn.Linear(
-            channels,
-            2 * bottleneck_dim,
-        )
-        # the gradients on in_proj are a little noisy, likely to do with the
-        # sigmoid in glu.
-
-        # after in_proj we put x through a gated linear unit (nn.functional.glu).
-        # For most layers the normal rms value of channels of x seems to be in the range 1 to 4,
-        # but sometimes, for some reason, for layer 0 the rms ends up being very large,
-        # between 50 and 100 for different channels.  This will cause very peaky and
-        # sparse derivatives for the sigmoid gating function, which will tend to make
-        # the loss function not learn effectively.  (for most layers the average absolute values
-        # are in the range 0.5..9.0, and the average p(x>0), i.e. positive proportion,
-        # at the output of pointwise_conv1.output is around 0.35 to 0.45 for different
-        # layers, which likely breaks down as 0.5 for the "linear" half and
-        # 0.2 to 0.3 for the part that goes into the sigmoid.  The idea is that if we
-        # constrain the rms values to a reasonable range via a constraint of max_abs=10.0,
-        # it will be in a better position to start learning something, i.e. to latch onto
-        # the correct range.
-        self.balancer1 = Balancer(
-            bottleneck_dim,
-            channel_dim=-1,
-            min_positive=ScheduledFloat((0.0, 0.05), (8000.0, 0.025)),
-            max_positive=1.0,
-            min_abs=1.5,
-            max_abs=ScheduledFloat((0.0, 5.0), (8000.0, 10.0), default=1.0),
-        )
-
-        self.activation1 = Identity()  # for diagnostics
-
-        self.sigmoid = nn.Sigmoid()
-
-        self.activation2 = Identity()  # for diagnostics
-
-        assert kernel_size % 2 == 1
-
-        self.depthwise_conv = nn.Conv1d(
-            in_channels=bottleneck_dim,
-            out_channels=bottleneck_dim,
-            groups=bottleneck_dim,
-            kernel_size=kernel_size,
-            padding=kernel_size // 2,
-        )
-
-        self.balancer2 = Balancer(
-            bottleneck_dim,
-            channel_dim=1,
-            min_positive=ScheduledFloat((0.0, 0.1), (8000.0, 0.05)),
-            max_positive=1.0,
-            min_abs=ScheduledFloat((0.0, 0.2), (20000.0, 0.5)),
-            max_abs=10.0,
-        )
-
-        self.whiten = Whiten(
-            num_groups=1,
-            whitening_limit=_whitening_schedule(7.5),
-            prob=(0.025, 0.25),
-            grad_scale=0.01,
-        )
-
-        self.out_proj = ActivationDropoutAndLinear(
-            bottleneck_dim,
-            channels,
-            activation="SwooshR",
-            dropout_p=0.0,
-            initial_scale=0.05,
-        )
-
-    def forward(
-        self,
-        x: Tensor,
-        src_key_padding_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """Compute convolution module.
-
-        Args:
-            x: Input tensor (#time, batch, channels).
-           src_key_padding_mask: the mask for the src keys per batch (optional):
-               (batch, #time), contains True in masked positions.
-
-        Returns:
-            Tensor: Output tensor (#time, batch, channels).
-
-        """
-
-        x = self.in_proj(x)  # (time, batch, 2*channels)
-
-        x, s = x.chunk(2, dim=2)
-        s = self.balancer1(s)
-        s = self.sigmoid(s)
-        x = self.activation1(x)  # identity.
-        x = x * s
-        x = self.activation2(x)  # identity
-
-        # (time, batch, channels)
-
-        # exchange the temporal dimension and the feature dimension
-        x = x.permute(1, 2, 0)  # (#batch, channels, time).
-
-        if src_key_padding_mask is not None:
-            x = x.masked_fill(src_key_padding_mask.unsqueeze(1).expand_as(x), 0.0)
-
-        x = self.depthwise_conv(x)
-
-        x = self.balancer2(x)
-        x = x.permute(2, 0, 1)  # (time, batch, channels)
-
-        x = self.whiten(x)  # (time, batch, channels)
-        x = self.out_proj(x)  # (time, batch, channels)
-
-        return x
diff --git a/egs/zipvoice/zipvoice/zipvoice_infer.py b/egs/zipvoice/zipvoice/zipvoice_infer.py
deleted file mode 100644
index 16cf8e039..000000000
--- a/egs/zipvoice/zipvoice/zipvoice_infer.py
+++ /dev/null
@@ -1,645 +0,0 @@
-#!/usr/bin/env python3
-# Copyright         2025  Xiaomi Corp.        (authors: Han Zhu)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-This script generates speech with our pre-trained ZipVoice or
-    ZipVoice-Distill models. Required models will be automatically
-    downloaded from HuggingFace.
-
-Usage:
-
-Note: If you having trouble connecting to HuggingFace,
-    try switching endpoint to mirror site:
-
-export HF_ENDPOINT=https://hf-mirror.com
-
-(1) Inference of a single sentence:
-
-python3 zipvoice/zipvoice_infer.py \
-    --model-name "zipvoice_distill" \
-    --prompt-wav prompt.wav \
-    --prompt-text "I am a prompt." \
-    --text "I am a sentence." \
-    --res-wav-path result.wav
-
-(2) Inference of a list of sentences:
-python3 zipvoice/zipvoice_infer.py \
-    --model-name "zipvoice-distill" \
-    --test-list test.tsv \
-    --res-dir results
-
-`--model-name` can be `zipvoice` or `zipvoice_distill`,
-    which are the models before and after distillation, respectively.
-
-Each line of `test.tsv` is in the format of
-    `{wav_name}\t{prompt_transcription}\t{prompt_wav}\t{text}`.
-"""
-
-import argparse
-import datetime as dt
-import os
-
-import numpy as np
-import safetensors.torch
-import torch
-import torch.nn as nn
-import torchaudio
-from feature import TorchAudioFbank, TorchAudioFbankConfig
-from huggingface_hub import hf_hub_download
-from lhotse.utils import fix_random_seed
-from model import get_distill_model, get_model
-from tokenizer import TokenizerEmilia
-from utils import AttributeDict
-from vocos import Vocos
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--model-name",
-        type=str,
-        default="zipvoice_distill",
-        choices=["zipvoice", "zipvoice_distill"],
-        help="The model used for inference",
-    )
-
-    parser.add_argument(
-        "--test-list",
-        type=str,
-        default=None,
-        help="The list of prompt speech, prompt_transcription, "
-        "and text to synthesizein the format of "
-        "'{wav_name}\t{prompt_transcription}\t{prompt_wav}\t{text}'.",
-    )
-
-    parser.add_argument(
-        "--prompt-wav",
-        type=str,
-        default=None,
-        help="The prompt wav to mimic",
-    )
-
-    parser.add_argument(
-        "--prompt-text",
-        type=str,
-        default=None,
-        help="The transcription of the prompt wav",
-    )
-
-    parser.add_argument(
-        "--text",
-        type=str,
-        default=None,
-        help="The text to synthesize",
-    )
-
-    parser.add_argument(
-        "--res-dir",
-        type=str,
-        default="results",
-        help="""
-        Path name of the generated wavs dir,
-        used when test-list is not None
-        """,
-    )
-
-    parser.add_argument(
-        "--res-wav-path",
-        type=str,
-        default="result.wav",
-        help="""
-        Path name of the generated wav path,
-        used when test-list is None
-        """,
-    )
-
-    parser.add_argument(
-        "--guidance-scale",
-        type=float,
-        default=None,
-        help="The scale of classifier-free guidance during inference.",
-    )
-
-    parser.add_argument(
-        "--num-step",
-        type=int,
-        default=None,
-        help="The number of sampling steps.",
-    )
-
-    parser.add_argument(
-        "--feat-scale",
-        type=float,
-        default=0.1,
-        help="The scale factor of fbank feature",
-    )
-
-    parser.add_argument(
-        "--speed",
-        type=float,
-        default=1.0,
-        help="Control speech speed, 1.0 means normal, >1.0 means speed up",
-    )
-
-    parser.add_argument(
-        "--t-shift",
-        type=float,
-        default=0.5,
-        help="Shift t to smaller ones if t_shift < 1.0",
-    )
-
-    parser.add_argument(
-        "--target-rms",
-        type=float,
-        default=0.1,
-        help="Target speech normalization rms value",
-    )
-
-    parser.add_argument(
-        "--seed",
-        type=int,
-        default=666,
-        help="Random seed",
-    )
-
-    add_model_arguments(parser)
-
-    return parser
-
-
-def add_model_arguments(parser: argparse.ArgumentParser):
-    parser.add_argument(
-        "--fm-decoder-downsampling-factor",
-        type=str,
-        default="1,2,4,2,1",
-        help="Downsampling factor for each stack of encoder layers.",
-    )
-
-    parser.add_argument(
-        "--fm-decoder-num-layers",
-        type=str,
-        default="2,2,4,4,4",
-        help="Number of zipformer encoder layers per stack, comma separated.",
-    )
-
-    parser.add_argument(
-        "--fm-decoder-cnn-module-kernel",
-        type=str,
-        default="31,15,7,15,31",
-        help="Sizes of convolutional kernels in convolution modules "
-        "in each encoder stack: a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--fm-decoder-feedforward-dim",
-        type=int,
-        default=1536,
-        help="Feedforward dimension of the zipformer encoder layers, "
-        "per stack, comma separated.",
-    )
-
-    parser.add_argument(
-        "--fm-decoder-num-heads",
-        type=int,
-        default=4,
-        help="Number of attention heads in the zipformer encoder layers: "
-        "a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--fm-decoder-dim",
-        type=int,
-        default=512,
-        help="Embedding dimension in encoder stacks: a single int "
-        "or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--text-encoder-downsampling-factor",
-        type=str,
-        default="1",
-        help="Downsampling factor for each stack of encoder layers.",
-    )
-
-    parser.add_argument(
-        "--text-encoder-num-layers",
-        type=str,
-        default="4",
-        help="Number of zipformer encoder layers per stack, comma separated.",
-    )
-
-    parser.add_argument(
-        "--text-encoder-feedforward-dim",
-        type=int,
-        default=512,
-        help="Feedforward dimension of the zipformer encoder layers, "
-        "per stack, comma separated.",
-    )
-
-    parser.add_argument(
-        "--text-encoder-cnn-module-kernel",
-        type=str,
-        default="9",
-        help="Sizes of convolutional kernels in convolution modules in "
-        "each encoder stack: a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--text-encoder-num-heads",
-        type=int,
-        default=4,
-        help="Number of attention heads in the zipformer encoder layers: "
-        "a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--text-encoder-dim",
-        type=int,
-        default=192,
-        help="Embedding dimension in encoder stacks: "
-        "a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--query-head-dim",
-        type=int,
-        default=32,
-        help="Query/key dimension per head in encoder stacks: "
-        "a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--value-head-dim",
-        type=int,
-        default=12,
-        help="Value dimension per head in encoder stacks: "
-        "a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--pos-head-dim",
-        type=int,
-        default=4,
-        help="Positional-encoding dimension per head in encoder stacks: "
-        "a single int or comma-separated list.",
-    )
-
-    parser.add_argument(
-        "--pos-dim",
-        type=int,
-        default=48,
-        help="Positional-encoding embedding dimension",
-    )
-
-    parser.add_argument(
-        "--time-embed-dim",
-        type=int,
-        default=192,
-        help="Embedding dimension of timestamps embedding.",
-    )
-
-    parser.add_argument(
-        "--text-embed-dim",
-        type=int,
-        default=192,
-        help="Embedding dimension of text embedding.",
-    )
-
-
-def get_params() -> AttributeDict:
-    params = AttributeDict(
-        {
-            "sampling_rate": 24000,
-            "frame_shift_ms": 256 / 24000 * 1000,
-            "feat_dim": 100,
-        }
-    )
-
-    return params
-
-
-def get_vocoder():
-    vocoder = Vocos.from_pretrained("charactr/vocos-mel-24khz")
-    return vocoder
-
-
-def generate_sentence(
-    save_path: str,
-    prompt_text: str,
-    prompt_wav: str,
-    text: str,
-    model: nn.Module,
-    vocoder: nn.Module,
-    tokenizer: TokenizerEmilia,
-    feature_extractor: TorchAudioFbank,
-    device: torch.device,
-    num_step: int = 16,
-    guidance_scale: float = 1.0,
-    speed: float = 1.0,
-    t_shift: float = 0.5,
-    target_rms: float = 0.1,
-    feat_scale: float = 0.1,
-    sampling_rate: int = 24000,
-):
-    """
-    Generate waveform of a text based on a given prompt
-        waveform and its transcription.
-
-    Args:
-        save_path (str): Path to save the generated wav.
-        prompt_text (str): Transcription of the prompt wav.
-        prompt_wav (str): Path to the prompt wav file.
-        text (str): Text to be synthesized into a waveform.
-        model (nn.Module): The model used for generation.
-        vocoder (nn.Module): The vocoder used to convert features to waveforms.
-        tokenizer (TokenizerEmilia): The tokenizer used to convert text to tokens.
-        feature_extractor (TorchAudioFbank): The feature extractor used to
-            extract acoustic features.
-        device (torch.device): The device on which computations are performed.
-        num_step (int, optional): Number of steps for decoding. Defaults to 16.
-        guidance_scale (float, optional): Scale for classifier-free guidance.
-            Defaults to 1.0.
-        speed (float, optional): Speed control. Defaults to 1.0.
-        t_shift (float, optional): Time shift. Defaults to 0.5.
-        target_rms (float, optional): Target RMS for waveform normalization.
-            Defaults to 0.1.
-        feat_scale (float, optional): Scale for features.
-            Defaults to 0.1.
-        sampling_rate (int, optional): Sampling rate for the waveform.
-            Defaults to 24000.
-    Returns:
-        metrics (dict): Dictionary containing time and real-time
-            factor metrics for processing.
-    """
-    # Convert text to tokens
-    tokens = tokenizer.texts_to_token_ids([text])
-    prompt_tokens = tokenizer.texts_to_token_ids([prompt_text])
-
-    # Load and preprocess prompt wav
-    prompt_wav, prompt_sampling_rate = torchaudio.load(prompt_wav)
-    prompt_rms = torch.sqrt(torch.mean(torch.square(prompt_wav)))
-    if prompt_rms < target_rms:
-        prompt_wav = prompt_wav * target_rms / prompt_rms
-
-    if prompt_sampling_rate != sampling_rate:
-        resampler = torchaudio.transforms.Resample(
-            orig_freq=prompt_sampling_rate, new_freq=sampling_rate
-        )
-        prompt_wav = resampler(prompt_wav)
-
-    # Extract features from prompt wav
-    prompt_features = feature_extractor.extract(
-        prompt_wav, sampling_rate=sampling_rate
-    ).to(device)
-    prompt_features = prompt_features.unsqueeze(0) * feat_scale
-    prompt_features_lens = torch.tensor([prompt_features.size(1)], device=device)
-
-    # Start timing
-    start_t = dt.datetime.now()
-
-    # Generate features
-    (
-        pred_features,
-        pred_features_lens,
-        pred_prompt_features,
-        pred_prompt_features_lens,
-    ) = model.sample(
-        tokens=tokens,
-        prompt_tokens=prompt_tokens,
-        prompt_features=prompt_features,
-        prompt_features_lens=prompt_features_lens,
-        speed=speed,
-        t_shift=t_shift,
-        duration="predict",
-        num_step=num_step,
-        guidance_scale=guidance_scale,
-    )
-
-    # Postprocess predicted features
-    pred_features = pred_features.permute(0, 2, 1) / feat_scale  # (B, C, T)
-
-    # Start vocoder processing
-    start_vocoder_t = dt.datetime.now()
-    wav = vocoder.decode(pred_features).squeeze(1).clamp(-1, 1)
-
-    # Calculate processing times and real-time factors
-    t = (dt.datetime.now() - start_t).total_seconds()
-    t_no_vocoder = (start_vocoder_t - start_t).total_seconds()
-    t_vocoder = (dt.datetime.now() - start_vocoder_t).total_seconds()
-    wav_seconds = wav.shape[-1] / sampling_rate
-    rtf = t / wav_seconds
-    rtf_no_vocoder = t_no_vocoder / wav_seconds
-    rtf_vocoder = t_vocoder / wav_seconds
-    metrics = {
-        "t": t,
-        "t_no_vocoder": t_no_vocoder,
-        "t_vocoder": t_vocoder,
-        "wav_seconds": wav_seconds,
-        "rtf": rtf,
-        "rtf_no_vocoder": rtf_no_vocoder,
-        "rtf_vocoder": rtf_vocoder,
-    }
-
-    # Adjust wav volume if necessary
-    if prompt_rms < target_rms:
-        wav = wav * prompt_rms / target_rms
-    torchaudio.save(save_path, wav.cpu(), sample_rate=sampling_rate)
-
-    return metrics
-
-
-def generate(
-    res_dir: str,
-    test_list: str,
-    model: nn.Module,
-    vocoder: nn.Module,
-    tokenizer: TokenizerEmilia,
-    feature_extractor: TorchAudioFbank,
-    device: torch.device,
-    num_step: int = 16,
-    guidance_scale: float = 1.0,
-    speed: float = 1.0,
-    t_shift: float = 0.5,
-    target_rms: float = 0.1,
-    feat_scale: float = 0.1,
-    sampling_rate: int = 24000,
-):
-    total_t = []
-    total_t_no_vocoder = []
-    total_t_vocoder = []
-    total_wav_seconds = []
-
-    with open(test_list, "r") as fr:
-        lines = fr.readlines()
-
-    for i, line in enumerate(lines):
-        wav_name, prompt_text, prompt_wav, text = line.strip().split("\t")
-        save_path = f"{res_dir}/{wav_name}.wav"
-        metrics = generate_sentence(
-            save_path=save_path,
-            prompt_text=prompt_text,
-            prompt_wav=prompt_wav,
-            text=text,
-            model=model,
-            vocoder=vocoder,
-            tokenizer=tokenizer,
-            feature_extractor=feature_extractor,
-            device=device,
-            num_step=num_step,
-            guidance_scale=guidance_scale,
-            speed=speed,
-            t_shift=t_shift,
-            target_rms=target_rms,
-            feat_scale=feat_scale,
-            sampling_rate=sampling_rate,
-        )
-        print(f"[Sentence: {i}] RTF: {metrics['rtf']:.4f}")
-        total_t.append(metrics["t"])
-        total_t_no_vocoder.append(metrics["t_no_vocoder"])
-        total_t_vocoder.append(metrics["t_vocoder"])
-        total_wav_seconds.append(metrics["wav_seconds"])
-
-    print(f"Average RTF: {np.sum(total_t)/np.sum(total_wav_seconds):.4f}")
-    print(
-        f"Average RTF w/o vocoder: "
-        f"{np.sum(total_t_no_vocoder)/np.sum(total_wav_seconds):.4f}"
-    )
-    print(
-        f"Average RTF vocoder: "
-        f"{np.sum(total_t_vocoder)/np.sum(total_wav_seconds):.4f}"
-    )
-
-
-@torch.inference_mode()
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-
-    params = get_params()
-    params.update(vars(args))
-
-    model_defaults = {
-        "zipvoice": {
-            "num_step": 16,
-            "guidance_scale": 1.0,
-        },
-        "zipvoice_distill": {
-            "num_step": 8,
-            "guidance_scale": 3.0,
-        },
-    }
-
-    model_specific_defaults = model_defaults.get(params.model_name, {})
-
-    for param, value in model_specific_defaults.items():
-        if getattr(params, param) == parser.get_default(param):
-            setattr(params, param, value)
-            print(f"Setting {param} to default value: {value}")
-
-    assert (params.test_list is not None) ^ (
-        (params.prompt_wav and params.prompt_text and params.text) is not None
-    ), (
-        "For inference, please provide prompts and text with either '--test-list'"
-        " or '--prompt-wav, --prompt-text and --text'."
-    )
-
-    if torch.cuda.is_available():
-        params.device = torch.device("cuda", 0)
-    else:
-        params.device = torch.device("cpu")
-
-    token_file = hf_hub_download("zhu-han/ZipVoice", filename="tokens_emilia.txt")
-
-    tokenizer = TokenizerEmilia(token_file)
-
-    params.vocab_size = tokenizer.vocab_size
-    params.pad_id = tokenizer.pad_id
-    fix_random_seed(params.seed)
-
-    if params.model_name == "zipvoice_distill":
-        model = get_distill_model(params)
-        model_ckpt = hf_hub_download(
-            "zhu-han/ZipVoice", filename="exp_zipvoice_distill/model.safetensors"
-        )
-    else:
-        model = get_model(params)
-        model_ckpt = hf_hub_download(
-            "zhu-han/ZipVoice", filename="exp_zipvoice/model.safetensors"
-        )
-
-    safetensors.torch.load_model(model, model_ckpt)
-
-    model = model.to(params.device)
-    model.eval()
-
-    vocoder = get_vocoder()
-    vocoder = vocoder.to(params.device)
-    vocoder.eval()
-
-    config = TorchAudioFbankConfig(
-        sampling_rate=params.sampling_rate,
-        n_mels=100,
-        n_fft=1024,
-        hop_length=256,
-    )
-    feature_extractor = TorchAudioFbank(config)
-
-    if params.test_list:
-        os.makedirs(params.res_dir, exist_ok=True)
-        generate(
-            res_dir=params.res_dir,
-            test_list=params.test_list,
-            model=model,
-            vocoder=vocoder,
-            tokenizer=tokenizer,
-            feature_extractor=feature_extractor,
-            device=params.device,
-            num_step=params.num_step,
-            guidance_scale=params.guidance_scale,
-            speed=params.speed,
-            t_shift=params.t_shift,
-            target_rms=params.target_rms,
-            feat_scale=params.feat_scale,
-            sampling_rate=params.sampling_rate,
-        )
-    else:
-        generate_sentence(
-            save_path=params.res_wav_path,
-            prompt_text=params.prompt_text,
-            prompt_wav=params.prompt_wav,
-            text=params.text,
-            model=model,
-            vocoder=vocoder,
-            tokenizer=tokenizer,
-            feature_extractor=feature_extractor,
-            device=params.device,
-            num_step=params.num_step,
-            guidance_scale=params.guidance_scale,
-            speed=params.speed,
-            t_shift=params.t_shift,
-            target_rms=params.target_rms,
-            feat_scale=params.feat_scale,
-            sampling_rate=params.sampling_rate,
-        )
-    print("Done")
-
-
-if __name__ == "__main__":
-    main()

From abd9437e6d5419a497707748eb935e50976c3b7b Mon Sep 17 00:00:00 2001
From: Fangjun Kuang <csukuangfj@gmail.com>
Date: Tue, 24 Jun 2025 14:49:16 +0800
Subject: [PATCH 20/22] Add more wheels for piper-phonemize (#1969)

---
 .../scripts/generate-piper-phonemize-page.py  | 67 ++++++++++++++++++-
 .github/workflows/build-doc.yml               |  4 +-
 2 files changed, 66 insertions(+), 5 deletions(-)

diff --git a/.github/scripts/generate-piper-phonemize-page.py b/.github/scripts/generate-piper-phonemize-page.py
index 3784d5fa5..e268acf03 100755
--- a/.github/scripts/generate-piper-phonemize-page.py
+++ b/.github/scripts/generate-piper-phonemize-page.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 
 
-def main():
+def get_v1_2_0_files():
     prefix = (
         "https://github.com/csukuangfj/piper-phonemize/releases/download/2023.12.5/"
     )
@@ -19,9 +19,70 @@ def main():
         "piper_phonemize-1.2.0-cp39-cp39-macosx_10_14_x86_64.whl",
         "piper_phonemize-1.2.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
     ]
+    ans = [prefix + f for f in files]
+    ans.sort()
+    return ans
+
+
+def get_v1_3_0_files():
+    prefix = (
+        "https://github.com/csukuangfj/piper-phonemize/releases/download/2025.06.23/"
+    )
+    files = [
+        "piper_phonemize-1.3.0-cp310-cp310-macosx_10_9_universal2.whl",
+        "piper_phonemize-1.3.0-cp310-cp310-macosx_10_9_x86_64.whl",
+        "piper_phonemize-1.3.0-cp310-cp310-macosx_11_0_arm64.whl",
+        "piper_phonemize-1.3.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
+        "piper_phonemize-1.3.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl",
+        "piper_phonemize-1.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
+        "piper_phonemize-1.3.0-cp310-cp310-win_amd64.whl",
+        "piper_phonemize-1.3.0-cp311-cp311-macosx_10_9_universal2.whl",
+        "piper_phonemize-1.3.0-cp311-cp311-macosx_10_9_x86_64.whl",
+        "piper_phonemize-1.3.0-cp311-cp311-macosx_11_0_arm64.whl",
+        "piper_phonemize-1.3.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
+        "piper_phonemize-1.3.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl",
+        "piper_phonemize-1.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
+        "piper_phonemize-1.3.0-cp311-cp311-win_amd64.whl",
+        "piper_phonemize-1.3.0-cp312-cp312-macosx_10_13_universal2.whl",
+        "piper_phonemize-1.3.0-cp312-cp312-macosx_10_13_x86_64.whl",
+        "piper_phonemize-1.3.0-cp312-cp312-macosx_11_0_arm64.whl",
+        "piper_phonemize-1.3.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
+        "piper_phonemize-1.3.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl",
+        "piper_phonemize-1.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
+        "piper_phonemize-1.3.0-cp312-cp312-win_amd64.whl",
+        "piper_phonemize-1.3.0-cp313-cp313-macosx_10_13_universal2.whl",
+        "piper_phonemize-1.3.0-cp313-cp313-macosx_10_13_x86_64.whl",
+        "piper_phonemize-1.3.0-cp313-cp313-macosx_11_0_arm64.whl",
+        "piper_phonemize-1.3.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
+        "piper_phonemize-1.3.0-cp313-cp313-manylinux_2_17_i686.manylinux2014_i686.whl",
+        "piper_phonemize-1.3.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
+        "piper_phonemize-1.3.0-cp313-cp313-win_amd64.whl",
+        "piper_phonemize-1.3.0-cp38-cp38-macosx_10_9_universal2.whl",
+        "piper_phonemize-1.3.0-cp38-cp38-macosx_10_9_x86_64.whl",
+        "piper_phonemize-1.3.0-cp38-cp38-macosx_11_0_arm64.whl",
+        "piper_phonemize-1.3.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
+        "piper_phonemize-1.3.0-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl",
+        "piper_phonemize-1.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
+        "piper_phonemize-1.3.0-cp38-cp38-win_amd64.whl",
+        "piper_phonemize-1.3.0-cp39-cp39-macosx_10_9_universal2.whl",
+        "piper_phonemize-1.3.0-cp39-cp39-macosx_10_9_x86_64.whl",
+        "piper_phonemize-1.3.0-cp39-cp39-macosx_11_0_arm64.whl",
+        "piper_phonemize-1.3.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl",
+        "piper_phonemize-1.3.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl",
+        "piper_phonemize-1.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl",
+        "piper_phonemize-1.3.0-cp39-cp39-win_amd64.whl",
+    ]
+    ans = [prefix + f for f in files]
+    ans.sort()
+    return ans
+
+
+def main():
+    files = get_v1_3_0_files() + get_v1_2_0_files()
+
     with open("piper_phonemize.html", "w") as f:
-        for file in files:
-            url = prefix + file
+        for url in files:
+            file = url.split("/")[-1]
             f.write(f'<a href="{url}">{file}</a><br/>\n')
 
 
diff --git a/.github/workflows/build-doc.yml b/.github/workflows/build-doc.yml
index ca96e6de5..bc6d9b6ac 100644
--- a/.github/workflows/build-doc.yml
+++ b/.github/workflows/build-doc.yml
@@ -34,7 +34,7 @@ concurrency:
 
 jobs:
   build-doc:
-    if: github.event.label.name == 'doc' || github.event_name == 'push'
+    # if: github.event.label.name == 'doc' || github.event_name == 'push'
     runs-on: ${{ matrix.os }}
     strategy:
       fail-fast: false
@@ -43,7 +43,7 @@ jobs:
         python-version: ["3.8"]
     steps:
       # refer to https://github.com/actions/checkout
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
         with:
           fetch-depth: 0
 

From 71377d21cd93a38cda89e6a123e4f579ca5094ba Mon Sep 17 00:00:00 2001
From: Fangjun Kuang <csukuangfj@gmail.com>
Date: Mon, 30 Jun 2025 19:01:15 +0800
Subject: [PATCH 21/22] Export streaming zipformer models with whisper feature
 to onnx (#1973)

