Export streaming zipformer to ncnn (#906)

.github/scripts/test-ncnn-export.sh

@@ -131,3 +131,102 @@ python3 ./lstm_transducer_stateless2/ncnn-decode.py \
 
 rm -rf $repo
 log "--------------------------------------------------------------------------"
+
+log "=========================================================================="
+repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
+GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+repo=$(basename $repo_url)
+
+pushd $repo
+git lfs pull --include "data/lang_bpe_500/bpe.model"
+git lfs pull --include "exp/pretrained.pt"
+
+cd exp
+ln -s pretrained.pt epoch-99.pt
+popd
+
+./pruned_transducer_stateless7_streaming/export-for-ncnn.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --exp-dir $repo/exp \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  \
+  --decode-chunk-len 32 \
+  --num-encoder-layers "2,4,3,2,4" \
+  --feedforward-dims "1024,1024,2048,2048,1024" \
+  --nhead "8,8,8,8,8" \
+  --encoder-dims "384,384,384,384,384" \
+  --attention-dims "192,192,192,192,192" \
+  --encoder-unmasked-dims "256,256,256,256,256" \
+  --zipformer-downsampling-factors "1,2,4,8,2" \
+  --cnn-module-kernels "31,31,31,31,31" \
+  --decoder-dim 512 \
+  --joiner-dim 512
+
+./ncnn/tools/pnnx/build/src/pnnx $repo/exp/encoder_jit_trace-pnnx.pt
+./ncnn/tools/pnnx/build/src/pnnx $repo/exp/decoder_jit_trace-pnnx.pt
+./ncnn/tools/pnnx/build/src/pnnx $repo/exp/joiner_jit_trace-pnnx.pt
+
+python3 ./pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  --encoder-param-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.param \
+  --encoder-bin-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.bin \
+  --decoder-param-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.param \
+  --decoder-bin-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.bin \
+  --joiner-param-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.param \
+  --joiner-bin-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.bin \
+  $repo/test_wavs/1089-134686-0001.wav
+
+rm -rf $repo
+log "--------------------------------------------------------------------------"
+
+log "=========================================================================="
+repo_url=https://huggingface.co/pfluo/k2fsa-zipformer-chinese-english-mixed
+GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+repo=$(basename $repo_url)
+
+pushd $repo
+git lfs pull --include "data/lang_char_bpe/L.pt"
+git lfs pull --include "data/lang_char_bpe/L_disambig.pt"
+git lfs pull --include "data/lang_char_bpe/Linv.pt"
+git lfs pull --include "exp/pretrained.pt"
+
+cd exp
+ln -s pretrained.pt epoch-99.pt
+popd
+
+./pruned_transducer_stateless7_streaming/export-for-ncnn-zh.py \
+  --lang-dir $repo/data/lang_char_bpe \
+  --exp-dir $repo/exp \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --decode-chunk-len 32 \
+  --num-encoder-layers "2,4,3,2,4" \
+  --feedforward-dims "1024,1024,1536,1536,1024" \
+  --nhead "8,8,8,8,8" \
+  --encoder-dims "384,384,384,384,384" \
+  --attention-dims "192,192,192,192,192" \
+  --encoder-unmasked-dims "256,256,256,256,256" \
+  --zipformer-downsampling-factors "1,2,4,8,2" \
+  --cnn-module-kernels "31,31,31,31,31" \
+  --decoder-dim 512 \
+  --joiner-dim 512
+
+./ncnn/tools/pnnx/build/src/pnnx $repo/exp/encoder_jit_trace-pnnx.pt
+./ncnn/tools/pnnx/build/src/pnnx $repo/exp/decoder_jit_trace-pnnx.pt
+./ncnn/tools/pnnx/build/src/pnnx $repo/exp/joiner_jit_trace-pnnx.pt
+
+python3 ./pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py \
+  --tokens $repo/data/lang_char_bpe/tokens.txt \
+  --encoder-param-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.param \
+  --encoder-bin-filename $repo/exp/encoder_jit_trace-pnnx.ncnn.bin \
+  --decoder-param-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.param \
+  --decoder-bin-filename $repo/exp/decoder_jit_trace-pnnx.ncnn.bin \
+  --joiner-param-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.param \
+  --joiner-bin-filename $repo/exp/joiner_jit_trace-pnnx.ncnn.bin \
+  $repo/test_wavs/0.wav
+
+rm -rf $repo
+log "--------------------------------------------------------------------------"

egs/librispeech/ASR/conv_emformer_transducer_stateless2/export-for-ncnn.py

@@ -310,6 +310,16 @@ def main():
     model.eval()
 
     convert_scaled_to_non_scaled(model, inplace=True)
+
+    encoder_num_param = sum([p.numel() for p in model.encoder.parameters()])
+    decoder_num_param = sum([p.numel() for p in model.decoder.parameters()])
+    joiner_num_param = sum([p.numel() for p in model.joiner.parameters()])
+    total_num_param = encoder_num_param + decoder_num_param + joiner_num_param
+    logging.info(f"encoder parameters: {encoder_num_param}")
+    logging.info(f"decoder parameters: {decoder_num_param}")
+    logging.info(f"joiner parameters: {joiner_num_param}")
+    logging.info(f"total parameters: {total_num_param}")
+
     logging.info("Using torch.jit.trace()")
 
     logging.info("Exporting encoder")

egs/librispeech/ASR/conv_emformer_transducer_stateless2/streaming-ncnn-decode.py

@@ -203,11 +203,8 @@ class Model:
                 #  (1, 512, 2) -> (512, 2)
                 ex.input(name, ncnn.Mat(states[i * 4 + 3].numpy()).clone())
 
