decode and adapt method is initialized

egs/tedlium3/ASR/local/prepare_ted_manifests.py

@@ -13,6 +13,39 @@ from lhotse import (
     validate_recordings_and_supervisions,
 )
 from lhotse.utils import Pathlike, safe_extract
+from tqdm.auto import tqdm
+
+def tqdm_urlretrieve_hook(t):
+    """Wraps tqdm instance.
+    Don't forget to close() or __exit__()
+    the tqdm instance once you're done with it (easiest using `with` syntax).
+    Example
+    -------
+    >>> from urllib.request import urlretrieve
+    >>> with tqdm(...) as t:
+    ...     reporthook = tqdm_urlretrieve_hook(t)
+    ...     urlretrieve(..., reporthook=reporthook)
+    Source: https://github.com/tqdm/tqdm/blob/master/examples/tqdm_wget.py
+    """
+    last_b = [0]
+
+    def update_to(b=1, bsize=1, tsize=None):
+        """
+        b  : int, optional
+            Number of blocks transferred so far [default: 1].
+        bsize  : int, optional
+            Size of each block (in tqdm units) [default: 1].
+        tsize  : int, optional
+            Total size (in tqdm units). If [default: None] or -1,
+            remains unchanged.
+        """
+        if tsize not in (None, -1):
+            t.total = tsize
+        displayed = t.update((b - last_b[0]) * bsize)
+        last_b[0] = b
+        return displayed
+
+    return update_to
 
 def urlretrieve_progress(url, filename=None, data=None, desc=None):
     """

egs/tedlium3/ASR/prepare.sh

@@ -228,14 +228,14 @@ if [ $stage -le 9 ] && [ $stop_stage -ge 9 ]; then
 
 fi
 
-if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then
-  log "Stage 10: Generate pseudo label"
-  for spk in {0..10}; do
-	  spk_id=${spk#*$dest\/}
-	  echo $spk_id
-	  ./pseudo.sh $spk_id $subset
-  done
-fi
+# if [ $stage -le 10 ] && [ $stop_stage -ge 10 ]; then
+#   log "Stage 10: Generate pseudo label"
+#   for spk in {0..10}; do
+# 	  spk_id=${spk#*$dest\/}
+# 	  echo $spk_id
+# 	  ./pseudo.sh $spk_id $subset
+#   done
+# fi
 
 
 : <<'END'

egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/asr_datamodule.py

@@ -39,6 +39,8 @@ from torch.utils.data import DataLoader
 
 from icefall.utils import str2bool
 
+from sampling import SingleUttSampler
+
 
 class TedLiumAsrDataModule:
     """
@@ -355,11 +357,20 @@ class TedLiumAsrDataModule:
                 return_cuts=self.args.return_cuts,
             )
 
-        test_sampler = DynamicBucketingSampler(
-            cuts_test,
-            max_duration=self.args.max_duration,
-            shuffle=False,
-        )
+        if self.args.bucketing_sampler:
+            logging.info("Using DynamicBucketingSampler.")
+            test_sampler = DynamicBucketingSampler(
+                cuts_test,
+                max_duration=self.args.max_duration,
+                shuffle=False,
+            )
+        else:
+            logging.info("Using SingleUttSampler.")
+            test_sampler = SingleUttSampler(
+                cuts_test,
+                max_duration=self.args.max_duration,
+                shuffle=False,
+            )
 
         logging.debug("About to create test dataloader")
         test_dl = DataLoader(
@@ -390,5 +401,5 @@ class TedLiumAsrDataModule:
     
     @lru_cache()
     def user_test_cuts(self, spk_id) -> CutSet:
-        logging.info("About to get test cuts")
+        logging.info(f"About to get test cuts : {spk_id}")
         return load_manifest_lazy(self.args.manifest_dir / f"tedlium_cuts_test_{spk_id}.jsonl.gz")

egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/decode.py

@@ -79,26 +79,26 @@ from icefall.utils import (
 )
 
 import fairseq
-from data2vec_audio import LoRAModule
+# from data2vec_audio import LoRAModule
 
 LOG_EPS = math.log(1e-10)
 
-class LoRAHook():
-    def __init__(self, module):
-        self.hook = module.register_forward_hook(self.hook_fn)
-        self.lora = LoRAModule(
-                           embedding_dim=768,
-                           rank=6,
-                           lora_alpha=10000,
-                    )
-    def hook_fn(self, module, input, output):
-        lora_out = self.lora(input[0])
-        output += lora_out
+# class LoRAHook():
+#     def __init__(self, module):
+#         self.hook = module.register_forward_hook(self.hook_fn)
+#         self.lora = LoRAModule(
+#                            embedding_dim=768,
+#                            rank=6,
+#                            lora_alpha=10000,
+#                     )
+#     def hook_fn(self, module, input, output):
+#         lora_out = self.lora(input[0])
+#         output += lora_out
 
