Replace bpe with tokens in export.py and pretrain.py

egs/aishell/ASR/pruned_transducer_stateless7_bbpe/decode.py

@@ -397,7 +397,6 @@ def decode_one_batch(
             beam=params.beam,
             max_contexts=params.max_contexts,
             max_states=params.max_states,
-            subtract_ilme=True,
             ilme_scale=params.ilme_scale,
         )
         for hyp in hyp_tokens:

egs/librispeech/ASR/pruned_transducer_stateless2/beam_search.py

@@ -47,8 +47,7 @@ def fast_beam_search_one_best(
     max_states: int,
     max_contexts: int,
     temperature: float = 1.0,
-    subtract_ilme: bool = False,
-    ilme_scale: float = 0.1,
+    ilme_scale: float = 0.0,
     blank_penalty: float = 0.0,
     return_timestamps: bool = False,
 ) -> Union[List[List[int]], DecodingResults]:
@@ -91,7 +90,6 @@ def fast_beam_search_one_best(
         max_states=max_states,
         max_contexts=max_contexts,
         temperature=temperature,
-        subtract_ilme=subtract_ilme,
         ilme_scale=ilme_scale,
         blank_penalty=blank_penalty,
     )
@@ -117,6 +115,7 @@ def fast_beam_search_nbest_LG(
     use_double_scores: bool = True,
     temperature: float = 1.0,
     blank_penalty: float = 0.0,
+    ilme_scale: float = 0.0,
     return_timestamps: bool = False,
 ) -> Union[List[List[int]], DecodingResults]:
     """It limits the maximum number of symbols per frame to 1.
@@ -172,6 +171,7 @@ def fast_beam_search_nbest_LG(
         max_contexts=max_contexts,
         temperature=temperature,
         blank_penalty=blank_penalty,
+        ilme_scale=ilme_scale,
     )
 
     nbest = Nbest.from_lattice(
@@ -440,8 +440,7 @@ def fast_beam_search(
     max_states: int,
     max_contexts: int,
     temperature: float = 1.0,
-    subtract_ilme: bool = False,
-    ilme_scale: float = 0.1,
+    ilme_scale: float = 0.0,
     blank_penalty: float = 0.0,
 ) -> k2.Fsa:
     """It limits the maximum number of symbols per frame to 1.
@@ -512,10 +511,13 @@ def fast_beam_search(
             project_input=False,
         )
         logits = logits.squeeze(1).squeeze(1)
+
         if blank_penalty != 0:
             logits[:, 0] -= blank_penalty
+
         log_probs = (logits / temperature).log_softmax(dim=-1)
-        if subtract_ilme:
+
+        if ilme_scale != 0:
             ilme_logits = model.joiner(
                 torch.zeros_like(
                     current_encoder_out, device=current_encoder_out.device
@@ -526,6 +528,7 @@ def fast_beam_search(
             ilme_logits = ilme_logits.squeeze(1).squeeze(1)
             ilme_log_probs = (ilme_logits / temperature).log_softmax(dim=-1)
             log_probs -= ilme_scale * ilme_log_probs
+
         decoding_streams.advance(log_probs)
     decoding_streams.terminate_and_flush_to_streams()
     lattice = decoding_streams.format_output(encoder_out_lens.tolist())

egs/librispeech/ASR/zipformer/export-onnx-streaming.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 #
-# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang)
+# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang, Wei Kang)
 # Copyright 2023 Danqing Fu (danqing.fu@gmail.com)
 
 """
@@ -19,7 +19,7 @@ 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 "data/lang_bpe_500/tokens.txt"
 git lfs pull --include "exp/pretrained.pt"
 
 cd exp
@@ -29,7 +29,7 @@ popd
 2. Export the model to ONNX
 
 ./zipformer/export-onnx-streaming.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --use-averaged-model 0 \
   --epoch 99 \
   --avg 1 \
@@ -69,8 +69,8 @@ import logging
 from pathlib import Path
 from typing import Dict, List, Tuple
 
+import k2
 import onnx
-import sentencepiece as spm
 import torch
 import torch.nn as nn
 from decoder import Decoder
@@ -85,7 +85,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import str2bool, make_pad_mask
+from icefall.utils import make_pad_mask, str2bool
 
 
 def get_parser():
@@ -142,9 +142,9 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
+        default="data/lang_bpe_500/tokens.txt",
         help="Path to the BPE model",
     )
 
@@ -585,12 +585,9 @@ def main():
 
     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()
+    symbol_table = k2.SymbolTable.from_file(params.tokens)
+    params.blank_id = symbol_table["<blk>"]
+    params.vocab_size = len(symbol_table)
 
     logging.info(params)
 

egs/librispeech/ASR/zipformer/export-onnx.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 #
-# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang)
+# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang, Wei Kang)
 # Copyright 2023 Danqing Fu (danqing.fu@gmail.com)
 
 """
@@ -19,7 +19,7 @@ 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 "data/lang_bpe_500/tokens.txt"
 git lfs pull --include "exp/pretrained.pt"
 
 cd exp
@@ -29,12 +29,11 @@ popd
 2. Export the model to ONNX
 
 ./zipformer/export-onnx.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
   --use-averaged-model 0 \
   --epoch 99 \
   --avg 1 \
   --exp-dir $repo/exp \
-  \
   --num-encoder-layers "2,2,3,4,3,2" \
   --downsampling-factor "1,2,4,8,4,2" \
   --feedforward-dim "512,768,1024,1536,1024,768" \
@@ -67,8 +66,8 @@ import logging
 from pathlib import Path
 from typing import Dict, Tuple
 
+import k2
 import onnx
-import sentencepiece as spm
 import torch
 import torch.nn as nn
 from decoder import Decoder
@@ -83,7 +82,7 @@ from icefall.checkpoint import (
     find_checkpoints,
     load_checkpoint,
 )
-from icefall.utils import str2bool, make_pad_mask
+from icefall.utils import make_pad_mask, str2bool
 
 
 def get_parser():
@@ -140,10 +139,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt",
     )
 
     parser.add_argument(
@@ -434,12 +433,9 @@ def main():
 