-            import pdb
-
-            #  pdb.set_trace()
             ret, ncnn_out0 = ex.extract("out0")
-            #  assert ret == 0, ret
+            assert ret == 0, ret
             encoder_out = torch.from_numpy(ncnn_out0.numpy()).clone()
 
             out_states: List[torch.Tensor] = []

egs/librispeech/ASR/lstm_transducer_stateless2/export-for-ncnn.py

@@ -99,7 +99,7 @@ def get_parser():
     parser.add_argument(
         "--exp-dir",
         type=str,
-        default="pruned_transducer_stateless2/exp",
+        default="lstm_transducer_stateless2/exp",
         help="""It specifies the directory where all training related
         files, e.g., checkpoints, log, etc, are saved
         """,
@@ -316,6 +316,16 @@ def main():
     model.eval()
 
     convert_scaled_to_non_scaled(model, inplace=True)
+
+    encoder_num_param = sum([p.numel() for p in model.encoder.parameters()])
+    decoder_num_param = sum([p.numel() for p in model.decoder.parameters()])
+    joiner_num_param = sum([p.numel() for p in model.joiner.parameters()])
+    total_num_param = encoder_num_param + decoder_num_param + joiner_num_param
+    logging.info(f"encoder parameters: {encoder_num_param}")
+    logging.info(f"decoder parameters: {decoder_num_param}")
+    logging.info(f"joiner parameters: {joiner_num_param}")
+    logging.info(f"total parameters: {total_num_param}")
+
     logging.info("Using torch.jit.trace()")
 
     logging.info("Exporting encoder")

egs/librispeech/ASR/pruned_transducer_stateless7/decoder.py

@@ -87,7 +87,11 @@ class Decoder(nn.Module):
         y = y.to(torch.int64)
         # this stuff about clamp() is a temporary fix for a mismatch
         # at utterance start, we use negative ids in beam_search.py
-        embedding_out = self.embedding(y.clamp(min=0)) * (y >= 0).unsqueeze(-1)
+        if torch.jit.is_tracing():
+            # This is for exporting to PNNX via ONNX
+            embedding_out = self.embedding(y)
+        else:
+            embedding_out = self.embedding(y.clamp(min=0)) * (y >= 0).unsqueeze(-1)
         if self.context_size > 1:
             embedding_out = embedding_out.permute(0, 2, 1)
             if need_pad is True:

egs/librispeech/ASR/pruned_transducer_stateless7/joiner.py

@@ -53,7 +53,6 @@ class Joiner(nn.Module):
         """
         assert encoder_out.ndim == decoder_out.ndim
         assert encoder_out.ndim in (2, 4)
-        assert encoder_out.shape[:-1] == decoder_out.shape[:-1]
 
         if project_input:
             logit = self.encoder_proj(encoder_out) + self.decoder_proj(decoder_out)

egs/librispeech/ASR/pruned_transducer_stateless7/scaling_converter.py

@@ -22,11 +22,101 @@ BasicNorm is replaced by a module with `exp` removed.
 """
 
 import copy
-from typing import List
+from typing import List, Tuple
 
 import torch
 import torch.nn as nn
 from scaling import ActivationBalancer, BasicNorm, Whiten
+from zipformer import PoolingModule
+
+
+class PoolingModuleNoProj(nn.Module):
+    def forward(
+        self,
+        x: torch.Tensor,
+        cached_len: torch.Tensor,
+        cached_avg: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            A tensor of shape (T, N, C)
+          cached_len:
+            A tensor of shape (N,)
+          cached_avg:
+            A tensor of shape (N, C)
+        Returns:
+          Return a tuple containing:
+            - new_x
+            - new_cached_len
+            - new_cached_avg
+        """
+        x = x.cumsum(dim=0)  # (T, N, C)
+        x = x + (cached_avg * cached_len.unsqueeze(1)).unsqueeze(0)
+        # Cumulated numbers of frames from start
+        cum_mask = torch.arange(1, x.size(0) + 1, device=x.device)
+        cum_mask = cum_mask.unsqueeze(1) + cached_len.unsqueeze(0)  # (T, N)
+        pooling_mask = (1.0 / cum_mask).unsqueeze(2)
+        # now pooling_mask: (T, N, 1)
+        x = x * pooling_mask  # (T, N, C)
+
+        cached_len = cached_len + x.size(0)
+        cached_avg = x[-1]
+
+        return x, cached_len, cached_avg
+
+
+class PoolingModuleWithProj(nn.Module):
+    def __init__(self, proj: torch.nn.Module):
+        super().__init__()
+        self.proj = proj
+        self.pooling = PoolingModuleNoProj()
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cached_len: torch.Tensor,
+        cached_avg: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            A tensor of shape (T, N, C)
+          cached_len:
+            A tensor of shape (N,)
+          cached_avg:
+            A tensor of shape (N, C)
+        Returns:
+          Return a tuple containing:
+            - new_x
+            - new_cached_len
+            - new_cached_avg
+        """
+        x, cached_len, cached_avg = self.pooling(x, cached_len, cached_avg)
+        return self.proj(x), cached_len, cached_avg
+
+    def streaming_forward(
+        self,
+        x: torch.Tensor,
+        cached_len: torch.Tensor,
+        cached_avg: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            A tensor of shape (T, N, C)
+          cached_len:
+            A tensor of shape (N,)
+          cached_avg:
+            A tensor of shape (N, C)
+        Returns:
+          Return a tuple containing:
+            - new_x
+            - new_cached_len
+            - new_cached_avg
+        """
+        x, cached_len, cached_avg = self.pooling(x, cached_len, cached_avg)
+        return self.proj(x), cached_len, cached_avg
 
 
 class NonScaledNorm(nn.Module):
@@ -53,7 +143,7 @@ class NonScaledNorm(nn.Module):
 
 
 def convert_basic_norm(basic_norm: BasicNorm) -> NonScaledNorm:
-    assert isinstance(basic_norm, BasicNorm), type(BasicNorm)
+    assert isinstance(basic_norm, BasicNorm), type(basic_norm)
     norm = NonScaledNorm(
         num_channels=basic_norm.num_channels,
         eps_exp=basic_norm.eps.data.exp().item(),
@@ -62,6 +152,11 @@ def convert_basic_norm(basic_norm: BasicNorm) -> NonScaledNorm:
     return norm
 