-    def save_checkpoint(self, i, iter_, save_dir):
-        if isinstance(self.lora, DDP):
-            lora = self.lora.module
-        torch.save(lora.state_dict(), f"{save_dir}/lora_{iter_}_{i}.pt")
+#     def save_checkpoint(self, i, iter_, save_dir):
+#         if isinstance(self.lora, DDP):
+#             lora = self.lora.module
+#         torch.save(lora.state_dict(), f"{save_dir}/lora_{iter_}_{i}.pt")
 
 
 def get_parser():
@@ -304,6 +304,21 @@ def get_parser():
 
     return parser
 
+from typing import Any, Dict, Optional, Tuple, Union
+
+from torch import Tensor
+
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    encode_supervisions,
+    setup_logger,
+    str2bool,
+    save_args,
+)
+import warnings
 
 def decode_one_batch(
     params: AttributeDict,
@@ -479,6 +494,8 @@ def decode_one_batch(
                 )
             hyps.append(sp.decode(hyp).split())
 
+    print(hyps)
+    exit()
     if params.decoding_method == "greedy_search":
         return {"greedy_search": hyps}
     elif "fast_beam_search" in params.decoding_method:
@@ -536,7 +553,7 @@ def decode_dataset(
         num_batches = "?"
 
     if params.decoding_method == "greedy_search":
-        log_interval = 50
+        log_interval = 1
     else:
         log_interval = 20
 
@@ -821,10 +838,12 @@ def main():
 
     # we need cut ids to display recognition results.
     args.return_cuts = True
+    args.bucketing_sampler= False
+    
     tedlium = TedLiumAsrDataModule(args)
     #valid_cuts = tedlium.dev_cuts()
-    test_cuts = tedlium.user_test_cuts(spk_id=params.spk_id)
     #valid_dl = tedlium.test_dataloaders(valid_cuts)
+    test_cuts = tedlium.user_test_cuts(spk_id=params.spk_id)
     test_dl = tedlium.test_dataloaders(test_cuts)
 
     #test_sets = ['dev', 'test']
@@ -844,7 +863,7 @@ def main():
         
         save_results(
             params=params,
-            test_set_name=test_set,
+            test_set_name=test_set + str(params.spk_id),
             results_dict=results_dict,
         )
 

egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/decode_and_adapt.py

@@ -0,0 +1,882 @@
+#!/usr/bin/env python3
+#
+# Copyright 2021-2022 Xiaomi Corporation (Author: 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 argparse
+import logging
+import math
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+import k2
+import sentencepiece as spm
+import torch
+import torch.nn as nn
+from asr_datamodule import TedLiumAsrDataModule
+from beam_search import (
+    beam_search,
+    fast_beam_search_nbest,
+    fast_beam_search_nbest_LG,
+    fast_beam_search_nbest_oracle,
+    fast_beam_search_one_best,
+    greedy_search,
+    greedy_search_batch,
+    modified_beam_search,
+)
+
+from train_tta import add_model_arguments, add_rep_arguments, get_params, get_transducer_model
+#from prompt_tuning import add_model_arguments, add_rep_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.lexicon import Lexicon
+from icefall.utils import (
+    AttributeDict,
+    setup_logger,
+    store_transcripts,
+    str2bool,
+    write_error_stats,
+)
+
+import fairseq
+
+LOG_EPS = math.log(1e-10)
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+    parser.add_argument(
+        "--model-name",
+        type=str,
+        default="",
+        help="""It specifies the model file name to use for decoding.""",
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=30,
+        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=9,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--use-averaged-model",
+        type=str2bool,
+        default=True,
+        help="Whether to load averaged model. Currently it only supports "
+        "using --epoch. If True, it would decode with the averaged model "
+        "over the epoch range from `epoch-avg` (excluded) to `epoch`."
+        "Actually only the models with epoch number of `epoch-avg` and "
+        "`epoch` are loaded for averaging. ",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless7_ctc/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=Path,
+        default="data/lang_bpe_500",
+        help="The lang dir containing word table and LG graph",
+    )
+
+    parser.add_argument(
+        "--decoding-method",
+        type=str,
+        default="greedy_search",
+        help="""Possible values are:
+          - greedy_search
+          - beam_search
+          - modified_beam_search
+          - fast_beam_search
+          - fast_beam_search_nbest
+          - fast_beam_search_nbest_oracle
+          - fast_beam_search_nbest_LG
+        If you use fast_beam_search_nbest_LG, you have to specify
+        `--lang-dir`, which should contain `LG.pt`.
+        """,
+    )
+
+    parser.add_argument(
+        "--beam-size",
+        type=int,
+        default=4,
+        help="""An integer indicating how many candidates we will keep for each
+        frame. Used only when --decoding-method is beam_search or
+        modified_beam_search.""",
+    )
+
+    parser.add_argument(
+        "--beam",
+        type=float,
+        default=20.0,
+        help="""A floating point value to calculate the cutoff score during beam
+        search (i.e., `cutoff = max-score - beam`), which is the same as the
+        `beam` in Kaldi.
+        Used only when --decoding-method is fast_beam_search,
+        fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle
+        """,
+    )
+
+    parser.add_argument(
+        "--ngram-lm-scale",
+        type=float,
+        default=0.01,
+        help="""
+        Used only when --decoding_method is fast_beam_search_nbest_LG.
+        It specifies the scale for n-gram LM scores.
+        """,
+    )
+
+    parser.add_argument(
+        "--max-contexts",
+        type=int,
+        default=8,
+        help="""Used only when --decoding-method is
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--max-states",
+        type=int,
+        default=64,
+        help="""Used only when --decoding-method is
+        fast_beam_search, fast_beam_search_nbest, fast_beam_search_nbest_LG,
+        and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--context-size",
+        type=int,
+        default=2,
+        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
+    )
+    parser.add_argument(
+        "--max-sym-per-frame",
+        type=int,
+        default=1,
+        help="""Maximum number of symbols per frame.
+        Used only when --decoding_method is greedy_search""",
+    )
+
+    parser.add_argument(
+        "--num-paths",
+        type=int,
+        default=200,
+        help="""Number of paths for nbest decoding.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--nbest-scale",
+        type=float,
+        default=0.5,
+        help="""Scale applied to lattice scores when computing nbest paths.
+        Used only when the decoding method is fast_beam_search_nbest,
+        fast_beam_search_nbest_LG, and fast_beam_search_nbest_oracle""",
+    )
+
+    parser.add_argument(
+        "--simulate-streaming",
+        type=str2bool,
+        default=False,
+        help="""Whether to simulate streaming in decoding, this is a good way to
+        test a streaming model.
+        """,
+    )
+
+    parser.add_argument(
+        "--decode-chunk-size",
+        type=int,
+        default=16,
+        help="The chunk size for decoding (in frames after subsampling)",
+    )
+
+    parser.add_argument(
+        "--left-context",
+        type=int,
+        default=64,
+        help="left context can be seen during decoding (in frames after subsampling)",
+    )
+    
+    parser.add_argument(
+        "--res-name",
+        type=str,
+    )
+
+    add_model_arguments(parser)
+    add_rep_arguments(parser)
+
+    return parser
+
+from typing import Any, Dict, Optional, Tuple, Union
+
+from torch import Tensor
+
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+from icefall.utils import (
+    AttributeDict,
+    MetricsTracker,
+    encode_supervisions,
+    setup_logger,
+    str2bool,
+    save_args,
+)
+import warnings
+
+def decode_one_batch(
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[List[str]]]:
+    """Decode one batch and return the result in a dict. The dict has the
+    following format:
+
+        - key: It indicates the setting used for decoding. For example,
+               if greedy_search is used, it would be "greedy_search"
+               If beam search with a beam size of 7 is used, it would be
+               "beam_7"
+        - value: It contains the decoding result. `len(value)` equals to
+                 batch size. `value[i]` is the decoding result for the i-th
+                 utterance in the given batch.
+    Args:
+      params:
+        It's the return value of :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      batch:
+        It is the return value from iterating
+        `lhotse.dataset.K2SpeechRecognitionDataset`. See its documentation
+        for the format of the `batch`.
+      word_table:
+        The word symbol table.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
+    Returns:
+      Return the decoding result. See above description for the format of
+      the returned dict.
+    """
+    device = next(model.parameters()).device
+    feature = batch["inputs"]
+    assert feature.ndim == 2 or feature.ndim == 3
+
+    feature = feature.to(device)
+    # at entry, feature is (N, T, C)
+
+    supervisions = batch["supervisions"]
+    #feature_lens = supervisions["num_frames"].to(device)
+    if feature.ndim == 2:
+        feature_lens = [] 
+        for supervision in supervisions['cut']:
+            try: feature_lens.append(supervision.tracks[0].cut.recording.num_samples)
+            except: feature_lens.append(supervision.recording.num_samples)
+        feature_lens = torch.tensor(feature_lens)
+
+    elif feature.ndim == 3:
+        feature_lens = supervisions["num_frames"].to(device)
+
+    if params.simulate_streaming:
+        feature_lens += params.left_context
+        feature = torch.nn.functional.pad(
+            feature,
+            pad=(0, 0, 0, params.left_context),
+            value=LOG_EPS,
+        )
+        encoder_out, encoder_out_lens, _ = model.encoder.streaming_forward(
+            x=feature,
+            x_lens=feature_lens,
+            chunk_size=params.decode_chunk_size,
+            left_context=params.left_context,
+            simulate_streaming=True,
+        )
+    else:
+        encoder_out, encoder_out_lens = model.encoder(x=feature, x_lens=feature_lens, prompt=model.prompt)
+    
+    hyps = []
+
+    if params.decoding_method == "fast_beam_search":
+        hyp_tokens = fast_beam_search_one_best(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_LG":
+        hyp_tokens = fast_beam_search_nbest_LG(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in hyp_tokens:
+            hyps.append([word_table[i] for i in hyp])
+    elif params.decoding_method == "fast_beam_search_nbest":
+        hyp_tokens = fast_beam_search_nbest(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "fast_beam_search_nbest_oracle":
+        hyp_tokens = fast_beam_search_nbest_oracle(
+            model=model,
+            decoding_graph=decoding_graph,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam,
+            max_contexts=params.max_contexts,
+            max_states=params.max_states,
+            num_paths=params.num_paths,
+            ref_texts=sp.encode(supervisions["text"]),
+            nbest_scale=params.nbest_scale,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "greedy_search" and params.max_sym_per_frame == 1:
+        hyp_tokens = greedy_search_batch(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    elif params.decoding_method == "modified_beam_search":
+        hyp_tokens = modified_beam_search(
+            model=model,
+            encoder_out=encoder_out,
+            encoder_out_lens=encoder_out_lens,
+            beam=params.beam_size,
+        )
+        for hyp in sp.decode(hyp_tokens):
+            hyps.append(hyp.split())
+    else:
+        batch_size = encoder_out.size(0)
+
+        for i in range(batch_size):
+            # fmt: off
+            encoder_out_i = encoder_out[i:i+1, :encoder_out_lens[i]]
+            # fmt: on
+            if params.decoding_method == "greedy_search":
+                hyp = greedy_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    max_sym_per_frame=params.max_sym_per_frame,
+                )
+            elif params.decoding_method == "beam_search":
+                hyp = beam_search(
+                    model=model,
+                    encoder_out=encoder_out_i,
+                    beam=params.beam_size,
+                )
+            else:
+                raise ValueError(
+                    f"Unsupported decoding method: {params.decoding_method}"
+                )
+            hyps.append(sp.decode(hyp).split())
+
+    if params.decoding_method == "greedy_search":
+        return {"greedy_search": hyps}
+    elif "fast_beam_search" in params.decoding_method:
+        key = f"beam_{params.beam}_"
+        key += f"max_contexts_{params.max_contexts}_"
+        key += f"max_states_{params.max_states}"
+        if "nbest" in params.decoding_method:
+            key += f"_num_paths_{params.num_paths}_"
+            key += f"nbest_scale_{params.nbest_scale}"
+            if "LG" in params.decoding_method:
+                key += f"_ngram_lm_scale_{params.ngram_lm_scale}"
+
+        return {key: hyps}
+    else:
+        return {f"beam_size_{params.beam_size}": hyps}
+    
+def decode_and_adapt(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    sp: spm.SentencePieceProcessor,
+    batch: dict,
+    is_training: bool,
+    num_iter: int
+) -> Tuple[Tensor, MetricsTracker]:
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+    feature = batch["inputs"]
+    # at entry, feature is (N, T, C)
+    assert feature.ndim == 2 or feature.ndim == 3
+    feature = feature.to(device)
+    
+    supervisions = batch["supervisions"]
+    if feature.ndim == 2:
+        feature_lens = []
+        for supervision in supervisions['cut']:
+            try: feature_lens.append(supervision.tracks[0].cut.recording.num_samples)