     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()
+    symbol_table = k2.SymbolTable.from_file(params.tokens)
+    params.blank_id = symbol_table["<blk>"]
+    params.vocab_size = len(symbol_table)
 
     logging.info(params)
 

egs/librispeech/ASR/zipformer/export.py

@@ -1,6 +1,8 @@
 #!/usr/bin/env python3
 #
-# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang, Zengwei Yao)
+# Copyright 2021-2023 Xiaomi Corporation (Author: Fangjun Kuang,
+#                                                 Zengwei Yao,
+#                                                 Wei Kang)
 #
 # See ../../../../LICENSE for clarification regarding multiple authors
 #
@@ -22,13 +24,16 @@
 
 Usage:
 
+Note: This is a example for librispeech dataset, if you are using different
+dataset, you should change the argument values according to your dataset.
+
 (1) Export to torchscript model using torch.jit.script()
 
 - For non-streaming model:
 
 ./zipformer/export.py \
   --exp-dir ./zipformer/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 30 \
   --avg 9 \
   --jit 1
@@ -48,7 +53,7 @@ for how to use the exported models outside of icefall.
   --causal 1 \
   --chunk-size 16 \
   --left-context-frames 128 \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 30 \
   --avg 9 \
   --jit 1
@@ -67,7 +72,7 @@ for how to use the exported models outside of icefall.
 
 ./zipformer/export.py \
   --exp-dir ./zipformer/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 30 \
   --avg 9
 
@@ -76,7 +81,7 @@ for how to use the exported models outside of icefall.
 ./zipformer/export.py \
   --exp-dir ./zipformer/exp \
   --causal 1 \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 30 \
   --avg 9
 
@@ -158,7 +163,7 @@ import logging
 from pathlib import Path
 from typing import List, Tuple
 
-import sentencepiece as spm
+import k2
 import torch
 from scaling_converter import convert_scaled_to_non_scaled
 from torch import Tensor, nn
@@ -227,10 +232,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt",
     )
 
     parser.add_argument(
@@ -397,12 +402,9 @@ def main():
 
     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()
+    symbol_table = k2.SymbolTable.from_file(params.tokens)
+    params.blank_id = symbol_table["<blk>"]
+    params.vocab_size = len(symbol_table)
 
     logging.info(params)
 

egs/librispeech/ASR/zipformer/generate_averaged_model.py

@@ -40,16 +40,11 @@ You can later load it by `torch.load("iter-22000-avg-5.pt")`.
 import argparse
 from pathlib import Path
 
-import sentencepiece as spm
+import k2
 import torch
-from asr_datamodule import LibriSpeechAsrDataModule
-
 from train import add_model_arguments, get_params, get_transducer_model
 
-from icefall.checkpoint import (
-    average_checkpoints_with_averaged_model,
-    find_checkpoints,
-)
+from icefall.checkpoint import average_checkpoints_with_averaged_model, find_checkpoints
 
 
 def get_parser():
@@ -93,10 +88,10 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        default="data/lang_bpe_500/bpe.model",
-        help="Path to the BPE model",
+        default="data/lang_bpe_500/tokens.txt",
+        help="Path to the tokens.txt",
     )
 
     parser.add_argument(
@@ -114,7 +109,6 @@ def get_parser():
 @torch.no_grad()
 def main():
     parser = get_parser()
-    LibriSpeechAsrDataModule.add_arguments(parser)
     args = parser.parse_args()
     args.exp_dir = Path(args.exp_dir)
 
@@ -131,13 +125,10 @@ def main():
     device = torch.device("cpu")
     print(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.unk_id = sp.piece_to_id("<unk>")
-    params.vocab_size = sp.get_piece_size()
+    symbol_table = k2.SymbolTable.from_file(params.tokens)
+    params.blank_id = symbol_table["<blk>"]
+    params.unk_id = symbol_table["<unk>"]
+    params.vocab_size = len(symbol_table)
 
     print("About to create model")
     model = get_transducer_model(params)

egs/librispeech/ASR/zipformer/jit_pretrained.py

@@ -21,7 +21,7 @@ You can use the following command to get the exported models:
 
 ./zipformer/export.py \
   --exp-dir ./zipformer/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 30 \
   --avg 9 \
   --jit 1
@@ -30,7 +30,7 @@ Usage of this script:
 
 ./zipformer/jit_pretrained.py \
   --nn-model-filename ./zipformer/exp/cpu_jit.pt \
-  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --tokens ./data/lang_bpe_500/tokens.txt \
   /path/to/foo.wav \
   /path/to/bar.wav
 """
@@ -40,8 +40,8 @@ import logging
 import math
 from typing import List
 
+import k2
 import kaldifeat
-import sentencepiece as spm
 import torch
 import torchaudio
 from torch.nn.utils.rnn import pad_sequence
@@ -60,9 +60,9 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        help="""Path to bpe.model.""",
+        help="""Path to tokens.txt.""",
     )
 
     parser.add_argument(
@@ -128,7 +128,7 @@ def greedy_search(
     )
 
     device = encoder_out.device
-    blank_id = 0  # hard-code to 0
+    blank_id = model.decoder.blank_id
 
     batch_size_list = packed_encoder_out.batch_sizes.tolist()
     N = encoder_out.size(0)
@@ -215,9 +215,6 @@ def main():
 
     model.to(device)
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(args.bpe_model)
-
     logging.info("Constructing Fbank computer")
     opts = kaldifeat.FbankOptions()
     opts.device = device
@@ -256,10 +253,21 @@ def main():
         encoder_out=encoder_out,
         encoder_out_lens=encoder_out_lens,
     )
+
     s = "\n"
+
+    symbol_table = k2.SymbolTable.from_file(args.tokens)
+
+    def token_ids_to_words(token_ids: List[int]) -> str:
+        text = ""
+        for i in token_ids:
+            text += symbol_table[i]
+        return text.replace("▁", " ").strip()
+
     for filename, hyp in zip(args.sound_files, hyps):
-        words = sp.decode(hyp)
-        s += f"{filename}:\n{words}\n\n"
+        words = token_ids_to_words(hyp)
+        s += f"{filename}:\n{words}\n"
+
     logging.info(s)
 
     logging.info("Decoding Done")

egs/librispeech/ASR/zipformer/jit_pretrained_streaming.py

@@ -25,7 +25,7 @@ You can use the following command to get the exported models:
   --causal 1 \
   --chunk-size 16 \
   --left-context-frames 128 \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 30 \
   --avg 9 \
   --jit 1
@@ -34,7 +34,7 @@ Usage of this script:
 