 
+def convert_pooling_module(pooling: PoolingModule) -> PoolingModuleWithProj:
+    assert isinstance(pooling, PoolingModule), type(pooling)
+    return PoolingModuleWithProj(proj=pooling.proj)
+
+
 # Copied from https://pytorch.org/docs/1.9.0/_modules/torch/nn/modules/module.html#Module.get_submodule  # noqa
 # get_submodule was added to nn.Module at v1.9.0
 def get_submodule(model, target):
@@ -83,6 +178,7 @@ def get_submodule(model, target):
 def convert_scaled_to_non_scaled(
     model: nn.Module,
     inplace: bool = False,
+    is_pnnx: bool = False,
 ):
     """
     Args:
@@ -91,6 +187,8 @@ def convert_scaled_to_non_scaled(
       inplace:
         If True, the input model is modified inplace.
         If False, the input model is copied and we modify the copied version.
+      is_pnnx:
+        True if we are going to export the model for PNNX.
     Return:
       Return a model without scaled layers.
     """
@@ -103,6 +201,8 @@ def convert_scaled_to_non_scaled(
             d[name] = convert_basic_norm(m)
         elif isinstance(m, (ActivationBalancer, Whiten)):
             d[name] = nn.Identity()
+        elif isinstance(m, PoolingModule) and is_pnnx:
+            d[name] = convert_pooling_module(m)
 
     for k, v in d.items():
         if "." in k:

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/README.md

@@ -1,3 +1,10 @@
 This recipe implements Streaming Zipformer-Transducer model.
 
 See https://k2-fsa.github.io/icefall/recipes/Streaming-ASR/librispeech/zipformer_transducer.html for detailed tutorials.
+
+[./emformer.py](./emformer.py) and [./train.py](./train.py)
+are basically the same as
+[./emformer2.py](./emformer2.py) and [./train2.py](./train2.py).
+The only purpose of [./emformer2.py](./emformer2.py) and [./train2.py](./train2.py)
+is for exporting to [sherpa-ncnn](https://github.com/k2-fsa/sherpa-ncnn).
+

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export-for-ncnn-zh.py

@@ -0,0 +1,367 @@
+#!/usr/bin/env python3
+
+"""
+Please see
+https://k2-fsa.github.io/icefall/model-export/export-ncnn.html
+for more details about how to use this file.
+
+We use
+https://huggingface.co/pfluo/k2fsa-zipformer-chinese-english-mixed
+to demonstrate the usage of this file.
+
+1. Download the pre-trained model
+
+cd egs/librispeech/ASR
+
+repo_url=https://huggingface.co/pfluo/k2fsa-zipformer-chinese-english-mixed
+GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+repo=$(basename $repo_url)
+
+pushd $repo
+git lfs pull --include "data/lang_char_bpe/L.pt"
+git lfs pull --include "data/lang_char_bpe/L_disambig.pt"
+git lfs pull --include "data/lang_char_bpe/Linv.pt"
+git lfs pull --include "exp/pretrained.pt"
+
+cd exp
+ln -s pretrained.pt epoch-99.pt
+popd
+
+2. Export to ncnn
+
+./pruned_transducer_stateless7_streaming/export-for-ncnn-zh.py \
+  --lang-dir $repo/data/lang_char_bpe \
+  --exp-dir $repo/exp \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --decode-chunk-len 32 \
+  --num-encoder-layers "2,4,3,2,4" \
+  --feedforward-dims "1024,1024,1536,1536,1024" \
+  --nhead "8,8,8,8,8" \
+  --encoder-dims "384,384,384,384,384" \
+  --attention-dims "192,192,192,192,192" \
+  --encoder-unmasked-dims "256,256,256,256,256" \
+  --zipformer-downsampling-factors "1,2,4,8,2" \
+  --cnn-module-kernels "31,31,31,31,31" \
+  --decoder-dim 512 \
+  --joiner-dim 512
+
+cd $repo/exp
+
+pnnx encoder_jit_trace-pnnx.pt
+pnnx decoder_jit_trace-pnnx.pt
+pnnx joiner_jit_trace-pnnx.pt
+
+You can find converted models at
+https://huggingface.co/csukuangfj/sherpa-ncnn-streaming-zipformer-bilingual-zh-en-2023-02-13
+
+See ./streaming-ncnn-decode.py
+and
+https://github.com/k2-fsa/sherpa-ncnn
+for usage.
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import torch
+from scaling_converter import convert_scaled_to_non_scaled
+from train2 import add_model_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 setup_logger, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="""It specifies the checkpoint to use for averaging.
+        Note: Epoch counts from 0.
+        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=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless7_streaming/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--lang-dir",
+        type=str,
+        default="data/lang_char",
+        help="The lang dir",
+    )
+
+    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(