+            except: feature_lens.append(supervision.recording.num_samples)
+        feature_lens = torch.tensor(feature_lens)
+
+    elif feature.ndim == 3:
+        feature_lens = supervisions["num_frames"].to(device)
+
+    batch_idx_train = params.batch_idx_train
+    warm_step = params.warm_step
+
+    texts = batch["supervisions"]["text"]
+    texts = [text.upper() for text in texts]
+    
+    token_ids = sp.encode(texts, out_type=int)
+    y = k2.RaggedTensor(token_ids).to(device)
+
+    model.train()
+    for i in range(num_iter):
+        with torch.set_grad_enabled(is_training):
+            simple_loss, pruned_loss, ctc_output = model(
+                x=feature,
+                x_lens=feature_lens,
+                y=y,
+                prune_range=params.prune_range,
+                am_scale=params.am_scale,
+                lm_scale=params.lm_scale,
+            )
+
+            s = params.simple_loss_scale
+            # take down the scale on the simple loss from 1.0 at the start
+            # to params.simple_loss scale by warm_step.
+            simple_loss_scale = (
+                s
+                if batch_idx_train >= warm_step
+                else 1.0 - (batch_idx_train / warm_step) * (1.0 - s)
+            )
+            pruned_loss_scale = (
+                1.0
+                if batch_idx_train >= warm_step
+                else 0.1 + 0.9 * (batch_idx_train / warm_step)
+            )
+
+            loss = simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
+        
+        if params.ctc_loss_scale > 0:
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                supervision_segments, token_ids = encode_supervisions(
+                    supervisions,
+                    subsampling_factor=params.subsampling_factor,
+                    token_ids=token_ids,
+                )
+            
+            # Works with a BPE model
+            decoding_graph = k2.ctc_graph(token_ids, modified=False, device=device)
+            dense_fsa_vec = k2.DenseFsaVec(
+                ctc_output,
+                supervision_segments,
+                allow_truncate=params.subsampling_factor - 1,
+            )
+
+            ctc_loss = k2.ctc_loss(
+                decoding_graph=decoding_graph,
+                dense_fsa_vec=dense_fsa_vec,
+                output_beam=params.beam_size,
+                reduction="sum",
+                use_double_scores=params.use_double_scores,
+            )
+            assert ctc_loss.requires_grad == is_training
+            loss += params.ctc_loss_scale * ctc_loss
+
+        # self.adapted_model_losses.append(loss.item())
+        # self.adapted_models.append(self.copy_model_and_optimizer(self.models[0]))
+        
+        self.optimizer.zero_grad()
+        loss.backward()
+        self.optimizer.step()
+
+
+    with torch.set_grad_enabled(is_training):
+        simple_loss, pruned_loss, ctc_output = model(
+            x=feature,
+            x_lens=feature_lens,
+            y=y,
+            prune_range=params.prune_range,
+            am_scale=params.am_scale,
+            lm_scale=params.lm_scale,
+        )
+
+        s = params.simple_loss_scale
+        # take down the scale on the simple loss from 1.0 at the start
+        # to params.simple_loss scale by warm_step.
+        simple_loss_scale = (
+            s
+            if batch_idx_train >= warm_step
+            else 1.0 - (batch_idx_train / warm_step) * (1.0 - s)
+        )
+        pruned_loss_scale = (
+            1.0
+            if batch_idx_train >= warm_step
+            else 0.1 + 0.9 * (batch_idx_train / warm_step)
+        )
+
+    
+
+        loss = simple_loss_scale * simple_loss + pruned_loss_scale * pruned_loss
+
+
+def decode_dataset(
+    dl: torch.utils.data.DataLoader,
+    params: AttributeDict,
+    model: nn.Module,
+    sp: spm.SentencePieceProcessor,
+    word_table: Optional[k2.SymbolTable] = None,
+    decoding_graph: Optional[k2.Fsa] = None,
+) -> Dict[str, List[Tuple[str, List[str], List[str]]]]:
+    """Decode dataset.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      sp:
+        The BPE model.
+      word_table:
+        The word symbol table.
+      decoding_graph:
+        The decoding graph. Can be either a `k2.trivial_graph` or HLG, Used
+        only when --decoding_method is fast_beam_search, fast_beam_search_nbest,
+        fast_beam_search_nbest_oracle, and fast_beam_search_nbest_LG.
+    Returns:
+      Return a dict, whose key may be "greedy_search" if greedy search
+      is used, or it may be "beam_7" if beam size of 7 is used.
+      Its value is a list of tuples. Each tuple contains two elements:
+      The first is the reference transcript, and the second is the
+      predicted result.
+    """
+    num_cuts = 0
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    if params.decoding_method == "greedy_search":
+        log_interval = 1
+    else:
+        log_interval = 20
+
+    results = defaultdict(list)
+    for batch_idx, batch in enumerate(dl):
+        model.eval()
+        texts = batch["supervisions"]["text"]
+        texts = [text.upper() for text in texts]
+
+        cut_ids = [cut.id for cut in batch["supervisions"]["cut"]]
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+            word_table=word_table,
+            batch=batch,
+        )
+        
+        # replace the supervision to pseudo labels
+        batch["supervision"]["text"] = "".join(hyps_dict[params.decoding_method] )
+
+        # augment the single utterance (augmentation automatically excued in d2v model)
+        batch["intputs"] = batch["intputs"].reapeat(4, 1)
+
+        decode_and_adapt(params, model, sp, batch, is_training=True, num_iter=10)
+
+        hyps_dict = decode_one_batch(
+            params=params,
+            model=model,
+            sp=sp,
+            decoding_graph=decoding_graph,
+            word_table=word_table,
+            batch=batch,
+        )
+        
+        for name, 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_words = ref_text.split()