 ./zipformer/jit_pretrained_streaming.py \
   --nn-model-filename ./zipformer/exp-causal/jit_script_chunk_16_left_128.pt \
-  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --tokens ./data/lang_bpe_500/tokens.txt \
   /path/to/foo.wav \
 """
 
@@ -43,8 +43,8 @@ import logging
 import math
 from typing import List, Optional
 
+import k2
 import kaldifeat
-import sentencepiece as spm
 import torch
 import torchaudio
 from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature
@@ -60,13 +60,13 @@ def get_parser():
         "--nn-model-filename",
         type=str,
         required=True,
-        help="Path to the torchscript model cpu_jit.pt",
+        help="Path to the torchscript model jit_script.pt",
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        help="""Path to bpe.model.""",
+        help="""Path to tokens.txt.""",
     )
 
     parser.add_argument(
@@ -120,8 +120,8 @@ def greedy_search(
     device: torch.device = torch.device("cpu"),
 ):
     assert encoder_out.ndim == 2
-    context_size = 2
-    blank_id = 0
+    context_size = decoder.context_size
+    blank_id = decoder.blank_id
 
     if decoder_out is None:
         assert hyp is None, hyp
@@ -190,8 +190,8 @@ def main():
     decoder = model.decoder
     joiner = model.joiner
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(args.bpe_model)
+    symbol_table = k2.SymbolTable.from_file(args.tokens)
+    context_size = decoder.context_size
 
     logging.info("Constructing Fbank computer")
     online_fbank = create_streaming_feature_extractor(args.sample_rate)
@@ -250,9 +250,13 @@ def main():
                 decoder, joiner, encoder_out.squeeze(0), decoder_out, hyp, device=device
             )
 
-    context_size = 2
+    text = ""
+    for i in hyp[context_size:]:
+        text += symbol_table[i]
+    text = text.replace("▁", " ").strip()
+
     logging.info(args.sound_file)
-    logging.info(sp.decode(hyp[context_size:]))
+    logging.info(text)
 
     logging.info("Decoding Done")
 

egs/librispeech/ASR/zipformer/onnx_check.py

@@ -0,0 +1,241 @@
+#!/usr/bin/env python3
+#
+# Copyright 2022 Xiaomi Corporation (Author: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script checks that exported onnx models produce the same output
+with the given torchscript model for the same input.
+
+We use the pre-trained model from
+https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
+as an example to show how to use this file.
+
+1. Download the pre-trained model
+
+cd egs/librispeech/ASR
+
+repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
+GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+repo=$(basename $repo_url)
+
+pushd $repo
+git lfs pull --include "data/lang_bpe_500/tokens.txt"
+git lfs pull --include "exp/pretrained.pt"
+
+cd exp
+ln -s pretrained.pt epoch-99.pt
+popd
+
+2. Export the model via torchscript (torch.jit.script())
+
+./zipformer/export.py \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp/ \
+  --jit 1
+
+It will generate the following file in $repo/exp:
+    - jit_script.pt
+
+3. Export the model to ONNX
+
+./zipformer/export-onnx.py \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp/
+
+It will generate the following 3 files inside $repo/exp:
+
+  - encoder-epoch-99-avg-1.onnx
+  - decoder-epoch-99-avg-1.onnx
+  - joiner-epoch-99-avg-1.onnx
+
+4. Run this file
+
+./zipformer/onnx_check.py \
+  --jit-filename $repo/exp/jit_script.pt \
+  --onnx-encoder-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
+  --onnx-decoder-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+  --onnx-joiner-filename $repo/exp/joiner-epoch-99-avg-1.onnx
+"""
+
+import argparse
+import logging
+
+import torch
+from onnx_pretrained import OnnxModel
+
+from icefall import is_module_available
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--jit-filename",
+        required=True,
+        type=str,
+        help="Path to the torchscript model",
+    )
+
+    parser.add_argument(
+        "--onnx-encoder-filename",
+        required=True,
+        type=str,
+        help="Path to the onnx encoder model",
+    )
+
+    parser.add_argument(
+        "--onnx-decoder-filename",
+        required=True,
+        type=str,
+        help="Path to the onnx decoder model",
+    )
+
+    parser.add_argument(
+        "--onnx-joiner-filename",
+        required=True,
+        type=str,
+        help="Path to the onnx joiner model",
+    )
+
+    return parser
+
+
+def test_encoder(
+    torch_model: torch.jit.ScriptModule,
+    onnx_model: OnnxModel,
+):
+    C = 80
+    for i in range(3):
+        N = torch.randint(low=1, high=20, size=(1,)).item()
+        T = torch.randint(low=30, high=50, size=(1,)).item()
+        logging.info(f"test_encoder: iter {i}, N={N}, T={T}")
+
+        x = torch.rand(N, T, C)
+        x_lens = torch.randint(low=30, high=T + 1, size=(N,))
+        x_lens[0] = T
+
+        torch_encoder_out, torch_encoder_out_lens = torch_model.encoder(x, x_lens)
+        torch_encoder_out = torch_model.joiner.encoder_proj(torch_encoder_out)
+
+        onnx_encoder_out, onnx_encoder_out_lens = onnx_model.run_encoder(x, x_lens)
+
+        assert torch.allclose(torch_encoder_out, onnx_encoder_out, atol=1e-05), (
+            (torch_encoder_out - onnx_encoder_out).abs().max()
+        )
+
+
+def test_decoder(
+    torch_model: torch.jit.ScriptModule,
+    onnx_model: OnnxModel,
+):
+    context_size = onnx_model.context_size
+    vocab_size = onnx_model.vocab_size
+    for i in range(10):
+        N = torch.randint(1, 100, size=(1,)).item()
+        logging.info(f"test_decoder: iter {i}, N={N}")
+        x = torch.randint(
+            low=1,
+            high=vocab_size,
+            size=(N, context_size),
+            dtype=torch.int64,
+        )
+        torch_decoder_out = torch_model.decoder(x, need_pad=torch.tensor([False]))
+        torch_decoder_out = torch_model.joiner.decoder_proj(torch_decoder_out)
+        torch_decoder_out = torch_decoder_out.squeeze(1)
+
+        onnx_decoder_out = onnx_model.run_decoder(x)
+        assert torch.allclose(torch_decoder_out, onnx_decoder_out, atol=1e-4), (
+            (torch_decoder_out - onnx_decoder_out).abs().max()
+        )
+
+
+def test_joiner(
+    torch_model: torch.jit.ScriptModule,
+    onnx_model: OnnxModel,
+):
+    encoder_dim = torch_model.joiner.encoder_proj.weight.shape[1]
+    decoder_dim = torch_model.joiner.decoder_proj.weight.shape[1]
+    for i in range(10):
+        N = torch.randint(1, 100, size=(1,)).item()
+        logging.info(f"test_joiner: iter {i}, N={N}")
+        encoder_out = torch.rand(N, encoder_dim)
+        decoder_out = torch.rand(N, decoder_dim)
+
+        projected_encoder_out = torch_model.joiner.encoder_proj(encoder_out)
+        projected_decoder_out = torch_model.joiner.decoder_proj(decoder_out)
+
+        torch_joiner_out = torch_model.joiner(encoder_out, decoder_out)
+        onnx_joiner_out = onnx_model.run_joiner(
+            projected_encoder_out, projected_decoder_out
+        )
+
+        assert torch.allclose(torch_joiner_out, onnx_joiner_out, atol=1e-4), (
+            (torch_joiner_out - onnx_joiner_out).abs().max()
+        )
+
+
+@torch.no_grad()
+def main():
+    args = get_parser().parse_args()
+    logging.info(vars(args))
+
+    torch_model = torch.jit.load(args.jit_filename)
+
+    onnx_model = OnnxModel(
+        encoder_model_filename=args.onnx_encoder_filename,
+        decoder_model_filename=args.onnx_decoder_filename,
+        joiner_model_filename=args.onnx_joiner_filename,
+    )
+
+    logging.info("Test encoder")
+    test_encoder(torch_model, onnx_model)
+
+    logging.info("Test decoder")
+    test_decoder(torch_model, onnx_model)
+
+    logging.info("Test joiner")
+    test_joiner(torch_model, onnx_model)
+    logging.info("Finished checking ONNX models")
+
+
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+# See https://github.com/pytorch/pytorch/issues/38342
+# and https://github.com/pytorch/pytorch/issues/33354
+#
+# If we don't do this, the delay increases whenever there is
+# a new request that changes the actual batch size.
+# If you use `py-spy dump --pid <server-pid> --native`, you will
+# see a lot of time is spent in re-compiling the torch script model.
+torch._C._jit_set_profiling_executor(False)
+torch._C._jit_set_profiling_mode(False)
+torch._C._set_graph_executor_optimize(False)
+if __name__ == "__main__":
+    torch.manual_seed(20220727)
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()