+        "--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. ",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def export_encoder_model_jit_trace(
+    encoder_model: torch.nn.Module,
+    encoder_filename: str,
+) -> None:
+    """Export the given encoder model with torch.jit.trace()
+
+    Note: The warmup argument is fixed to 1.
+
+    Args:
+      encoder_model:
+        The input encoder model
+      encoder_filename:
+        The filename to save the exported model.
+    """
+    encoder_model.__class__.forward = encoder_model.__class__.streaming_forward
+
+    decode_chunk_len = encoder_model.decode_chunk_size * 2
+    pad_length = 7
+    T = decode_chunk_len + pad_length  # 32 + 7 = 39
+
+    logging.info(f"decode_chunk_len: {decode_chunk_len}")
+    logging.info(f"T: {T}")
+
+    x = torch.zeros(1, T, 80, dtype=torch.float32)
+    states = encoder_model.get_init_state()
+
+    traced_model = torch.jit.trace(encoder_model, (x, states))
+    traced_model.save(encoder_filename)
+    logging.info(f"Saved to {encoder_filename}")
+
+
+def export_decoder_model_jit_trace(
+    decoder_model: torch.nn.Module,
+    decoder_filename: str,
+) -> None:
+    """Export the given decoder model with torch.jit.trace()
+
+    Note: The argument need_pad is fixed to False.
+
+    Args:
+      decoder_model:
+        The input decoder model
+      decoder_filename:
+        The filename to save the exported model.
+    """
+    y = torch.zeros(10, decoder_model.context_size, dtype=torch.int64)
+    need_pad = torch.tensor([False])
+
+    traced_model = torch.jit.trace(decoder_model, (y, need_pad))
+    traced_model.save(decoder_filename)
+    logging.info(f"Saved to {decoder_filename}")
+
+
+def export_joiner_model_jit_trace(
+    joiner_model: torch.nn.Module,
+    joiner_filename: str,
+) -> None:
+    """Export the given joiner model with torch.jit.trace()
+
+    Note: The argument project_input is fixed to True. A user should not
+    project the encoder_out/decoder_out by himself/herself. The exported joiner
+    will do that for the user.
+
+    Args:
+      joiner_model:
+        The input joiner model
+      joiner_filename:
+        The filename to save the exported model.
+
+    """
+    encoder_out_dim = joiner_model.encoder_proj.weight.shape[1]
+    decoder_out_dim = joiner_model.decoder_proj.weight.shape[1]
+    encoder_out = torch.rand(1, encoder_out_dim, dtype=torch.float32)
+    decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32)
+
+    traced_model = torch.jit.trace(joiner_model, (encoder_out, decoder_out))
+    traced_model.save(joiner_filename)
+    logging.info(f"Saved to {joiner_filename}")
+
+
+@torch.no_grad()
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+
+    setup_logger(f"{params.exp_dir}/log-export/log-export-ncnn")
+
+    logging.info(f"device: {device}")
+
+    lexicon = Lexicon(params.lang_dir)
+    params.blank_id = 0
+    params.vocab_size = max(lexicon.tokens) + 1
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    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(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        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(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to("cpu")
+    model.eval()
+
+    convert_scaled_to_non_scaled(model, inplace=True, is_pnnx=True)
+
+    encoder_num_param = sum([p.numel() for p in model.encoder.parameters()])
+    decoder_num_param = sum([p.numel() for p in model.decoder.parameters()])
+    joiner_num_param = sum([p.numel() for p in model.joiner.parameters()])
+    total_num_param = encoder_num_param + decoder_num_param + joiner_num_param
+    logging.info(f"encoder parameters: {encoder_num_param}")
+    logging.info(f"decoder parameters: {decoder_num_param}")
+    logging.info(f"joiner parameters: {joiner_num_param}")
+    logging.info(f"total parameters: {total_num_param}")
+
+    logging.info("Using torch.jit.trace()")
+
+    logging.info("Exporting encoder")
+    encoder_filename = params.exp_dir / "encoder_jit_trace-pnnx.pt"
+    export_encoder_model_jit_trace(model.encoder, encoder_filename)
+
+    logging.info("Exporting decoder")
+    decoder_filename = params.exp_dir / "decoder_jit_trace-pnnx.pt"
+    export_decoder_model_jit_trace(model.decoder, decoder_filename)
+
+    logging.info("Exporting joiner")
+    joiner_filename = params.exp_dir / "joiner_jit_trace-pnnx.pt"
+    export_joiner_model_jit_trace(model.joiner, joiner_filename)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    main()