+                this_batch.append((cut_id, ref_words, hyp_words))
+
+            results[name].extend(this_batch)
+
+        num_cuts += len(texts)
+
+        if batch_idx % log_interval == 0:
+            batch_str = f"{batch_idx}/{num_batches}"
+
+            logging.info(f"batch {batch_str}, cuts processed until now is {num_cuts}")
+    return results
+
+
+def save_results(
+    params: AttributeDict,
+    test_set_name: str,
+    results_dict: Dict[str, List[Tuple[str, List[str], List[str]]]],
+):
+    test_set_wers = dict()
+    for key, results in results_dict.items():
+        recog_path = (
+            params.res_dir / f"recogs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        results = sorted(results)
+        store_transcripts(filename=recog_path, texts=results)
+        logging.info(f"The transcripts are stored in {recog_path}")
+
+        # The following prints out WERs, per-word error statistics and aligned
+        # ref/hyp pairs.
+        errs_filename = (
+            params.res_dir / f"errs-{test_set_name}-{key}-{params.suffix}.txt"
+        )
+        with open(errs_filename, "w") as f:
+            wer = write_error_stats(
+                f, f"{test_set_name}-{key}", results, enable_log=True
+            )
+            test_set_wers[key] = wer
+
+        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.res_dir / f"wer-summary-{test_set_name}-{key}-{params.suffix}.txt"
+    )
+    with open(errs_info, "w") as f:
+        print("settings\tWER", file=f)
+        for key, val in test_set_wers:
+            print("{}\t{}".format(key, val), file=f)
+    
+    spk = None
+    wer = None
+    s = "\nFor {}, WER of different settings are:\n".format(test_set_name)
+    note = "\tbest for {}".format(test_set_name)
+    for key, val in test_set_wers:
+        s += "{}\t{}{}\n".format(key, val, note)
+        note = ""
+        spk = str(test_set_name)
+        wer = str(val)
+    logging.info(s)
+    with open(f'./{params.res_name}.txt', 'a') as f:
+        f.write(f"{spk} {wer}\n")
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    TedLiumAsrDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    assert params.decoding_method in (
+        "greedy_search",
+        "beam_search",
+        "fast_beam_search",
+        "fast_beam_search_nbest",
+        "fast_beam_search_nbest_LG",
+        "fast_beam_search_nbest_oracle",
+        "modified_beam_search",
+    )
+    params.res_dir = params.exp_dir / params.decoding_method
+
+    if params.iter > 0:
+        params.suffix = f"iter-{params.iter}-avg-{params.avg}"
+    else:
+        params.suffix = f"epoch-{params.epoch}-avg-{params.avg}"
+
+    if params.simulate_streaming:
+        params.suffix += f"-streaming-chunk-size-{params.decode_chunk_size}"
+        params.suffix += f"-left-context-{params.left_context}"
+
+    if "fast_beam_search" in params.decoding_method:
+        params.suffix += f"-beam-{params.beam}"
+        params.suffix += f"-max-contexts-{params.max_contexts}"
+        params.suffix += f"-max-states-{params.max_states}"
+        if "nbest" in params.decoding_method:
+            params.suffix += f"-nbest-scale-{params.nbest_scale}"
+            params.suffix += f"-num-paths-{params.num_paths}"
+            if "LG" in params.decoding_method:
+                params.suffix += f"-ngram-lm-scale-{params.ngram_lm_scale}"
+    elif "beam_search" in params.decoding_method:
+        params.suffix += f"-{params.decoding_method}-beam-size-{params.beam_size}"
+    else:
+        params.suffix += f"-context-{params.context_size}"
+        params.suffix += f"-max-sym-per-frame-{params.max_sym_per_frame}"
+
+    if params.use_averaged_model:
+        params.suffix += "-use-averaged-model"
+
+    setup_logger(f"{params.res_dir}/log-decode-{params.suffix}")
+    logging.info("Decoding started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    logging.info(f"Device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> and <unk> are defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.unk_id = sp.piece_to_id("<unk>")
+    params.vocab_size = sp.get_piece_size()
+
+    if params.simulate_streaming:
+        assert (
+            params.causal_convolution
+        ), "Decoding in streaming requires causal convolution"
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    load_checkpoint(f"{params.exp_dir}/{params.model_name}", model)
+
+    model.to(device)
+    model.eval()
+
+    if "fast_beam_search" in params.decoding_method:
+        if params.decoding_method == "fast_beam_search_nbest_LG":
+            lexicon = Lexicon(params.lang_dir)
+            word_table = lexicon.word_table
+            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)
+            )
+            decoding_graph.scores *= params.ngram_lm_scale
+        else:
+            word_table = None
+            decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
+    else:
+        decoding_graph = None
+        word_table = None
+
+    num_param = sum([p.numel() for p in model.parameters()])
+    logging.info(f"Number of model parameters: {num_param}")
+
+    # we need cut ids to display recognition results.
+    args.return_cuts = True
+    args.bucketing_sampler= False
+    
+    tedlium = TedLiumAsrDataModule(args)
+    #valid_cuts = tedlium.dev_cuts()
+    #valid_dl = tedlium.test_dataloaders(valid_cuts)
+    test_cuts = tedlium.user_test_cuts(spk_id=params.spk_id)
+    test_dl = tedlium.test_dataloaders(test_cuts)
+
+    #test_sets = ['dev', 'test']
+    #test_dl = [valid_dl, test_dl]
+    test_sets = ['test']
+    test_dl = [test_dl]
+ 
+    for test_set, test_dl in zip(test_sets, test_dl):
+        results_dict = decode_dataset(
+            dl=test_dl,
+            params=params,
+            model=model,
+            sp=sp,
+            word_table=word_table,
+            decoding_graph=decoding_graph,
+        )
+        
+        save_results(
+            params=params,
+            test_set_name=test_set + str(params.spk_id),
+            results_dict=results_dict,
+        )
+
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()