egs/librispeech/ASR/zipformer/onnx_pretrained.py

@@ -1 +0,0 @@
-../pruned_transducer_stateless7/onnx_pretrained.py

egs/librispeech/ASR/zipformer/onnx_pretrained.py

@@ -0,0 +1,419 @@
+#!/usr/bin/env python3
+# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This script loads ONNX models and uses them to decode waves.
+You can use the following command to get the exported models:
+
+We use the pre-trained model from
+https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
+as an example to show how to use this file.
+
+1. Download the pre-trained model
+
+cd egs/librispeech/ASR
+
+repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-zipformer-2023-05-15
+GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
+repo=$(basename $repo_url)
+
+pushd $repo
+git lfs pull --include "data/lang_bpe_500/tokens.txt"
+git lfs pull --include "exp/pretrained.pt"
+
+cd exp
+ln -s pretrained.pt epoch-99.pt
+popd
+
+2. Export the model to ONNX
+
+./zipformer/export-onnx.py \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  --use-averaged-model 0 \
+  --epoch 99 \
+  --avg 1 \
+  --exp-dir $repo/exp \
+  --causal False
+
+It will generate the following 3 files inside $repo/exp:
+
+  - encoder-epoch-99-avg-1.onnx
+  - decoder-epoch-99-avg-1.onnx
+  - joiner-epoch-99-avg-1.onnx
+
+3. Run this file
+
+./pruned_transducer_stateless3/onnx_pretrained.py \
+  --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
+  --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
+  --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
+  --tokens $repo/data/lang_bpe_500/tokens.txt \
+  $repo/test_wavs/1089-134686-0001.wav \
+  $repo/test_wavs/1221-135766-0001.wav \
+  $repo/test_wavs/1221-135766-0002.wav
+"""
+
+import argparse
+import logging
+import math
+from typing import List, Tuple
+
+import k2
+import kaldifeat
+import onnxruntime as ort
+import torch
+import torchaudio
+from torch.nn.utils.rnn import pad_sequence
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--encoder-model-filename",
+        type=str,
+        required=True,
+        help="Path to the encoder onnx model. ",
+    )
+
+    parser.add_argument(
+        "--decoder-model-filename",
+        type=str,
+        required=True,
+        help="Path to the decoder onnx model. ",
+    )
+
+    parser.add_argument(
+        "--joiner-model-filename",
+        type=str,
+        required=True,
+        help="Path to the joiner onnx model. ",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        help="""Path to tokens.txt.""",
+    )
+
+    parser.add_argument(
+        "sound_files",
+        type=str,
+        nargs="+",
+        help="The input sound file(s) to transcribe. "
+        "Supported formats are those supported by torchaudio.load(). "
+        "For example, wav and flac are supported. "
+        "The sample rate has to be 16kHz.",
+    )
+
+    parser.add_argument(
+        "--sample-rate",
+        type=int,
+        default=16000,
+        help="The sample rate of the input sound file",
+    )
+
+    return parser
+
+
+class OnnxModel:
+    def __init__(
+        self,
+        encoder_model_filename: str,
+        decoder_model_filename: str,
+        joiner_model_filename: str,
+    ):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 4
+
+        self.session_opts = session_opts
+
+        self.init_encoder(encoder_model_filename)
+        self.init_decoder(decoder_model_filename)
+        self.init_joiner(joiner_model_filename)
+
+    def init_encoder(self, encoder_model_filename: str):
+        self.encoder = ort.InferenceSession(
+            encoder_model_filename,
+            sess_options=self.session_opts,
+        )
+
+    def init_decoder(self, decoder_model_filename: str):
+        self.decoder = ort.InferenceSession(
+            decoder_model_filename,
+            sess_options=self.session_opts,
+        )
+
+        decoder_meta = self.decoder.get_modelmeta().custom_metadata_map
+        self.context_size = int(decoder_meta["context_size"])
+        self.vocab_size = int(decoder_meta["vocab_size"])
+
+        logging.info(f"context_size: {self.context_size}")
+        logging.info(f"vocab_size: {self.vocab_size}")
+
+    def init_joiner(self, joiner_model_filename: str):
+        self.joiner = ort.InferenceSession(
+            joiner_model_filename,
+            sess_options=self.session_opts,
+        )
+
+        joiner_meta = self.joiner.get_modelmeta().custom_metadata_map
+        self.joiner_dim = int(joiner_meta["joiner_dim"])
+
+        logging.info(f"joiner_dim: {self.joiner_dim}")
+
+    def run_encoder(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Args:
+          x:
+            A 3-D tensor of shape (N, T, C)
+          x_lens:
+            A 2-D tensor of shape (N,). Its dtype is torch.int64
+        Returns:
+          Return a tuple containing:
+            - encoder_out, its shape is (N, T', joiner_dim)
+            - encoder_out_lens, its shape is (N,)
+        """
+        out = self.encoder.run(
+            [
+                self.encoder.get_outputs()[0].name,
+                self.encoder.get_outputs()[1].name,
+            ],
+            {
+                self.encoder.get_inputs()[0].name: x.numpy(),
+                self.encoder.get_inputs()[1].name: x_lens.numpy(),
+            },
+        )
+        return torch.from_numpy(out[0]), torch.from_numpy(out[1])
+
+    def run_decoder(self, decoder_input: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+          decoder_input:
+            A 2-D tensor of shape (N, context_size)
+        Returns:
+          Return a 2-D tensor of shape (N, joiner_dim)
+        """
+        out = self.decoder.run(
+            [self.decoder.get_outputs()[0].name],
+            {self.decoder.get_inputs()[0].name: decoder_input.numpy()},
+        )[0]
+
+        return torch.from_numpy(out)
+
+    def run_joiner(
+        self, encoder_out: torch.Tensor, decoder_out: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Args:
+          encoder_out:
+            A 2-D tensor of shape (N, joiner_dim)
+          decoder_out:
+            A 2-D tensor of shape (N, joiner_dim)
+        Returns:
+          Return a 2-D tensor of shape (N, vocab_size)
+        """
+        out = self.joiner.run(
+            [self.joiner.get_outputs()[0].name],
+            {
+                self.joiner.get_inputs()[0].name: encoder_out.numpy(),
+                self.joiner.get_inputs()[1].name: decoder_out.numpy(),
+            },
+        )[0]
+
+        return torch.from_numpy(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 greedy_search(
+    model: OnnxModel,
+    encoder_out: torch.Tensor,
+    encoder_out_lens: torch.Tensor,
+) -> List[List[int]]:
+    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
+    Args:
+      model:
+        The transducer model.
+      encoder_out:
+        A 3-D tensor of shape (N, T, joiner_dim)
+      encoder_out_lens:
+        A 1-D tensor of shape (N,).
+    Returns:
+      Return the decoded results for each utterance.
+    """
+    assert encoder_out.ndim == 3, encoder_out.shape
+    assert encoder_out.size(0) >= 1, encoder_out.size(0)
+
+    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
+        input=encoder_out,
+        lengths=encoder_out_lens.cpu(),
+        batch_first=True,
+        enforce_sorted=False,
+    )
+
+    blank_id = 0  # hard-code to 0
+
+    batch_size_list = packed_encoder_out.batch_sizes.tolist()
+    N = encoder_out.size(0)
+
+    assert torch.all(encoder_out_lens > 0), encoder_out_lens
+    assert N == batch_size_list[0], (N, batch_size_list)
+
+    context_size = model.context_size
+    hyps = [[blank_id] * context_size for _ in range(N)]
+
+    decoder_input = torch.tensor(
+        hyps,
+        dtype=torch.int64,
+    )  # (N, context_size)
+
+    decoder_out = model.run_decoder(decoder_input)
+
+    offset = 0
+    for batch_size in batch_size_list:
+        start = offset
+        end = offset + batch_size
+        current_encoder_out = packed_encoder_out.data[start:end]
+        # current_encoder_out's shape: (batch_size, joiner_dim)
+        offset = end
+
+        decoder_out = decoder_out[:batch_size]
+        logits = model.run_joiner(current_encoder_out, decoder_out)
+
+        # logits'shape (batch_size, vocab_size)
+
+        assert logits.ndim == 2, logits.shape
+        y = logits.argmax(dim=1).tolist()
+        emitted = False
+        for i, v in enumerate(y):
+            if v != blank_id:
+                hyps[i].append(v)
+                emitted = True
+        if emitted:
+            # update decoder output
+            decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
+            decoder_input = torch.tensor(
+                decoder_input,
+                dtype=torch.int64,
+            )
+            decoder_out = model.run_decoder(decoder_input)
+
+    sorted_ans = [h[context_size:] for h in hyps]
+    ans = []
+    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
+    for i in range(N):
+        ans.append(sorted_ans[unsorted_indices[i]])
+
+    return ans
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    args = parser.parse_args()
+    logging.info(vars(args))
+    model = OnnxModel(
+        encoder_model_filename=args.encoder_model_filename,
+        decoder_model_filename=args.decoder_model_filename,
+        joiner_model_filename=args.joiner_model_filename,
+    )
+
+    logging.info("Constructing Fbank computer")
+    opts = kaldifeat.FbankOptions()
+    opts.device = "cpu"
+    opts.frame_opts.dither = 0
+    opts.frame_opts.snip_edges = False
+    opts.frame_opts.samp_freq = args.sample_rate
+    opts.mel_opts.num_bins = 80
+
+    fbank = kaldifeat.Fbank(opts)
+
+    logging.info(f"Reading sound files: {args.sound_files}")
+    waves = read_sound_files(
+        filenames=args.sound_files,
+        expected_sample_rate=args.sample_rate,
+    )
+
+    logging.info("Decoding started")
+    features = fbank(waves)
+    feature_lengths = [f.size(0) for f in features]
+
+    features = pad_sequence(
+        features,
+        batch_first=True,
+        padding_value=math.log(1e-10),
+    )
+
+    feature_lengths = torch.tensor(feature_lengths, dtype=torch.int64)
+    encoder_out, encoder_out_lens = model.run_encoder(features, feature_lengths)
+
+    hyps = greedy_search(
+        model=model,
+        encoder_out=encoder_out,
+        encoder_out_lens=encoder_out_lens,
+    )
+    s = "\n"
+
+    symbol_table = k2.SymbolTable.from_file(args.tokens)
+
+    def token_ids_to_words(token_ids: List[int]) -> str:
+        text = ""
+        for i in token_ids:
+            text += symbol_table[i]
+        return text.replace("▁", " ").strip()
+
+    for filename, hyp in zip(args.sound_files, hyps):
+        words = token_ids_to_words(hyp)
+        s += f"{filename}:\n{words}\n"
+    logging.info(s)
+
+    logging.info("Decoding Done")
+
+
+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/zipformer/pretrained.py