---
 .github/scripts/docker/Dockerfile             |   4 +-
 .../scripts/docker/generate_build_matrix.py   |  21 +-
 .github/scripts/multi-zh-hans.sh              | 200 -----
 .github/scripts/multi_zh-hans/ASR/run.sh      | 756 ++++++++++++++++++
 .github/workflows/multi-zh-hans.yml           |  85 +-
 .../ASR/zipformer/export-onnx-ctc.py          |  24 +-
 .../zipformer/export-onnx-streaming-ctc.py    |  97 ++-
 .../ASR/zipformer/export-onnx-streaming.py    |  97 ++-
 egs/librispeech/ASR/zipformer/export-onnx.py  |  22 +-
 9 files changed, 1017 insertions(+), 289 deletions(-)
 delete mode 100755 .github/scripts/multi-zh-hans.sh
 create mode 100755 .github/scripts/multi_zh-hans/ASR/run.sh

diff --git a/.github/scripts/docker/Dockerfile b/.github/scripts/docker/Dockerfile
index cf0523401..1b6d0026f 100644
--- a/.github/scripts/docker/Dockerfile
+++ b/.github/scripts/docker/Dockerfile
@@ -55,9 +55,9 @@ RUN pip install --no-cache-dir \
       "numpy<2.0" \
       onnxoptimizer \
       onnxsim \
-      onnx \
+      onnx==1.17.0 \
       onnxmltools \
-      onnxruntime \
+      onnxruntime==1.17.1 \
       piper_phonemize -f https://k2-fsa.github.io/icefall/piper_phonemize.html \
       pypinyin==0.50.0 \
       pytest \
diff --git a/.github/scripts/docker/generate_build_matrix.py b/.github/scripts/docker/generate_build_matrix.py
index 638e19498..7f36e278d 100755
--- a/.github/scripts/docker/generate_build_matrix.py
+++ b/.github/scripts/docker/generate_build_matrix.py
@@ -63,23 +63,24 @@ def get_torchaudio_version(torch_version):
 
 
 def get_matrix(min_torch_version, specified_torch_version, specified_python_version):
-    k2_version = "1.24.4.dev20241029"
-    kaldifeat_version = "1.25.5.dev20241029"
-    version = "20241218"
+    k2_version = "1.24.4.dev20250630"
+    kaldifeat_version = "1.25.5.dev20250630"
+    version = "20250630"
 
     # torchaudio 2.5.0 does not support python 3.13
-    python_version = ["3.8", "3.9", "3.10", "3.11", "3.12"]
+    python_version = ["3.8", "3.9", "3.10", "3.11", "3.12", "3.13"]
     torch_version = []
     torch_version += ["1.13.0", "1.13.1"]
     torch_version += ["2.0.0", "2.0.1"]
-    #  torch_version += ["2.1.0", "2.1.1", "2.1.2"]
-    #  torch_version += ["2.2.0", "2.2.1", "2.2.2"]
+    torch_version += ["2.1.0", "2.1.1", "2.1.2"]
+    torch_version += ["2.2.0", "2.2.1", "2.2.2"]
     # Test only torch >= 2.3.0
     torch_version += ["2.3.0", "2.3.1"]
     torch_version += ["2.4.0"]
     torch_version += ["2.4.1"]
     torch_version += ["2.5.0"]
     torch_version += ["2.5.1"]
+    torch_version += ["2.6.0", "2.7.0", "2.7.1"]
 
     if specified_torch_version:
         torch_version = [specified_torch_version]
@@ -109,12 +110,8 @@ def get_matrix(min_torch_version, specified_torch_version, specified_python_vers
                 # torch>=2.5 requires python 3.10
                 continue
 
-            if t == "2.5.1":
-                k2_version_2 = "1.24.4.dev20241122"
-                kaldifeat_version_2 = "1.25.5.dev20241126"
-            else:
-                k2_version_2 = k2_version
-                kaldifeat_version_2 = kaldifeat_version
+            k2_version_2 = k2_version
+            kaldifeat_version_2 = kaldifeat_version
 
             matrix.append(
                 {
diff --git a/.github/scripts/multi-zh-hans.sh b/.github/scripts/multi-zh-hans.sh
deleted file mode 100755
index e254419ff..000000000
--- a/.github/scripts/multi-zh-hans.sh
+++ /dev/null
@@ -1,200 +0,0 @@
-#!/usr/bin/env bash
-
-set -ex
-
-git config --global user.name "k2-fsa"
-git config --global user.email "csukuangfj@gmail.com"
-git config --global lfs.allowincompletepush true
-
-log() {
-  # This function is from espnet
-  local fname=${BASH_SOURCE[1]##*/}
-  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
-}
-
-log "pwd: $PWD"
-
-cd egs/multi_zh-hans/ASR
-
-repo_url=https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-2023-9-2
-log "Downloading pre-trained model from $repo_url"
-GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
-repo=$(basename $repo_url)
-pushd $repo
-cd exp
-git lfs pull --include pretrained.pt
-ln -s pretrained.pt epoch-99.pt
-cd ../data/lang_bpe_2000
-ls -lh
-git lfs pull --include L.pt L_disambig.pt Linv.pt bpe.model
-git lfs pull --include "*.model"
-ls -lh
-popd
-
-log "--------------------------------------------"
-log "Export non-streaming ONNX transducer models "
-log "--------------------------------------------"
-./zipformer/export-onnx.py \
-  --tokens $repo/data/lang_bpe_2000/tokens.txt \
-  --use-averaged-model 0 \
-  --epoch 99 \
-  --avg 1 \
-  --exp-dir $repo/exp \
-  --causal False
-
-ls -lh $repo/exp
-
-./zipformer/onnx_pretrained.py \
-  --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
-  --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
-  --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
-  --tokens $repo/data/lang_bpe_2000/tokens.txt \
-  $repo/test_wavs/DEV_T0000000000.wav \
-  $repo/test_wavs/DEV_T0000000001.wav \
-  $repo/test_wavs/DEV_T0000000002.wav \
-  $repo/test_wavs/TEST_MEETING_T0000000113.wav \
-  $repo/test_wavs/TEST_MEETING_T0000000219.wav \
-  $repo/test_wavs/TEST_MEETING_T0000000351.wav
-
-rm -rf $repo
-
-repo_url=https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-ctc-streaming-2023-11-05
-log "Downloading pre-trained model from $repo_url"
-GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
-repo=$(basename $repo_url)
-
-pushd $repo
-cd exp/
-git lfs pull --include pretrained.pt
-rm -fv epoch-20.pt
-rm -fv *.onnx
-ln -s pretrained.pt epoch-20.pt
-cd ../data/lang_bpe_2000
-ls -lh
-git lfs pull --include L.pt L_disambig.pt Linv.pt bpe.model
-git lfs pull --include "*.model"
-ls -lh
-popd
-
-log "----------------------------------------"
-log "Export streaming ONNX CTC models "
-log "----------------------------------------"
-./zipformer/export-onnx-streaming-ctc.py \
-  --exp-dir $repo/exp \
-  --tokens $repo/data/lang_bpe_2000/tokens.txt \
-  --causal 1 \
-  --avg 1 \
-  --epoch 20 \
-  --use-averaged-model 0 \
-  --chunk-size 16 \
-  --left-context-frames 128 \
-  --use-ctc 1
-
-ls -lh $repo/exp/
-
-log "------------------------------------------------------------"
-log "Test exported streaming ONNX CTC models (greedy search)     "
-log "------------------------------------------------------------"
-
-test_wavs=(
-DEV_T0000000000.wav
-DEV_T0000000001.wav
-DEV_T0000000002.wav
-TEST_MEETING_T0000000113.wav
-TEST_MEETING_T0000000219.wav
-TEST_MEETING_T0000000351.wav
-)
-
-for w in ${test_wavs[@]}; do
-  ./zipformer/onnx_pretrained-streaming-ctc.py \
-    --model-filename $repo/exp/ctc-epoch-20-avg-1-chunk-16-left-128.int8.onnx \
-    --tokens $repo/data/lang_bpe_2000/tokens.txt \
-    $repo/test_wavs/$w
-done
-
-log "Upload onnx CTC models to huggingface"
-url=https://huggingface.co/k2-fsa/sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13
-GIT_LFS_SKIP_SMUDGE=1 git clone $url
-dst=$(basename $url)
-cp -v $repo/exp/ctc*.onnx $dst
-cp -v $repo/data/lang_bpe_2000/tokens.txt $dst
-cp -v $repo/data/lang_bpe_2000/bpe.model $dst
-mkdir -p $dst/test_wavs
-cp -v $repo/test_wavs/*.wav $dst/test_wavs
-cd $dst
-git lfs track "*.onnx" "bpe.model"
-ls -lh
-file bpe.model
-git status
-git add .
-git commit -m "upload model" && git push https://k2-fsa:${HF_TOKEN}@huggingface.co/k2-fsa/$dst main || true
-
-log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
-rm -rf .git
-rm -fv .gitattributes
-cd ..
-tar cjfv $dst.tar.bz2 $dst
-ls -lh *.tar.bz2
-mv -v $dst.tar.bz2 ../../../
-
-log "----------------------------------------"
-log "Export streaming ONNX transducer models "
-log "----------------------------------------"
-
-./zipformer/export-onnx-streaming.py \
-  --exp-dir $repo/exp \
-  --tokens $repo/data/lang_bpe_2000/tokens.txt \
-  --causal 1 \
-  --avg 1 \
-  --epoch 20 \
-  --use-averaged-model 0 \
-  --chunk-size 16 \
-  --left-context-frames 128 \
-  --use-ctc 0
-
-ls -lh $repo/exp
-
-log "------------------------------------------------------------"
-log "Test exported streaming ONNX transducer models (Python code)"
-log "------------------------------------------------------------"
-
-log "test fp32"
-./zipformer/onnx_pretrained-streaming.py \
-  --encoder-model-filename $repo/exp/encoder-epoch-20-avg-1-chunk-16-left-128.onnx \
-  --decoder-model-filename $repo/exp/decoder-epoch-20-avg-1-chunk-16-left-128.onnx \
-  --joiner-model-filename $repo/exp/joiner-epoch-20-avg-1-chunk-16-left-128.onnx \
-  --tokens $repo/data/lang_bpe_2000/tokens.txt \
-  $repo/test_wavs/DEV_T0000000000.wav
-
-log "test int8"
-./zipformer/onnx_pretrained-streaming.py \
-  --encoder-model-filename $repo/exp/encoder-epoch-20-avg-1-chunk-16-left-128.int8.onnx \
-  --decoder-model-filename $repo/exp/decoder-epoch-20-avg-1-chunk-16-left-128.onnx \
-  --joiner-model-filename $repo/exp/joiner-epoch-20-avg-1-chunk-16-left-128.int8.onnx \
-  --tokens $repo/data/lang_bpe_2000/tokens.txt \
-  $repo/test_wavs/DEV_T0000000000.wav
-
-log "Upload onnx transducer models to huggingface"
-
-url=https://huggingface.co/k2-fsa/sherpa-onnx-streaming-zipformer-multi-zh-hans-2023-12-12
-GIT_LFS_SKIP_SMUDGE=1 git clone $url
-dst=$(basename $url)
-cp -v $repo/exp/encoder*.onnx $dst
-cp -v $repo/exp/decoder*.onnx $dst
-cp -v $repo/exp/joiner*.onnx $dst
-cp -v $repo/data/lang_bpe_2000/tokens.txt $dst
-cp -v $repo/data/lang_bpe_2000/bpe.model $dst
-mkdir -p $dst/test_wavs
-cp -v $repo/test_wavs/*.wav $dst/test_wavs
-cd $dst
-git lfs track "*.onnx" bpe.model
-git add .
-git commit -m "upload model" && git push https://k2-fsa:${HF_TOKEN}@huggingface.co/k2-fsa/$dst main || true
-
-log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
-rm -rf .git
-rm -fv .gitattributes
-cd ..
-tar cjfv $dst.tar.bz2 $dst
-ls -lh *.tar.bz2
-mv -v $dst.tar.bz2 ../../../
diff --git a/.github/scripts/multi_zh-hans/ASR/run.sh b/.github/scripts/multi_zh-hans/ASR/run.sh
new file mode 100755
index 000000000..345b64cf0
--- /dev/null
+++ b/.github/scripts/multi_zh-hans/ASR/run.sh
@@ -0,0 +1,756 @@
+#!/usr/bin/env bash
+
+set -ex
+
+git config --global user.name "k2-fsa"
+git config --global user.email "csukuangfj@gmail.com"
+git config --global lfs.allowincompletepush true
+
+python3 -m pip install onnxmltools==1.13.0 onnx==1.17.0 onnxruntime==1.17.1 sherpa-onnx
+
+log() {
+  # This function is from espnet
+  local fname=${BASH_SOURCE[1]##*/}
+  echo -e "$(date '+%Y-%m-%d %H:%M:%S') (${fname}:${BASH_LINENO[0]}:${FUNCNAME[1]}) $*"
+}
+
+cd egs/multi_zh-hans/ASR
+
+log "pwd: $PWD"
+
+function run_2023_9_2() {
+  repo_url=https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-2023-9-2
+  log "Downloading pre-trained model from $repo_url"
+  GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+  repo=$(basename $repo_url)
+  pushd $repo
+  cd exp
+  git lfs pull --include pretrained.pt
+  ln -s pretrained.pt epoch-99.pt
+  cd ../data/lang_bpe_2000
+  ls -lh
+  git lfs pull --include L.pt L_disambig.pt Linv.pt bpe.model
+  git lfs pull --include "*.model"
+  ls -lh
+  popd
+
+  log "--------------------------------------------"
+  log "Export non-streaming ONNX transducer models "
+  log "--------------------------------------------"
+  ./zipformer/export-onnx.py \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    --use-averaged-model 0 \
+    --epoch 99 \
+    --avg 1 \
+    --exp-dir $repo/exp \
+    --causal False \
+    --fp16 1
+
+  ls -lh $repo/exp
+
+  ./zipformer/onnx_pretrained.py \
+    --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
+    --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+    --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    $repo/test_wavs/DEV_T0000000000.wav \
+    $repo/test_wavs/DEV_T0000000001.wav \
+    $repo/test_wavs/DEV_T0000000002.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000113.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000219.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000351.wav
+
+  ./zipformer/onnx_pretrained.py \
+    --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.int8.onnx \
+    --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+    --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.int8.onnx \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    $repo/test_wavs/DEV_T0000000000.wav \
+    $repo/test_wavs/DEV_T0000000001.wav \
+    $repo/test_wavs/DEV_T0000000002.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000113.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000219.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000351.wav
+
+  ./zipformer/onnx_pretrained.py \
+    --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.fp16.onnx \
+    --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.fp16.onnx \
+    --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.fp16.onnx \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    $repo/test_wavs/DEV_T0000000000.wav \
+    $repo/test_wavs/DEV_T0000000001.wav \
+    $repo/test_wavs/DEV_T0000000002.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000113.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000219.wav \
+    $repo/test_wavs/TEST_MEETING_T0000000351.wav
+
+  rm -rf $repo
+}
+
+function run_2023_11_05_streaming() {
+  repo_url=https://huggingface.co/zrjin/icefall-asr-multi-zh-hans-zipformer-ctc-streaming-2023-11-05
+  log "Downloading pre-trained model from $repo_url"
+  GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+  repo=$(basename $repo_url)
+
+  pushd $repo
+  cd exp/
+  git lfs pull --include pretrained.pt
+  rm -fv epoch-20.pt
+  rm -fv *.onnx
+  ln -s pretrained.pt epoch-20.pt
+  cd ../data/lang_bpe_2000
+  ls -lh
+  git lfs pull --include L.pt L_disambig.pt Linv.pt bpe.model
+  git lfs pull --include "*.model"
+  ls -lh
+  popd
+
+  log "----------------------------------------"
+  log "Export streaming ONNX CTC models "
+  log "----------------------------------------"
+  ./zipformer/export-onnx-streaming-ctc.py \
+    --exp-dir $repo/exp \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    --causal 1 \
+    --avg 1 \
+    --epoch 20 \
+    --use-averaged-model 0 \
+    --chunk-size 16 \
+    --left-context-frames 128 \
+    --use-ctc 1 \
+    --fp16 1
+
+  ls -lh $repo/exp/
+
+  log "------------------------------------------------------------"
+  log "Test exported streaming ONNX CTC models (greedy search)     "
+  log "------------------------------------------------------------"
+
+  test_wavs=(
+    DEV_T0000000000.wav
+    DEV_T0000000001.wav
+    DEV_T0000000002.wav
+    TEST_MEETING_T0000000113.wav
+    TEST_MEETING_T0000000219.wav
+    TEST_MEETING_T0000000351.wav
+  )
+
+  for w in ${test_wavs[@]}; do
+    log "----fp32----"
+    ./zipformer/onnx_pretrained-streaming-ctc.py \
+      --model-filename $repo/exp/ctc-epoch-20-avg-1-chunk-16-left-128.onnx \
+      --tokens $repo/data/lang_bpe_2000/tokens.txt \
+      $repo/test_wavs/$w
+
+    log "----int8----"
+
+    ./zipformer/onnx_pretrained-streaming-ctc.py \
+      --model-filename $repo/exp/ctc-epoch-20-avg-1-chunk-16-left-128.int8.onnx \
+      --tokens $repo/data/lang_bpe_2000/tokens.txt \
+      $repo/test_wavs/$w
+
+    log "----fp16----"
+
+    ./zipformer/onnx_pretrained-streaming-ctc.py \
+      --model-filename $repo/exp/ctc-epoch-20-avg-1-chunk-16-left-128.fp16.onnx \
+      --tokens $repo/data/lang_bpe_2000/tokens.txt \
+      $repo/test_wavs/$w
+  done
+
+  log "Upload onnx CTC models to huggingface"
+  name=(
+    sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13
+    sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-int8-2023-12-13
+    sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-fp16-2023-12-13
+    )
+  for n in ${name[@]}; do
+      url=https://huggingface.co/k2-fsa/$n
+      GIT_LFS_SKIP_SMUDGE=1 git clone $url
+      dst=$(basename $url)
+      if [[ $n == sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-2023-12-13 ]]; then
+        cp -v $repo/exp/ctc-epoch-20-avg-1-chunk-16-left-128.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-int8-2023-12-13 ]]; then
+        cp -v $repo/exp/ctc-epoch-20-avg-1-chunk-16-left-128.int8.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-ctc-multi-zh-hans-fp16-2023-12-13 ]]; then
+        cp -v $repo/exp/ctc-epoch-20-avg-1-chunk-16-left-128.fp16.onnx $dst
+      fi
+
+      cp -v $repo/data/lang_bpe_2000/tokens.txt $dst
+      cp -v $repo/data/lang_bpe_2000/bpe.model $dst
+      mkdir -p $dst/test_wavs
+      cp -v $repo/test_wavs/*.wav $dst/test_wavs
+      cd $dst
+      git lfs track "*.onnx" "bpe.model" "*.wav"
+      ls -lh
+      file bpe.model
+      git status
+      git add .
+      git commit -m "upload model" && git push https://k2-fsa:${HF_TOKEN}@huggingface.co/k2-fsa/$dst main || true
+
+      log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+      rm -rf .git
+      rm -fv .gitattributes
+      cd ..
+      tar cjfv $dst.tar.bz2 $dst
+      ls -lh *.tar.bz2
+      mv -v $dst.tar.bz2 ../../../
+  done
+
+  log "----------------------------------------"
+  log "Export streaming ONNX transducer models "
+  log "----------------------------------------"
+
+  ./zipformer/export-onnx-streaming.py \
+    --exp-dir $repo/exp \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    --causal 1 \
+    --avg 1 \
+    --epoch 20 \
+    --use-averaged-model 0 \
+    --chunk-size 16 \
+    --left-context-frames 128 \
+    --use-ctc 0 \
+    --fp16 1
+
+  ls -lh $repo/exp
+
+  log "------------------------------------------------------------"
+  log "Test exported streaming ONNX transducer models (Python code)"
+  log "------------------------------------------------------------"
+
+  log "test fp32"
+  ./zipformer/onnx_pretrained-streaming.py \
+    --encoder-model-filename $repo/exp/encoder-epoch-20-avg-1-chunk-16-left-128.onnx \
+    --decoder-model-filename $repo/exp/decoder-epoch-20-avg-1-chunk-16-left-128.onnx \
+    --joiner-model-filename $repo/exp/joiner-epoch-20-avg-1-chunk-16-left-128.onnx \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    $repo/test_wavs/DEV_T0000000000.wav
+
+  log "test int8"
+  ./zipformer/onnx_pretrained-streaming.py \
+    --encoder-model-filename $repo/exp/encoder-epoch-20-avg-1-chunk-16-left-128.int8.onnx \
+    --decoder-model-filename $repo/exp/decoder-epoch-20-avg-1-chunk-16-left-128.onnx \
+    --joiner-model-filename $repo/exp/joiner-epoch-20-avg-1-chunk-16-left-128.int8.onnx \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    $repo/test_wavs/DEV_T0000000000.wav
+
+  log "test fp16"
+  ./zipformer/onnx_pretrained-streaming.py \
+    --encoder-model-filename $repo/exp/encoder-epoch-20-avg-1-chunk-16-left-128.fp16.onnx \
+    --decoder-model-filename $repo/exp/decoder-epoch-20-avg-1-chunk-16-left-128.fp16.onnx \
+    --joiner-model-filename $repo/exp/joiner-epoch-20-avg-1-chunk-16-left-128.fp16.onnx \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    $repo/test_wavs/DEV_T0000000000.wav
+
+  name=(
+    sherpa-onnx-streaming-zipformer-multi-zh-hans-2023-12-13
+    sherpa-onnx-streaming-zipformer-multi-zh-hans-int8-2023-12-13
+    sherpa-onnx-streaming-zipformer-multi-zh-hans-fp16-2023-12-13
+  )
+
+  for n in ${name[@]}; do
+      url=https://huggingface.co/csukuangfj/$n
+      GIT_LFS_SKIP_SMUDGE=1 git clone $url
+      dst=$(basename $url)
+      if [[ $n == sherpa-onnx-streaming-zipformer-multi-zh-hans-2023-12-13 ]]; then
+        cp -v $repo/exp/encoder-epoch-20-avg-1-chunk-16-left-128.onnx $dst
+        cp -v $repo/exp/decoder-epoch-20-avg-1-chunk-16-left-128.onnx $dst
+        cp -v $repo/exp/joiner-epoch-20-avg-1-chunk-16-left-128.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-multi-zh-hans-int8-2023-12-13 ]]; then
+        cp -v $repo/exp/encoder-epoch-20-avg-1-chunk-16-left-128.int8.onnx $dst
+        cp -v $repo/exp/decoder-epoch-20-avg-1-chunk-16-left-128.onnx $dst
+        cp -v $repo/exp/joiner-epoch-20-avg-1-chunk-16-left-128.int8.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-multi-zh-hans-fp16-2023-12-13 ]]; then
+        cp -v $repo/exp/encoder-epoch-20-avg-1-chunk-16-left-128.fp16.onnx $dst
+        cp -v $repo/exp/decoder-epoch-20-avg-1-chunk-16-left-128.fp16.onnx $dst
+        cp -v $repo/exp/joiner-epoch-20-avg-1-chunk-16-left-128.fp16.onnx $dst
+      fi
+
+      cp -v $repo/data/lang_bpe_2000/tokens.txt $dst
+      cp -v $repo/data/lang_bpe_2000/bpe.model $dst
+      mkdir -p $dst/test_wavs
+      cp -v $repo/test_wavs/*.wav $dst/test_wavs
+      cd $dst
+      git lfs track "*.onnx" "bpe.model" "*.wav"
+      ls -lh
+      file bpe.model
+      git status
+      git add .
+      git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$dst main || true
+
+      log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+      rm -rf .git
+      rm -fv .gitattributes
+      cd ..
+      tar cjfv $dst.tar.bz2 $dst
+      ls -lh *.tar.bz2
+      mv -v $dst.tar.bz2 ../../../
+  done
+}
+
+function run_2023_12_12_streaming() {
+  log "Upload onnx transducer models to huggingface"
+
+  url=https://huggingface.co/k2-fsa/sherpa-onnx-streaming-zipformer-multi-zh-hans-2023-12-12
+  GIT_LFS_SKIP_SMUDGE=1 git clone $url
+  dst=$(basename $url)
+  cp -v $repo/exp/encoder*.onnx $dst
+  cp -v $repo/exp/decoder*.onnx $dst
+  cp -v $repo/exp/joiner*.onnx $dst
+  cp -v $repo/data/lang_bpe_2000/tokens.txt $dst
+  cp -v $repo/data/lang_bpe_2000/bpe.model $dst
+  mkdir -p $dst/test_wavs
+  cp -v $repo/test_wavs/*.wav $dst/test_wavs
+  cd $dst
+  git lfs track "*.onnx" bpe.model "*.wav"
+  git add .
+  git commit -m "upload model" && git push https://k2-fsa:${HF_TOKEN}@huggingface.co/k2-fsa/$dst main || true
+
+  log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+  rm -rf .git
+  rm -fv .gitattributes
+  cd ..
+  tar cjfv $dst.tar.bz2 $dst
+  ls -lh *.tar.bz2
+  mv -v $dst.tar.bz2 ../../../
+}
+
+function run_yuekai_large() {
+  repo_url=https://csukuangfj:${HF_TOKEN}@huggingface.co/yuekai/icefall-asr-multi-zh-hans-zipformer-large
+  log "Downloading pre-trained model from $repo_url"
+  GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+  repo=$(basename $repo_url)
+  pushd $repo
+  git lfs pull --include pretrained.pt
+  mv pretrained.pt epoch-99.pt
+  curl -SL -O https://huggingface.co/pingzxy/icefall-asr-multi-zh-hans-zipformer-large-onnx/resolve/main/tokens.txt
+  popd
+
+  log "----------------------------------------"
+  log "Export streaming ONNX CTC models "
+  log "----------------------------------------"
+  ./zipformer/export-onnx-streaming-ctc.py \
+    --exp-dir $repo/ \
+    --tokens $repo/tokens.txt \
+    --causal 1 \
+    --avg 1 \
+    --epoch 99 \
+    --use-averaged-model 0 \
+    --chunk-size 16 \
+    --left-context-frames 128 \
+    --use-ctc 1 \
+    \
+    --num-encoder-layers 2,2,4,5,4,2 \
+    --feedforward-dim 768,1024,1536,2048,1536,768 \
+    --encoder-dim 256,384,512,768,512,256 \
+    --encoder-unmasked-dim 192,192,256,320,256,192 \
+    \
+    --fp16 1 \
+    --use-whisper-features 1
+
+
+  ls -lh $repo/
+  pushd $repo
+
+cat >README.md <<EOF
+# Introduction
+
+This model is converted
+from
+https://huggingface.co/yuekai/icefall-asr-multi-zh-hans-zipformer-large
+
+The training code can be found at
+https://github.com/k2-fsa/icefall/blob/master/egs/multi_zh-hans/ASR/RESULTS.md#multi-chinese-datasets-char-based-training-results-streaming-on-zipformer-large-model
+EOF
+
+  mv -v ctc-epoch-99-avg-1-chunk-16-left-128.fp16.onnx model.fp16.onnx
+  mv -v ctc-epoch-99-avg-1-chunk-16-left-128.int8.onnx model.int8.onnx
+  mv -v ctc-epoch-99-avg-1-chunk-16-left-128.onnx model.onnx
+
+  ls -lh *.onnx
+
+  mkdir test_wavs
+  cd test_wavs
+  curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-small-ctc-zh-int8-2025-04-01/resolve/main/test_wavs/0.wav
+  curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-small-ctc-zh-int8-2025-04-01/resolve/main/test_wavs/1.wav
+  curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-small-ctc-zh-int8-2025-04-01/resolve/main/test_wavs/8k.wav
+  popd
+
+  for w in 0.wav 1.wav 8k.wav; do
+    log "---fp32---"
+    sherpa-onnx \
+      --zipformer2-ctc-model=$repo/model.onnx \
+      --tokens=$repo/tokens.txt \
+      $repo/test_wavs/$w
+
+    log "---int8---"
+
+    sherpa-onnx \
+      --zipformer2-ctc-model=$repo/model.int8.onnx \
+      --tokens=$repo/tokens.txt \
+      $repo/test_wavs/$w
+
+    log "---fp16---"
+
+    sherpa-onnx \
+      --zipformer2-ctc-model=$repo/model.fp16.onnx \
+      --tokens=$repo/tokens.txt \
+      $repo/test_wavs/$w
+  done
+
+  name=(
+    sherpa-onnx-streaming-zipformer-ctc-zh-2025-06-30
+    sherpa-onnx-streaming-zipformer-ctc-zh-int8-2025-06-30
+    sherpa-onnx-streaming-zipformer-ctc-zh-fp16-2025-06-30
+  )
+  for n in ${name[@]}; do
+      url=https://huggingface.co/csukuangfj/$n
+      GIT_LFS_SKIP_SMUDGE=1 git clone $url
+      dst=$(basename $url)
+      if [[ $n == sherpa-onnx-streaming-zipformer-ctc-zh-2025-06-30 ]]; then
+        cp -v $repo/model.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-ctc-zh-int8-2025-06-30 ]]; then
+        cp -v $repo/model.int8.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-ctc-zh-fp16-2025-06-30 ]]; then
+        cp -v $repo/model.fp16.onnx $dst
+      fi
+
+      cp -v $repo/tokens.txt $dst
+      cp -v $repo/README.md $dst
+      mkdir -p $dst/test_wavs
+      cp -v $repo/test_wavs/*.wav $dst/test_wavs
+      cd $dst
+      git lfs track "*.onnx" "*.wav"
+      ls -lh
+      git status
+      git add .
+      git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$dst main || true
+
+      log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+      rm -rf .git
+      rm -fv .gitattributes
+      cd ..
+      tar cjfv $dst.tar.bz2 $dst
+      ls -lh *.tar.bz2
+      mv -v $dst.tar.bz2 ../../../
+  done
+
+  rm $repo/*.onnx
+
+  log "----------------------------------------"
+  log "Export streaming ONNX transducer models "
+  log "----------------------------------------"
+
+  ./zipformer/export-onnx-streaming.py \
+    --exp-dir $repo \
+    --tokens $repo/tokens.txt \
+    --causal 1 \
+    --avg 1 \
+    --epoch 99 \
+    --use-averaged-model 0 \
+    --chunk-size 16 \
+    --left-context-frames 128 \
+    --use-ctc 0 \
+    \
+    --num-encoder-layers 2,2,4,5,4,2 \
+    --feedforward-dim 768,1024,1536,2048,1536,768 \
+    --encoder-dim 256,384,512,768,512,256 \
+    --encoder-unmasked-dim 192,192,256,320,256,192 \
+    \
+    --fp16 1 \
+    --use-whisper-features 1
+
+  ls -lh $repo
+  pushd $repo
+  for m in encoder decoder joiner; do
+    mv -v $m-epoch-99-avg-1-chunk-16-left-128.onnx $m.onnx
+    mv -v $m-epoch-99-avg-1-chunk-16-left-128.fp16.onnx $m.fp16.onnx
+    mv -v $m-epoch-99-avg-1-chunk-16-left-128.int8.onnx $m.int8.onnx
+  done
+  ls -lh *.onnx
+  popd
+
+  for w in 0.wav 1.wav 8k.wav; do
+    log "---fp32---"
+      sherpa-onnx \
+        --encoder=$repo/encoder.onnx \
+        --decoder=$repo/decoder.onnx \
+        --joiner=$repo/joiner.onnx \
+        --tokens=$repo/tokens.txt \
+        $repo/test_wavs/$w
+
+    log "---int8---"
+
+      sherpa-onnx \
+        --encoder=$repo/encoder.int8.onnx \
+        --decoder=$repo/decoder.onnx \
+        --joiner=$repo/joiner.int8.onnx \
+        --tokens=$repo/tokens.txt \
+        $repo/test_wavs/$w
+
+    log "---fp16---"
+
+      sherpa-onnx \
+        --encoder=$repo/encoder.fp16.onnx \
+        --decoder=$repo/decoder.fp16.onnx \
+        --joiner=$repo/joiner.fp16.onnx \
+        --tokens=$repo/tokens.txt \
+        $repo/test_wavs/$w
+  done
+
+  name=(
+    sherpa-onnx-streaming-zipformer-zh-2025-06-30
+    sherpa-onnx-streaming-zipformer-zh-int8-2025-06-30
+    sherpa-onnx-streaming-zipformer-zh-fp16-2025-06-30
+  )
+  for n in ${name[@]}; do
+      url=https://huggingface.co/csukuangfj/$n
+      GIT_LFS_SKIP_SMUDGE=1 git clone $url
+      dst=$(basename $url)
+      if [[ $n == sherpa-onnx-streaming-zipformer-zh-2025-06-30 ]]; then
+        cp -v $repo/encoder.onnx $dst
+        cp -v $repo/decoder.onnx $dst
+        cp -v $repo/joiner.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-zh-int8-2025-06-30 ]]; then
+        cp -v $repo/encoder.int8.onnx $dst
+        cp -v $repo/decoder.onnx $dst
+        cp -v $repo/joiner.int8.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-zh-fp16-2025-06-30 ]]; then
+        cp -v $repo/encoder.fp16.onnx $dst
+        cp -v $repo/decoder.fp16.onnx $dst
+        cp -v $repo/joiner.fp16.onnx $dst
+      fi
+
+      cp -v $repo/tokens.txt $dst
+      cp -v $repo/README.md $dst
+      mkdir -p $dst/test_wavs
+      cp -v $repo/test_wavs/*.wav $dst/test_wavs
+      cd $dst
+      git lfs track "*.onnx" "*.wav"
+      ls -lh
+      git status
+      git add .
+      git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$dst main || true
+
+      log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+      rm -rf .git
+      rm -fv .gitattributes
+      cd ..
+      tar cjfv $dst.tar.bz2 $dst
+      ls -lh *.tar.bz2
+      mv -v $dst.tar.bz2 ../../../
+  done
+}
+
+function run_yuekai_xl() {
+  repo_url=https://csukuangfj:${HF_TOKEN}@huggingface.co/yuekai/icefall-asr-multi-zh-hans-zipformer-xl
+  log "Downloading pre-trained model from $repo_url"
+  GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+  repo=$(basename $repo_url)
+
+  pushd $repo
+  git lfs pull --include pretrained.pt
+  git lfs pull --include data/lang_bpe_2000/bpe.model
+  mv pretrained.pt epoch-99.pt
+  ls -lh *.pt
+  popd
+
+  log "----------------------------------------"
+  log "Export streaming ONNX CTC models "
+  log "----------------------------------------"
+  ./zipformer/export-onnx-streaming-ctc.py \
+    --exp-dir $repo/ \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    --causal 1 \
+    --avg 1 \
+    --epoch 99 \
+    --use-averaged-model 0 \
+    --chunk-size 16 \
+    --left-context-frames 128 \
+    --use-ctc 1 \
+    \
+    --num-encoder-layers 2,3,5,6,5,3 \
+    --feedforward-dim 1536,2048,3072,4096,3072,1536 \
+    --encoder-dim 512,768,1024,1536,1024,512 \
+    --encoder-unmasked-dim 192,192,256,320,256,192 \
+    --decoder-dim 768 --joiner-dim 768 \
+    --value-head-dim 18 \
+    --query-head-dim 48 \
+    --num-heads 4,4,4,8,4,4 \
+    \
+    --fp16 1 \
+    --use-whisper-features 1 \
+    --use-external-data 1
+
+  mv -v ctc-epoch-99-avg-1-chunk-16-left-128.int8.onnx model.int8.onnx
+  mv -v ctc-epoch-99-avg-1-chunk-16-left-128.fp16.onnx model.fp16.onnx
+
+  ls -lh *.onnx
+
+  mkdir test_wavs
+  pushd test_wavs
+  curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-small-ctc-zh-int8-2025-04-01/resolve/main/test_wavs/0.wav
+  curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-small-ctc-zh-int8-2025-04-01/resolve/main/test_wavs/1.wav
+  curl -SL -O https://huggingface.co/csukuangfj/sherpa-onnx-streaming-zipformer-small-ctc-zh-int8-2025-04-01/resolve/main/test_wavs/8k.wav
+  popd
+
+  for w in 0.wav 1.wav 8k.wav; do
+    log "---int8---"
+
+    sherpa-onnx \
+      --zipformer2-ctc-model=./model.int8.onnx \
+      --tokens=$repo/data/lang_bpe_2000/tokens.txt \
+      test_wavs/$w
+
+    log "---fp16---"
+
+    sherpa-onnx \
+      --zipformer2-ctc-model=./model.fp16.onnx \
+      --tokens=$repo/data/lang_bpe_2000/tokens.txt \
+      test_wavs/$w
+  done
+
+  pushd $repo
+cat >README.md <<EOF
+# Introduction
+
+This model is converted
+from
+https://huggingface.co/yuekai/icefall-asr-multi-zh-hans-zipformer-xl
+
+The training code can be found at
+https://github.com/k2-fsa/icefall/blob/master/egs/multi_zh-hans/ASR/RESULTS.md#multi-chinese-datasets-char-based-training-results-streaming-on-zipformer-xl-model
+EOF
+  popd
+
+  name=(
+    sherpa-onnx-streaming-zipformer-ctc-zh-xlarge-int8-2025-06-30
+    sherpa-onnx-streaming-zipformer-ctc-zh-xlarge-fp16-2025-06-30
+  )
+
+  for n in ${name[@]}; do
+      url=https://huggingface.co/csukuangfj/$n
+      GIT_LFS_SKIP_SMUDGE=1 git clone $url
+      dst=$(basename $url)
+      if [[ $n == sherpa-onnx-streaming-zipformer-ctc-zh-xlarge-fp16-2025-06-30 ]]; then
+        cp -v model.fp16.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-ctc-zh-xlarge-int8-2025-06-30 ]]; then
+        cp -v model.int8.onnx $dst
+      fi
+
+      cp -v $repo/data/lang_bpe_2000/tokens.txt $dst
+      cp -v $repo/data/lang_bpe_2000/bpe.model $dst
+      cp -v $repo/README.md $dst
+      mkdir -p $dst/test_wavs
+      cp -v ./test_wavs/*.wav $dst/test_wavs
+      cd $dst
+      git lfs track "*.onnx" "*.wav" "bpe.model"
+      ls -lh
+      git status
+      git add .
+      git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$dst main || true
+
+      log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+      rm -rf .git
+      rm -fv .gitattributes
+      cd ..
+
+      ls -lh $dst
+      tar cjfv $dst.tar.bz2 $dst
+      ls -lh *.tar.bz2
+      mv -v $dst.tar.bz2 ../../../
+  done
+
+  rm -fv *.onnx *.weights
+
+  log "----------------------------------------"
+  log "Export streaming ONNX transducer models "
+  log "----------------------------------------"
+
+  ./zipformer/export-onnx-streaming.py \
+    --exp-dir $repo/ \
+    --tokens $repo/data/lang_bpe_2000/tokens.txt \
+    --causal 1 \
+    --avg 1 \
+    --epoch 99 \
+    --use-averaged-model 0 \
+    --chunk-size 16 \
+    --left-context-frames 128 \
+    --use-ctc 0 \
+    \
+    --num-encoder-layers 2,3,5,6,5,3 \
+    --feedforward-dim 1536,2048,3072,4096,3072,1536 \
+    --encoder-dim 512,768,1024,1536,1024,512 \
+    --encoder-unmasked-dim 192,192,256,320,256,192 \
+    --decoder-dim 768 --joiner-dim 768 \
+    --value-head-dim 18 \
+    --query-head-dim 48 \
+    --num-heads 4,4,4,8,4,4 \
+    \
+    --fp16 1 \
+    --use-whisper-features 1 \
+    --use-external-data 1
+
+    ls -lh *.onnx
+    ls -lh *.weights
+
+    mv encoder-epoch-99-avg-1-chunk-16-left-128.fp16.onnx encoder.fp16.onnx
+    mv encoder-epoch-99-avg-1-chunk-16-left-128.int8.onnx encoder.int8.onnx
+
+    mv $repo/decoder-epoch-99-avg-1-chunk-16-left-128.onnx decoder.onnx
+    mv $repo/decoder-epoch-99-avg-1-chunk-16-left-128.fp16.onnx decoder.fp16.onnx
+
+    mv $repo/joiner-epoch-99-avg-1-chunk-16-left-128.int8.onnx joiner.int8.onnx
+    mv $repo/joiner-epoch-99-avg-1-chunk-16-left-128.fp16.onnx joiner.fp16.onnx
+
+  name=(
+    sherpa-onnx-streaming-zipformer-zh-xlarge-int8-2025-06-30
+    sherpa-onnx-streaming-zipformer-zh-xlarge-fp16-2025-06-30
+  )
+
+  for n in ${name[@]}; do
+      url=https://huggingface.co/csukuangfj/$n
+      GIT_LFS_SKIP_SMUDGE=1 git clone $url
+      dst=$(basename $url)
+      if [[ $n == sherpa-onnx-streaming-zipformer-zh-xlarge-fp16-2025-06-30 ]]; then
+        cp -v encoder.fp16.onnx $dst
+        cp -v decoder.fp16.onnx $dst
+        cp -v joiner.fp16.onnx $dst
+      elif [[ $n == sherpa-onnx-streaming-zipformer-zh-xlarge-int8-2025-06-30 ]]; then
+        cp -v encoder.int8.onnx $dst
+        cp -v decoder.onnx $dst
+        cp -v joiner.int8.onnx $dst
+      fi
+
+      cp -v $repo/data/lang_bpe_2000/tokens.txt $dst
+      cp -v $repo/data/lang_bpe_2000/bpe.model $dst
+      cp -v $repo/README.md $dst
+      mkdir -p $dst/test_wavs
+      cp -v ./test_wavs/*.wav $dst/test_wavs
+      cd $dst
+      git lfs track "*.onnx" "*.wav" "bpe.model"
+      ls -lh
+      git status
+      git add .
+      git commit -m "upload model" && git push https://csukuangfj:${HF_TOKEN}@huggingface.co/csukuangfj/$dst main || true
+
+      log "Upload models to https://github.com/k2-fsa/sherpa-onnx"
+      rm -rf .git
+      rm -fv .gitattributes
+      cd ..
+
+      ls -lh $dst
+      tar cjfv $dst.tar.bz2 $dst
+      ls -lh *.tar.bz2
+      mv -v $dst.tar.bz2 ../../../
+  done
+
+  rm -fv *.onnx *.weights
+}
+
+# run_yuekai_large
+# run_yuekai_xl
+# run_2023_9_2
+run_2023_11_05_streaming
+# run_2023_12_12_streaming
diff --git a/.github/workflows/multi-zh-hans.yml b/.github/workflows/multi-zh-hans.yml
index 9081047de..4da0a784a 100644
--- a/.github/workflows/multi-zh-hans.yml
+++ b/.github/workflows/multi-zh-hans.yml
@@ -1,4 +1,4 @@
-name: run-multi-zh-hans
+name: multi-zh-hans
 