egs/tedlium3/ASR/pruned_transducer_stateless_d2v_v2/sampling.py

@@ -0,0 +1,224 @@
+import warnings
+from typing import Any, Dict, Optional
+
+from lhotse import CutSet, Seconds
+from lhotse.dataset.sampling.base import CutSampler, TimeConstraint
+from lhotse.dataset.sampling.data_source import DataSource
+
+
+class SingleUttSampler(CutSampler):
+    """
+    Samples cuts from a CutSet to satisfy the input constraints.
+    It behaves like an iterable that yields lists of strings (cut IDs).
+
+    When one of :attr:`max_frames`, :attr:`max_samples`, or :attr:`max_duration` is specified,
+    the batch size is dynamic.
+    Exactly zero or one of those constraints can be specified.
+    Padding required to collate the batch does not contribute to max frames/samples/duration.
+
+    Example usage::
+
+        >>> dataset = K2SpeechRecognitionDataset(cuts)
+        >>> sampler = SimpleCutSampler(cuts, shuffle=True)
+        >>> loader = DataLoader(dataset, sampler=sampler, batch_size=None)
+        >>> for epoch in range(start_epoch, n_epochs):
+        ...     sampler.set_epoch(epoch)
+        ...     train(loader)
+
+    """
+
+    def __init__(
+        self,
+        cuts: CutSet,
+        max_duration: Seconds = None,
+        max_cuts: Optional[int] = None,
+        shuffle: bool = False,
+        drop_last: bool = False,
+        world_size: Optional[int] = None,
+        rank: Optional[int] = None,
+        seed: int = 0,
+    ):
+        """
+        SimpleCutSampler's constructor.
+
+        :param cuts: the ``CutSet`` to sample data from.
+        :param max_duration: The maximum total recording duration from ``cuts``.
+        :param max_cuts: The maximum number of cuts sampled to form a mini-batch.
+            By default, this constraint is off.
+        :param shuffle: When ``True``, the cuts will be shuffled at the start of iteration.
+            Convenient when mini-batch loop is inside an outer epoch-level loop, e.g.:
+            `for epoch in range(10): for batch in dataset: ...` as every epoch will see a
+            different cuts order.
+        :param drop_last: When ``True``, the last batch is dropped if it's incomplete.
+        :param world_size: Total number of distributed nodes. We will try to infer it by default.
+        :param rank: Index of distributed node. We will try to infer it by default.
+        :param seed: Random seed used to consistently shuffle the dataset across different processes.
+        """
+        super().__init__(
+            drop_last=drop_last,
+            shuffle=shuffle,
+            world_size=world_size,
+            rank=rank,
+            seed=seed,
+        )
+        self.data_source = DataSource(cuts)
+        self.time_constraint = TimeConstraint(
+            max_duration=max_duration,
+            max_cuts=max_cuts,
+        )
+
+    @property
+    def remaining_duration(self) -> Optional[float]:
+        """
+        Remaining duration of data left in the sampler (may be inexact due to float arithmetic).
+        Not available when the CutSet is read in lazy mode (returns None).
+        """
+        return self.data_source.remaining_duration
+
+    @property
+    def remaining_cuts(self) -> Optional[int]:
+        """
+        Remaining number of cuts in the sampler.
+        Not available when the CutSet is read in lazy mode (returns None).
+        """
+        return self.data_source.remaining_cuts
+
+    @property
+    def num_cuts(self) -> Optional[int]:
+        """
+        Total number of cuts in the sampler.
+        Not available when the CutSet is read in lazy mode (returns None).
+        """
+        if self.data_source.is_lazy:
+            return None
+        return len(self.data_source)
+
+    def state_dict(self) -> Dict[str, Any]:
+        """
+        Return the current state of the sampler in a state_dict.
+        Together with ``load_state_dict()``, this can be used to restore the
+        training loop's state to the one stored in the state_dict.
+        """
+        state_dict = super().state_dict()
+        state_dict.update(
+            {
+                "time_constraint": self.time_constraint.state_dict(),
+            }
+        )
+        return state_dict
+
+    def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
+        """
+        Restore the state of the sampler that is described in a state_dict.
+        This will result in the sampler yielding batches from where the previous training left it off.
+
+        .. caution::
+            The samplers are expected to be initialized with the same CutSets,
+            but this is not explicitly checked anywhere.
+
+        .. caution::
+            The input ``state_dict`` is being mutated: we remove each consumed key, and expect
+            it to be empty at the end of loading. If you don't want this behavior, pass a copy
+            inside of this function (e.g., using ``import deepcopy``).