@@ -18,11 +18,14 @@
 This script loads a checkpoint and uses it to decode waves.
 You can generate the checkpoint with the following command:
 
+Note: This is a example for librispeech dataset, if you are using different
+dataset, you should change the argument values according to your dataset.
+
 - For non-streaming model:
 
 ./zipformer/export.py \
   --exp-dir ./zipformer/exp \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 30 \
   --avg 9
 
@@ -31,7 +34,7 @@ You can generate the checkpoint with the following command:
 ./zipformer/export.py \
   --exp-dir ./zipformer/exp \
   --causal 1 \
-  --bpe-model data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --epoch 30 \
   --avg 9
 
@@ -42,7 +45,7 @@ Usage of this script:
 (1) greedy search
 ./zipformer/pretrained.py \
   --checkpoint ./zipformer/exp/pretrained.pt \
-  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --tokens data/lang_bpe_500/tokens.txt \
   --method greedy_search \
   /path/to/foo.wav \
   /path/to/bar.wav
@@ -50,7 +53,7 @@ Usage of this script:
 (2) modified beam search
 ./zipformer/pretrained.py \
   --checkpoint ./zipformer/exp/pretrained.pt \
-  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --tokens ./data/lang_bpe_500/tokens.txt \
   --method modified_beam_search \
   /path/to/foo.wav \
   /path/to/bar.wav
@@ -58,7 +61,7 @@ Usage of this script:
 (3) fast beam search
 ./zipformer/pretrained.py \
   --checkpoint ./zipformer/exp/pretrained.pt \
-  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --tokens ./data/lang_bpe_500/tokens.txt \
   --method fast_beam_search \
   /path/to/foo.wav \
   /path/to/bar.wav
@@ -71,7 +74,7 @@ Usage of this script:
   --causal 1 \
   --chunk-size 16 \
   --left-context-frames 128 \
-  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --tokens ./data/lang_bpe_500/tokens.txt \
   --method greedy_search \
   /path/to/foo.wav \
   /path/to/bar.wav
@@ -82,7 +85,7 @@ Usage of this script:
   --causal 1 \
   --chunk-size 16 \
   --left-context-frames 128 \
-  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --tokens ./data/lang_bpe_500/tokens.txt \
   --method modified_beam_search \
   /path/to/foo.wav \
   /path/to/bar.wav
@@ -93,7 +96,7 @@ Usage of this script:
   --causal 1 \
   --chunk-size 16 \
   --left-context-frames 128 \
-  --bpe-model ./data/lang_bpe_500/bpe.model \
+  --tokens ./data/lang_bpe_500/tokens.txt \
   --method fast_beam_search \
   /path/to/foo.wav \
   /path/to/bar.wav
@@ -112,7 +115,6 @@ from typing import List
 
 import k2
 import kaldifeat
-import sentencepiece as spm
 import torch
 import torchaudio
 from beam_search import (
@@ -120,10 +122,11 @@ from beam_search import (
     greedy_search_batch,
     modified_beam_search,
 )
-from icefall.utils import make_pad_mask
 from torch.nn.utils.rnn import pad_sequence
 from train import add_model_arguments, get_params, get_transducer_model
 
+from icefall.utils import make_pad_mask
+
 
 def get_parser():
     parser = argparse.ArgumentParser(
@@ -140,9 +143,9 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--bpe-model",
+        "--tokens",
         type=str,
-        help="""Path to bpe.model.""",
+        help="""Path to tokens.txt.""",
     )
 
     parser.add_argument(
@@ -259,13 +262,11 @@ def main():
 
     params.update(vars(args))
 
-    sp = spm.SentencePieceProcessor()
-    sp.load(params.bpe_model)
+    symbol_table = k2.SymbolTable.from_file(params.tokens)
 
-    # <blk> is 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()
+    params.blank_id = symbol_table["<blk>"]
+    params.unk_id = symbol_table["<unk>"]
+    params.vocab_size = len(symbol_table)
 
     logging.info(f"{params}")
 
@@ -323,15 +324,19 @@ def main():
     src_key_padding_mask = make_pad_mask(x_lens)
     x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
 
-    encoder_out, encoder_out_lens = model.encoder(
-        x, x_lens, src_key_padding_mask
-    )
+    encoder_out, encoder_out_lens = model.encoder(x, x_lens, src_key_padding_mask)
     encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
 
     hyps = []
     msg = f"Using {params.method}"
     logging.info(msg)
 
+    def token_ids_to_words(token_ids: List[int]) -> str:
+        text = ""
+        for i in token_ids:
+            text += symbol_table[i]
+        return text.replace("▁", " ").strip()
+
     if params.method == "fast_beam_search":
         decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
         hyp_tokens = fast_beam_search_one_best(
@@ -343,8 +348,8 @@ def main():
             max_contexts=params.max_contexts,
             max_states=params.max_states,
         )
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     elif params.method == "modified_beam_search":
         hyp_tokens = modified_beam_search(
             model=model,
@@ -353,23 +358,22 @@ def main():
             beam=params.beam_size,
         )
 