 on:
   push:
@@ -8,65 +8,72 @@ on:
   workflow_dispatch:
 
 concurrency:
-  group: run-multi-zh-hans-${{ github.ref }}
+  group: multi-zh-hans-${{ github.ref }}
   cancel-in-progress: true
 
 permissions:
   contents: write
 
 jobs:
+  generate_build_matrix:
+    if: github.repository_owner == 'csukuangfj' || github.repository_owner == 'k2-fsa'
+    # see https://github.com/pytorch/pytorch/pull/50633
+    runs-on: ubuntu-latest
+    outputs:
+      matrix: ${{ steps.set-matrix.outputs.matrix }}
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+      - name: Generating build matrix
+        id: set-matrix
+        run: |
+          # outputting for debugging purposes
+          python ./.github/scripts/docker/generate_build_matrix.py --torch-version "2.7.0" --python-version "3.11"
+          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --torch-version "2.7.0" --python-version "3.11")
+          echo "::set-output name=matrix::${MATRIX}"
   multi-zh-hans:
-    runs-on: ${{ matrix.os }}
+    needs: generate_build_matrix
+    name: py${{ matrix.python-version }} torch${{ matrix.torch-version }} v${{ matrix.version }}
+    runs-on: ubuntu-latest
     strategy:
       fail-fast: false
       matrix:
-        os: [ubuntu-latest]
-        python-version: [3.8]
+        ${{ fromJson(needs.generate_build_matrix.outputs.matrix) }}
 
     steps:
       - uses: actions/checkout@v4
         with:
           fetch-depth: 0
 
-      - name: Setup Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
-        with:
-          python-version: ${{ matrix.python-version }}
-          cache: 'pip'
-          cache-dependency-path: '**/requirements-ci.txt'
-
-      - name: Install Python dependencies
-        run: |
-          grep -v '^#' ./requirements-ci.txt  | xargs -n 1 -L 1 pip install
-          pip uninstall -y protobuf
-          pip install --no-binary protobuf protobuf==3.20.*
-
-      - name: Cache kaldifeat
-        id: my-cache
-        uses: actions/cache@v2
-        with:
-          path: |
-            ~/tmp/kaldifeat
-          key: cache-tmp-${{ matrix.python-version }}-2023-05-22
-
-      - name: Install kaldifeat
-        if: steps.my-cache.outputs.cache-hit != 'true'
+      - name: Free space
         shell: bash
         run: |
-          .github/scripts/install-kaldifeat.sh
+          df -h
+          rm -rf /opt/hostedtoolcache
+          df -h
+          echo "pwd: $PWD"
+          echo "github.workspace ${{ github.workspace }}"
 
-      - name: export-model
+      - name: Test with multi_zh-hans
+        uses: addnab/docker-run-action@v3
+        with:
+            image: ghcr.io/${{ github.repository_owner }}/icefall:cpu-py${{ matrix.python-version }}-torch${{ matrix.torch-version }}-v${{ matrix.version }}
+            options: |
+              --volume ${{ github.workspace }}/:/icefall
+            shell: bash
+            run: |
+              export PYTHONPATH=/icefall:$PYTHONPATH
+              export HF_TOKEN=${{ secrets.HF_TOKEN }}
+              cd /icefall
+              git config --global --add safe.directory /icefall
+
+              .github/scripts/multi_zh-hans/ASR/run.sh
+
+      - name: Show models
         shell: bash
-        env:
-          HF_TOKEN: ${{ secrets.HF_TOKEN }}
         run: |
-          sudo apt-get -qq install git-lfs tree
-          export PYTHONPATH=$PWD:$PYTHONPATH
-          export PYTHONPATH=~/tmp/kaldifeat/kaldifeat/python:$PYTHONPATH
-          export PYTHONPATH=~/tmp/kaldifeat/build/lib:$PYTHONPATH
-
-          .github/scripts/multi-zh-hans.sh
-          ls -lh
+          ls -lh *.tar.bz2
 
       - name: upload model to https://github.com/k2-fsa/sherpa-onnx
         uses: svenstaro/upload-release-action@v2
diff --git a/egs/librispeech/ASR/zipformer/export-onnx-ctc.py b/egs/librispeech/ASR/zipformer/export-onnx-ctc.py
index 99685f2fe..413b5bb1e 100755
--- a/egs/librispeech/ASR/zipformer/export-onnx-ctc.py
+++ b/egs/librispeech/ASR/zipformer/export-onnx-ctc.py
@@ -153,6 +153,13 @@ def get_parser():
         help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
     )
 
+    parser.add_argument(
+        "--fp16",
+        type=str2bool,
+        default=False,
+        help="Whether to export models in fp16",
+    )
+
     add_model_arguments(parser)
 
     return parser
@@ -176,6 +183,15 @@ def add_meta_data(filename: str, meta_data: Dict[str, str]):
     onnx.save(model, filename)
 
 
+def export_onnx_fp16(onnx_fp32_path, onnx_fp16_path):
+    import onnxmltools
+    from onnxmltools.utils.float16_converter import convert_float_to_float16
+
+    onnx_fp32_model = onnxmltools.utils.load_model(onnx_fp32_path)
+    onnx_fp16_model = convert_float_to_float16(onnx_fp32_model, keep_io_types=True)
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
+
+
 class OnnxModel(nn.Module):
     """A wrapper for encoder_embed, Zipformer, and ctc_output layer"""
 
@@ -407,7 +423,7 @@ def main():
     opset_version = 13
 
     logging.info("Exporting ctc model")
-    filename = params.exp_dir / f"model.onnx"
+    filename = params.exp_dir / "model.onnx"
     export_ctc_model_onnx(
         model,
         filename,
@@ -420,7 +436,7 @@ def main():
 
     logging.info("Generate int8 quantization models")
 
-    filename_int8 = params.exp_dir / f"model.int8.onnx"
+    filename_int8 = params.exp_dir / "model.int8.onnx"
     quantize_dynamic(
         model_input=filename,
         model_output=filename_int8,
@@ -428,6 +444,10 @@ def main():
         weight_type=QuantType.QInt8,
     )
 
+    if params.fp16:
+        filename_fp16 = params.exp_dir / "model.fp16.onnx"
+        export_onnx_fp16(filename, filename_fp16)
+
 
 if __name__ == "__main__":
     formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
diff --git a/egs/librispeech/ASR/zipformer/export-onnx-streaming-ctc.py b/egs/librispeech/ASR/zipformer/export-onnx-streaming-ctc.py
index 1eba6093b..003b4bf2c 100755
--- a/egs/librispeech/ASR/zipformer/export-onnx-streaming-ctc.py
+++ b/egs/librispeech/ASR/zipformer/export-onnx-streaming-ctc.py
@@ -136,12 +136,35 @@ def get_parser():
         help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
     )
 
+    parser.add_argument(
+        "--use-whisper-features",
+        type=str2bool,
+        default=False,
+        help="True to use whisper features. Must match the one used in training",
+    )
+
+    parser.add_argument(
+        "--fp16",
+        type=str2bool,
+        default=False,
+        help="Whether to export models in fp16",
+    )
+
+    parser.add_argument(
+        "--use-external-data",
+        type=str2bool,
+        default=False,
+        help="Set it to true for model file size > 2GB",
+    )
+
     add_model_arguments(parser)
 
     return parser
 
 
-def add_meta_data(filename: str, meta_data: Dict[str, str]):
+def add_meta_data(
+    filename: str, meta_data: Dict[str, str], use_external_data: bool = False
+):
     """Add meta data to an ONNX model. It is changed in-place.
 
     Args:
@@ -150,13 +173,46 @@ def add_meta_data(filename: str, meta_data: Dict[str, str]):
       meta_data:
         Key-value pairs.
     """
+    filename = str(filename)
+
     model = onnx.load(filename)
     for key, value in meta_data.items():
         meta = model.metadata_props.add()
         meta.key = key
         meta.value = value
 
-    onnx.save(model, filename)
+    if use_external_data:
+        # For models file size > 2GB
+        external_filename = Path(filename).stem
+
+        onnx.save(
+            model,
+            filename,
+            save_as_external_data=True,
+            all_tensors_to_one_file=True,
+            location=external_filename + ".weights",
+        )
+    else:
+        onnx.save(model, filename)
+
+
+def export_onnx_fp16(onnx_fp32_path, onnx_fp16_path):
+    import onnxmltools
+    from onnxmltools.utils.float16_converter import convert_float_to_float16
+
+    onnx_fp32_model = onnxmltools.utils.load_model(onnx_fp32_path)
+    onnx_fp16_model = convert_float_to_float16(onnx_fp32_model, keep_io_types=True)
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
+
+
+def export_onnx_fp16_large_2gb(onnx_fp32_path, onnx_fp16_path):
+    import onnxmltools
+    from onnxmltools.utils.float16_converter import convert_float_to_float16_model_path
+
+    onnx_fp16_model = convert_float_to_float16_model_path(
+        onnx_fp32_path, keep_io_types=True
+    )
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
 
 
 class OnnxModel(nn.Module):
@@ -270,6 +326,8 @@ def export_streaming_ctc_model_onnx(
     model: OnnxModel,
     encoder_filename: str,
     opset_version: int = 11,
+    use_whisper_features: bool = False,
+    use_external_data: bool = False,
 ) -> None:
     model.encoder.__class__.forward = model.encoder.__class__.streaming_forward
 
@@ -367,6 +425,10 @@ def export_streaming_ctc_model_onnx(
         "value_head_dims": value_head_dims,
         "num_heads": num_heads,
     }
+
+    if use_whisper_features:
+        meta_data["feature"] = "whisper"
+
     logging.info(f"meta_data: {meta_data}")
 
     for i in range(len(init_state[:-2]) // 6):
@@ -411,7 +473,11 @@ def export_streaming_ctc_model_onnx(
         },
     )
 
-    add_meta_data(filename=encoder_filename, meta_data=meta_data)
+    add_meta_data(
+        filename=encoder_filename,
+        meta_data=meta_data,
+        use_external_data=use_external_data,
+    )
 
 
 @torch.no_grad()
@@ -542,11 +608,18 @@ def main():
     opset_version = 13
 
     logging.info("Exporting model")
-    model_filename = params.exp_dir / f"ctc-{suffix}.onnx"
+
+    if params.use_external_data:
+        model_filename = f"ctc-{suffix}.onnx"
+    else:
+        model_filename = params.exp_dir / f"ctc-{suffix}.onnx"
+
     export_streaming_ctc_model_onnx(
         model,
-        model_filename,
+        str(model_filename),
         opset_version=opset_version,
+        use_whisper_features=params.use_whisper_features,
+        use_external_data=params.use_external_data,
     )
     logging.info(f"Exported model to {model_filename}")
 
@@ -555,7 +628,11 @@ def main():
 
     logging.info("Generate int8 quantization models")
 
-    model_filename_int8 = params.exp_dir / f"ctc-{suffix}.int8.onnx"
+    if params.use_external_data:
+        model_filename_int8 = f"ctc-{suffix}.int8.onnx"
+    else:
+        model_filename_int8 = params.exp_dir / f"ctc-{suffix}.int8.onnx"
+
     quantize_dynamic(
         model_input=model_filename,
         model_output=model_filename_int8,
@@ -563,6 +640,14 @@ def main():
         weight_type=QuantType.QInt8,
     )
 
+    if params.fp16:
+        if params.use_external_data:
+            model_filename_fp16 = f"ctc-{suffix}.fp16.onnx"
+            export_onnx_fp16_large_2gb(model_filename, model_filename_fp16)
+        else:
+            model_filename_fp16 = params.exp_dir / f"ctc-{suffix}.fp16.onnx"
+            export_onnx_fp16(model_filename, model_filename_fp16)
+
 
 if __name__ == "__main__":
     formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
diff --git a/egs/librispeech/ASR/zipformer/export-onnx-streaming.py b/egs/librispeech/ASR/zipformer/export-onnx-streaming.py
index a35eb5287..29541570b 100755
--- a/egs/librispeech/ASR/zipformer/export-onnx-streaming.py
+++ b/egs/librispeech/ASR/zipformer/export-onnx-streaming.py
@@ -162,12 +162,47 @@ def get_parser():
         help="Whether to export models in fp16",
     )
 
+    parser.add_argument(
+        "--use-whisper-features",
+        type=str2bool,
+        default=False,
+        help="True to use whisper features. Must match the one used in training",
+    )
+
+    parser.add_argument(
+        "--use-external-data",
+        type=str2bool,
+        default=False,
+        help="Set it to true for model file size > 2GB",
+    )
+
     add_model_arguments(parser)
 
     return parser
 
 
-def add_meta_data(filename: str, meta_data: Dict[str, str]):
+def export_onnx_fp16(onnx_fp32_path, onnx_fp16_path):
+    import onnxmltools
+    from onnxmltools.utils.float16_converter import convert_float_to_float16
+
+    onnx_fp32_model = onnxmltools.utils.load_model(onnx_fp32_path)
+    onnx_fp16_model = convert_float_to_float16(onnx_fp32_model, keep_io_types=True)
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
+
+
+def export_onnx_fp16_large_2gb(onnx_fp32_path, onnx_fp16_path):
+    import onnxmltools
+    from onnxmltools.utils.float16_converter import convert_float_to_float16_model_path
+
+    onnx_fp16_model = convert_float_to_float16_model_path(
+        onnx_fp32_path, keep_io_types=True
+    )
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
+
+
+def add_meta_data(
+    filename: str, meta_data: Dict[str, str], use_external_data: bool = False
+):
     """Add meta data to an ONNX model. It is changed in-place.
 