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/export-for-ncnn.py

@@ -0,0 +1,369 @@
+#!/usr/bin/env python3
+
+"""
+Please see
+https://k2-fsa.github.io/icefall/model-export/export-ncnn.html
+for more details about how to use this file.
+
+We use
+https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
+to demonstrate the usage of this file.
+
+1. Download the pre-trained model
+
+cd egs/librispeech/ASR
+
+repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-pruned-transducer-stateless7-streaming-2022-12-29
+GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+repo=$(basename $repo_url)
+
+pushd $repo
+git lfs pull --include "data/lang_bpe/bpe.model"
+git lfs pull --include "exp/pretrained.pt"
+
+cd exp
+ln -s pretrained.pt epoch-99.pt
+popd
+
+2. Export to ncnn
+
+./pruned_transducer_stateless7_streaming/export-for-ncnn.py \
+  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --exp-dir $repo/exp \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  \
+  --decode-chunk-len 32 \
+  --num-encoder-layers "2,4,3,2,4" \
+  --feedforward-dims "1024,1024,2048,2048,1024" \
+  --nhead "8,8,8,8,8" \
+  --encoder-dims "384,384,384,384,384" \
+  --attention-dims "192,192,192,192,192" \
+  --encoder-unmasked-dims "256,256,256,256,256" \
+  --zipformer-downsampling-factors "1,2,4,8,2" \
+  --cnn-module-kernels "31,31,31,31,31" \
+  --decoder-dim 512 \
+  --joiner-dim 512
+
+cd $repo/exp
+
+pnnx encoder_jit_trace-pnnx.pt
+pnnx decoder_jit_trace-pnnx.pt
+pnnx joiner_jit_trace-pnnx.pt
+
+You can find converted models at
+https://huggingface.co/csukuangfj/sherpa-ncnn-streaming-zipformer-en-2023-02-13
+
+See ./streaming-ncnn-decode.py
+and
+https://github.com/k2-fsa/sherpa-ncnn
+for usage.
+"""
+
+import argparse
+import logging
+from pathlib import Path
+
+import sentencepiece as spm
+import torch
+from scaling_converter import convert_scaled_to_non_scaled
+from train2 import add_model_arguments, get_params, get_transducer_model
+
+from icefall.checkpoint import (
+    average_checkpoints,
+    average_checkpoints_with_averaged_model,
+    find_checkpoints,
+    load_checkpoint,
+)
+from icefall.utils import setup_logger, str2bool
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=28,
+        help="""It specifies the checkpoint to use for averaging.
+        Note: Epoch counts from 0.
+        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=15,
+        help="Number of checkpoints to average. Automatically select "
+        "consecutive checkpoints before the checkpoint specified by "
+        "'--epoch' and '--iter'",
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="pruned_transducer_stateless7_streaming/exp",
+        help="""It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--bpe-model",
+        type=str,
+        default="data/lang_bpe_500/bpe.model",
+        help="Path to the BPE model",
+    )
+
+    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(
+        "--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. ",
+    )
+
+    add_model_arguments(parser)
+
+    return parser
+
+
+def export_encoder_model_jit_trace(
+    encoder_model: torch.nn.Module,
+    encoder_filename: str,
+) -> None:
+    """Export the given encoder model with torch.jit.trace()
+
+    Note: The warmup argument is fixed to 1.
+
+    Args:
+      encoder_model:
+        The input encoder model
+      encoder_filename:
+        The filename to save the exported model.
+    """
+    encoder_model.__class__.forward = encoder_model.__class__.streaming_forward
+
+    decode_chunk_len = encoder_model.decode_chunk_size * 2
+    pad_length = 7
+    T = decode_chunk_len + pad_length  # 32 + 7 = 39
+
+    logging.info(f"decode_chunk_len: {decode_chunk_len}")
+    logging.info(f"T: {T}")
+
+    x = torch.zeros(1, T, 80, dtype=torch.float32)
+    states = encoder_model.get_init_state()
+
+    traced_model = torch.jit.trace(encoder_model, (x, states))
+    traced_model.save(encoder_filename)
+    logging.info(f"Saved to {encoder_filename}")
+
+
+def export_decoder_model_jit_trace(
+    decoder_model: torch.nn.Module,
+    decoder_filename: str,
+) -> None:
+    """Export the given decoder model with torch.jit.trace()
+
+    Note: The argument need_pad is fixed to False.
+
+    Args:
+      decoder_model:
+        The input decoder model
+      decoder_filename:
+        The filename to save the exported model.
+    """
+    y = torch.zeros(10, decoder_model.context_size, dtype=torch.int64)
+    need_pad = torch.tensor([False])
+
+    traced_model = torch.jit.trace(decoder_model, (y, need_pad))
+    traced_model.save(decoder_filename)
+    logging.info(f"Saved to {decoder_filename}")
+
+
+def export_joiner_model_jit_trace(
+    joiner_model: torch.nn.Module,
+    joiner_filename: str,
+) -> None:
+    """Export the given joiner model with torch.jit.trace()
+
+    Note: The argument project_input is fixed to True. A user should not
+    project the encoder_out/decoder_out by himself/herself. The exported joiner
+    will do that for the user.
+
+    Args:
+      joiner_model:
+        The input joiner model
+      joiner_filename:
+        The filename to save the exported model.
+
+    """
+    encoder_out_dim = joiner_model.encoder_proj.weight.shape[1]
+    decoder_out_dim = joiner_model.decoder_proj.weight.shape[1]
+    encoder_out = torch.rand(1, encoder_out_dim, dtype=torch.float32)
+    decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32)
+
+    traced_model = torch.jit.trace(joiner_model, (encoder_out, decoder_out))
+    traced_model.save(joiner_filename)
+    logging.info(f"Saved to {joiner_filename}")
+
+
+@torch.no_grad()
+def main():
+    args = get_parser().parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    device = torch.device("cpu")
+
+    setup_logger(f"{params.exp_dir}/log-export/log-export-ncnn")
+
+    logging.info(f"device: {device}")
+
+    sp = spm.SentencePieceProcessor()
+    sp.load(params.bpe_model)
+
+    # <blk> is defined in local/train_bpe_model.py
+    params.blank_id = sp.piece_to_id("<blk>")
+    params.vocab_size = sp.get_piece_size()
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_transducer_model(params)
+
+    if not params.use_averaged_model:
+        if params.iter > 0:
+            filenames = find_checkpoints(params.exp_dir, iteration=-params.iter)[
+                : params.avg
+            ]
+            if len(filenames) == 0:
+                raise ValueError(
+                    f"No checkpoints found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            elif len(filenames) < params.avg:
+                raise ValueError(
+                    f"Not enough checkpoints ({len(filenames)}) found for"
+                    f" --iter {params.iter}, --avg {params.avg}"
+                )
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+        elif params.avg == 1:
+            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+        else:
+            start = params.epoch - params.avg + 1
+            filenames = []
+            for i in range(start, params.epoch + 1):
+                if i >= 1:
+                    filenames.append(f"{params.exp_dir}/epoch-{i}.pt")
+            logging.info(f"averaging {filenames}")
+            model.to(device)
+            model.load_state_dict(average_checkpoints(filenames, device=device))
+    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(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+        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(device)
+            model.load_state_dict(
+                average_checkpoints_with_averaged_model(
+                    filename_start=filename_start,
+                    filename_end=filename_end,
+                    device=device,
+                )
+            )
+
+    model.to("cpu")
+    model.eval()
+
+    convert_scaled_to_non_scaled(model, inplace=True, is_pnnx=True)
+
+    encoder_num_param = sum([p.numel() for p in model.encoder.parameters()])
+    decoder_num_param = sum([p.numel() for p in model.decoder.parameters()])
+    joiner_num_param = sum([p.numel() for p in model.joiner.parameters()])
+    total_num_param = encoder_num_param + decoder_num_param + joiner_num_param
+    logging.info(f"encoder parameters: {encoder_num_param}")
+    logging.info(f"decoder parameters: {decoder_num_param}")
+    logging.info(f"joiner parameters: {joiner_num_param}")
+    logging.info(f"total parameters: {total_num_param}")
+
+    logging.info("Using torch.jit.trace()")
+
+    logging.info("Exporting encoder")
+    encoder_filename = params.exp_dir / "encoder_jit_trace-pnnx.pt"
+    export_encoder_model_jit_trace(model.encoder, encoder_filename)
+
+    logging.info("Exporting decoder")
+    decoder_filename = params.exp_dir / "decoder_jit_trace-pnnx.pt"
+    export_decoder_model_jit_trace(model.decoder, decoder_filename)
+
+    logging.info("Exporting joiner")
+    joiner_filename = params.exp_dir / "joiner_jit_trace-pnnx.pt"
+    export_joiner_model_jit_trace(model.joiner, joiner_filename)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    main()