-        for hyp in sp.decode(hyp_tokens):
-            hyps.append(hyp.split())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     elif params.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())
+        for hyp in hyp_tokens:
+            hyps.append(token_ids_to_words(hyp))
     else:
         raise ValueError(f"Unsupported method: {params.method}")
 
     s = "\n"
     for filename, hyp in zip(params.sound_files, hyps):
-        words = " ".join(hyp)
-        s += f"{filename}:\n{words}\n\n"
+        s += f"{filename}:\n{hyp}\n\n"
     logging.info(s)
 
     logging.info("Decoding Done")

egs/wenetspeech/ASR/zipformer/export-onnx-streaming.py

@@ -1,738 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang)
-# Copyright 2023 Danqing Fu (danqing.fu@gmail.com)
-
-"""
-This script exports a transducer model from PyTorch to ONNX.
-
-1. Export the model to ONNX
-
-./zipformer/export-onnx-streaming.py \
-  --lang-dir data/lang_char \
-  --epoch 12 \
-  --avg 4 \
-  --exp-dir zipformer/exp \
-  --causal True \
-  --chunk-size 16 \
-  --left-context-frames 128
-
-The --chunk-size in training is "16,32,64,-1", so we select one of them
-(excluding -1) during streaming export. The same applies to `--left-context`,
-whose value is "64,128,256,-1".
-
-It will generate the following 3 files inside $repo/exp:
-
-  - encoder-epoch-12-avg-4.onnx
-  - decoder-epoch-12-avg-4.onnx
-  - joiner-epoch-12-avg-4.onnx
-
-See ./onnx_pretrained-streaming.py for how to use the exported ONNX models.
-"""
-
-import argparse
-import logging
-from pathlib import Path
-from typing import Dict, List, Tuple
-
-import onnx
-import torch
-import torch.nn as nn
-from decoder import Decoder
-from onnxruntime.quantization import QuantType, quantize_dynamic
-from scaling_converter import convert_scaled_to_non_scaled
-from train import add_model_arguments, get_params, get_transducer_model
-from zipformer import Zipformer2
-
-from icefall.checkpoint import (
-    average_checkpoints,
-    average_checkpoints_with_averaged_model,
-    find_checkpoints,
-    load_checkpoint,
-)
-from icefall.lexicon import Lexicon
-from icefall.utils import make_pad_mask, 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(
-        "--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="zipformer/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="Path to the lang dir(containing lexicon, tokens etc.)",
-    )
-
-    parser.add_argument(
-        "--context-size",
-        type=int,
-        default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
-    )
-
-    add_model_arguments(parser)
-
-    return parser
-
-
-def add_meta_data(filename: str, meta_data: Dict[str, str]):
-    """Add meta data to an ONNX model. It is changed in-place.
-
-    Args:
-      filename:
-        Filename of the ONNX model to be changed.
-      meta_data:
-        Key-value pairs.
-    """
-    model = onnx.load(filename)
-    for key, value in meta_data.items():
-        meta = model.metadata_props.add()
-        meta.key = key
-        meta.value = value
-
-    onnx.save(model, filename)
-
-
-class OnnxEncoder(nn.Module):
-    """A wrapper for Zipformer and the encoder_proj from the joiner"""
-
-    def __init__(
-        self, encoder: Zipformer2, encoder_embed: nn.Module, encoder_proj: nn.Linear
-    ):
-        """
-        Args:
-          encoder:
-            A Zipformer encoder.
-          encoder_proj:
-            The projection layer for encoder from the joiner.
-        """
-        super().__init__()
-        self.encoder = encoder
-        self.encoder_embed = encoder_embed
-        self.encoder_proj = encoder_proj
-        self.chunk_size = encoder.chunk_size[0]
-        self.left_context_len = encoder.left_context_frames[0]
-        self.pad_length = 7 + 2 * 3
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        states: List[torch.Tensor],
-    ) -> Tuple[torch.Tensor, torch.Tensor, List[torch.Tensor]]:
-        N = x.size(0)
-        T = self.chunk_size * 2 + self.pad_length
-        x_lens = torch.tensor([T] * N, device=x.device)
-        left_context_len = self.left_context_len
-
-        cached_embed_left_pad = states[-2]
-        x, x_lens, new_cached_embed_left_pad = self.encoder_embed.streaming_forward(
-            x=x,
-            x_lens=x_lens,
-            cached_left_pad=cached_embed_left_pad,
-        )
-        assert x.size(1) == self.chunk_size, (x.size(1), self.chunk_size)
-
-        src_key_padding_mask = make_pad_mask(x_lens)
-
-        # processed_mask is used to mask out initial states
-        processed_mask = torch.arange(left_context_len, device=x.device).expand(
-            x.size(0), left_context_len
-        )
-        processed_lens = states[-1]  # (batch,)
-        # (batch, left_context_size)
-        processed_mask = (processed_lens.unsqueeze(1) <= processed_mask).flip(1)
-        # Update processed lengths
-        new_processed_lens = processed_lens + x_lens
-        # (batch, left_context_size + chunk_size)
-        src_key_padding_mask = torch.cat([processed_mask, src_key_padding_mask], dim=1)
-
-        x = x.permute(1, 0, 2)
-        encoder_states = states[:-2]
-        logging.info(f"len_encoder_states={len(encoder_states)}")
-        (
-            encoder_out,
-            encoder_out_lens,
-            new_encoder_states,
-        ) = self.encoder.streaming_forward(
-            x=x,
-            x_lens=x_lens,
-            states=encoder_states,
-            src_key_padding_mask=src_key_padding_mask,
-        )
-        encoder_out = encoder_out.permute(1, 0, 2)
-        encoder_out = self.encoder_proj(encoder_out)
-        # Now encoder_out is of shape (N, T, joiner_dim)
-
-        new_states = new_encoder_states + [
-            new_cached_embed_left_pad,
-            new_processed_lens,
-        ]
-
-        return encoder_out, new_states
-
-    def get_init_states(
-        self,
-        batch_size: int = 1,
-        device: torch.device = torch.device("cpu"),
-    ) -> List[torch.Tensor]:
-        """
-        Returns a list of cached tensors of all encoder layers. For layer-i, states[i*6:(i+1)*6]
-        is (cached_key, cached_nonlin_attn, cached_val1, cached_val2, cached_conv1, cached_conv2).
-        states[-2] is the cached left padding for ConvNeXt module,