     Args:
@@ -182,7 +217,19 @@ def add_meta_data(filename: str, meta_data: Dict[str, str]):
         meta.key = key
         meta.value = value
 
-    onnx.save(model, filename)
+    if use_external_data:
+        # For models file size > 2GB
+        external_filename = Path(filename).stem
+
+        onnx.save(
+            model,
+            filename,
+            save_as_external_data=True,
+            all_tensors_to_one_file=True,
+            location=external_filename + ".weights",
+        )
+    else:
+        onnx.save(model, filename)
 
 
 class OnnxEncoder(nn.Module):
@@ -342,6 +389,8 @@ def export_encoder_model_onnx(
     encoder_filename: str,
     opset_version: int = 11,
     feature_dim: int = 80,
+    use_whisper_features: bool = False,
+    use_external_data: bool = False,
 ) -> None:
     encoder_model.encoder.__class__.forward = (
         encoder_model.encoder.__class__.streaming_forward
@@ -441,6 +490,9 @@ def export_encoder_model_onnx(
         "value_head_dims": value_head_dims,
         "num_heads": num_heads,
     }
+    if use_whisper_features:
+        meta_data["feature"] = "whisper"
+
     logging.info(f"meta_data: {meta_data}")
 
     for i in range(len(init_state[:-2]) // 6):
@@ -485,7 +537,11 @@ def export_encoder_model_onnx(
         },
     )
 
-    add_meta_data(filename=encoder_filename, meta_data=meta_data)
+    add_meta_data(
+        filename=encoder_filename,
+        meta_data=meta_data,
+        use_external_data=use_external_data,
+    )
 
 
 def export_decoder_model_onnx(
@@ -728,12 +784,17 @@ def main():
     opset_version = 13
 
     logging.info("Exporting encoder")
-    encoder_filename = params.exp_dir / f"encoder-{suffix}.onnx"
+    if params.use_external_data:
+        encoder_filename = f"encoder-{suffix}.onnx"
+    else:
+        encoder_filename = params.exp_dir / f"encoder-{suffix}.onnx"
     export_encoder_model_onnx(
         encoder,
-        encoder_filename,
+        str(encoder_filename),
         opset_version=opset_version,
         feature_dim=params.feature_dim,
+        use_whisper_features=params.use_whisper_features,
+        use_external_data=params.use_external_data,
     )
     logging.info(f"Exported encoder to {encoder_filename}")
 
@@ -756,31 +817,31 @@ def main():
     logging.info(f"Exported joiner to {joiner_filename}")
 
     if params.fp16:
-        from onnxconverter_common import float16
-
         logging.info("Generate fp16 models")
 
-        encoder = onnx.load(encoder_filename)
-        encoder_fp16 = float16.convert_float_to_float16(encoder, keep_io_types=True)
-        encoder_filename_fp16 = params.exp_dir / f"encoder-{suffix}.fp16.onnx"
-        onnx.save(encoder_fp16, encoder_filename_fp16)
+        if params.use_external_data:
+            encoder_filename_fp16 = f"encoder-{suffix}.fp16.onnx"
+            export_onnx_fp16_large_2gb(encoder_filename, encoder_filename_fp16)
+        else:
+            encoder_filename_fp16 = params.exp_dir / f"encoder-{suffix}.fp16.onnx"
+            export_onnx_fp16(encoder_filename, encoder_filename_fp16)
 
-        decoder = onnx.load(decoder_filename)
-        decoder_fp16 = float16.convert_float_to_float16(decoder, keep_io_types=True)
         decoder_filename_fp16 = params.exp_dir / f"decoder-{suffix}.fp16.onnx"
-        onnx.save(decoder_fp16, decoder_filename_fp16)
+        export_onnx_fp16(decoder_filename, decoder_filename_fp16)
 
-        joiner = onnx.load(joiner_filename)
-        joiner_fp16 = float16.convert_float_to_float16(joiner, keep_io_types=True)
         joiner_filename_fp16 = params.exp_dir / f"joiner-{suffix}.fp16.onnx"
-        onnx.save(joiner_fp16, joiner_filename_fp16)
+        export_onnx_fp16(joiner_filename, joiner_filename_fp16)
 
     # Generate int8 quantization models
     # See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection
 
     logging.info("Generate int8 quantization models")
 
-    encoder_filename_int8 = params.exp_dir / f"encoder-{suffix}.int8.onnx"
+    if params.use_external_data:
+        encoder_filename_int8 = f"encoder-{suffix}.int8.onnx"
+    else:
+        encoder_filename_int8 = params.exp_dir / f"encoder-{suffix}.int8.onnx"
+
     quantize_dynamic(
         model_input=encoder_filename,
         model_output=encoder_filename_int8,
diff --git a/egs/librispeech/ASR/zipformer/export-onnx.py b/egs/librispeech/ASR/zipformer/export-onnx.py
index a56a7a3e6..03c7d6f82 100755
--- a/egs/librispeech/ASR/zipformer/export-onnx.py
+++ b/egs/librispeech/ASR/zipformer/export-onnx.py
@@ -70,7 +70,6 @@ import onnx
 import torch
 import torch.nn as nn
 from decoder import Decoder
-from onnxconverter_common import float16
 from onnxruntime.quantization import QuantType, quantize_dynamic
 from scaling_converter import convert_scaled_to_non_scaled
 from train import add_model_arguments, get_model, get_params
@@ -182,6 +181,15 @@ def add_meta_data(filename: str, meta_data: Dict[str, str]):
     onnx.save(model, filename)
 
 
+def export_onnx_fp16(onnx_fp32_path, onnx_fp16_path):
+    import onnxmltools
+    from onnxmltools.utils.float16_converter import convert_float_to_float16
+
+    onnx_fp32_model = onnxmltools.utils.load_model(onnx_fp32_path)
+    onnx_fp16_model = convert_float_to_float16(onnx_fp32_model, keep_io_types=True)
+    onnxmltools.utils.save_model(onnx_fp16_model, onnx_fp16_path)
+
+
 class OnnxEncoder(nn.Module):
     """A wrapper for Zipformer and the encoder_proj from the joiner"""
 
@@ -595,20 +603,14 @@ def main():
     if params.fp16:
         logging.info("Generate fp16 models")
 
-        encoder = onnx.load(encoder_filename)
-        encoder_fp16 = float16.convert_float_to_float16(encoder, keep_io_types=True)
         encoder_filename_fp16 = params.exp_dir / f"encoder-{suffix}.fp16.onnx"
-        onnx.save(encoder_fp16, encoder_filename_fp16)
+        export_onnx_fp16(encoder_filename, encoder_filename_fp16)
 
-        decoder = onnx.load(decoder_filename)
-        decoder_fp16 = float16.convert_float_to_float16(decoder, keep_io_types=True)
         decoder_filename_fp16 = params.exp_dir / f"decoder-{suffix}.fp16.onnx"
-        onnx.save(decoder_fp16, decoder_filename_fp16)
+        export_onnx_fp16(decoder_filename, decoder_filename_fp16)
 
-        joiner = onnx.load(joiner_filename)
-        joiner_fp16 = float16.convert_float_to_float16(joiner, keep_io_types=True)
         joiner_filename_fp16 = params.exp_dir / f"joiner-{suffix}.fp16.onnx"
-        onnx.save(joiner_fp16, joiner_filename_fp16)
+        export_onnx_fp16(joiner_filename, joiner_filename_fp16)
 
     # Generate int8 quantization models
     # See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection

From fba5e67d5e14c808cea7f2bf5ccc7fa0c248cc5c Mon Sep 17 00:00:00 2001
From: Fangjun Kuang <csukuangfj@gmail.com>
Date: Tue, 1 Jul 2025 13:47:55 +0800
Subject: [PATCH 22/22] Fix CI tests. (#1974)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Introduce unified AMP helpers (create_grad_scaler, torch_autocast) to handle
  deprecations in PyTorch ≥2.3.0

- Replace direct uses of torch.cuda.amp.GradScaler and torch.cuda.amp.autocast
  with the new utilities across all training and inference scripts

- Update all torch.load calls to include weights_only=False for compatibility with
  newer PyTorch versions
---
 .github/workflows/aishell.yml                 |  6 +--
 .github/workflows/audioset.yml                | 10 ++---
 .github/workflows/baker_zh.yml                | 20 ++++-----
 .github/workflows/librispeech.yml             |  5 ++-
 .github/workflows/ljspeech.yml                | 22 ++++-----
 .github/workflows/test.yml                    |  4 +-
 .github/workflows/yesno.yml                   |  5 ++-
 .../ASR/pruned_transducer_stateless2/train.py | 12 +++--
 .../ASR/pruned_transducer_stateless2/train.py | 12 +++--
 .../ASR/pruned_transducer_stateless3/model.py |  6 +--
 .../ASR/pruned_transducer_stateless3/train.py | 12 +++--
 .../ASR/pruned_transducer_stateless7/train.py |  5 ++-
 .../train.py                                  |  5 ++-
 .../train.py                                  | 12 +++--
 egs/aishell/ASR/whisper/train.py              |  5 ++-
 egs/aishell/ASR/zipformer/train.py            |  5 ++-
 egs/aishell/ASR/zipformer/train_bbpe.py       |  5 ++-
 .../ASR/pruned_transducer_stateless5/train.py | 12 +++--
 .../ASR/pruned_transducer_stateless5/train.py | 12 +++--
 .../ASR/pruned_transducer_stateless2/train.py | 12 +++--
 .../pruned_transducer_stateless7/train.py     | 12 +++--
 .../ASR/pruned_transducer_stateless7/train.py | 12 +++--
 egs/ami/SURT/dprnn_zipformer/train.py         | 10 ++++-
 egs/ami/SURT/dprnn_zipformer/train_adapt.py   | 10 ++++-
 egs/audioset/AT/zipformer/train.py            |  5 ++-
 .../TTS/local/compute_fbank_baker_zh.py       |  2 +
 .../ASR/pruned_transducer_stateless7/train.py |  5 ++-
 .../finetune.py                               |  5 ++-
 .../train.py                                  | 12 +++--
 egs/commonvoice/ASR/zipformer/train.py        |  5 ++-
 egs/commonvoice/ASR/zipformer/train_char.py   |  5 ++-
 .../train.py                                  | 12 +++--
 .../ASR/pruned_transducer_stateless2/train.py | 12 +++--
 egs/gigaspeech/ASR/zipformer/train.py         |  5 ++-
 egs/gigaspeech/KWS/zipformer/train.py         |  5 ++-
 .../train.py                                  | 12 +++--
 egs/ksponspeech/ASR/zipformer/train.py        |  5 ++-
 egs/librispeech/ASR/conformer_ctc/decode.py   |  8 +++-
 .../ASR/conformer_ctc/pretrained.py           | 10 +++--
 egs/librispeech/ASR/conformer_ctc2/decode.py  |  8 +++-
 egs/librispeech/ASR/conformer_ctc2/train.py   | 13 +++---
 egs/librispeech/ASR/conformer_ctc3/decode.py  |  8 +++-
 .../ASR/conformer_ctc3/jit_pretrained.py      |  8 +++-
 .../ASR/conformer_ctc3/pretrained.py          | 10 +++--
 egs/librispeech/ASR/conformer_ctc3/train.py   | 13 +++---
 egs/librispeech/ASR/conformer_mmi/decode.py   |  8 +++-
 .../train.py                                  | 20 ++++++---
 .../train.py                                  | 20 ++++++---
 egs/librispeech/ASR/local/compile_hlg.py      |  4 +-
 egs/librispeech/ASR/local/compile_lg.py       |  4 +-
 .../ASR/lstm_transducer_stateless/decode.py   |  2 +-
 .../ASR/lstm_transducer_stateless/model.py    |  6 +--
 .../lstm_transducer_stateless/pretrained.py   |  2 +-
 .../ASR/lstm_transducer_stateless/train.py    | 13 +++---
 .../ASR/lstm_transducer_stateless2/decode.py  |  2 +-
 .../ASR/lstm_transducer_stateless2/model.py   |  6 +--
 .../lstm_transducer_stateless2/pretrained.py  |  2 +-
 .../ASR/lstm_transducer_stateless2/train.py   | 13 +++---
 .../ASR/lstm_transducer_stateless3/decode.py  |  2 +-
 .../lstm_transducer_stateless3/pretrained.py  |  2 +-
 .../ASR/lstm_transducer_stateless3/train.py   | 13 +++---
 .../ASR/pruned2_knowledge/model.py            |  6 +--
 .../ASR/pruned2_knowledge/sampling.py         |  8 ++--
 .../ASR/pruned2_knowledge/train.py            | 20 ++++++---
 .../pruned_stateless_emformer_rnnt2/train.py  | 20 ++++++---
 .../ASR/pruned_transducer_stateless/decode.py |  2 +-
 .../pruned_transducer_stateless2/decode.py    |  2 +-
 .../ASR/pruned_transducer_stateless2/model.py |  6 +--
 .../pretrained.py                             |  2 +-
 .../ASR/pruned_transducer_stateless2/train.py | 13 +++---
 .../pruned_transducer_stateless3/decode.py    |  4 +-
 .../ASR/pruned_transducer_stateless3/model.py |  6 +--
 .../pretrained.py                             |  2 +-
 .../ASR/pruned_transducer_stateless3/train.py | 13 +++---
 .../pruned_transducer_stateless4/decode.py    |  2 +-
 .../ASR/pruned_transducer_stateless4/train.py | 13 +++---
 .../pruned_transducer_stateless5/decode.py    |  2 +-
 .../pretrained.py                             |  2 +-
 .../ASR/pruned_transducer_stateless5/train.py | 13 +++---
 .../ASR/pruned_transducer_stateless6/model.py |  6 +--
 .../ASR/pruned_transducer_stateless6/train.py | 13 +++---
 .../pruned_transducer_stateless6/vq_utils.py  |  4 +-
 .../compute_ali.py                            |  2 +-
 .../pruned_transducer_stateless7/decode.py    |  2 +-
 .../decode_gigaspeech.py                      |  2 +-
 .../pruned_transducer_stateless7/finetune.py  | 15 ++++---
 .../ASR/pruned_transducer_stateless7/model.py |  6 +--
 .../pretrained.py                             |  2 +-
 .../pruned_transducer_stateless7/scaling.py   |  8 ++--
 .../ASR/pruned_transducer_stateless7/train.py | 13 +++---
 .../pruned_transducer_stateless7/zipformer.py |  4 +-
 .../ctc_decode.py                             |  8 +++-
 .../decode.py                                 |  2 +-
 .../jit_pretrained_ctc.py                     |  8 +++-
 .../pruned_transducer_stateless7_ctc/model.py |  6 +--
 .../pretrained.py                             |  2 +-
 .../pretrained_ctc.py                         | 10 +++--
 .../pruned_transducer_stateless7_ctc/train.py | 13 +++---
 .../ctc_decode.py                             |  8 +++-
 .../ctc_guide_decode_bs.py                    |  2 +-
 .../decode.py                                 |  2 +-
 .../jit_pretrained_ctc.py                     |  8 +++-
 .../model.py                                  |  6 +--
 .../pretrained.py                             |  2 +-
 .../pretrained_ctc.py                         | 10 +++--
 .../train.py                                  | 13 +++---
 .../decode.py                                 |  2 +-
 .../pretrained.py                             |  2 +-
 .../train.py                                  | 20 ++++++---
 .../zipformer.py                              |  4 +-
 .../decode.py                                 |  2 +-
 .../decode_gigaspeech.py                      |  2 +-
 .../train.py                                  | 20 ++++++---
 .../pruned_transducer_stateless8/decode.py    |  2 +-
 .../ASR/pruned_transducer_stateless8/model.py |  6 +--
 .../pretrained.py                             |  2 +-
 .../ASR/pruned_transducer_stateless8/train.py | 20 ++++++---
 egs/librispeech/ASR/tdnn_lstm_ctc/decode.py   |  8 +++-
 .../ASR/tdnn_lstm_ctc/pretrained.py           | 10 +++--
 .../ASR/tiny_transducer_ctc/ctc_decode.py     |  8 +++-
 .../ASR/tiny_transducer_ctc/decode.py         |  2 +-
 .../tiny_transducer_ctc/jit_pretrained_ctc.py |  8 +++-
 .../ASR/tiny_transducer_ctc/pretrained.py     |  2 +-
 .../ASR/tiny_transducer_ctc/pretrained_ctc.py | 10 +++--
 .../ASR/tiny_transducer_ctc/train.py          | 13 +++---
 egs/librispeech/ASR/transducer/pretrained.py  |  2 +-
 egs/librispeech/ASR/zipformer/ctc_decode.py   |  8 +++-
 egs/librispeech/ASR/zipformer/decode.py       |  2 +-
 .../ASR/zipformer/decode_gigaspeech.py        |  2 +-
 egs/librispeech/ASR/zipformer/finetune.py     | 15 ++++---
 .../ASR/zipformer/jit_pretrained_ctc.py       |  8 +++-
 egs/librispeech/ASR/zipformer/model.py        |  6 +--
 egs/librispeech/ASR/zipformer/pretrained.py   |  2 +-
 .../ASR/zipformer/pretrained_ctc.py           | 10 +++--
 egs/librispeech/ASR/zipformer/scaling.py      | 12 ++---
 egs/librispeech/ASR/zipformer/train.py        | 17 +++----
 egs/librispeech/ASR/zipformer/zipformer.py    |  4 +-
 .../ASR/zipformer_adapter/decode.py           |  2 +-
 .../zipformer_adapter/decode_gigaspeech.py    |  2 +-
 .../ASR/zipformer_adapter/train.py            | 15 ++++---
 .../ASR/zipformer_adapter/zipformer.py        |  4 +-
 egs/librispeech/ASR/zipformer_ctc/decode.py   |  8 +++-
 egs/librispeech/ASR/zipformer_ctc/train.py    | 18 +++++---
 .../ASR/zipformer_lora/decode_gigaspeech.py   |  2 +-
 .../ASR/zipformer_lora/finetune.py            | 15 ++++---
 egs/librispeech/ASR/zipformer_lora/scaling.py | 12 ++---
 egs/librispeech/ASR/zipformer_lora/train.py   | 13 +++---
 .../ASR/zipformer_lora/zipformer.py           |  4 +-
 egs/librispeech/ASR/zipformer_mmi/decode.py   | 10 ++++-
 .../ASR/zipformer_mmi/jit_pretrained.py       |  8 +++-
 .../ASR/zipformer_mmi/pretrained.py           | 10 +++--
 egs/librispeech/ASR/zipformer_mmi/train.py    | 13 +++---
 egs/librispeech/SSL/hubert/finetune.py        |  5 ++-
 egs/librispeech/SSL/hubert/finetune_ce.py     |  5 ++-
 egs/librispeech/SSL/hubert/model.py           |  6 +--
 egs/librispeech/SSL/hubert/pretrain.py        |  5 ++-
 egs/librispeech/SSL/hubert/pretrain_ce.py     |  5 ++-
 egs/librispeech/SSL/zipformer/finetune.py     |  5 ++-
 egs/librispeech/SSL/zipformer/model.py        |  6 +--
 egs/librispeech/SSL/zipformer/pretrain.py     |  5 ++-
 egs/librispeech/SSL/zipformer/zipformer.py    |  3 +-
 egs/librispeech/WSASR/conformer_ctc2/train.py |  5 ++-
 .../WSASR/conformer_ctc2/train_phone.py       |  5 ++-
 .../TTS/local/compute_fbank_ljspeech.py       |  2 +
 egs/yesno/ASR/local/compile_hlg.py            |  2 +-
 egs/yesno/ASR/tdnn/decode.py                  |  4 +-
 egs/yesno/ASR/tdnn/jit_pretrained.py          |  4 +-
 egs/yesno/ASR/tdnn/onnx_pretrained.py         |  4 +-
 egs/yesno/ASR/tdnn/pretrained.py              |  6 ++-
 icefall/checkpoint.py                         | 21 +++++----
 icefall/diagnostics.py                        | 10 ++++-
 icefall/hooks.py                              |  6 ++-
 icefall/rnn_lm/train.py                       | 12 +++--
 icefall/transformer_lm/train.py               | 12 +++--
 icefall/utils.py                              | 45 +++++++++++++++++++
 requirements.txt                              |  1 +
 176 files changed, 881 insertions(+), 501 deletions(-)

diff --git a/.github/workflows/aishell.yml b/.github/workflows/aishell.yml
index 8b0599fca..4572c0c7f 100644
--- a/.github/workflows/aishell.yml
+++ b/.github/workflows/aishell.yml
@@ -17,7 +17,7 @@ concurrency:
 
 jobs:
   generate_build_matrix:
-    if: (github.repository_owner == 'csukuangfj' || github.repository_owner == 'k2-fsa') && (github.event.label.name == 'ready' || github.event_name == 'push' || github.event_name == 'aishell')
+    if: github.repository_owner == 'csukuangfj' || github.repository_owner == 'k2-fsa'
 
     # see https://github.com/pytorch/pytorch/pull/50633
     runs-on: ubuntu-latest
@@ -31,8 +31,8 @@ jobs:
         id: set-matrix
         run: |
           # outputting for debugging purposes
-          python ./.github/scripts/docker/generate_build_matrix.py
-          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py)
+          python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10"
+          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10")
           echo "::set-output name=matrix::${MATRIX}"
   aishell:
     needs: generate_build_matrix
diff --git a/.github/workflows/audioset.yml b/.github/workflows/audioset.yml
index 9c9446239..90d418513 100644
--- a/.github/workflows/audioset.yml
+++ b/.github/workflows/audioset.yml
@@ -30,8 +30,8 @@ jobs:
         id: set-matrix
         run: |
           # outputting for debugging purposes
-          python ./.github/scripts/docker/generate_build_matrix.py
-          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py)
+          python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10"
+          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10")
           echo "::set-output name=matrix::${MATRIX}"
 
   audioset:
@@ -83,7 +83,7 @@ jobs:
           ls -lh ./model-onnx/*
 
       - name: Upload model to huggingface
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
         env:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
         uses: nick-fields/retry@v3
@@ -116,7 +116,7 @@ jobs:
             rm -rf huggingface
 
       - name: Prepare for release
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
         shell: bash
         run: |
           d=sherpa-onnx-zipformer-audio-tagging-2024-04-09
@@ -125,7 +125,7 @@ jobs:
           ls -lh
 
       - name: Release exported onnx models
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
         uses: svenstaro/upload-release-action@v2
         with:
           file_glob: true
diff --git a/.github/workflows/baker_zh.yml b/.github/workflows/baker_zh.yml
index 7805ab5ab..044919fd8 100644
--- a/.github/workflows/baker_zh.yml
+++ b/.github/workflows/baker_zh.yml
@@ -31,8 +31,8 @@ jobs:
         id: set-matrix
         run: |
           # outputting for debugging purposes
-          python ./.github/scripts/docker/generate_build_matrix.py --min-torch-version "2.3"
-          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --min-torch-version "2.3")
+          python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10"
+          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10")
           echo "::set-output name=matrix::${MATRIX}"
 
   baker_zh:
@@ -84,43 +84,43 @@ jobs:
           ls -lh
 
       - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         with:
           name: generated-test-files-${{ matrix.python-version }}-${{ matrix.torch-version }}
           path: ./*.wav
 
       - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         with:
           name: step-2
           path: ./model-steps-2.onnx
 
       - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         with:
           name: step-3
           path: ./model-steps-3.onnx
 
       - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         with:
           name: step-4
           path: ./model-steps-4.onnx
 
       - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         with:
           name: step-5
           path: ./model-steps-5.onnx
 
       - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         with:
           name: step-6
           path: ./model-steps-6.onnx
 
       - name: Upload models to huggingface
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
         shell: bash
         env:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
@@ -141,7 +141,7 @@ jobs:
           popd
 
       - name: Release exported onnx models
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
         uses: svenstaro/upload-release-action@v2
         with:
           file_glob: true
diff --git a/.github/workflows/librispeech.yml b/.github/workflows/librispeech.yml
index 6e087b10a..19037c11b 100644
--- a/.github/workflows/librispeech.yml
+++ b/.github/workflows/librispeech.yml
@@ -29,8 +29,9 @@ jobs:
         id: set-matrix
         run: |
           # outputting for debugging purposes
-          python ./.github/scripts/docker/generate_build_matrix.py
-          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py)
+          python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10"
+          # MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10")
+          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10" --min-torch-version "2.6.0")
           echo "::set-output name=matrix::${MATRIX}"
   librispeech:
     needs: generate_build_matrix
diff --git a/.github/workflows/ljspeech.yml b/.github/workflows/ljspeech.yml
index 31a65cd94..52a3b1a3f 100644
--- a/.github/workflows/ljspeech.yml
+++ b/.github/workflows/ljspeech.yml
@@ -30,8 +30,8 @@ jobs:
         id: set-matrix
         run: |
           # outputting for debugging purposes
-          python ./.github/scripts/docker/generate_build_matrix.py --min-torch-version "2.3"
-          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --min-torch-version "2.3")
+          python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10"
+          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10")
           echo "::set-output name=matrix::${MATRIX}"
 
   ljspeech:
@@ -83,13 +83,13 @@ jobs:
           ls -lh
 
       - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         with:
           name: generated-test-files-${{ matrix.python-version }}-${{ matrix.torch-version }}
           path: ./*.wav
 
       - name: Release exported onnx models
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0' && github.event_name == 'push'
         uses: svenstaro/upload-release-action@v2
         with:
           file_glob: true
@@ -100,37 +100,37 @@ jobs:
           tag: tts-models
 
       - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         with:
           name: step-2
           path: ./model-steps-2.onnx
 
       - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         with:
           name: step-3
           path: ./model-steps-3.onnx
 
       - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         with:
           name: step-4
           path: ./model-steps-4.onnx
 
       - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         with:
           name: step-5
           path: ./model-steps-5.onnx
 
       - uses: actions/upload-artifact@v4
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         with:
           name: step-6
           path: ./model-steps-6.onnx
 
       - name: Upload models to huggingface
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         shell: bash
         env:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
@@ -155,7 +155,7 @@ jobs:
           popd
 
       - name: Release exported onnx models
-        if: matrix.python-version == '3.9' && matrix.torch-version == '2.3.0'
+        if: matrix.python-version == '3.10' && matrix.torch-version == '2.3.0'
         uses: svenstaro/upload-release-action@v2
         with:
           file_glob: true
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index c22f2edb5..ed0e62330 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -30,8 +30,8 @@ jobs:
         id: set-matrix
         run: |
           # outputting for debugging purposes
-          python ./.github/scripts/docker/generate_build_matrix.py
-          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py)
+          python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10"
+          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10")
           echo "::set-output name=matrix::${MATRIX}"
   test:
     needs: generate_build_matrix
diff --git a/.github/workflows/yesno.yml b/.github/workflows/yesno.yml
index a9d65516f..a5832df9d 100644
--- a/.github/workflows/yesno.yml
+++ b/.github/workflows/yesno.yml
@@ -30,8 +30,9 @@ jobs:
         id: set-matrix
         run: |
           # outputting for debugging purposes
-          python ./.github/scripts/docker/generate_build_matrix.py
-          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py)
+          python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10"
+          MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10")
+          # MATRIX=$(python ./.github/scripts/docker/generate_build_matrix.py --python-version "3.10" --min-torch-version "2.5.0")
           echo "::set-output name=matrix::${MATRIX}"
   yesno:
     needs: generate_build_matrix
diff --git a/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/train.py b/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/train.py
index fa809b768..9060cdb26 100644
--- a/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/train.py
+++ b/egs/aidatatang_200zh/ASR/pruned_transducer_stateless2/train.py
@@ -79,7 +79,13 @@ from icefall.checkpoint import save_checkpoint_with_global_batch_idx
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -638,7 +644,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -912,7 +918,7 @@ def scan_pessimistic_batches_for_oom(
             # warmup = 0.0 is so that the derivs for the pruned loss stay zero
             # (i.e. are not remembered by the decaying-average in adam), because
             # we want to avoid these params being subject to shrinkage in adam.
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/aishell/ASR/pruned_transducer_stateless2/train.py b/egs/aishell/ASR/pruned_transducer_stateless2/train.py
index 60f014c48..457b564fe 100755
--- a/egs/aishell/ASR/pruned_transducer_stateless2/train.py
+++ b/egs/aishell/ASR/pruned_transducer_stateless2/train.py
@@ -72,7 +72,13 @@ from icefall.checkpoint import save_checkpoint_with_global_batch_idx
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -688,7 +694,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -989,7 +995,7 @@ def scan_pessimistic_batches_for_oom(
             # warmup = 0.0 is so that the derivs for the pruned loss stay zero
             # (i.e. are not remembered by the decaying-average in adam), because
             # we want to avoid these params being subject to shrinkage in adam.
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/aishell/ASR/pruned_transducer_stateless3/model.py b/egs/aishell/ASR/pruned_transducer_stateless3/model.py
index a4dda0d6d..3b9dad55e 100644
--- a/egs/aishell/ASR/pruned_transducer_stateless3/model.py
+++ b/egs/aishell/ASR/pruned_transducer_stateless3/model.py
@@ -23,7 +23,7 @@ import torch.nn as nn
 from encoder_interface import EncoderInterface
 from scaling import ScaledLinear
 