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py

@@ -0,0 +1,419 @@
+#!/usr/bin/env python3
+#
+# Copyright      2022  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.
+"""
+Usage:
+
+./pruned_transducer_stateless7_streaming/streaming-ncnn-decode.py \
+  --tokens ./sherpa-ncnn-streaming-zipformer-en-2023-02-13/tokens.txt \
+  --encoder-param-filename ./sherpa-ncnn-streaming-zipformer-en-2023-02-13/encoder_jit_trace-pnnx.ncnn.param \
+  --encoder-bin-filename ./sherpa-ncnn-streaming-zipformer-en-2023-02-13/encoder_jit_trace-pnnx.ncnn.bin \
+  --decoder-param-filename ./sherpa-ncnn-streaming-zipformer-en-2023-02-13/decoder_jit_trace-pnnx.ncnn.param \
+  --decoder-bin-filename ./sherpa-ncnn-streaming-zipformer-en-2023-02-13/decoder_jit_trace-pnnx.ncnn.bin \
+  --joiner-param-filename ./sherpa-ncnn-streaming-zipformer-en-2023-02-13/joiner_jit_trace-pnnx.ncnn.param \
+  --joiner-bin-filename ./sherpa-ncnn-streaming-zipformer-en-2023-02-13/joiner_jit_trace-pnnx.ncnn.bin \
+  ./sherpa-ncnn-streaming-zipformer-en-2023-02-13/test_wavs/1089-134686-0001.wav
+
+You can find pretrained models at
+- English: https://huggingface.co/csukuangfj/sherpa-ncnn-streaming-zipformer-en-2023-02-13
+- Bilingual (Chinese + English): https://huggingface.co/csukuangfj/sherpa-ncnn-streaming-zipformer-bilingual-zh-en-2023-02-13
+"""
+
+import argparse
+import logging
+from typing import List, Optional, Tuple
+
+import k2
+import ncnn
+import torch
+import torchaudio
+from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        help="Path to tokens.txt",
+    )
+
+    parser.add_argument(
+        "--encoder-param-filename",
+        type=str,
+        help="Path to encoder.ncnn.param",
+    )
+
+    parser.add_argument(
+        "--encoder-bin-filename",
+        type=str,
+        help="Path to encoder.ncnn.bin",
+    )
+
+    parser.add_argument(
+        "--decoder-param-filename",
+        type=str,
+        help="Path to decoder.ncnn.param",
+    )
+
+    parser.add_argument(
+        "--decoder-bin-filename",
+        type=str,
+        help="Path to decoder.ncnn.bin",
+    )
+
+    parser.add_argument(
+        "--joiner-param-filename",
+        type=str,
+        help="Path to joiner.ncnn.param",
+    )
+
+    parser.add_argument(
+        "--joiner-bin-filename",
+        type=str,
+        help="Path to joiner.ncnn.bin",
+    )
+
+    parser.add_argument(
+        "sound_filename",
+        type=str,
+        help="Path to foo.wav",
+    )
+
+    return parser.parse_args()
+
+
+def to_int_tuple(s: str):
+    return tuple(map(int, s.split(",")))
+
+
+class Model:
+    def __init__(self, args):
+        self.init_encoder(args)
+        self.init_decoder(args)
+        self.init_joiner(args)
+
+        # Please change the parameters according to your model
+        self.num_encoder_layers = to_int_tuple("2,4,3,2,4")
+        self.encoder_dims = to_int_tuple("384,384,384,384,384")  # also known as d_model
+        self.attention_dims = to_int_tuple("192,192,192,192,192")
+        self.zipformer_downsampling_factors = to_int_tuple("1,2,4,8,2")
+        self.cnn_module_kernels = to_int_tuple("31,31,31,31,31")
+
+        self.decode_chunk_size = 32 // 2
+        num_left_chunks = 4
+        self.left_context_length = self.decode_chunk_size * num_left_chunks  # 64
+
+        self.chunk_length = self.decode_chunk_size * 2
+        pad_length = 7
+        self.T = self.chunk_length + pad_length
+
+    def get_init_states(self) -> List[torch.Tensor]:
+        cached_len_list = []
+        cached_avg_list = []
+        cached_key_list = []
+        cached_val_list = []
+        cached_val2_list = []
+        cached_conv1_list = []
+        cached_conv2_list = []
+
+        for i in range(len(self.num_encoder_layers)):
+            num_layers = self.num_encoder_layers[i]
+            ds = self.zipformer_downsampling_factors[i]
+            attention_dim = self.attention_dims[i]
+            left_context_length = self.left_context_length // ds
+            encoder_dim = self.encoder_dims[i]
+            cnn_module_kernel = self.cnn_module_kernels[i]
+
+            cached_len_list.append(torch.zeros(num_layers))
+            cached_avg_list.append(torch.zeros(num_layers, encoder_dim))
+            cached_key_list.append(
+                torch.zeros(num_layers, left_context_length, attention_dim)
+            )
+            cached_val_list.append(
+                torch.zeros(num_layers, left_context_length, attention_dim // 2)
+            )
+            cached_val2_list.append(
+                torch.zeros(num_layers, left_context_length, attention_dim // 2)
+            )
+            cached_conv1_list.append(
+                torch.zeros(num_layers, encoder_dim, cnn_module_kernel - 1)
+            )
+            cached_conv2_list.append(
+                torch.zeros(num_layers, encoder_dim, cnn_module_kernel - 1)
+            )
+
+        states = (
+            cached_len_list
+            + cached_avg_list
+            + cached_key_list
+            + cached_val_list
+            + cached_val2_list
+            + cached_conv1_list
+            + cached_conv2_list
+        )
+
+        return states
+
+    def init_encoder(self, args):
+        encoder_net = ncnn.Net()
+        encoder_net.opt.use_packing_layout = False
+        encoder_net.opt.use_fp16_storage = False
+        encoder_net.opt.num_threads = 4
+
+        encoder_param = args.encoder_param_filename
+        encoder_model = args.encoder_bin_filename
+
+        encoder_net.load_param(encoder_param)
+        encoder_net.load_model(encoder_model)
+
+        self.encoder_net = encoder_net
+
+    def init_decoder(self, args):
+        decoder_param = args.decoder_param_filename
+        decoder_model = args.decoder_bin_filename
+
+        decoder_net = ncnn.Net()
+        decoder_net.opt.num_threads = 4
+
+        decoder_net.load_param(decoder_param)
+        decoder_net.load_model(decoder_model)
+
+        self.decoder_net = decoder_net
+
+    def init_joiner(self, args):
+        joiner_param = args.joiner_param_filename
+        joiner_model = args.joiner_bin_filename
+        joiner_net = ncnn.Net()
+        joiner_net.opt.num_threads = 4
+
+        joiner_net.load_param(joiner_param)
+        joiner_net.load_model(joiner_model)
+
+        self.joiner_net = joiner_net
+
+    def run_encoder(
+        self,
+        x: torch.Tensor,
+        states: List[torch.Tensor],
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        """
+        Args:
+          x:
+            A tensor of shape (T, C)
+          states:
+            A list of tensors. len(states) == self.num_layers * 4
+        Returns:
+          Return a tuple containing:
+           - encoder_out, a tensor of shape (T, encoder_dim).
+           - next_states, a list of tensors containing the next states
+        """
+        with self.encoder_net.create_extractor() as ex:
+            ex.input("in0", ncnn.Mat(x.numpy()).clone())
+
+            for i in range(len(states)):
+                name = f"in{i+1}"
+                ex.input(name, ncnn.Mat(states[i].squeeze().numpy()).clone())
+
+            ret, ncnn_out0 = ex.extract("out0")
+            assert ret == 0, ret
+            encoder_out = torch.from_numpy(ncnn_out0.numpy()).clone()
+
+            out_states: List[torch.Tensor] = []
+            for i in range(len(states)):
+                name = f"out{i+1}"
+                ret, ncnn_out_state = ex.extract(name)
+                assert ret == 0, ret
+                ncnn_out_state = torch.from_numpy(ncnn_out_state.numpy())
+
+                if i < len(self.num_encoder_layers):
+                    # for cached_len, we need to discard the last dim
+                    ncnn_out_state = ncnn_out_state.squeeze(1)
+
+                out_states.append(ncnn_out_state)
+
+            return encoder_out, out_states
+
+    def run_decoder(self, decoder_input):
+        assert decoder_input.dtype == torch.int32
+
+        with self.decoder_net.create_extractor() as ex:
+            ex.input("in0", ncnn.Mat(decoder_input.numpy()).clone())
+            ret, ncnn_out0 = ex.extract("out0")
+            assert ret == 0, ret
+            decoder_out = torch.from_numpy(ncnn_out0.numpy()).clone()
+            return decoder_out
+
+    def run_joiner(self, encoder_out, decoder_out):
+        with self.joiner_net.create_extractor() as ex:
+            ex.input("in0", ncnn.Mat(encoder_out.numpy()).clone())
+            ex.input("in1", ncnn.Mat(decoder_out.numpy()).clone())
+            ret, ncnn_out0 = ex.extract("out0")
+            assert ret == 0, ret
+            joiner_out = torch.from_numpy(ncnn_out0.numpy()).clone()
+            return joiner_out
+
+
+def read_sound_files(
+    filenames: List[str], expected_sample_rate: float
+) -> List[torch.Tensor]:
+    """Read a list of sound files into a list 1-D float32 torch tensors.
+    Args:
+      filenames:
+        A list of sound filenames.
+      expected_sample_rate:
+        The expected sample rate of the sound files.
+    Returns:
+      Return a list of 1-D float32 torch tensors.
+    """
+    ans = []
+    for f in filenames:
+        wave, sample_rate = torchaudio.load(f)
+        assert (
+            sample_rate == expected_sample_rate
+        ), f"expected sample rate: {expected_sample_rate}. Given: {sample_rate}"
+        # We use only the first channel
+        ans.append(wave[0])
+    return ans
+
+
+def create_streaming_feature_extractor() -> OnlineFeature:
+    """Create a CPU streaming feature extractor.
+
+    At present, we assume it returns a fbank feature extractor with
+    fixed options. In the future, we will support passing in the options
+    from outside.
+
+    Returns:
+      Return a CPU streaming feature extractor.
+    """
+    opts = FbankOptions()
+    opts.device = "cpu"
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = 16000
+    opts.mel_opts.num_bins = 80
+    return OnlineFbank(opts)
+
+
+def greedy_search(
+    model: Model,
+    encoder_out: torch.Tensor,
+    decoder_out: Optional[torch.Tensor] = None,
+    hyp: Optional[List[int]] = None,
+):
+    context_size = 2
+    blank_id = 0
+
+    if decoder_out is None:
+        assert hyp is None, hyp
+        hyp = [blank_id] * context_size
+        decoder_input = torch.tensor(hyp, dtype=torch.int32)  # (1, context_size)
+        decoder_out = model.run_decoder(decoder_input).squeeze(0)
+    else:
+        assert decoder_out.ndim == 1
+        assert hyp is not None, hyp
+
+    T = encoder_out.size(0)
+    for t in range(T):
+        cur_encoder_out = encoder_out[t]
+
+        joiner_out = model.run_joiner(cur_encoder_out, decoder_out)
+        y = joiner_out.argmax(dim=0).item()
+        if y != blank_id:
+            hyp.append(y)
+            decoder_input = hyp[-context_size:]
+            decoder_input = torch.tensor(decoder_input, dtype=torch.int32)
+            decoder_out = model.run_decoder(decoder_input).squeeze(0)
+
+    return hyp, decoder_out
+
+
+def main():
+    args = get_args()
+    logging.info(vars(args))
+
+    model = Model(args)
+
+    sound_file = args.sound_filename
+
+    sample_rate = 16000
+
+    logging.info("Constructing Fbank computer")
+    online_fbank = create_streaming_feature_extractor()
+
+    logging.info(f"Reading sound files: {sound_file}")
+    wave_samples = read_sound_files(
+        filenames=[sound_file],
+        expected_sample_rate=sample_rate,
+    )[0]
+    logging.info(wave_samples.shape)
+
+    tail_padding = torch.zeros(int(0.3 * sample_rate), dtype=torch.float32)
+
+    wave_samples = torch.cat([wave_samples, tail_padding])
+
+    states = model.get_init_states()
+    logging.info(f"number of states: {len(states)}")
+
+    hyp = None
+    decoder_out = None
+
+    num_processed_frames = 0
+    segment = model.T
+    offset = model.chunk_length
+
+    chunk = int(1 * sample_rate)  # 0.2 second
+
+    start = 0
+    while start < wave_samples.numel():
+        end = min(start + chunk, wave_samples.numel())
+        samples = wave_samples[start:end]
+        start += chunk
+
+        online_fbank.accept_waveform(
+            sampling_rate=sample_rate,
+            waveform=samples,
+        )
+        while online_fbank.num_frames_ready - num_processed_frames >= segment:
+            frames = []
+            for i in range(segment):
+                frames.append(online_fbank.get_frame(num_processed_frames + i))
+            num_processed_frames += offset
+            frames = torch.cat(frames, dim=0)
+            encoder_out, states = model.run_encoder(frames, states)
+            hyp, decoder_out = greedy_search(model, encoder_out, decoder_out, hyp)
+
+    symbol_table = k2.SymbolTable.from_file(args.tokens)
+
+    context_size = 2
+    text = ""
+    for i in hyp[context_size:]:
+        text += symbol_table[i]
+    text = text.replace("▁", " ").strip()
+
+    logging.info(sound_file)
+    logging.info(text)
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+
+    main()