+
+        .. note::
+            For implementers of sub-classes of CutSampler: the flag ``self._just_restored_state`` has to be
+            handled in ``__iter__`` to make it avoid resetting the just-restored state (only once).
+        """
+        time_constraint = TimeConstraint(**state_dict.pop("time_constraint"))
+        if self.time_constraint != time_constraint:
+            warnings.warn(
+                "SimpleCutSampler.load_state_dict(): Inconsistent time_constraint:\n"
+                f"expected {self.time_constraint}\n"
+                f"received {time_constraint}\n"
+                f"We will overwrite the settings with the received state_dict."
+            )
+        self.time_constraint = time_constraint
+
+        super().load_state_dict(state_dict)
+
+        # Restore the data source's state
+        if self.shuffle:
+            self.data_source.shuffle(self.seed + self.epoch)
+        self.data_source.fast_forward(self.diagnostics.current_epoch_stats.total_cuts)
+
+    def __iter__(self) -> "SimpleCutSampler":
+        """
+        Prepare the dataset for iterating over a new epoch. Will shuffle the data if requested.
+        """
+        # Restored state with load_state_dict()? Skip resetting only this once.
+        if self._just_restored_state:
+            return self
+        # Why reset the current epoch?
+        # Either we are iterating the epoch for the first time and it's a no-op,
+        # or we are iterating the same epoch again, in which case setting more steps
+        # than are actually available per epoch would have broken the checkpoint restoration.
+        self.diagnostics.reset_current_epoch()
+        # Reset the state to the beginning of the epoch.
+        if self.shuffle:
+            self.data_source.shuffle(self.seed + self.epoch)
+        iter(self.data_source)
+        return self
+
+    def _next_batch(self) -> CutSet:
+        # Keep iterating the underlying CutSet as long as we hit or exceed the constraints
+        # provided by user (the max number of frames or max number of cuts).
+        # Note: no actual data is loaded into memory yet because the manifests contain all the metadata
+        # required to do this operation.
+        self.time_constraint.reset()
+        cuts = []
+        while True:
+
+            # Check that we have not reached the end of the dataset.
+            try:
+                # If this doesn't raise (typical case), it's not the end: keep processing.
+                next_cut = next(self.data_source)
+            except StopIteration:
+                # No more cuts to sample from: if we have a partial batch,
+                # we may output it, unless the user requested to drop it.
+                # We also check if the batch is "almost there" to override drop_last.
+                if cuts and (
+                    not self.drop_last or self.time_constraint.close_to_exceeding()
+                ):
+                    # We have a partial batch and we can return it.
+                    return CutSet.from_cuts(cuts)
+                else:
+                    # There is nothing more to return or it's discarded:
+                    # signal the iteration code to stop.
+                    self.diagnostics.discard(cuts)
+                    raise StopIteration()
+
+            # Check whether the cut we're about to sample satisfies optional user-requested predicate.
+            if not self._filter_fn(next_cut):
+                # No - try another one.
+                self.diagnostics.discard_single(next_cut)
+                continue
+
+            # Track the duration/frames/etc. constraints.
+            self.time_constraint.add(next_cut)
+
+            cuts.append(next_cut)
+            break
+
+            # # Did we exceed the max_frames and max_cuts constraints?
+            # if not self.time_constraint.exceeded():
+            #     # No - add the next cut to the batch, and keep trying.
+            #     cuts.append(next_cut)
+            # else:
+            #     # Yes. Do we have at least one cut in the batch?
+            #     if cuts:
+            #         # Yes. Return the batch, but keep the currently drawn cut for later.
+            #         self.data_source.take_back(next_cut)
+            #         break
+            #     else:
+            #         # No. We'll warn the user that the constrains might be too tight,
+            #         # and return the cut anyway.
+            #         warnings.warn(
+            #             "The first cut drawn in batch collection violates "
+            #             "the max_frames, max_cuts, or max_duration constraints - "
+            #             "we'll return it anyway. "
+            #             "Consider increasing max_frames/max_cuts/max_duration."
+            #         )
+            #         cuts.append(next_cut)
+
+        return CutSet.from_cuts(cuts)