-from icefall.utils import add_sos
+from icefall.utils import add_sos, torch_autocast
 
 
 class Transducer(nn.Module):
@@ -184,7 +184,7 @@ class Transducer(nn.Module):
         lm = simple_lm_proj(decoder_out)
         am = simple_am_proj(encoder_out)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -219,7 +219,7 @@ class Transducer(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/aishell/ASR/pruned_transducer_stateless3/train.py b/egs/aishell/ASR/pruned_transducer_stateless3/train.py
index 7c23041ca..ad9f40e25 100755
--- a/egs/aishell/ASR/pruned_transducer_stateless3/train.py
+++ b/egs/aishell/ASR/pruned_transducer_stateless3/train.py
@@ -94,7 +94,13 @@ from icefall.checkpoint import (
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -797,7 +803,7 @@ def train_one_epoch(
         aishell = is_aishell(batch["supervisions"]["cut"][0])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1202,7 +1208,7 @@ def scan_pessimistic_batches_for_oom(
             # warmup = 0.0 is so that the derivs for the pruned loss stay zero
             # (i.e. are not remembered by the decaying-average in adam), because
             # we want to avoid these params being subject to shrinkage in adam.
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/aishell/ASR/pruned_transducer_stateless7/train.py b/egs/aishell/ASR/pruned_transducer_stateless7/train.py
index 2dc835f3b..85a51278b 100755
--- a/egs/aishell/ASR/pruned_transducer_stateless7/train.py
+++ b/egs/aishell/ASR/pruned_transducer_stateless7/train.py
@@ -94,6 +94,7 @@ from icefall.utils import (
     filter_uneven_sized_batch,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -809,7 +810,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1206,7 +1207,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/aishell/ASR/pruned_transducer_stateless7_bbpe/train.py b/egs/aishell/ASR/pruned_transducer_stateless7_bbpe/train.py
index 811269989..a07216de8 100755
--- a/egs/aishell/ASR/pruned_transducer_stateless7_bbpe/train.py
+++ b/egs/aishell/ASR/pruned_transducer_stateless7_bbpe/train.py
@@ -87,6 +87,7 @@ from icefall.utils import (
     setup_logger,
     str2bool,
     tokenize_by_CJK_char,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -802,7 +803,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1202,7 +1203,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/aishell/ASR/pruned_transducer_stateless7_streaming/train.py b/egs/aishell/ASR/pruned_transducer_stateless7_streaming/train.py
index f3b0f1e11..a8373d755 100755
--- a/egs/aishell/ASR/pruned_transducer_stateless7_streaming/train.py
+++ b/egs/aishell/ASR/pruned_transducer_stateless7_streaming/train.py
@@ -81,7 +81,13 @@ from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.lexicon import Lexicon
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -812,7 +818,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1202,7 +1208,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/aishell/ASR/whisper/train.py b/egs/aishell/ASR/whisper/train.py
index d77f8c270..af4d6442e 100755
--- a/egs/aishell/ASR/whisper/train.py
+++ b/egs/aishell/ASR/whisper/train.py
@@ -81,6 +81,7 @@ from icefall.utils import (
     filter_uneven_sized_batch,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -514,7 +515,7 @@ def compute_validation_loss(
     tot_loss = MetricsTracker()
 
     for batch_idx, batch in enumerate(valid_dl):
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 tokenizer=tokenizer,
@@ -608,7 +609,7 @@ def train_one_epoch(
                 )
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     tokenizer=tokenizer,
diff --git a/egs/aishell/ASR/zipformer/train.py b/egs/aishell/ASR/zipformer/train.py
index dddfe52fa..0c389db55 100755
--- a/egs/aishell/ASR/zipformer/train.py
+++ b/egs/aishell/ASR/zipformer/train.py
@@ -95,6 +95,7 @@ from icefall.utils import (
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -910,7 +911,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1302,7 +1303,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/aishell/ASR/zipformer/train_bbpe.py b/egs/aishell/ASR/zipformer/train_bbpe.py
index dbc262c5c..b9d7fe8ad 100755
--- a/egs/aishell/ASR/zipformer/train_bbpe.py
+++ b/egs/aishell/ASR/zipformer/train_bbpe.py
@@ -92,6 +92,7 @@ from icefall.utils import (
     setup_logger,
     str2bool,
     tokenize_by_CJK_char,
+    torch_autocast,
 )
 
 
@@ -495,7 +496,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -895,7 +896,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/aishell2/ASR/pruned_transducer_stateless5/train.py b/egs/aishell2/ASR/pruned_transducer_stateless5/train.py
index 8c7448d4c..84cd2ffca 100755
--- a/egs/aishell2/ASR/pruned_transducer_stateless5/train.py
+++ b/egs/aishell2/ASR/pruned_transducer_stateless5/train.py
@@ -90,7 +90,13 @@ from icefall.checkpoint import (
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -734,7 +740,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1062,7 +1068,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/aishell4/ASR/pruned_transducer_stateless5/train.py b/egs/aishell4/ASR/pruned_transducer_stateless5/train.py
index a354f761e..ab97f8677 100755
--- a/egs/aishell4/ASR/pruned_transducer_stateless5/train.py
+++ b/egs/aishell4/ASR/pruned_transducer_stateless5/train.py
@@ -83,7 +83,13 @@ from icefall.checkpoint import (
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -727,7 +733,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
         # print(batch["supervisions"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -1034,7 +1040,7 @@ def scan_pessimistic_batches_for_oom(
             # warmup = 0.0 is so that the derivs for the pruned loss stay zero
             # (i.e. are not remembered by the decaying-average in adam), because
             # we want to avoid these params being subject to shrinkage in adam.
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/alimeeting/ASR/pruned_transducer_stateless2/train.py b/egs/alimeeting/ASR/pruned_transducer_stateless2/train.py
index 30154291d..172d94862 100644
--- a/egs/alimeeting/ASR/pruned_transducer_stateless2/train.py
+++ b/egs/alimeeting/ASR/pruned_transducer_stateless2/train.py
@@ -79,7 +79,13 @@ from icefall.checkpoint import save_checkpoint_with_global_batch_idx
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -638,7 +644,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -912,7 +918,7 @@ def scan_pessimistic_batches_for_oom(
             # warmup = 0.0 is so that the derivs for the pruned loss stay zero
             # (i.e. are not remembered by the decaying-average in adam), because
             # we want to avoid these params being subject to shrinkage in adam.
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/alimeeting/ASR_v2/pruned_transducer_stateless7/train.py b/egs/alimeeting/ASR_v2/pruned_transducer_stateless7/train.py
index 30879d8d2..855aeca12 100755
--- a/egs/alimeeting/ASR_v2/pruned_transducer_stateless7/train.py
+++ b/egs/alimeeting/ASR_v2/pruned_transducer_stateless7/train.py
@@ -73,7 +73,13 @@ from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.lexicon import Lexicon
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -782,7 +788,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1127,7 +1133,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/ami/ASR/pruned_transducer_stateless7/train.py b/egs/ami/ASR/pruned_transducer_stateless7/train.py
index d62cdadb7..8922717ef 100755
--- a/egs/ami/ASR/pruned_transducer_stateless7/train.py
+++ b/egs/ami/ASR/pruned_transducer_stateless7/train.py
@@ -71,7 +71,13 @@ from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -773,7 +779,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1134,7 +1140,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/ami/SURT/dprnn_zipformer/train.py b/egs/ami/SURT/dprnn_zipformer/train.py
index adc6a8495..3572acd04 100755
--- a/egs/ami/SURT/dprnn_zipformer/train.py
+++ b/egs/ami/SURT/dprnn_zipformer/train.py
@@ -76,7 +76,13 @@ from icefall.checkpoint import (
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -1067,7 +1073,7 @@ def train_one_epoch(
         batch_size = batch["inputs"].shape[0]
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/ami/SURT/dprnn_zipformer/train_adapt.py b/egs/ami/SURT/dprnn_zipformer/train_adapt.py
index ac5b0dadc..313a5c46a 100755
--- a/egs/ami/SURT/dprnn_zipformer/train_adapt.py
+++ b/egs/ami/SURT/dprnn_zipformer/train_adapt.py
@@ -76,7 +76,13 @@ from icefall.checkpoint import (
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -1058,7 +1064,7 @@ def train_one_epoch(
         batch_size = batch["inputs"].shape[0]
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/audioset/AT/zipformer/train.py b/egs/audioset/AT/zipformer/train.py
index 67c703364..caf8accb2 100644
--- a/egs/audioset/AT/zipformer/train.py
+++ b/egs/audioset/AT/zipformer/train.py
@@ -74,6 +74,7 @@ from icefall.utils import (
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -799,7 +800,7 @@ def train_one_epoch(
         num_samples += batch_size
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1148,7 +1149,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/baker_zh/TTS/local/compute_fbank_baker_zh.py b/egs/baker_zh/TTS/local/compute_fbank_baker_zh.py
index 0720158f2..deb344d14 100755
--- a/egs/baker_zh/TTS/local/compute_fbank_baker_zh.py
+++ b/egs/baker_zh/TTS/local/compute_fbank_baker_zh.py
@@ -73,6 +73,8 @@ def compute_fbank_baker_zh(num_jobs: int):
         f_min=0,
         f_max=8000,
     )
+    if not torch.cuda.is_available():
+        config.device = "cpu"
 
     prefix = "baker_zh"
     suffix = "jsonl.gz"
diff --git a/egs/commonvoice/ASR/pruned_transducer_stateless7/train.py b/egs/commonvoice/ASR/pruned_transducer_stateless7/train.py
index 5e98084ec..7a859ff38 100755
--- a/egs/commonvoice/ASR/pruned_transducer_stateless7/train.py
+++ b/egs/commonvoice/ASR/pruned_transducer_stateless7/train.py
@@ -88,6 +88,7 @@ from icefall.utils import (
     filter_uneven_sized_batch,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -825,7 +826,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1220,7 +1221,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/finetune.py b/egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/finetune.py
index 976004eca..fb812b391 100755
--- a/egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/finetune.py
+++ b/egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/finetune.py
@@ -90,6 +90,7 @@ from icefall.utils import (
     filter_uneven_sized_batch,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -895,7 +896,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1293,7 +1294,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/train.py b/egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/train.py
index 67e1a8133..f1e9b6d43 100755
--- a/egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/train.py
+++ b/egs/commonvoice/ASR/pruned_transducer_stateless7_streaming/train.py
@@ -81,7 +81,13 @@ from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -840,7 +846,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1237,7 +1243,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/commonvoice/ASR/zipformer/train.py b/egs/commonvoice/ASR/zipformer/train.py
index 271014db0..c6940def5 100755
--- a/egs/commonvoice/ASR/zipformer/train.py
+++ b/egs/commonvoice/ASR/zipformer/train.py
@@ -97,6 +97,7 @@ from icefall.utils import (
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -969,7 +970,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1365,7 +1366,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/commonvoice/ASR/zipformer/train_char.py b/egs/commonvoice/ASR/zipformer/train_char.py
index 0aa7856cc..f44232c0e 100755
--- a/egs/commonvoice/ASR/zipformer/train_char.py
+++ b/egs/commonvoice/ASR/zipformer/train_char.py
@@ -97,6 +97,7 @@ from icefall.utils import (
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -604,7 +605,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -784,7 +785,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/csj/ASR/pruned_transducer_stateless7_streaming/train.py b/egs/csj/ASR/pruned_transducer_stateless7_streaming/train.py
index ef7ea9013..5862cd660 100755
--- a/egs/csj/ASR/pruned_transducer_stateless7_streaming/train.py
+++ b/egs/csj/ASR/pruned_transducer_stateless7_streaming/train.py
@@ -83,7 +83,13 @@ from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 LOG_EPS = math.log(1e-10)
@@ -838,7 +844,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1245,7 +1251,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/gigaspeech/ASR/pruned_transducer_stateless2/train.py b/egs/gigaspeech/ASR/pruned_transducer_stateless2/train.py
index a7772b62f..56371e59a 100755
--- a/egs/gigaspeech/ASR/pruned_transducer_stateless2/train.py
+++ b/egs/gigaspeech/ASR/pruned_transducer_stateless2/train.py
@@ -77,7 +77,13 @@ from icefall.checkpoint import (
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -675,7 +681,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -944,7 +950,7 @@ def scan_pessimistic_batches_for_oom(
             # warmup = 0.0 is so that the derivs for the pruned loss stay zero
             # (i.e. are not remembered by the decaying-average in adam), because
             # we want to avoid these params being subject to shrinkage in adam.
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/gigaspeech/ASR/zipformer/train.py b/egs/gigaspeech/ASR/zipformer/train.py
index 4c122effe..8cf8f9fc7 100755
--- a/egs/gigaspeech/ASR/zipformer/train.py
+++ b/egs/gigaspeech/ASR/zipformer/train.py
@@ -97,6 +97,7 @@ from icefall.utils import (
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -958,7 +959,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1317,7 +1318,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/gigaspeech/KWS/zipformer/train.py b/egs/gigaspeech/KWS/zipformer/train.py
index 39d8fc6cd..2d88b6e55 100755
--- a/egs/gigaspeech/KWS/zipformer/train.py
+++ b/egs/gigaspeech/KWS/zipformer/train.py
@@ -97,6 +97,7 @@ from icefall.utils import (
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -961,7 +962,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1320,7 +1321,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/ksponspeech/ASR/pruned_transducer_stateless7_streaming/train.py b/egs/ksponspeech/ASR/pruned_transducer_stateless7_streaming/train.py
index bf50bf5ea..63a38a4cc 100755
--- a/egs/ksponspeech/ASR/pruned_transducer_stateless7_streaming/train.py
+++ b/egs/ksponspeech/ASR/pruned_transducer_stateless7_streaming/train.py
@@ -77,7 +77,13 @@ from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -805,7 +811,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1196,7 +1202,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/ksponspeech/ASR/zipformer/train.py b/egs/ksponspeech/ASR/zipformer/train.py
index 485ea69c9..406749f22 100755
--- a/egs/ksponspeech/ASR/zipformer/train.py
+++ b/egs/ksponspeech/ASR/zipformer/train.py
@@ -92,6 +92,7 @@ from icefall.utils import (
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -942,7 +943,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1333,7 +1334,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/conformer_ctc/decode.py b/egs/librispeech/ASR/conformer_ctc/decode.py
index 7e0bf5b7b..fc866f83b 100755
--- a/egs/librispeech/ASR/conformer_ctc/decode.py
+++ b/egs/librispeech/ASR/conformer_ctc/decode.py
@@ -667,7 +667,9 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(
+                f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False
+            )
         )
         assert HLG.requires_grad is False
 
@@ -707,7 +709,9 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
+            d = torch.load(
+                params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False
+            )
             G = k2.Fsa.from_dict(d)
 
         if params.method in [
diff --git a/egs/librispeech/ASR/conformer_ctc/pretrained.py b/egs/librispeech/ASR/conformer_ctc/pretrained.py
index 38b60fcb9..5b3a021ad 100755
--- a/egs/librispeech/ASR/conformer_ctc/pretrained.py
+++ b/egs/librispeech/ASR/conformer_ctc/pretrained.py
@@ -271,7 +271,7 @@ def main():
         use_feat_batchnorm=params.use_feat_batchnorm,
     )
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
@@ -351,7 +351,9 @@ def main():
         "attention-decoder",
     ]:
         logging.info(f"Loading HLG from {params.HLG}")
-        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = k2.Fsa.from_dict(
+            torch.load(params.HLG, map_location="cpu", weights_only=False)
+        )
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder
@@ -362,7 +364,9 @@ def main():
             "attention-decoder",
         ]:
             logging.info(f"Loading G from {params.G}")
-            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = k2.Fsa.from_dict(
+                torch.load(params.G, map_location="cpu", weights_only=False)
+            )
             # Add epsilon self-loops to G as we will compose
             # it with the whole lattice later
             G = G.to(device)
diff --git a/egs/librispeech/ASR/conformer_ctc2/decode.py b/egs/librispeech/ASR/conformer_ctc2/decode.py
index 0b271a51c..349e8f02d 100755
--- a/egs/librispeech/ASR/conformer_ctc2/decode.py
+++ b/egs/librispeech/ASR/conformer_ctc2/decode.py
@@ -774,7 +774,9 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(
+                f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False
+            )
         )
         assert HLG.requires_grad is False
 
@@ -814,7 +816,9 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
+            d = torch.load(
+                params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False
+            )
             G = k2.Fsa.from_dict(d)
 
         if params.method in [
diff --git a/egs/librispeech/ASR/conformer_ctc2/train.py b/egs/librispeech/ASR/conformer_ctc2/train.py
index c4a13b101..14c132ada 100755
--- a/egs/librispeech/ASR/conformer_ctc2/train.py
+++ b/egs/librispeech/ASR/conformer_ctc2/train.py
@@ -65,7 +65,6 @@ from lhotse.dataset.sampling.base import CutSampler
 from lhotse.utils import fix_random_seed
 from optim import Eden, Eve
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -84,9 +83,11 @@ from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     encode_supervisions,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -420,7 +421,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -629,7 +630,7 @@ def train_one_epoch(
     scheduler: LRSchedulerType,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -676,7 +677,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -965,7 +966,7 @@ def run(rank, world_size, args):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1036,7 +1037,7 @@ def scan_pessimistic_batches_for_oom(
             # warmup = 0.0 is so that the derivs for the pruned loss stay zero
             # (i.e. are not remembered by the decaying-average in adam), because
             # we want to avoid these params being subject to shrinkage in adam.
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/conformer_ctc3/decode.py b/egs/librispeech/ASR/conformer_ctc3/decode.py
index e6327bb5e..cf58fd18d 100755
--- a/egs/librispeech/ASR/conformer_ctc3/decode.py
+++ b/egs/librispeech/ASR/conformer_ctc3/decode.py
@@ -868,7 +868,9 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(
+                f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False
+            )
         )
         assert HLG.requires_grad is False
 
@@ -907,7 +909,9 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
+            d = torch.load(
+                params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False
+            )
             G = k2.Fsa.from_dict(d)
 
         if params.decoding_method == "whole-lattice-rescoring":
diff --git a/egs/librispeech/ASR/conformer_ctc3/jit_pretrained.py b/egs/librispeech/ASR/conformer_ctc3/jit_pretrained.py
index 19b26361e..f8e3fa43b 100755
--- a/egs/librispeech/ASR/conformer_ctc3/jit_pretrained.py
+++ b/egs/librispeech/ASR/conformer_ctc3/jit_pretrained.py
@@ -334,7 +334,9 @@ def main():
         "whole-lattice-rescoring",
     ]:
         logging.info(f"Loading HLG from {params.HLG}")
-        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = k2.Fsa.from_dict(
+            torch.load(params.HLG, map_location="cpu", weights_only=False)
+        )
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder
@@ -345,7 +347,9 @@ def main():
             "whole-lattice-rescoring",
         ]:
             logging.info(f"Loading G from {params.G}")
-            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = k2.Fsa.from_dict(
+                torch.load(params.G, map_location="cpu", weights_only=False)
+            )
             G = G.to(device)
             if params.method == "whole-lattice-rescoring":
                 # Add epsilon self-loops to G as we will compose
diff --git a/egs/librispeech/ASR/conformer_ctc3/pretrained.py b/egs/librispeech/ASR/conformer_ctc3/pretrained.py
index a0cdfcf03..e528b2cb8 100755
--- a/egs/librispeech/ASR/conformer_ctc3/pretrained.py
+++ b/egs/librispeech/ASR/conformer_ctc3/pretrained.py
@@ -290,7 +290,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
@@ -386,7 +386,9 @@ def main():
         "whole-lattice-rescoring",
     ]:
         logging.info(f"Loading HLG from {params.HLG}")
-        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = k2.Fsa.from_dict(
+            torch.load(params.HLG, map_location="cpu", weights_only=False)
+        )
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder
@@ -397,7 +399,9 @@ def main():
             "whole-lattice-rescoring",
         ]:
             logging.info(f"Loading G from {params.G}")
-            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = k2.Fsa.from_dict(
+                torch.load(params.G, map_location="cpu", weights_only=False)
+            )
             G = G.to(device)
             if params.method == "whole-lattice-rescoring":
                 # Add epsilon self-loops to G as we will compose
diff --git a/egs/librispeech/ASR/conformer_ctc3/train.py b/egs/librispeech/ASR/conformer_ctc3/train.py
index a2f1125ca..64e77f421 100755
--- a/egs/librispeech/ASR/conformer_ctc3/train.py
+++ b/egs/librispeech/ASR/conformer_ctc3/train.py
@@ -76,7 +76,6 @@ from lhotse.utils import fix_random_seed
 from model import CTCModel
 from optim import Eden, Eve
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -95,9 +94,11 @@ from icefall.lexicon import Lexicon
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     encode_supervisions,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -493,7 +494,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -694,7 +695,7 @@ def train_one_epoch(
     graph_compiler: Union[BpeCtcTrainingGraphCompiler, CtcTrainingGraphCompiler],
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -743,7 +744,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -1004,7 +1005,7 @@ def run(rank, world_size, args):
             warmup=0.0 if params.start_epoch == 1 else 1.0,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1073,7 +1074,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/conformer_mmi/decode.py b/egs/librispeech/ASR/conformer_mmi/decode.py
index 74f6e73fa..01fcf0685 100755
--- a/egs/librispeech/ASR/conformer_mmi/decode.py
+++ b/egs/librispeech/ASR/conformer_mmi/decode.py
@@ -574,7 +574,9 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu")
+            torch.load(
+                f"{params.lang_dir}/HLG.pt", map_location="cpu", weights_only=False
+            )
         )
         HLG = HLG.to(device)
         assert HLG.requires_grad is False
@@ -609,7 +611,9 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location="cpu")
+            d = torch.load(
+                params.lm_dir / "G_4_gram.pt", map_location="cpu", weights_only=False
+            )
             G = k2.Fsa.from_dict(d).to(device)
 
         if params.method in ["whole-lattice-rescoring", "attention-decoder"]:
diff --git a/egs/librispeech/ASR/conv_emformer_transducer_stateless/train.py b/egs/librispeech/ASR/conv_emformer_transducer_stateless/train.py
index ca21bd6bf..fc33f9512 100755
--- a/egs/librispeech/ASR/conv_emformer_transducer_stateless/train.py
+++ b/egs/librispeech/ASR/conv_emformer_transducer_stateless/train.py
@@ -80,7 +80,6 @@ from lhotse.utils import fix_random_seed
 from model import Transducer
 from optim import Eden, Eve
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -93,7 +92,14 @@ from icefall.checkpoint import (
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    create_grad_scaler,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -560,7 +566,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -727,7 +733,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -772,7 +778,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -1002,7 +1008,7 @@ def run(rank, world_size, args):
             warmup=0.0 if params.start_epoch == 1 else 1.0,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1071,7 +1077,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/conv_emformer_transducer_stateless2/train.py b/egs/librispeech/ASR/conv_emformer_transducer_stateless2/train.py
index 23ddb6bec..b00cc6cc6 100755
--- a/egs/librispeech/ASR/conv_emformer_transducer_stateless2/train.py
+++ b/egs/librispeech/ASR/conv_emformer_transducer_stateless2/train.py
@@ -80,7 +80,6 @@ from lhotse.utils import fix_random_seed
 from model import Transducer
 from optim import Eden, Eve
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -93,7 +92,14 @@ from icefall.checkpoint import (
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    create_grad_scaler,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -560,7 +566,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -727,7 +733,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -772,7 +778,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -1001,7 +1007,7 @@ def run(rank, world_size, args):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1072,7 +1078,7 @@ def scan_pessimistic_batches_for_oom(
             # warmup = 0.0 is so that the derivs for the pruned loss stay zero
             # (i.e. are not remembered by the decaying-average in adam), because
             # we want to avoid these params being subject to shrinkage in adam.
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/local/compile_hlg.py b/egs/librispeech/ASR/local/compile_hlg.py
index d19d50ae6..ec39d5b36 100755
--- a/egs/librispeech/ASR/local/compile_hlg.py
+++ b/egs/librispeech/ASR/local/compile_hlg.py
@@ -72,11 +72,11 @@ def compile_HLG(lang_dir: str, lm: str = "G_3_gram") -> k2.Fsa:
     max_token_id = max(lexicon.tokens)
     logging.info(f"Building ctc_topo. max_token_id: {max_token_id}")
     H = k2.ctc_topo(max_token_id)
-    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
+    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt", weights_only=False))
 
     if Path(f"data/lm/{lm}.pt").is_file():
         logging.info(f"Loading pre-compiled {lm}")
-        d = torch.load(f"data/lm/{lm}.pt")
+        d = torch.load(f"data/lm/{lm}.pt", weights_only=False)
         G = k2.Fsa.from_dict(d)
     else:
         logging.info(f"Loading {lm}.fst.txt")
diff --git a/egs/librispeech/ASR/local/compile_lg.py b/egs/librispeech/ASR/local/compile_lg.py
index 709b14070..bd25cfa29 100755
--- a/egs/librispeech/ASR/local/compile_lg.py
+++ b/egs/librispeech/ASR/local/compile_lg.py
@@ -66,11 +66,11 @@ def compile_LG(lang_dir: str, lm: str = "G_3_gram") -> k2.Fsa:
       An FSA representing LG.
     """
     lexicon = Lexicon(lang_dir)
-    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
+    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt", weights_only=False))
 
     if Path(f"data/lm/{lm}.pt").is_file():
         logging.info(f"Loading pre-compiled {lm}")
-        d = torch.load(f"data/lm/{lm}.pt")
+        d = torch.load(f"data/lm/{lm}.pt", weights_only=False)
         G = k2.Fsa.from_dict(d)
     else:
         logging.info(f"Loading {lm}.fst.txt")
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless/decode.py b/egs/librispeech/ASR/lstm_transducer_stateless/decode.py
index 856c9d945..8c75eb871 100755
--- a/egs/librispeech/ASR/lstm_transducer_stateless/decode.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless/decode.py
@@ -750,7 +750,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless/model.py b/egs/librispeech/ASR/lstm_transducer_stateless/model.py
index e7bad7ed8..9f148b348 100644
--- a/egs/librispeech/ASR/lstm_transducer_stateless/model.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless/model.py
@@ -23,7 +23,7 @@ import torch.nn as nn
 from encoder_interface import EncoderInterface
 from scaling import ScaledLinear
 
-from icefall.utils import add_sos
+from icefall.utils import add_sos, torch_autocast
 
 
 class Transducer(nn.Module):
@@ -156,7 +156,7 @@ class Transducer(nn.Module):
         lm = self.simple_lm_proj(decoder_out)
         am = self.simple_am_proj(encoder_out)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -192,7 +192,7 @@ class Transducer(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = self.joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless/pretrained.py b/egs/librispeech/ASR/lstm_transducer_stateless/pretrained.py
index 42c3a5d7f..f29d1d9db 100755
--- a/egs/librispeech/ASR/lstm_transducer_stateless/pretrained.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless/pretrained.py
@@ -238,7 +238,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless/train.py b/egs/librispeech/ASR/lstm_transducer_stateless/train.py
index feb81d500..e23da3b56 100755
--- a/egs/librispeech/ASR/lstm_transducer_stateless/train.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless/train.py
@@ -66,7 +66,6 @@ from lstm import RNN
 from model import Transducer
 from optim import Eden, Eve
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -82,9 +81,11 @@ from icefall.env import get_env_info
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     display_and_save_batch,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -521,7 +522,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -717,7 +718,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -763,7 +764,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1023,7 +1024,7 @@ def run(rank, world_size, args):
             warmup=0.0 if params.start_epoch == 1 else 1.0,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1092,7 +1093,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless2/decode.py b/egs/librispeech/ASR/lstm_transducer_stateless2/decode.py
index 1a724830b..cfbbb334c 100755
--- a/egs/librispeech/ASR/lstm_transducer_stateless2/decode.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless2/decode.py
@@ -935,7 +935,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless2/model.py b/egs/librispeech/ASR/lstm_transducer_stateless2/model.py
index 4957d14b1..5aafe10af 100644
--- a/egs/librispeech/ASR/lstm_transducer_stateless2/model.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless2/model.py
@@ -23,7 +23,7 @@ import torch.nn as nn
 from encoder_interface import EncoderInterface
 from scaling import ScaledLinear
 
-from icefall.utils import add_sos
+from icefall.utils import add_sos, torch_autocast
 
 
 class Transducer(nn.Module):
@@ -195,7 +195,7 @@ class Transducer(nn.Module):
         lm = simple_lm_proj(decoder_out)
         am = simple_am_proj(encoder_out)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -231,7 +231,7 @@ class Transducer(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless2/pretrained.py b/egs/librispeech/ASR/lstm_transducer_stateless2/pretrained.py
index dcff088e2..888f9931e 100755
--- a/egs/librispeech/ASR/lstm_transducer_stateless2/pretrained.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless2/pretrained.py
@@ -241,7 +241,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless2/train.py b/egs/librispeech/ASR/lstm_transducer_stateless2/train.py
index 4fc4fa7f8..1b31b5485 100755
--- a/egs/librispeech/ASR/lstm_transducer_stateless2/train.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless2/train.py
@@ -74,7 +74,6 @@ from lstm import RNN
 from model import Transducer
 from optim import Eden, Eve
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -90,9 +89,11 @@ from icefall.env import get_env_info
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     display_and_save_batch,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -560,7 +561,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -772,7 +773,7 @@ def train_one_epoch(
     giga_train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
     rng: random.Random,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -848,7 +849,7 @@ def train_one_epoch(
         libri = is_libri(batch["supervisions"]["cut"][0])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1176,7 +1177,7 @@ def run(rank, world_size, args):
         else:
             logging.info("Skip scan_pessimistic_batches_for_oom")
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1247,7 +1248,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless3/decode.py b/egs/librispeech/ASR/lstm_transducer_stateless3/decode.py
index a2b4f9e1a..e25b79e2e 100755
--- a/egs/librispeech/ASR/lstm_transducer_stateless3/decode.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless3/decode.py
@@ -815,7 +815,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless3/pretrained.py b/egs/librispeech/ASR/lstm_transducer_stateless3/pretrained.py
index e39637bd8..619e783b0 100755
--- a/egs/librispeech/ASR/lstm_transducer_stateless3/pretrained.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless3/pretrained.py
@@ -239,7 +239,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/lstm_transducer_stateless3/train.py b/egs/librispeech/ASR/lstm_transducer_stateless3/train.py
index 2c1cef3a3..e169b499f 100755
--- a/egs/librispeech/ASR/lstm_transducer_stateless3/train.py
+++ b/egs/librispeech/ASR/lstm_transducer_stateless3/train.py
@@ -66,7 +66,6 @@ from lstm import RNN
 from model import Transducer
 from optim import Eden, Eve
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -82,9 +81,11 @@ from icefall.env import get_env_info
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     display_and_save_batch,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -551,7 +552,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -747,7 +748,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -793,7 +794,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1067,7 +1068,7 @@ def run(rank, world_size, args):
             warmup=0.0 if params.start_epoch == 1 else 1.0,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1136,7 +1137,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_.autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned2_knowledge/model.py b/egs/librispeech/ASR/pruned2_knowledge/model.py
index ca8c28af1..3b6ce9b89 100644
--- a/egs/librispeech/ASR/pruned2_knowledge/model.py
+++ b/egs/librispeech/ASR/pruned2_knowledge/model.py
@@ -21,7 +21,7 @@ import torch.nn as nn
 from encoder_interface import EncoderInterface
 from scaling import ScaledLinear
 
-from icefall.utils import add_sos
+from icefall.utils import add_sos, torch_autocast
 
 
 class Transducer(nn.Module):
@@ -141,7 +141,7 @@ class Transducer(nn.Module):
         lm = self.simple_lm_proj(decoder_out)
         am = self.simple_am_proj(encoder_out)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -176,7 +176,7 @@ class Transducer(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = self.joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/librispeech/ASR/pruned2_knowledge/sampling.py b/egs/librispeech/ASR/pruned2_knowledge/sampling.py
index 5b595c76c..5850555cd 100644
--- a/egs/librispeech/ASR/pruned2_knowledge/sampling.py
+++ b/egs/librispeech/ASR/pruned2_knowledge/sampling.py
@@ -10,9 +10,11 @@ from typing import Optional, Tuple
 import torch
 from scaling import ScaledLinear
 from torch import Tensor, nn
-from torch.cuda.amp import GradScaler, custom_bwd, custom_fwd
+from torch.cuda.amp import custom_bwd, custom_fwd
 from torch_scheduled_sampling import sample_combined
 
+from icefall.utils import create_grad_scaler, torch_autocast
+
 # The main exports of this file are the module KnowledgeBaseLookup and the
 # function create_knowledge_base.
 