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/zipformer.py

@@ -44,7 +44,6 @@ from scaling import (
 )
 from torch import Tensor, nn
 
-from icefall.dist import get_rank
 from icefall.utils import make_pad_mask, subsequent_chunk_mask
 
 
@@ -271,7 +270,6 @@ class Zipformer(EncoderInterface):
         num_encoder_layers (int): number of encoder layers
         dropout (float): dropout rate
         cnn_module_kernels (int): Kernel size of convolution module
-        vgg_frontend (bool): whether to use vgg frontend.
         warmup_batches (float): number of batches to warm up over
     """
 
@@ -388,9 +386,9 @@ class Zipformer(EncoderInterface):
     def _init_skip_modules(self):
         """
         If self.zipformer_downsampling_factors = (1, 2, 4, 8, 4, 2), then at the input of layer
-        indexed 4 (in zero indexing), with has subsapling_factor=4, we combine the output of
+        indexed 4 (in zero indexing), which has subsampling_factor=4, we combine the output of
-        layers 2 and 3; and at the input of layer indexed 5, which which has subsampling_factor=2,
+        layers 2 and 3; and at the input of layer indexed 5, which has subsampling_factor=2,
-        we combine the outputs of layers 1 and 5.
+        we combine the outputs of layers 1 and 4.
         """
         skip_layers = []
         skip_modules = []
@@ -1272,8 +1270,7 @@ class ZipformerEncoder(nn.Module):
 
         Shape:
             src: (S, N, E).
-            cached_len: (N,)
+            cached_len: (num_layers,)
-              N is the batch size.
             cached_avg: (num_layers, N, C).
               N is the batch size, C is the feature dimension.
             cached_key: (num_layers, left_context_len, N, K).
@@ -1289,8 +1286,8 @@ class ZipformerEncoder(nn.Module):
 
         Returns: A tuple of 8 tensors:
             - output tensor
-            - updated cached number of past frmaes.
+            - updated cached number of past frames.
-            - updated cached average of past frmaes.
+            - updated cached average of past frames.
             - updated cached key tensor of of the first attention module.
             - updated cached value tensor of of the first attention module.
             - updated cached value tensor of of the second attention module.
@@ -1522,9 +1519,6 @@ class AttentionDownsample(torch.nn.Module):
     """
 
     def __init__(self, in_channels: int, out_channels: int, downsample: int):
-        """
-        Require out_channels > in_channels.
-        """
         super(AttentionDownsample, self).__init__()
         self.query = nn.Parameter(torch.randn(in_channels) * (in_channels**-0.5))
 
@@ -1902,8 +1896,6 @@ class RelPositionMultiheadAttention(nn.Module):
         Args:
             x: input to be projected to query, key, value
             pos_emb: Positional embedding tensor
-            attn_mask: 2D or 3D mask that prevents attention to certain positions. A 2D mask will be broadcasted for all
-                the batches while a 3D mask allows to specify a different mask for the entries of each batch.
 
         Shape:
             - Inputs:
@@ -1911,13 +1903,6 @@ class RelPositionMultiheadAttention(nn.Module):
             the embedding dimension.
             - pos_emb: :math:`(N, 2*L-1, E)` where L is the target sequence length, N is the batch size, E is
             the embedding dimension.
-            - attn_mask: 2D mask :math:`(L, S)` where L is the target sequence length, S is the source sequence length.
-            3D mask :math:`(N*num_heads, L, S)` where N is the batch size, L is the target sequence length,
-            S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
-            positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
-            while the zero positions will be unchanged. If a BoolTensor is provided, positions with ``True``
-            is not allowed to attend while ``False`` values will be unchanged. If a FloatTensor
-            is provided, it will be added to the attention weight.
             - cached_key: :math:`(left_context_len, N, K)`, where N is the batch size, K is the key dimension.
             - cached_val: :math:`(left_context_len, N, V)`, where N is the batch size, V is the value dimension.