@@ -330,14 +332,14 @@ def _test_knowledge_base_lookup_autocast():
     optimizer = Eve(m.parameters(), lr=0.005, eps=1.0e-04)
     m = m.to(device)
 
-    scaler = GradScaler(enabled=True)
+    scaler = create_grad_scaler(enabled=True)
 
     start = timeit.default_timer()
 
     for epoch in range(150):
         for n, (x, y) in enumerate(train_pairs):
             y_out = m(x)
-            with torch.cuda.amp.autocast(enabled=True):
+            with torch_autocast(enabled=True):
                 loss = ((y_out - y) ** 2).mean() * 100.0
             if n % 10 == 0 and epoch % 10 == 0:
                 print(f"Epoch {epoch}, batch {n}, loss {loss.item()}")
diff --git a/egs/librispeech/ASR/pruned2_knowledge/train.py b/egs/librispeech/ASR/pruned2_knowledge/train.py
index 931341cc4..0611fd8cb 100755
--- a/egs/librispeech/ASR/pruned2_knowledge/train.py
+++ b/egs/librispeech/ASR/pruned2_knowledge/train.py
@@ -66,7 +66,6 @@ from lhotse.utils import fix_random_seed
 from model import Transducer
 from optim import Eden, Eve
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -76,7 +75,14 @@ from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
 from icefall.checkpoint import save_checkpoint_with_global_batch_idx
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    create_grad_scaler,
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -453,7 +459,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -608,7 +614,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
     rank: int = 0,
@@ -650,7 +656,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -868,7 +874,7 @@ def run(rank, world_size, args):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -937,7 +943,7 @@ def scan_pessimistic_batches_for_oom(
             # warmup = 0.0 is so that the derivs for the pruned loss stay zero
             # (i.e. are not remembered by the decaying-average in adam), because
             # we want to avoid these params being subject to shrinkage in adam.
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/train.py b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/train.py
index 2b872f1d5..2af8f3f4c 100755
--- a/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/train.py
+++ b/egs/librispeech/ASR/pruned_stateless_emformer_rnnt2/train.py
@@ -55,7 +55,6 @@ from lhotse.utils import fix_random_seed
 from model import Transducer
 from noam import Noam
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -68,7 +67,14 @@ from icefall.checkpoint import (
 )
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    create_grad_scaler,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 
 def add_model_arguments(parser: argparse.ArgumentParser):
@@ -496,7 +502,7 @@ def save_checkpoint(
     model_avg: Optional[nn.Module] = None,
     optimizer: Optional[torch.optim.Optimizer] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, and training stats to file.
@@ -650,7 +656,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -693,7 +699,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -939,7 +945,7 @@ def run(rank, world_size, args):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1004,7 +1010,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless/decode.py
index 3c4500087..6d1da7440 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless/decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless/decode.py
@@ -741,7 +741,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py
index c57514193..5a4a74ebb 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless2/decode.py
@@ -754,7 +754,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/model.py b/egs/librispeech/ASR/pruned_transducer_stateless2/model.py
index 272d06c37..6a69332aa 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless2/model.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless2/model.py
@@ -23,7 +23,7 @@ import torch.nn as nn
 from encoder_interface import EncoderInterface
 from scaling import ScaledLinear
 
-from icefall.utils import add_sos
+from icefall.utils import add_sos, torch_autocast
 
 
 class Transducer(nn.Module):
@@ -157,7 +157,7 @@ class Transducer(nn.Module):
         lm = self.simple_lm_proj(decoder_out)
         am = self.simple_am_proj(encoder_out)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -193,7 +193,7 @@ class Transducer(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = self.joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless2/pretrained.py
index 6923f4d40..e6ddcab25 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless2/pretrained.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless2/pretrained.py
@@ -265,7 +265,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless2/train.py b/egs/librispeech/ASR/pruned_transducer_stateless2/train.py
index 6c19f2cb0..ce6c89614 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless2/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless2/train.py
@@ -78,7 +78,6 @@ from lhotse.utils import fix_random_seed
 from model import Transducer
 from optim import Eden, Eve
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -91,9 +90,11 @@ from icefall.env import get_env_info
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     display_and_save_batch,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -523,7 +524,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -716,7 +717,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
     rank: int = 0,
@@ -759,7 +760,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1000,7 +1001,7 @@ def run(rank, world_size, args):
             warmup=0.0 if params.start_epoch == 0 else 1.0,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1067,7 +1068,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py
index 7c62bfa58..18a3792b0 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless3/decode.py
@@ -921,7 +921,7 @@ def load_ngram_LM(
 
     if pt_file.is_file():
         logging.info(f"Loading pre-compiled {pt_file}")
-        d = torch.load(pt_file, map_location=device)
+        d = torch.load(pt_file, map_location=device, weights_only=False)
         G = k2.Fsa.from_dict(d)
         G = k2.add_epsilon_self_loops(G)
         G = k2.arc_sort(G)
@@ -1101,7 +1101,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         elif params.decoding_method in [
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/model.py b/egs/librispeech/ASR/pruned_transducer_stateless3/model.py
index d45f6dadc..fbc4db921 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless3/model.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless3/model.py
@@ -23,7 +23,7 @@ import torch.nn as nn
 from encoder_interface import EncoderInterface
 from scaling import ScaledLinear
 
-from icefall.utils import add_sos
+from icefall.utils import add_sos, torch_autocast
 
 
 class Transducer(nn.Module):
@@ -195,7 +195,7 @@ class Transducer(nn.Module):
         lm = simple_lm_proj(decoder_out)
         am = simple_am_proj(encoder_out)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -231,7 +231,7 @@ class Transducer(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless3/pretrained.py
index 05e6a6fba..19143fb5d 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless3/pretrained.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless3/pretrained.py
@@ -274,7 +274,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless3/train.py b/egs/librispeech/ASR/pruned_transducer_stateless3/train.py
index fdafa5a87..50670d1b2 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless3/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless3/train.py
@@ -74,7 +74,6 @@ from librispeech import LibriSpeech
 from model import Transducer
 from optim import Eden, Eve
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -87,9 +86,11 @@ from icefall.env import get_env_info
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     display_and_save_batch,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -546,7 +547,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -755,7 +756,7 @@ def train_one_epoch(
     giga_train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
     rng: random.Random,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
     rank: int = 0,
@@ -827,7 +828,7 @@ def train_one_epoch(
 
         libri = is_libri(batch["supervisions"]["cut"][0])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -1126,7 +1127,7 @@ def run(rank, world_size, args):
                 warmup=0.0 if params.start_epoch == 0 else 1.0,
             )
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1195,7 +1196,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py
index 5195a4ef6..925c01c7b 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless4/decode.py
@@ -913,7 +913,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless4/train.py b/egs/librispeech/ASR/pruned_transducer_stateless4/train.py
index 875b03f7f..c35f52309 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless4/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless4/train.py
@@ -80,7 +80,6 @@ from lhotse.utils import fix_random_seed
 from model import Transducer
 from optim import Eden, Eve
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -96,9 +95,11 @@ from icefall.env import get_env_info
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     display_and_save_batch,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -548,7 +549,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -744,7 +745,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -789,7 +790,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -1047,7 +1048,7 @@ def run(rank, world_size, args):
             warmup=0.0 if params.start_epoch == 1 else 1.0,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1116,7 +1117,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless5/decode.py
index 7a3e63218..404d7a3d3 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless5/decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless5/decode.py
@@ -972,7 +972,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless5/pretrained.py
index a9ce75a7b..9e2669379 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless5/pretrained.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless5/pretrained.py
@@ -238,7 +238,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless5/train.py b/egs/librispeech/ASR/pruned_transducer_stateless5/train.py
index 66dc5f991..6f9f92623 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless5/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless5/train.py
@@ -68,7 +68,6 @@ from lhotse.utils import fix_random_seed
 from model import Transducer
 from optim import Eden, Eve
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -84,9 +83,11 @@ from icefall.env import get_env_info
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     display_and_save_batch,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -571,7 +572,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -768,7 +769,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -814,7 +815,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1078,7 +1079,7 @@ def run(rank, world_size, args):
             warmup=0.0 if params.start_epoch == 1 else 1.0,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1147,7 +1148,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/model.py b/egs/librispeech/ASR/pruned_transducer_stateless6/model.py
index daadb70c9..a5d2457f9 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless6/model.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless6/model.py
@@ -23,7 +23,7 @@ import torch.nn as nn
 from encoder_interface import EncoderInterface
 from scaling import ScaledLinear
 
-from icefall.utils import add_sos
+from icefall.utils import add_sos, torch_autocast
 
 
 class Transducer(nn.Module):
@@ -185,7 +185,7 @@ class Transducer(nn.Module):
         lm = self.simple_lm_proj(decoder_out)
         am = self.simple_am_proj(encoder_out)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -220,7 +220,7 @@ class Transducer(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = self.joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/train.py b/egs/librispeech/ASR/pruned_transducer_stateless6/train.py
index 8f033cb9a..35ee74f15 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless6/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless6/train.py
@@ -80,7 +80,6 @@ from lhotse.utils import fix_random_seed
 from model import Transducer
 from optim import Eden, Eve
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 
@@ -96,9 +95,11 @@ from icefall.env import get_env_info
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     display_and_save_batch,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -519,7 +520,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -736,7 +737,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -781,7 +782,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -1039,7 +1040,7 @@ def run(rank, world_size, args):
             warmup=0.0 if params.start_epoch == 1 else 1.0,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16)
+    scaler = create_grad_scaler(enabled=params.use_fp16)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1108,7 +1109,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py b/egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py
index 3bca7db2c..4f3fbaa81 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless6/vq_utils.py
@@ -348,7 +348,9 @@ class CodebookIndexExtractor:
             num_codebooks=self.params.num_codebooks,
             codebook_size=256,
         )
-        quantizer.load_state_dict(torch.load(self.quantizer_file_path))
+        quantizer.load_state_dict(
+            torch.load(self.quantizer_file_path, weights_only=False)
+        )
         quantizer.to(self.params.device)
         return quantizer
 
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/compute_ali.py b/egs/librispeech/ASR/pruned_transducer_stateless7/compute_ali.py
index 27ef0a244..949a497ce 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/compute_ali.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/compute_ali.py
@@ -289,7 +289,7 @@ def main():
     logging.info("About to create model")
     model = get_transducer_model(params)
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7/decode.py
index eb8841cc4..048de7bb9 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/decode.py
@@ -910,7 +910,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/decode_gigaspeech.py b/egs/librispeech/ASR/pruned_transducer_stateless7/decode_gigaspeech.py
index 7095c3cc8..da1bf17fc 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/decode_gigaspeech.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/decode_gigaspeech.py
@@ -813,7 +813,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/finetune.py b/egs/librispeech/ASR/pruned_transducer_stateless7/finetune.py
index e7546ec45..d3d996b4a 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/finetune.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/finetune.py
@@ -66,7 +66,6 @@ from lhotse.utils import fix_random_seed
 from model import Transducer
 from optim import Eden, ScaledAdam
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer
@@ -85,9 +84,11 @@ from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     filter_uneven_sized_batch,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -635,7 +636,7 @@ def load_model_params(
 
     """
     logging.info(f"Loading checkpoint from {ckpt}")
-    checkpoint = torch.load(ckpt, map_location="cpu")
+    checkpoint = torch.load(ckpt, map_location="cpu", weights_only=False)
 
     # if module list is empty, load the whole model from ckpt
     if not init_modules:
@@ -678,7 +679,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -857,7 +858,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -903,7 +904,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1219,7 +1220,7 @@ def run(rank, world_size, args):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1319,7 +1320,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/model.py b/egs/librispeech/ASR/pruned_transducer_stateless7/model.py
index add0e6a18..ed990b689 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/model.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/model.py
@@ -23,7 +23,7 @@ import torch.nn as nn
 from encoder_interface import EncoderInterface
 from scaling import penalize_abs_values_gt
 
-from icefall.utils import add_sos
+from icefall.utils import add_sos, torch_autocast
 
 
 class Transducer(nn.Module):
@@ -150,7 +150,7 @@ class Transducer(nn.Module):
         # if self.training and random.random() < 0.25:
         #    am = penalize_abs_values_gt(am, 30.0, 1.0e-04)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -185,7 +185,7 @@ class Transducer(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = self.joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless7/pretrained.py
index 4bf11ac24..fabda3aaa 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/pretrained.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/pretrained.py
@@ -247,7 +247,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py b/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
index 30a737061..5a317083c 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
@@ -28,6 +28,8 @@ import torch.nn.functional as F
 from torch import Tensor
 from torch.nn import Embedding as ScaledEmbedding
 
+from icefall.utils import torch_autocast
+
 
 class ActivationBalancerFunction(torch.autograd.Function):
     @staticmethod
@@ -289,7 +291,7 @@ class SoftmaxFunction(torch.autograd.Function):
     @staticmethod
     def backward(ctx, ans_grad: Tensor):
         (ans,) = ctx.saved_tensors
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             ans_grad = ans_grad.to(torch.float32)
             ans = ans.to(torch.float32)
             x_grad = ans_grad * ans
@@ -669,7 +671,7 @@ class WhiteningPenaltyFunction(torch.autograd.Function):
     def backward(ctx, x_grad: Tensor):
         (x_orig,) = ctx.saved_tensors
         with torch.enable_grad():
-            with torch.cuda.amp.autocast(enabled=False):
+            with torch_autocast(enabled=False):
                 x_detached = x_orig.to(torch.float32).detach()
                 x_detached.requires_grad = True
 
@@ -867,7 +869,7 @@ class MaxEig(torch.nn.Module):
         ):
             return _no_op(x)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             eps = 1.0e-20
             orig_x = x
             x = x.to(torch.float32)
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/train.py b/egs/librispeech/ASR/pruned_transducer_stateless7/train.py
index 436ec53b4..f94da9788 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/train.py
@@ -67,7 +67,6 @@ from lhotse.utils import fix_random_seed
 from model import Transducer
 from optim import Eden, ScaledAdam
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer
@@ -86,10 +85,12 @@ from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     filter_uneven_sized_batch,
     setup_logger,
     str2bool,
     symlink_or_copy,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -581,7 +582,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -763,7 +764,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -809,7 +810,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1106,7 +1107,7 @@ def run(rank, world_size, args):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1206,7 +1207,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
index cbde2a2e4..ee05627ae 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
@@ -44,7 +44,7 @@ from scaling import (
 from torch import Tensor, nn
 
 from icefall.dist import get_rank
-from icefall.utils import is_jit_tracing, make_pad_mask
+from icefall.utils import is_jit_tracing, make_pad_mask, torch_autocast
 
 
 class Zipformer(EncoderInterface):
@@ -1421,7 +1421,7 @@ class RelPositionMultiheadAttention(nn.Module):
         bsz = n // num_heads
 
         with torch.no_grad():
-            with torch.cuda.amp.autocast(enabled=False):
+            with torch_autocast(enabled=False):
                 attn_weights = attn_weights.to(torch.float32)
                 attn_output = attn_output.to(torch.float32)
                 attn_weights_entropy = (
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/ctc_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/ctc_decode.py
index 629bec058..3b181bf23 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/ctc_decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/ctc_decode.py
@@ -633,7 +633,9 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(
+                f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False
+            )
         )
         assert HLG.requires_grad is False
 
@@ -672,7 +674,9 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
+            d = torch.load(
+                params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False
+            )
             G = k2.Fsa.from_dict(d)
 
         if params.decoding_method == "whole-lattice-rescoring":
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/decode.py
index 7641fa5af..9e16c3fd7 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/decode.py
@@ -786,7 +786,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/jit_pretrained_ctc.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/jit_pretrained_ctc.py
index d1b7eec65..f7dd07f8d 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/jit_pretrained_ctc.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/jit_pretrained_ctc.py
@@ -347,7 +347,9 @@ def main():
         "whole-lattice-rescoring",
     ]:
         logging.info(f"Loading HLG from {params.HLG}")
-        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = k2.Fsa.from_dict(
+            torch.load(params.HLG, map_location="cpu", weights_only=False)
+        )
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder
@@ -358,7 +360,9 @@ def main():
             "whole-lattice-rescoring",
         ]:
             logging.info(f"Loading G from {params.G}")
-            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = k2.Fsa.from_dict(
+                torch.load(params.G, map_location="cpu", weights_only=False)
+            )
             G = G.to(device)
             if params.method == "whole-lattice-rescoring":
                 # Add epsilon self-loops to G as we will compose
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/model.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/model.py
index a6e919e2f..f1ab2a3ec 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/model.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/model.py
@@ -22,7 +22,7 @@ import torch
 import torch.nn as nn
 from encoder_interface import EncoderInterface
 
-from icefall.utils import add_sos
+from icefall.utils import add_sos, torch_autocast
 
 
 class Transducer(nn.Module):
@@ -150,7 +150,7 @@ class Transducer(nn.Module):
         lm = self.simple_lm_proj(decoder_out)
         am = self.simple_am_proj(encoder_out)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -185,7 +185,7 @@ class Transducer(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = self.joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/pretrained.py
index 323ba2642..a13952dfa 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/pretrained.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/pretrained.py
@@ -247,7 +247,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/pretrained_ctc.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/pretrained_ctc.py
index 1e638aa7d..32242c94e 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/pretrained_ctc.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/pretrained_ctc.py
@@ -286,7 +286,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
@@ -365,7 +365,9 @@ def main():
         "whole-lattice-rescoring",
     ]:
         logging.info(f"Loading HLG from {params.HLG}")
-        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = k2.Fsa.from_dict(
+            torch.load(params.HLG, map_location="cpu", weights_only=False)
+        )
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder
@@ -376,7 +378,9 @@ def main():
             "whole-lattice-rescoring",
         ]:
             logging.info(f"Loading G from {params.G}")
-            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = k2.Fsa.from_dict(
+                torch.load(params.G, map_location="cpu", weights_only=False)
+            )
             G = G.to(device)
             if params.method == "whole-lattice-rescoring":
                 # Add epsilon self-loops to G as we will compose
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/train.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/train.py
index b35e56abc..a26f11c82 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc/train.py
@@ -67,7 +67,6 @@ from lhotse.utils import fix_random_seed
 from model import Transducer
 from optim import Eden, ScaledAdam
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer
@@ -86,9 +85,11 @@ from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     encode_supervisions,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -588,7 +589,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -787,7 +788,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -833,7 +834,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1128,7 +1129,7 @@ def run(rank, world_size, args):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1228,7 +1229,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/ctc_decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/ctc_decode.py
index fa7144f0f..3af3ada2c 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/ctc_decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/ctc_decode.py
@@ -624,7 +624,9 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(
+                f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False
+            )
         )
         assert HLG.requires_grad is False
 
@@ -663,7 +665,9 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
+            d = torch.load(
+                params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False
+            )
             G = k2.Fsa.from_dict(d)
 
         if params.decoding_method == "whole-lattice-rescoring":
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/ctc_guide_decode_bs.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/ctc_guide_decode_bs.py
index e2f08abc6..233f00236 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/ctc_guide_decode_bs.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/ctc_guide_decode_bs.py
@@ -808,7 +808,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/decode.py
index e497787d3..025b146b9 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/decode.py
@@ -786,7 +786,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/jit_pretrained_ctc.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/jit_pretrained_ctc.py
index 80604ef4a..70d9841bf 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/jit_pretrained_ctc.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/jit_pretrained_ctc.py
@@ -347,7 +347,9 @@ def main():
         "whole-lattice-rescoring",
     ]:
         logging.info(f"Loading HLG from {params.HLG}")
-        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = k2.Fsa.from_dict(
+            torch.load(params.HLG, map_location="cpu", weights_only=False)
+        )
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder
@@ -358,7 +360,9 @@ def main():
             "whole-lattice-rescoring",
         ]:
             logging.info(f"Loading G from {params.G}")
-            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = k2.Fsa.from_dict(
+                torch.load(params.G, map_location="cpu", weights_only=False)
+            )
             G = G.to(device)
             if params.method == "whole-lattice-rescoring":
                 # Add epsilon self-loops to G as we will compose
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/model.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/model.py
index 0582b289f..bf0faf9f1 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/model.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/model.py
@@ -22,7 +22,7 @@ import torch
 import torch.nn as nn
 from encoder_interface import EncoderInterface
 
-from icefall.utils import add_sos, make_pad_mask
+from icefall.utils import add_sos, make_pad_mask, torch_autocast
 
 
 class Transducer(nn.Module):
@@ -178,7 +178,7 @@ class Transducer(nn.Module):
         am = self.simple_am_proj(encoder_out_fr)
         lm = self.simple_lm_proj(decoder_out)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -213,7 +213,7 @@ class Transducer(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = self.joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/pretrained.py
index a82f3562b..9ceec5f5a 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/pretrained.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/pretrained.py
@@ -247,7 +247,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/pretrained_ctc.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/pretrained_ctc.py
index b98756a54..431760f9a 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/pretrained_ctc.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/pretrained_ctc.py
@@ -286,7 +286,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
@@ -362,7 +362,9 @@ def main():
         "whole-lattice-rescoring",
     ]:
         logging.info(f"Loading HLG from {params.HLG}")
-        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = k2.Fsa.from_dict(
+            torch.load(params.HLG, map_location="cpu", weights_only=False)
+        )
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder
@@ -373,7 +375,9 @@ def main():
             "whole-lattice-rescoring",
         ]:
             logging.info(f"Loading G from {params.G}")
-            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = k2.Fsa.from_dict(
+                torch.load(params.G, map_location="cpu", weights_only=False)
+            )
             G = G.to(device)
             if params.method == "whole-lattice-rescoring":
                 # Add epsilon self-loops to G as we will compose
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/train.py b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/train.py
index c2d877a93..5585d74de 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_ctc_bs/train.py
@@ -63,7 +63,6 @@ from lhotse.utils import fix_random_seed
 from model import Transducer
 from optim import Eden, ScaledAdam
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer
@@ -82,9 +81,11 @@ from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     encode_supervisions,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -581,7 +582,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -778,7 +779,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -822,7 +823,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1118,7 +1119,7 @@ def run(rank, world_size, args):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1217,7 +1218,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/decode.py
index 02029c108..aa2fe8e38 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/decode.py
@@ -936,7 +936,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/pretrained.py
index aa2dd17fb..f98851f50 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/pretrained.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/pretrained.py
@@ -247,7 +247,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train.py
index 8bd00bbef..4d8a2644d 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train.py
@@ -66,7 +66,6 @@ from lhotse.utils import fix_random_seed
 from model import Transducer
 from optim import Eden, ScaledAdam
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer
@@ -82,7 +81,14 @@ from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    create_grad_scaler,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -597,7 +603,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -764,7 +770,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -810,7 +816,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1124,7 +1130,7 @@ def run(rank, world_size, args):
     #         params=params,
     #     )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1224,7 +1230,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/zipformer.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/zipformer.py
index c7e45564f..640d72b67 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/zipformer.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming/zipformer.py
@@ -44,7 +44,7 @@ from scaling import (
 )
 from torch import Tensor, nn
 
-from icefall.utils import make_pad_mask, subsequent_chunk_mask
+from icefall.utils import make_pad_mask, subsequent_chunk_mask, torch_autocast
 
 
 def stack_states(state_list: List[List[Tensor]]) -> List[Tensor]:
@@ -2408,7 +2408,7 @@ class RelPositionMultiheadAttention(nn.Module):
         bsz = n // num_heads
 
         with torch.no_grad():
-            with torch.cuda.amp.autocast(enabled=False):
+            with torch_autocast(enabled=False):
                 attn_weights = attn_weights.to(torch.float32)
                 attn_output = attn_output.to(torch.float32)
                 attn_weights_entropy = (
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode.py
index 35158ced4..61c1a9663 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode.py
@@ -768,7 +768,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode_gigaspeech.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode_gigaspeech.py
index a4f52ad7f..e95bb3357 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode_gigaspeech.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/decode_gigaspeech.py
@@ -788,7 +788,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train.py b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train.py
index da5e144c9..4b97575e6 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7_streaming_multi/train.py
@@ -70,7 +70,6 @@ from librispeech import LibriSpeech
 from model import Transducer
 from optim import Eden, ScaledAdam
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer
@@ -86,7 +85,14 @@ from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    create_grad_scaler,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -615,7 +621,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -795,7 +801,7 @@ def train_one_epoch(
     giga_train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
     rng: random.Random,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -866,7 +872,7 @@ def train_one_epoch(
         libri = is_libri(batch["supervisions"]["cut"][0])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1218,7 +1224,7 @@ def run(rank, world_size, args):
     #         params=params,
     #     )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1320,7 +1326,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless8/decode.py b/egs/librispeech/ASR/pruned_transducer_stateless8/decode.py
index e07777c9f..3cad83a0b 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless8/decode.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless8/decode.py
@@ -747,7 +747,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless8/model.py b/egs/librispeech/ASR/pruned_transducer_stateless8/model.py
index 39a360796..e06594c27 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless8/model.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless8/model.py
@@ -24,7 +24,7 @@ import torch.nn as nn
 from encoder_interface import EncoderInterface
 from scaling import penalize_abs_values_gt
 
-from icefall.utils import add_sos
+from icefall.utils import add_sos, torch_autocast
 
 
 class Transducer(nn.Module):
@@ -172,7 +172,7 @@ class Transducer(nn.Module):
         # if self.training and random.random() < 0.25:
         #    am = penalize_abs_values_gt(am, 30.0, 1.0e-04)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -207,7 +207,7 @@ class Transducer(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless8/pretrained.py b/egs/librispeech/ASR/pruned_transducer_stateless8/pretrained.py
index c29b8d8c9..693db2beb 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless8/pretrained.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless8/pretrained.py
@@ -247,7 +247,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless8/train.py b/egs/librispeech/ASR/pruned_transducer_stateless8/train.py
index 646f30ca1..ad14ec9dc 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless8/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless8/train.py
@@ -75,7 +75,6 @@ from librispeech import LibriSpeech
 from model import Transducer
 from optim import Eden, ScaledAdam
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer
@@ -91,7 +90,14 @@ from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    create_grad_scaler,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
 
@@ -608,7 +614,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -790,7 +796,7 @@ def train_one_epoch(
     giga_train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
     rng: random.Random,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -866,7 +872,7 @@ def train_one_epoch(
         libri = is_libri(batch["supervisions"]["cut"][0])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1219,7 +1225,7 @@ def run(rank, world_size, args):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1321,7 +1327,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py
index 92529e06c..db12ab827 100755
--- a/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py
+++ b/egs/librispeech/ASR/tdnn_lstm_ctc/decode.py
@@ -398,7 +398,9 @@ def main():
 
     logging.info(f"device: {device}")
 
-    HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu"))
+    HLG = k2.Fsa.from_dict(
+        torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu", weights_only=False)
+    )
     HLG = HLG.to(device)
     assert HLG.requires_grad is False
 
@@ -428,7 +430,9 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location="cpu")
+            d = torch.load(
+                params.lm_dir / "G_4_gram.pt", map_location="cpu", weights_only=False
+            )
             G = k2.Fsa.from_dict(d).to(device)
 
         if params.method == "whole-lattice-rescoring":
diff --git a/egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py b/egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py
index b3dfab64a..4ad7cb016 100755
--- a/egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py
+++ b/egs/librispeech/ASR/tdnn_lstm_ctc/pretrained.py
@@ -167,13 +167,15 @@ def main():
         subsampling_factor=params.subsampling_factor,
     )
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"])
     model.to(device)
     model.eval()
 
     logging.info(f"Loading HLG from {params.HLG}")
-    HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+    HLG = k2.Fsa.from_dict(
+        torch.load(params.HLG, map_location="cpu", weights_only=False)
+    )
     HLG = HLG.to(device)
     if not hasattr(HLG, "lm_scores"):
         # For whole-lattice-rescoring and attention-decoder
@@ -181,7 +183,9 @@ def main():
 
     if params.method == "whole-lattice-rescoring":
         logging.info(f"Loading G from {params.G}")
-        G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+        G = k2.Fsa.from_dict(
+            torch.load(params.G, map_location="cpu", weights_only=False)
+        )
         # Add epsilon self-loops to G as we will compose
         # it with the whole lattice later
         G = G.to(device)
diff --git a/egs/librispeech/ASR/tiny_transducer_ctc/ctc_decode.py b/egs/librispeech/ASR/tiny_transducer_ctc/ctc_decode.py
index cda03b56e..ec700626a 100644
--- a/egs/librispeech/ASR/tiny_transducer_ctc/ctc_decode.py
+++ b/egs/librispeech/ASR/tiny_transducer_ctc/ctc_decode.py
@@ -589,7 +589,9 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(
+                f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False
+            )
         )
         assert HLG.requires_grad is False
 
@@ -628,7 +630,9 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
+            d = torch.load(
+                params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False
+            )
             G = k2.Fsa.from_dict(d)
 
         if params.decoding_method == "whole-lattice-rescoring":
diff --git a/egs/librispeech/ASR/tiny_transducer_ctc/decode.py b/egs/librispeech/ASR/tiny_transducer_ctc/decode.py
index cc4471e2b..1b329e8f3 100644
--- a/egs/librispeech/ASR/tiny_transducer_ctc/decode.py
+++ b/egs/librispeech/ASR/tiny_transducer_ctc/decode.py
@@ -663,7 +663,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/tiny_transducer_ctc/jit_pretrained_ctc.py b/egs/librispeech/ASR/tiny_transducer_ctc/jit_pretrained_ctc.py
index 92dea3aa1..4b234a328 100755
--- a/egs/librispeech/ASR/tiny_transducer_ctc/jit_pretrained_ctc.py
+++ b/egs/librispeech/ASR/tiny_transducer_ctc/jit_pretrained_ctc.py
@@ -347,7 +347,9 @@ def main():
         "whole-lattice-rescoring",
     ]:
         logging.info(f"Loading HLG from {params.HLG}")
-        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = k2.Fsa.from_dict(
+            torch.load(params.HLG, map_location="cpu", weights_only=False)
+        )
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder
@@ -358,7 +360,9 @@ def main():
             "whole-lattice-rescoring",
         ]:
             logging.info(f"Loading G from {params.G}")
-            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = k2.Fsa.from_dict(
+                torch.load(params.G, map_location="cpu", weights_only=False)
+            )
             G = G.to(device)
             if params.method == "whole-lattice-rescoring":
                 # Add epsilon self-loops to G as we will compose
diff --git a/egs/librispeech/ASR/tiny_transducer_ctc/pretrained.py b/egs/librispeech/ASR/tiny_transducer_ctc/pretrained.py
index 5c6956324..9714aa537 100755
--- a/egs/librispeech/ASR/tiny_transducer_ctc/pretrained.py
+++ b/egs/librispeech/ASR/tiny_transducer_ctc/pretrained.py
@@ -249,7 +249,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/tiny_transducer_ctc/pretrained_ctc.py b/egs/librispeech/ASR/tiny_transducer_ctc/pretrained_ctc.py
index 7698ada79..a2ea1dd06 100755
--- a/egs/librispeech/ASR/tiny_transducer_ctc/pretrained_ctc.py
+++ b/egs/librispeech/ASR/tiny_transducer_ctc/pretrained_ctc.py
@@ -286,7 +286,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
@@ -365,7 +365,9 @@ def main():
         "whole-lattice-rescoring",
     ]:
         logging.info(f"Loading HLG from {params.HLG}")
-        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = k2.Fsa.from_dict(
+            torch.load(params.HLG, map_location="cpu", weights_only=False)
+        )
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder
@@ -376,7 +378,9 @@ def main():
             "whole-lattice-rescoring",
         ]:
             logging.info(f"Loading G from {params.G}")
-            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = k2.Fsa.from_dict(
+                torch.load(params.G, map_location="cpu", weights_only=False)
+            )
             G = G.to(device)
             if params.method == "whole-lattice-rescoring":
                 # Add epsilon self-loops to G as we will compose
diff --git a/egs/librispeech/ASR/tiny_transducer_ctc/train.py b/egs/librispeech/ASR/tiny_transducer_ctc/train.py
index 1bfd071de..368bd20fa 100644
--- a/egs/librispeech/ASR/tiny_transducer_ctc/train.py
+++ b/egs/librispeech/ASR/tiny_transducer_ctc/train.py
@@ -51,7 +51,6 @@ from lhotse.dataset.sampling.base import CutSampler
 from lhotse.utils import fix_random_seed
 from model import Transducer
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import AdamW
 from torch.optim.lr_scheduler import StepLR
@@ -72,9 +71,11 @@ from icefall.lexicon import UniqLexicon
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     encode_supervisions,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = torch.optim.lr_scheduler._LRScheduler
@@ -550,7 +551,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -757,7 +758,7 @@ def train_one_epoch(
     phone_lexicon: UniqLexicon,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -809,7 +810,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1092,7 +1093,7 @@ def run(rank, world_size, args):
     #         params=params,
     #     )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1198,7 +1199,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/transducer/pretrained.py b/egs/librispeech/ASR/transducer/pretrained.py
index 4d9bbf4b1..06b1c05b9 100755
--- a/egs/librispeech/ASR/transducer/pretrained.py
+++ b/egs/librispeech/ASR/transducer/pretrained.py
@@ -222,7 +222,7 @@ def main():
     logging.info("Creating model")
     model = get_transducer_model(params)
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/zipformer/ctc_decode.py b/egs/librispeech/ASR/zipformer/ctc_decode.py
index fe9347b95..e17407c5f 100755
--- a/egs/librispeech/ASR/zipformer/ctc_decode.py
+++ b/egs/librispeech/ASR/zipformer/ctc_decode.py
@@ -947,7 +947,9 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(
+                f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False
+            )
         )
         assert HLG.requires_grad is False
 
@@ -987,7 +989,9 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
+            d = torch.load(
+                params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False
+            )
             G = k2.Fsa.from_dict(d)
 
         if params.decoding_method in [
diff --git a/egs/librispeech/ASR/zipformer/decode.py b/egs/librispeech/ASR/zipformer/decode.py
index cbfb3728e..6462d22f8 100755
--- a/egs/librispeech/ASR/zipformer/decode.py
+++ b/egs/librispeech/ASR/zipformer/decode.py
@@ -1013,7 +1013,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/zipformer/decode_gigaspeech.py b/egs/librispeech/ASR/zipformer/decode_gigaspeech.py
index 3cda337c0..a4da83949 100755
--- a/egs/librispeech/ASR/zipformer/decode_gigaspeech.py
+++ b/egs/librispeech/ASR/zipformer/decode_gigaspeech.py
@@ -1049,7 +1049,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/zipformer/finetune.py b/egs/librispeech/ASR/zipformer/finetune.py
index 2c869a57a..94e8b273a 100755
--- a/egs/librispeech/ASR/zipformer/finetune.py
+++ b/egs/librispeech/ASR/zipformer/finetune.py
@@ -78,7 +78,6 @@ from optim import Eden, ScaledAdam
 from scaling import ScheduledFloat
 from subsampling import Conv2dSubsampling
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer2
@@ -95,11 +94,13 @@ from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
+    create_grad_scaler,
     AttributeDict,
     MetricsTracker,
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -765,7 +766,7 @@ def load_model_params(
 
     """
     logging.info(f"Loading checkpoint from {ckpt}")
-    checkpoint = torch.load(ckpt, map_location="cpu")
+    checkpoint = torch.load(ckpt, map_location="cpu", weights_only=False)
 
     # if module list is empty, load the whole model from ckpt
     if not init_modules:
@@ -808,7 +809,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -985,7 +986,7 @@ def train_one_epoch(
     train_dl: torch.utils.data.DataLoader,
     valid_dls: torch.utils.data.DataLoader,
     valid_sets: List[str],
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -1049,7 +1050,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1373,7 +1374,7 @@ def run(rank, world_size, args):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1474,7 +1475,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/zipformer/jit_pretrained_ctc.py b/egs/librispeech/ASR/zipformer/jit_pretrained_ctc.py
index fcd07ae34..d1978df52 100755
--- a/egs/librispeech/ASR/zipformer/jit_pretrained_ctc.py
+++ b/egs/librispeech/ASR/zipformer/jit_pretrained_ctc.py
@@ -346,7 +346,9 @@ def main():
         "whole-lattice-rescoring",
     ]:
         logging.info(f"Loading HLG from {params.HLG}")
-        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = k2.Fsa.from_dict(
+            torch.load(params.HLG, map_location="cpu", weights_only=False)
+        )
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder
@@ -357,7 +359,9 @@ def main():
             "whole-lattice-rescoring",
         ]:
             logging.info(f"Loading G from {params.G}")
-            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = k2.Fsa.from_dict(
+                torch.load(params.G, map_location="cpu", weights_only=False)
+            )
             G = G.to(device)
             if params.method == "whole-lattice-rescoring":
                 # Add epsilon self-loops to G as we will compose
diff --git a/egs/librispeech/ASR/zipformer/model.py b/egs/librispeech/ASR/zipformer/model.py
index f2791e51f..6ef250819 100644
--- a/egs/librispeech/ASR/zipformer/model.py
+++ b/egs/librispeech/ASR/zipformer/model.py
@@ -25,7 +25,7 @@ from encoder_interface import EncoderInterface
 from lhotse.dataset import SpecAugment
 from scaling import ScaledLinear
 
-from icefall.utils import add_sos, make_pad_mask, time_warp
+from icefall.utils import add_sos, make_pad_mask, time_warp, torch_autocast
 
 
 class AsrModel(nn.Module):
@@ -285,7 +285,7 @@ class AsrModel(nn.Module):
         # if self.training and random.random() < 0.25:
         #    am = penalize_abs_values_gt(am, 30.0, 1.0e-04)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -320,7 +320,7 @@ class AsrModel(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = self.joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/librispeech/ASR/zipformer/pretrained.py b/egs/librispeech/ASR/zipformer/pretrained.py
index 9f3571b08..65ea7c7f2 100755
--- a/egs/librispeech/ASR/zipformer/pretrained.py
+++ b/egs/librispeech/ASR/zipformer/pretrained.py
@@ -289,7 +289,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
diff --git a/egs/librispeech/ASR/zipformer/pretrained_ctc.py b/egs/librispeech/ASR/zipformer/pretrained_ctc.py
index 4341ef61f..90a6ff5b8 100755
--- a/egs/librispeech/ASR/zipformer/pretrained_ctc.py
+++ b/egs/librispeech/ASR/zipformer/pretrained_ctc.py
@@ -305,7 +305,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
@@ -389,7 +389,9 @@ def main():
         "whole-lattice-rescoring",
     ]:
         logging.info(f"Loading HLG from {params.HLG}")
-        HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+        HLG = k2.Fsa.from_dict(
+            torch.load(params.HLG, map_location="cpu", weights_only=False)
+        )
         HLG = HLG.to(device)
         if not hasattr(HLG, "lm_scores"):
             # For whole-lattice-rescoring and attention-decoder
@@ -400,7 +402,9 @@ def main():
             "whole-lattice-rescoring",
         ]:
             logging.info(f"Loading G from {params.G}")
-            G = k2.Fsa.from_dict(torch.load(params.G, map_location="cpu"))
+            G = k2.Fsa.from_dict(
+                torch.load(params.G, map_location="cpu", weights_only=False)
+            )
             G = G.to(device)
             if params.method == "whole-lattice-rescoring":
                 # Add epsilon self-loops to G as we will compose
diff --git a/egs/librispeech/ASR/zipformer/scaling.py b/egs/librispeech/ASR/zipformer/scaling.py
index 11375385e..22aa1b1ca 100644
--- a/egs/librispeech/ASR/zipformer/scaling.py
+++ b/egs/librispeech/ASR/zipformer/scaling.py
@@ -26,6 +26,8 @@ import torch.nn as nn
 from torch import Tensor
 from torch.cuda.amp import custom_bwd, custom_fwd
 
+from icefall.utils import torch_autocast
+
 
 def logaddexp_onnx(x: Tensor, y: Tensor) -> Tensor:
     max_value = torch.max(x, y)
@@ -308,7 +310,7 @@ class SoftmaxFunction(torch.autograd.Function):
     @staticmethod
     def backward(ctx, ans_grad: Tensor):
         (ans,) = ctx.saved_tensors
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             ans_grad = ans_grad.to(torch.float32)
             ans = ans.to(torch.float32)
             x_grad = ans_grad * ans
@@ -761,7 +763,7 @@ class BalancerFunction(torch.autograd.Function):
 
         try:
             with torch.enable_grad():
-                with torch.cuda.amp.autocast(enabled=False):
+                with torch_autocast(enabled=False):
                     x = x.to(torch.float32)
                     x = x.detach()
                     x.requires_grad = True
@@ -1016,7 +1018,7 @@ class WhiteningPenaltyFunction(torch.autograd.Function):
 
         try:
             with torch.enable_grad():
-                with torch.cuda.amp.autocast(enabled=False):
+                with torch_autocast(enabled=False):
                     x_detached = x_orig.to(torch.float32).detach()
                     x_detached.requires_grad = True
 
@@ -1355,7 +1357,7 @@ class SwooshLFunction(torch.autograd.Function):
 
         coeff = -0.08
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             with torch.enable_grad():
                 x = x.detach()
                 x.requires_grad = True
@@ -1432,7 +1434,7 @@ class SwooshRFunction(torch.autograd.Function):
 
         zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             with torch.enable_grad():
                 x = x.detach()
                 x.requires_grad = True
diff --git a/egs/librispeech/ASR/zipformer/train.py b/egs/librispeech/ASR/zipformer/train.py
index f8864d58b..42ae9b9f2 100755
--- a/egs/librispeech/ASR/zipformer/train.py
+++ b/egs/librispeech/ASR/zipformer/train.py
@@ -79,7 +79,6 @@ from optim import Eden, ScaledAdam
 from scaling import ScheduledFloat
 from subsampling import Conv2dSubsampling
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer2
@@ -98,9 +97,11 @@ from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -829,7 +830,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -1034,7 +1035,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     spec_augment: Optional[SpecAugment] = None,
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
@@ -1101,9 +1102,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(
-                enabled=params.use_autocast, dtype=params.dtype
-            ):
+            with torch_autocast(enabled=params.use_autocast, dtype=params.dtype):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1449,7 +1448,7 @@ def run(rank, world_size, args):
             spec_augment=spec_augment,
         )
 
-    scaler = GradScaler(enabled=params.use_autocast, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_autocast, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1551,9 +1550,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(
-                enabled=params.use_autocast, dtype=params.dtype
-            ):
+            with torch_autocast(enabled=params.use_autocast, dtype=params.dtype):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/zipformer/zipformer.py b/egs/librispeech/ASR/zipformer/zipformer.py
index 2a0ae0129..e83a89400 100644
--- a/egs/librispeech/ASR/zipformer/zipformer.py
+++ b/egs/librispeech/ASR/zipformer/zipformer.py
@@ -47,6 +47,8 @@ from scaling import (
 )
 from torch import Tensor, nn
 
+from icefall.utils import torch_autocast
+
 
 class Zipformer2(EncoderInterface):
     """
@@ -1873,7 +1875,7 @@ class RelPositionMultiheadAttentionWeights(nn.Module):
         (num_heads, batch_size, seq_len, seq_len) = attn_weights.shape
 
         with torch.no_grad():
-            with torch.cuda.amp.autocast(enabled=False):
+            with torch_autocast(enabled=False):
                 attn_weights = attn_weights.to(torch.float32)
                 attn_weights_entropy = (
                     -((attn_weights + 1.0e-20).log() * attn_weights)
diff --git a/egs/librispeech/ASR/zipformer_adapter/decode.py b/egs/librispeech/ASR/zipformer_adapter/decode.py
index 91533be8d..e8798aed6 100755
--- a/egs/librispeech/ASR/zipformer_adapter/decode.py
+++ b/egs/librispeech/ASR/zipformer_adapter/decode.py
@@ -1005,7 +1005,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/zipformer_adapter/decode_gigaspeech.py b/egs/librispeech/ASR/zipformer_adapter/decode_gigaspeech.py
index bbc582f50..66c401761 100755
--- a/egs/librispeech/ASR/zipformer_adapter/decode_gigaspeech.py
+++ b/egs/librispeech/ASR/zipformer_adapter/decode_gigaspeech.py
@@ -1050,7 +1050,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/zipformer_adapter/train.py b/egs/librispeech/ASR/zipformer_adapter/train.py
index 3511590da..fcd7272e9 100755
--- a/egs/librispeech/ASR/zipformer_adapter/train.py
+++ b/egs/librispeech/ASR/zipformer_adapter/train.py
@@ -67,7 +67,6 @@ from optim import Eden, ScaledAdam
 from scaling import ScheduledFloat
 from subsampling import Conv2dSubsampling
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer2
@@ -86,9 +85,11 @@ from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -762,7 +763,7 @@ def load_model_params(
 
     """
     logging.info(f"Loading checkpoint from {ckpt}")
-    checkpoint = torch.load(ckpt, map_location="cpu")
+    checkpoint = torch.load(ckpt, map_location="cpu", weights_only=False)
 
     # if module list is empty, load the whole model from ckpt
     if not init_modules:
@@ -805,7 +806,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -982,7 +983,7 @@ def train_one_epoch(
     train_dl: torch.utils.data.DataLoader,
     valid_dls: torch.utils.data.DataLoader,
     valid_sets: List[str],
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -1052,7 +1053,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1397,7 +1398,7 @@ def run(rank, world_size, args):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1498,7 +1499,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/zipformer_adapter/zipformer.py b/egs/librispeech/ASR/zipformer_adapter/zipformer.py
index 8e2dfdd72..8bc163db5 100644
--- a/egs/librispeech/ASR/zipformer_adapter/zipformer.py
+++ b/egs/librispeech/ASR/zipformer_adapter/zipformer.py
@@ -50,6 +50,8 @@ from scaling import (
 )
 from torch import Tensor, nn
 
+from icefall.utils import torch_autocast
+
 
 class Zipformer2(EncoderInterface):
     """
@@ -1916,7 +1918,7 @@ class RelPositionMultiheadAttentionWeights(nn.Module):
         (num_heads, batch_size, seq_len, seq_len) = attn_weights.shape
 
         with torch.no_grad():
-            with torch.cuda.amp.autocast(enabled=False):
+            with torch_autocast(enabled=False):
                 attn_weights = attn_weights.to(torch.float32)
                 attn_weights_entropy = (
                     -((attn_weights + 1.0e-20).log() * attn_weights)
diff --git a/egs/librispeech/ASR/zipformer_ctc/decode.py b/egs/librispeech/ASR/zipformer_ctc/decode.py
index 7f605e2c8..b9eed099c 100755
--- a/egs/librispeech/ASR/zipformer_ctc/decode.py
+++ b/egs/librispeech/ASR/zipformer_ctc/decode.py
@@ -679,7 +679,9 @@ def main():
         H = None
         bpe_model = None
         HLG = k2.Fsa.from_dict(
-            torch.load(f"{params.lang_dir}/HLG.pt", map_location=device)
+            torch.load(
+                f"{params.lang_dir}/HLG.pt", map_location=device, weights_only=False
+            )
         )
         assert HLG.requires_grad is False
 
@@ -719,7 +721,9 @@ def main():
                 torch.save(G.as_dict(), params.lm_dir / "G_4_gram.pt")
         else:
             logging.info("Loading pre-compiled G_4_gram.pt")
-            d = torch.load(params.lm_dir / "G_4_gram.pt", map_location=device)
+            d = torch.load(
+                params.lm_dir / "G_4_gram.pt", map_location=device, weights_only=False
+            )
             G = k2.Fsa.from_dict(d)
 
         if params.method in [
diff --git a/egs/librispeech/ASR/zipformer_ctc/train.py b/egs/librispeech/ASR/zipformer_ctc/train.py
index 60112a84e..bd3bfa332 100755
--- a/egs/librispeech/ASR/zipformer_ctc/train.py
+++ b/egs/librispeech/ASR/zipformer_ctc/train.py
@@ -46,7 +46,6 @@ from lhotse.utils import fix_random_seed
 from model import CTCModel
 from optim import Eden, LRScheduler, ScaledAdam
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.nn.utils import clip_grad_norm_
 from torch.utils.tensorboard import SummaryWriter
@@ -65,7 +64,14 @@ from icefall.env import get_env_info
 from icefall.err import raise_grad_scale_is_too_small_error
 from icefall.hooks import register_inf_check_hooks
 from icefall.lexicon import Lexicon
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    create_grad_scaler,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, LRScheduler]
 
@@ -533,7 +539,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -687,7 +693,7 @@ def train_one_epoch(
     graph_compiler: BpeCtcTrainingGraphCompiler,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -726,7 +732,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -987,7 +993,7 @@ def run(rank, world_size, args):
         params=params,
     )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
diff --git a/egs/librispeech/ASR/zipformer_lora/decode_gigaspeech.py b/egs/librispeech/ASR/zipformer_lora/decode_gigaspeech.py
index 4d93a905f..acc814a00 100755
--- a/egs/librispeech/ASR/zipformer_lora/decode_gigaspeech.py
+++ b/egs/librispeech/ASR/zipformer_lora/decode_gigaspeech.py
@@ -1050,7 +1050,7 @@ def main():
             lg_filename = params.lang_dir / "LG.pt"
             logging.info(f"Loading {lg_filename}")
             decoding_graph = k2.Fsa.from_dict(
-                torch.load(lg_filename, map_location=device)
+                torch.load(lg_filename, map_location=device, weights_only=False)
             )
             decoding_graph.scores *= params.ngram_lm_scale
         else:
diff --git a/egs/librispeech/ASR/zipformer_lora/finetune.py b/egs/librispeech/ASR/zipformer_lora/finetune.py
index 3f36f229f..c26a2f5cc 100755
--- a/egs/librispeech/ASR/zipformer_lora/finetune.py
+++ b/egs/librispeech/ASR/zipformer_lora/finetune.py
@@ -78,7 +78,6 @@ from optim import Eden, ScaledAdam
 from scaling import ScheduledFloat
 from subsampling import Conv2dSubsampling
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer2
@@ -96,9 +95,11 @@ from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -775,7 +776,7 @@ def load_model_params(
 
     """
     logging.info(f"Loading checkpoint from {ckpt}")
-    checkpoint = torch.load(ckpt, map_location="cpu")
+    checkpoint = torch.load(ckpt, map_location="cpu", weights_only=False)
 
     # if module list is empty, load the whole model from ckpt
     if not init_modules:
@@ -818,7 +819,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -995,7 +996,7 @@ def train_one_epoch(
     train_dl: torch.utils.data.DataLoader,
     valid_dls: torch.utils.data.DataLoader,
     valid_sets: List[str],
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -1065,7 +1066,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1406,7 +1407,7 @@ def run(rank, world_size, args):
     #         params=params,
     #     )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1507,7 +1508,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/zipformer_lora/scaling.py b/egs/librispeech/ASR/zipformer_lora/scaling.py
index 8d7aa8027..1347570df 100644
--- a/egs/librispeech/ASR/zipformer_lora/scaling.py
+++ b/egs/librispeech/ASR/zipformer_lora/scaling.py
@@ -27,6 +27,8 @@ import torch.nn.functional as F
 from torch import Tensor
 from torch.cuda.amp import custom_bwd, custom_fwd
 
+from icefall.utils import torch_autocast
+
 
 def logaddexp_onnx(x: Tensor, y: Tensor) -> Tensor:
     max_value = torch.max(x, y)
@@ -307,7 +309,7 @@ class SoftmaxFunction(torch.autograd.Function):
     @staticmethod
     def backward(ctx, ans_grad: Tensor):
         (ans,) = ctx.saved_tensors
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             ans_grad = ans_grad.to(torch.float32)
             ans = ans.to(torch.float32)
             x_grad = ans_grad * ans
@@ -863,7 +865,7 @@ class BalancerFunction(torch.autograd.Function):
 
         try:
             with torch.enable_grad():
-                with torch.cuda.amp.autocast(enabled=False):
+                with torch_autocast(enabled=False):
                     x = x.to(torch.float32)
                     x = x.detach()
                     x.requires_grad = True
@@ -1118,7 +1120,7 @@ class WhiteningPenaltyFunction(torch.autograd.Function):
 
         try:
             with torch.enable_grad():
-                with torch.cuda.amp.autocast(enabled=False):
+                with torch_autocast(enabled=False):
                     x_detached = x_orig.to(torch.float32).detach()
                     x_detached.requires_grad = True
 
@@ -1457,7 +1459,7 @@ class SwooshLFunction(torch.autograd.Function):
 
         coeff = -0.08
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             with torch.enable_grad():
                 x = x.detach()
                 x.requires_grad = True
@@ -1534,7 +1536,7 @@ class SwooshRFunction(torch.autograd.Function):
 
         zero = torch.tensor(0.0, dtype=x.dtype, device=x.device)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             with torch.enable_grad():
                 x = x.detach()
                 x.requires_grad = True
diff --git a/egs/librispeech/ASR/zipformer_lora/train.py b/egs/librispeech/ASR/zipformer_lora/train.py
index 9ab214e86..2b83d58ef 100755
--- a/egs/librispeech/ASR/zipformer_lora/train.py
+++ b/egs/librispeech/ASR/zipformer_lora/train.py
@@ -76,7 +76,6 @@ from optim import Eden, ScaledAdam
 from scaling import ScheduledFloat
 from subsampling import Conv2dSubsampling
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer2
@@ -94,9 +93,11 @@ from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -707,7 +708,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -883,7 +884,7 @@ def train_one_epoch(
     sp: spm.SentencePieceProcessor,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -947,7 +948,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1252,7 +1253,7 @@ def run(rank, world_size, args):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1352,7 +1353,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/ASR/zipformer_lora/zipformer.py b/egs/librispeech/ASR/zipformer_lora/zipformer.py
index 43865609a..b84b1c32a 100644
--- a/egs/librispeech/ASR/zipformer_lora/zipformer.py
+++ b/egs/librispeech/ASR/zipformer_lora/zipformer.py
@@ -49,6 +49,8 @@ from scaling import (
 )
 from torch import Tensor, nn
 
+from icefall.utils import torch_autocast
+
 
 class Zipformer2(EncoderInterface):
     """
@@ -1905,7 +1907,7 @@ class RelPositionMultiheadAttentionWeights(nn.Module):
         (num_heads, batch_size, seq_len, seq_len) = attn_weights.shape
 
         with torch.no_grad():
-            with torch.cuda.amp.autocast(enabled=False):
+            with torch_autocast(enabled=False):
                 attn_weights = attn_weights.to(torch.float32)
                 attn_weights_entropy = (
                     -((attn_weights + 1.0e-20).log() * attn_weights)
diff --git a/egs/librispeech/ASR/zipformer_mmi/decode.py b/egs/librispeech/ASR/zipformer_mmi/decode.py
index 33c0bf199..bd3ce21f5 100755
--- a/egs/librispeech/ASR/zipformer_mmi/decode.py
+++ b/egs/librispeech/ASR/zipformer_mmi/decode.py
@@ -569,7 +569,9 @@ def main():
     if params.decoding_method == "nbest-rescoring-LG":
         lg_filename = params.lang_dir / "LG.pt"
         logging.info(f"Loading {lg_filename}")
-        LG = k2.Fsa.from_dict(torch.load(lg_filename, map_location=device))
+        LG = k2.Fsa.from_dict(
+            torch.load(lg_filename, map_location=device, weights_only=False)
+        )
         LG = k2.Fsa.from_fsas([LG]).to(device)
         LG.lm_scores = LG.scores.clone()
 
@@ -602,7 +604,11 @@ def main():
                 torch.save(G.as_dict(), params.lang_dir / f"{order}gram.pt")
         else:
             logging.info(f"Loading pre-compiled {order}gram.pt")
-            d = torch.load(params.lang_dir / f"{order}gram.pt", map_location=device)
+            d = torch.load(
+                params.lang_dir / f"{order}gram.pt",
+                map_location=device,
+                weights_only=False,
+            )
             G = k2.Fsa.from_dict(d)
 
         G.lm_scores = G.scores.clone()
diff --git a/egs/librispeech/ASR/zipformer_mmi/jit_pretrained.py b/egs/librispeech/ASR/zipformer_mmi/jit_pretrained.py
index 6990c90a0..d5667cafa 100755
--- a/egs/librispeech/ASR/zipformer_mmi/jit_pretrained.py
+++ b/egs/librispeech/ASR/zipformer_mmi/jit_pretrained.py
@@ -308,7 +308,9 @@ def main():
     if method == "nbest-rescoring-LG":
         lg_filename = params.lang_dir / "LG.pt"
         logging.info(f"Loading {lg_filename}")
-        LG = k2.Fsa.from_dict(torch.load(lg_filename, map_location=device))
+        LG = k2.Fsa.from_dict(
+            torch.load(lg_filename, map_location=device, weights_only=False)
+        )
         LG = k2.Fsa.from_fsas([LG]).to(device)
         LG.lm_scores = LG.scores.clone()
         LM = LG
@@ -317,7 +319,9 @@ def main():
         assert order in ("3", "4")
         order = int(order)
         logging.info(f"Loading pre-compiled {order}gram.pt")
-        d = torch.load(params.lang_dir / f"{order}gram.pt", map_location=device)
+        d = torch.load(
+            params.lang_dir / f"{order}gram.pt", map_location=device, weights_only=False
+        )
         G = k2.Fsa.from_dict(d)
         G.lm_scores = G.scores.clone()
         LM = G
diff --git a/egs/librispeech/ASR/zipformer_mmi/pretrained.py b/egs/librispeech/ASR/zipformer_mmi/pretrained.py
index 1e7afc777..ca860b877 100755
--- a/egs/librispeech/ASR/zipformer_mmi/pretrained.py
+++ b/egs/librispeech/ASR/zipformer_mmi/pretrained.py
@@ -269,7 +269,7 @@ def main():
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"], strict=False)
     model.to(device)
     model.eval()
@@ -331,7 +331,9 @@ def main():
     if method == "nbest-rescoring-LG":
         lg_filename = params.lang_dir / "LG.pt"
         logging.info(f"Loading {lg_filename}")
-        LG = k2.Fsa.from_dict(torch.load(lg_filename, map_location=device))
+        LG = k2.Fsa.from_dict(
+            torch.load(lg_filename, map_location=device, weights_only=False)
+        )
         LG = k2.Fsa.from_fsas([LG]).to(device)
         LG.lm_scores = LG.scores.clone()
         LM = LG
@@ -340,7 +342,9 @@ def main():
         assert order in ("3", "4")
         order = int(order)
         logging.info(f"Loading pre-compiled {order}gram.pt")
-        d = torch.load(params.lang_dir / f"{order}gram.pt", map_location=device)
+        d = torch.load(
+            params.lang_dir / f"{order}gram.pt", map_location=device, weights_only=False
+        )
         G = k2.Fsa.from_dict(d)
         G.lm_scores = G.scores.clone()
         LM = G
diff --git a/egs/librispeech/ASR/zipformer_mmi/train.py b/egs/librispeech/ASR/zipformer_mmi/train.py
index c1785a328..e0ca0a6a5 100755
--- a/egs/librispeech/ASR/zipformer_mmi/train.py
+++ b/egs/librispeech/ASR/zipformer_mmi/train.py
@@ -64,7 +64,6 @@ from lhotse.utils import fix_random_seed
 from model import CTCModel
 from optim import Eden, ScaledAdam
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.utils.tensorboard import SummaryWriter
 from zipformer import Zipformer
@@ -87,9 +86,11 @@ from icefall.mmi_graph_compiler import MmiTrainingGraphCompiler
 from icefall.utils import (
     AttributeDict,
     MetricsTracker,
+    create_grad_scaler,
     encode_supervisions,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -514,7 +515,7 @@ def save_checkpoint(
     optimizer: Optional[torch.optim.Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
     sampler: Optional[CutSampler] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     rank: int = 0,
 ) -> None:
     """Save model, optimizer, scheduler and training stats to file.
@@ -696,7 +697,7 @@ def train_one_epoch(
     mmi_graph_compiler: MmiTrainingGraphCompiler,
     train_dl: torch.utils.data.DataLoader,
     valid_dl: torch.utils.data.DataLoader,
-    scaler: GradScaler,
+    scaler: "GradScaler",
     model_avg: Optional[nn.Module] = None,
     tb_writer: Optional[SummaryWriter] = None,
     world_size: int = 1,
@@ -744,7 +745,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1037,7 +1038,7 @@ def run(rank, world_size, args):
             params=params,
         )
 
-    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    scaler = create_grad_scaler(enabled=params.use_fp16, init_scale=1.0)
     if checkpoints and "grad_scaler" in checkpoints:
         logging.info("Loading grad scaler state dict")
         scaler.load_state_dict(checkpoints["grad_scaler"])
@@ -1138,7 +1139,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/SSL/hubert/finetune.py b/egs/librispeech/SSL/hubert/finetune.py
index 17daa3c9d..0080513f3 100644
--- a/egs/librispeech/SSL/hubert/finetune.py
+++ b/egs/librispeech/SSL/hubert/finetune.py
@@ -86,6 +86,7 @@ from icefall.utils import (
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -816,7 +817,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1207,7 +1208,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/SSL/hubert/finetune_ce.py b/egs/librispeech/SSL/hubert/finetune_ce.py
index 2723cc770..1ff2b03c0 100644
--- a/egs/librispeech/SSL/hubert/finetune_ce.py
+++ b/egs/librispeech/SSL/hubert/finetune_ce.py
@@ -81,6 +81,7 @@ from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
+    torch_autocast,
     AttributeDict,
     MetricsTracker,
     get_parameter_groups_with_lrs,
@@ -816,7 +817,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1207,7 +1208,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/SSL/hubert/model.py b/egs/librispeech/SSL/hubert/model.py
index 46a968b69..2c2077376 100644
--- a/egs/librispeech/SSL/hubert/model.py
+++ b/egs/librispeech/SSL/hubert/model.py
@@ -24,7 +24,7 @@ import torch
 import torch.nn as nn
 from scaling import ScaledLinear
 
-from icefall.utils import add_sos
+from icefall.utils import add_sos, torch_autocast
 
 
 class AsrModel(nn.Module):
@@ -221,7 +221,7 @@ class AsrModel(nn.Module):
         # if self.training and random.random() < 0.25:
         #    am = penalize_abs_values_gt(am, 30.0, 1.0e-04)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -256,7 +256,7 @@ class AsrModel(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = self.joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/librispeech/SSL/hubert/pretrain.py b/egs/librispeech/SSL/hubert/pretrain.py
index f183d90fd..240cd2c0d 100644
--- a/egs/librispeech/SSL/hubert/pretrain.py
+++ b/egs/librispeech/SSL/hubert/pretrain.py
@@ -80,6 +80,7 @@ from icefall.utils import (
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -644,7 +645,7 @@ def train_one_epoch(
         batch_size = batch["kmeans"].shape[0]
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1036,7 +1037,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/SSL/hubert/pretrain_ce.py b/egs/librispeech/SSL/hubert/pretrain_ce.py
index 94948695d..12f95c16f 100644
--- a/egs/librispeech/SSL/hubert/pretrain_ce.py
+++ b/egs/librispeech/SSL/hubert/pretrain_ce.py
@@ -80,6 +80,7 @@ from icefall.utils import (
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -644,7 +645,7 @@ def train_one_epoch(
         batch_size = batch["kmeans"].shape[0]
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1036,7 +1037,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/SSL/zipformer/finetune.py b/egs/librispeech/SSL/zipformer/finetune.py
index c907b41c5..5bebf60f0 100644
--- a/egs/librispeech/SSL/zipformer/finetune.py
+++ b/egs/librispeech/SSL/zipformer/finetune.py
@@ -81,6 +81,7 @@ from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
 from icefall.hooks import register_inf_check_hooks
 from icefall.utils import (
+    torch_autocast,
     AttributeDict,
     MetricsTracker,
     get_parameter_groups_with_lrs,
@@ -1115,7 +1116,7 @@ def train_one_epoch(
         batch_size = len(batch["supervisions"]["text"])
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1504,7 +1505,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/SSL/zipformer/model.py b/egs/librispeech/SSL/zipformer/model.py
index 46a968b69..2c2077376 100644
--- a/egs/librispeech/SSL/zipformer/model.py
+++ b/egs/librispeech/SSL/zipformer/model.py
@@ -24,7 +24,7 @@ import torch
 import torch.nn as nn
 from scaling import ScaledLinear
 
-from icefall.utils import add_sos
+from icefall.utils import add_sos, torch_autocast
 
 
 class AsrModel(nn.Module):
@@ -221,7 +221,7 @@ class AsrModel(nn.Module):
         # if self.training and random.random() < 0.25:
         #    am = penalize_abs_values_gt(am, 30.0, 1.0e-04)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             simple_loss, (px_grad, py_grad) = k2.rnnt_loss_smoothed(
                 lm=lm.float(),
                 am=am.float(),
@@ -256,7 +256,7 @@ class AsrModel(nn.Module):
         # prior to do_rnnt_pruning (this is an optimization for speed).
         logits = self.joiner(am_pruned, lm_pruned, project_input=False)
 
-        with torch.cuda.amp.autocast(enabled=False):
+        with torch_autocast(enabled=False):
             pruned_loss = k2.rnnt_loss_pruned(
                 logits=logits.float(),
                 symbols=y_padded,
diff --git a/egs/librispeech/SSL/zipformer/pretrain.py b/egs/librispeech/SSL/zipformer/pretrain.py
index 937fb382e..d772f56d0 100644
--- a/egs/librispeech/SSL/zipformer/pretrain.py
+++ b/egs/librispeech/SSL/zipformer/pretrain.py
@@ -78,6 +78,7 @@ from icefall.utils import (
     get_parameter_groups_with_lrs,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -944,7 +945,7 @@ def train_one_epoch(
         batch_size = batch["kmeans"].shape[0]
 
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, loss_info = compute_loss(
                     params=params,
                     model=model,
@@ -1334,7 +1335,7 @@ def scan_pessimistic_batches_for_oom(
     for criterion, cuts in batches.items():
         batch = train_dl.dataset[cuts]
         try:
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/SSL/zipformer/zipformer.py b/egs/librispeech/SSL/zipformer/zipformer.py
index e9eff3357..5071a91a8 100644
--- a/egs/librispeech/SSL/zipformer/zipformer.py
+++ b/egs/librispeech/SSL/zipformer/zipformer.py
@@ -22,6 +22,7 @@ import math
 import random
 import warnings
 from typing import List, Optional, Tuple, Union
+from icefall.utils import torch_autocast
 
 import torch
 from encoder_interface import EncoderInterface
@@ -1849,7 +1850,7 @@ class RelPositionMultiheadAttentionWeights(nn.Module):
         (num_heads, batch_size, seq_len, seq_len) = attn_weights.shape
 
         with torch.no_grad():
-            with torch.cuda.amp.autocast(enabled=False):
+            with torch_autocast(enabled=False):
                 attn_weights = attn_weights.to(torch.float32)
                 attn_weights_entropy = (
                     -((attn_weights + 1.0e-20).log() * attn_weights)
diff --git a/egs/librispeech/WSASR/conformer_ctc2/train.py b/egs/librispeech/WSASR/conformer_ctc2/train.py
index 82c68803f..19cce1708 100755
--- a/egs/librispeech/WSASR/conformer_ctc2/train.py
+++ b/egs/librispeech/WSASR/conformer_ctc2/train.py
@@ -84,6 +84,7 @@ from icefall.utils import (
     get_texts,
     setup_logger,
     str2bool,
+    torch_autocast,
 )
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -757,7 +758,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -1076,7 +1077,7 @@ def scan_pessimistic_batches_for_oom(
             # warmup = 0.0 is so that the derivs for the pruned loss stay zero
             # (i.e. are not remembered by the decaying-average in adam), because
             # we want to avoid these params being subject to shrinkage in adam.
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/librispeech/WSASR/conformer_ctc2/train_phone.py b/egs/librispeech/WSASR/conformer_ctc2/train_phone.py
index b276d0587..a32183bf7 100755
--- a/egs/librispeech/WSASR/conformer_ctc2/train_phone.py
+++ b/egs/librispeech/WSASR/conformer_ctc2/train_phone.py
@@ -79,6 +79,7 @@ from icefall.env import get_env_info
 from icefall.lexicon import Lexicon
 from icefall.otc_phone_graph_compiler import OtcPhoneTrainingGraphCompiler
 from icefall.utils import (
+    torch_autocast,
     AttributeDict,
     MetricsTracker,
     encode_supervisions_otc,
@@ -758,7 +759,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         batch_size = len(batch["supervisions"]["text"])
 
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 params=params,
                 model=model,
@@ -1078,7 +1079,7 @@ def scan_pessimistic_batches_for_oom(
             # warmup = 0.0 is so that the derivs for the pruned loss stay zero
             # (i.e. are not remembered by the decaying-average in adam), because
             # we want to avoid these params being subject to shrinkage in adam.
-            with torch.cuda.amp.autocast(enabled=params.use_fp16):
+            with torch_autocast(enabled=params.use_fp16):
                 loss, _ = compute_loss(
                     params=params,
                     model=model,
diff --git a/egs/ljspeech/TTS/local/compute_fbank_ljspeech.py b/egs/ljspeech/TTS/local/compute_fbank_ljspeech.py
index 296f9a4f4..906025b7f 100755
--- a/egs/ljspeech/TTS/local/compute_fbank_ljspeech.py
+++ b/egs/ljspeech/TTS/local/compute_fbank_ljspeech.py
@@ -73,6 +73,8 @@ def compute_fbank_ljspeech(num_jobs: int):
         f_min=0,
         f_max=8000,
     )
+    if not torch.cuda.is_available():
+        config.device = "cpu"
 
     prefix = "ljspeech"
     suffix = "jsonl.gz"
diff --git a/egs/yesno/ASR/local/compile_hlg.py b/egs/yesno/ASR/local/compile_hlg.py
index e0a94bf08..3de7136ec 100755
--- a/egs/yesno/ASR/local/compile_hlg.py
+++ b/egs/yesno/ASR/local/compile_hlg.py
@@ -47,7 +47,7 @@ def compile_HLG(lang_dir: str) -> k2.Fsa:
     max_token_id = max(lexicon.tokens)
     logging.info(f"Building ctc_topo. max_token_id: {max_token_id}")
     H = k2.ctc_topo(max_token_id)
-    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt"))
+    L = k2.Fsa.from_dict(torch.load(f"{lang_dir}/L_disambig.pt", weights_only=False))
 
     logging.info("Loading G.fst.txt")
     with open("data/lm/G.fst.txt") as f:
diff --git a/egs/yesno/ASR/tdnn/decode.py b/egs/yesno/ASR/tdnn/decode.py
index f520607af..479e195fa 100755
--- a/egs/yesno/ASR/tdnn/decode.py
+++ b/egs/yesno/ASR/tdnn/decode.py
@@ -271,7 +271,9 @@ def main():
 
     logging.info(f"device: {device}")
 
-    HLG = k2.Fsa.from_dict(torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu"))
+    HLG = k2.Fsa.from_dict(
+        torch.load(f"{params.lang_dir}/HLG.pt", map_location="cpu", weights_only=False)
+    )
     HLG = HLG.to(device)
     assert HLG.requires_grad is False
 
diff --git a/egs/yesno/ASR/tdnn/jit_pretrained.py b/egs/yesno/ASR/tdnn/jit_pretrained.py
index 6c643c263..2a0879045 100755
--- a/egs/yesno/ASR/tdnn/jit_pretrained.py
+++ b/egs/yesno/ASR/tdnn/jit_pretrained.py
@@ -131,7 +131,9 @@ def main():
     model.to(device)
 
     logging.info(f"Loading HLG from {params.HLG}")
-    HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+    HLG = k2.Fsa.from_dict(
+        torch.load(params.HLG, map_location="cpu", weights_only=False)
+    )
     HLG = HLG.to(device)
 
     logging.info("Constructing Fbank computer")
diff --git a/egs/yesno/ASR/tdnn/onnx_pretrained.py b/egs/yesno/ASR/tdnn/onnx_pretrained.py
index 968a9e9a8..e6471d2db 100755
--- a/egs/yesno/ASR/tdnn/onnx_pretrained.py
+++ b/egs/yesno/ASR/tdnn/onnx_pretrained.py
@@ -176,7 +176,9 @@ def main():
     model = OnnxModel(params.nn_model)
 
     logging.info(f"Loading HLG from {args.HLG}")
-    HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+    HLG = k2.Fsa.from_dict(
+        torch.load(params.HLG, map_location="cpu", weights_only=False)
+    )
     HLG = HLG.to(device)
 
     logging.info("Constructing Fbank computer")
diff --git a/egs/yesno/ASR/tdnn/pretrained.py b/egs/yesno/ASR/tdnn/pretrained.py
index bea520998..d4f3ae39f 100755
--- a/egs/yesno/ASR/tdnn/pretrained.py
+++ b/egs/yesno/ASR/tdnn/pretrained.py
@@ -148,13 +148,15 @@ def main():
         num_classes=params.num_classes,
     )
 
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
+    checkpoint = torch.load(args.checkpoint, map_location="cpu", weights_only=False)
     model.load_state_dict(checkpoint["model"])
     model.to(device)
     model.eval()
 
     logging.info(f"Loading HLG from {params.HLG}")
-    HLG = k2.Fsa.from_dict(torch.load(params.HLG, map_location="cpu"))
+    HLG = k2.Fsa.from_dict(
+        torch.load(params.HLG, map_location="cpu", weights_only=False)
+    )
     HLG = HLG.to(device)
 
     logging.info("Constructing Fbank computer")
diff --git a/icefall/checkpoint.py b/icefall/checkpoint.py
index d31ce1301..4ab685684 100644
--- a/icefall/checkpoint.py
+++ b/icefall/checkpoint.py
@@ -27,7 +27,6 @@ import torch
 import torch.nn as nn
 from lhotse.dataset.sampling.base import CutSampler
 from torch import Tensor
-from torch.cuda.amp import GradScaler
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import Optimizer
 
@@ -43,7 +42,7 @@ def save_checkpoint(
     params: Optional[Dict[str, Any]] = None,
     optimizer: Optional[Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     sampler: Optional[CutSampler] = None,
     rank: int = 0,
 ) -> None:
@@ -102,7 +101,7 @@ def load_checkpoint(
     model_avg: Optional[nn.Module] = None,
     optimizer: Optional[Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     sampler: Optional[CutSampler] = None,
     strict: bool = False,
 ) -> Dict[str, Any]:
@@ -110,7 +109,7 @@ def load_checkpoint(
     TODO: document it
     """
     logging.info(f"Loading checkpoint from {filename}")
-    checkpoint = torch.load(filename, map_location="cpu")
+    checkpoint = torch.load(filename, map_location="cpu", weights_only=False)
 
     if next(iter(checkpoint["model"])).startswith("module."):
         logging.info("Loading checkpoint saved by DDP")
@@ -163,7 +162,7 @@ def average_checkpoints(
     """
     n = len(filenames)
 
-    avg = torch.load(filenames[0], map_location=device)["model"]
+    avg = torch.load(filenames[0], map_location=device, weights_only=False)["model"]
 
     # Identify shared parameters. Two parameters are said to be shared
     # if they have the same data_ptr
@@ -178,7 +177,9 @@ def average_checkpoints(
     uniqued_names = list(uniqued.values())
 
     for i in range(1, n):
-        state_dict = torch.load(filenames[i], map_location=device)["model"]
+        state_dict = torch.load(filenames[i], map_location=device, weights_only=False)[
+            "model"
+        ]
         for k in uniqued_names:
             avg[k] += state_dict[k]
 
@@ -199,7 +200,7 @@ def save_checkpoint_with_global_batch_idx(
     params: Optional[Dict[str, Any]] = None,
     optimizer: Optional[Optimizer] = None,
     scheduler: Optional[LRSchedulerType] = None,
-    scaler: Optional[GradScaler] = None,
+    scaler: Optional["GradScaler"] = None,
     sampler: Optional[CutSampler] = None,
     rank: int = 0,
 ):
@@ -421,8 +422,10 @@ def average_checkpoints_with_averaged_model(
       device:
         Move checkpoints to this device before averaging.
     """
-    state_dict_start = torch.load(filename_start, map_location=device)
-    state_dict_end = torch.load(filename_end, map_location=device)
+    state_dict_start = torch.load(
+        filename_start, map_location=device, weights_only=False
+    )
+    state_dict_end = torch.load(filename_end, map_location=device, weights_only=False)
 
     average_period = state_dict_start["average_period"]
 
diff --git a/icefall/diagnostics.py b/icefall/diagnostics.py
index e5eaba619..d923e8842 100644
--- a/icefall/diagnostics.py
+++ b/icefall/diagnostics.py
@@ -631,7 +631,10 @@ def attach_diagnostics(
                         )
 
         module.register_forward_hook(forward_hook)
-        module.register_backward_hook(backward_hook)
+        if hasattr(module, "register_full_backward_hook"):
+            module.register_full_backward_hook(backward_hook)
+        else:
+            module.register_backward_hook(backward_hook)
 
         if type(module).__name__ in [
             "Sigmoid",
@@ -665,7 +668,10 @@ def attach_diagnostics(
                 _model_diagnostic[f"{_name}.scalar"].accumulate_output_grad(_output)
 
             module.register_forward_hook(scalar_forward_hook)
-            module.register_backward_hook(scalar_backward_hook)
+            if hasattr(module, "register_full_backward_hook"):
+                module.register_full_backward_hook(scalar_backward_hook)
+            else:
+                module.register_backward_hook(scalar_backward_hook)
 
     for name, parameter in model.named_parameters():
 
diff --git a/icefall/hooks.py b/icefall/hooks.py
index 85583acbe..b543190be 100644
--- a/icefall/hooks.py
+++ b/icefall/hooks.py
@@ -77,7 +77,11 @@ def register_inf_check_hooks(model: nn.Module) -> None:
                         logging.warning(f"The sum of {_name}.grad[{i}] is not finite")
 
         module.register_forward_hook(forward_hook)
-        module.register_backward_hook(backward_hook)
+
+        if hasattr(module, "register_full_backward_hook"):
+            module.register_full_backward_hook(backward_hook)
+        else:
+            module.register_backward_hook(backward_hook)
 
     for name, parameter in model.named_parameters():
 
diff --git a/icefall/rnn_lm/train.py b/icefall/rnn_lm/train.py
index 0178b80bf..023afb5a5 100755
--- a/icefall/rnn_lm/train.py
+++ b/icefall/rnn_lm/train.py
@@ -53,7 +53,13 @@ from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
 from icefall.checkpoint import save_checkpoint_with_global_batch_idx
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 
 def get_parser():
@@ -401,7 +407,7 @@ def compute_validation_loss(
 
     for batch_idx, batch in enumerate(valid_dl):
         x, y, sentence_lengths = batch
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 model=model,
                 x=x,
@@ -470,7 +476,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         x, y, sentence_lengths = batch
         batch_size = x.size(0)
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 model=model,
                 x=x,
diff --git a/icefall/transformer_lm/train.py b/icefall/transformer_lm/train.py
index c36abfcdf..acec95e94 100644
--- a/icefall/transformer_lm/train.py
+++ b/icefall/transformer_lm/train.py
@@ -50,7 +50,13 @@ from icefall.checkpoint import load_checkpoint
 from icefall.checkpoint import save_checkpoint as save_checkpoint_impl
 from icefall.dist import cleanup_dist, setup_dist
 from icefall.env import get_env_info
-from icefall.utils import AttributeDict, MetricsTracker, setup_logger, str2bool
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    setup_logger,
+    str2bool,
+    torch_autocast,
+)
 
 
 def get_parser():
@@ -341,7 +347,7 @@ def compute_validation_loss(
 
     for batch_idx, batch in enumerate(valid_dl):
         x, y, sentence_lengths = batch
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 model=model,
                 x=x,
@@ -403,7 +409,7 @@ def train_one_epoch(
         params.batch_idx_train += 1
         x, y, sentence_lengths = batch
         batch_size = x.size(0)
-        with torch.cuda.amp.autocast(enabled=params.use_fp16):
+        with torch_autocast(enabled=params.use_fp16):
             loss, loss_info = compute_loss(
                 model=model,
                 x=x,
diff --git a/icefall/utils.py b/icefall/utils.py
index ffb926566..022f83b3b 100644
--- a/icefall/utils.py
+++ b/icefall/utils.py
@@ -26,6 +26,7 @@ import pathlib
 import random
 import re
 import subprocess
+import warnings
 from collections import defaultdict
 from contextlib import contextmanager
 from dataclasses import dataclass
@@ -42,6 +43,7 @@ import torch
 import torch.distributed as dist
 import torch.nn as nn
 from lhotse.dataset.signal_transforms import time_warp as time_warp_impl
+from packaging import version
 from pypinyin import lazy_pinyin, pinyin
 from pypinyin.contrib.tone_convert import to_finals, to_finals_tone, to_initials
 from torch.utils.tensorboard import SummaryWriter
@@ -50,6 +52,48 @@ from icefall.checkpoint import average_checkpoints
 
 Pathlike = Union[str, Path]
 
+TORCH_VERSION = version.parse(torch.__version__)
+
+
+def create_grad_scaler(device="cuda", **kwargs):
+    """
+    Creates a GradScaler compatible with both torch < 2.3.0 and >= 2.3.0.
+    Accepts all kwargs like: enabled, init_scale, growth_factor, etc.
+
+    /icefall/egs/librispeech/ASR/./zipformer/train.py:1451: FutureWarning:
+    `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use
+    `torch.amp.GradScaler('cuda', args...)` instead.
+    """
+    if TORCH_VERSION >= version.parse("2.3.0"):
+        from torch.amp import GradScaler
+
+        return GradScaler(device=device, **kwargs)
+    else:
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=FutureWarning)
+            return torch.cuda.amp.GradScaler(**kwargs)
+
+
+@contextmanager
+def torch_autocast(device_type="cuda", **kwargs):
+    """
+    To fix the following warnings:
+    /icefall/egs/librispeech/ASR/zipformer/model.py:323:
+    FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated.
+    Please use `torch.amp.autocast('cuda', args...)` instead.
+      with torch.cuda.amp.autocast(enabled=False):
+    """
+    if TORCH_VERSION >= version.parse("2.3.0"):
+        # Use new unified API
+        with torch.amp.autocast(device_type=device_type, **kwargs):
+            yield
+    else:
+        # Suppress deprecation warning and use old CUDA-specific autocast
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", category=FutureWarning)
+            with torch.cuda.amp.autocast(**kwargs):
+                yield
+
 
 # Pytorch issue: https://github.com/pytorch/pytorch/issues/47379
 # Fixed: https://github.com/pytorch/pytorch/pull/49853
@@ -1551,6 +1595,7 @@ def optim_step_and_measure_param_change(
     and the L2 norm of the original parameter. It is given by the formula:
 
         .. math::
+
             \begin{aligned}
                 \delta = \frac{\Vert\theta - \theta_{new}\Vert^2}{\Vert\theta\Vert^2}
             \end{aligned}
diff --git a/requirements.txt b/requirements.txt
index d97263142..885bf2fc3 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -21,3 +21,4 @@ flake8==5.0.4
 
 # cantonese word segment support
 pycantonese==3.4.0
+packaging