-        of shape (batch_size, num_channels, left_pad, num_freqs)
-        states[-1] is processed_lens of shape (batch,), which records the number
-        of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch.
-        """
-        states = self.encoder.get_init_states(batch_size, device)
-
-        embed_states = self.encoder_embed.get_init_states(batch_size, device)
-        states.append(embed_states)
-
-        processed_lens = torch.zeros(batch_size, dtype=torch.int64, device=device)
-        states.append(processed_lens)
-
-        return states
-
-
-class OnnxDecoder(nn.Module):
-    """A wrapper for Decoder and the decoder_proj from the joiner"""
-
-    def __init__(self, decoder: Decoder, decoder_proj: nn.Linear):
-        super().__init__()
-        self.decoder = decoder
-        self.decoder_proj = decoder_proj
-
-    def forward(self, y: torch.Tensor) -> torch.Tensor:
-        """
-        Args:
-          y:
-            A 2-D tensor of shape (N, context_size).
-        Returns
-          Return a 2-D tensor of shape (N, joiner_dim)
-        """
-        need_pad = False
-        decoder_output = self.decoder(y, need_pad=need_pad)
-        decoder_output = decoder_output.squeeze(1)
-        output = self.decoder_proj(decoder_output)
-
-        return output
-
-
-class OnnxJoiner(nn.Module):
-    """A wrapper for the joiner"""
-
-    def __init__(self, output_linear: nn.Linear):
-        super().__init__()
-        self.output_linear = output_linear
-
-    def forward(
-        self,
-        encoder_out: torch.Tensor,
-        decoder_out: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Args:
-          encoder_out:
-            A 2-D tensor of shape (N, joiner_dim)
-          decoder_out:
-            A 2-D tensor of shape (N, joiner_dim)
-        Returns:
-          Return a 2-D tensor of shape (N, vocab_size)
-        """
-        logit = encoder_out + decoder_out
-        logit = self.output_linear(torch.tanh(logit))
-        return logit
-
-
-def export_encoder_model_onnx(
-    encoder_model: OnnxEncoder,
-    encoder_filename: str,
-    opset_version: int = 11,
-) -> None:
-    encoder_model.encoder.__class__.forward = (
-        encoder_model.encoder.__class__.streaming_forward
-    )
-
-    decode_chunk_len = encoder_model.chunk_size * 2
-    # The encoder_embed subsample features (T - 7) // 2
-    # The ConvNeXt module needs (7 - 1) // 2 = 3 frames of right padding after subsampling
-    T = decode_chunk_len + encoder_model.pad_length
-
-    x = torch.rand(1, T, 80, dtype=torch.float32)
-    init_state = encoder_model.get_init_states()
-    num_encoders = len(encoder_model.encoder.encoder_dim)
-    logging.info(f"num_encoders: {num_encoders}")
-    logging.info(f"len(init_state): {len(init_state)}")
-
-    inputs = {}
-    input_names = ["x"]
-
-    outputs = {}
-    output_names = ["encoder_out"]
-
-    def build_inputs_outputs(tensors, i):
-        assert len(tensors) == 6, len(tensors)
-
-        # (downsample_left, batch_size, key_dim)
-        name = f"cached_key_{i}"
-        logging.info(f"{name}.shape: {tensors[0].shape}")
-        inputs[name] = {1: "N"}
-        outputs[f"new_{name}"] = {1: "N"}
-        input_names.append(name)
-        output_names.append(f"new_{name}")
-
-        # (1, batch_size, downsample_left, nonlin_attn_head_dim)
-        name = f"cached_nonlin_attn_{i}"
-        logging.info(f"{name}.shape: {tensors[1].shape}")
-        inputs[name] = {1: "N"}
-        outputs[f"new_{name}"] = {1: "N"}
-        input_names.append(name)
-        output_names.append(f"new_{name}")
-
-        # (downsample_left, batch_size, value_dim)
-        name = f"cached_val1_{i}"
-        logging.info(f"{name}.shape: {tensors[2].shape}")
-        inputs[name] = {1: "N"}
-        outputs[f"new_{name}"] = {1: "N"}
-        input_names.append(name)
-        output_names.append(f"new_{name}")
-
-        # (downsample_left, batch_size, value_dim)
-        name = f"cached_val2_{i}"
-        logging.info(f"{name}.shape: {tensors[3].shape}")
-        inputs[name] = {1: "N"}
-        outputs[f"new_{name}"] = {1: "N"}
-        input_names.append(name)
-        output_names.append(f"new_{name}")
-
-        # (batch_size, embed_dim, conv_left_pad)
-        name = f"cached_conv1_{i}"
-        logging.info(f"{name}.shape: {tensors[4].shape}")
-        inputs[name] = {0: "N"}
-        outputs[f"new_{name}"] = {0: "N"}
-        input_names.append(name)
-        output_names.append(f"new_{name}")
-
-        # (batch_size, embed_dim, conv_left_pad)
-        name = f"cached_conv2_{i}"
-        logging.info(f"{name}.shape: {tensors[5].shape}")
-        inputs[name] = {0: "N"}
-        outputs[f"new_{name}"] = {0: "N"}
-        input_names.append(name)
-        output_names.append(f"new_{name}")
-
-    num_encoder_layers = ",".join(map(str, encoder_model.encoder.num_encoder_layers))
-    encoder_dims = ",".join(map(str, encoder_model.encoder.encoder_dim))
-    cnn_module_kernels = ",".join(map(str, encoder_model.encoder.cnn_module_kernel))
-    ds = encoder_model.encoder.downsampling_factor
-    left_context_len = encoder_model.left_context_len
-    left_context_len = [left_context_len // k for k in ds]
-    left_context_len = ",".join(map(str, left_context_len))
-    query_head_dims = ",".join(map(str, encoder_model.encoder.query_head_dim))
-    value_head_dims = ",".join(map(str, encoder_model.encoder.value_head_dim))
-    num_heads = ",".join(map(str, encoder_model.encoder.num_heads))
-
-    meta_data = {
-        "model_type": "zipformer2",
-        "version": "1",
-        "model_author": "k2-fsa",
-        "comment": "streaming zipformer2",
-        "decode_chunk_len": str(decode_chunk_len),  # 32
-        "T": str(T),  # 32+7+2*3=45
-        "num_encoder_layers": num_encoder_layers,
-        "encoder_dims": encoder_dims,
-        "cnn_module_kernels": cnn_module_kernels,
-        "left_context_len": left_context_len,
-        "query_head_dims": query_head_dims,
-        "value_head_dims": value_head_dims,
-        "num_heads": num_heads,
-    }
-    logging.info(f"meta_data: {meta_data}")
-
-    for i in range(len(init_state[:-2]) // 6):
-        build_inputs_outputs(init_state[i * 6 : (i + 1) * 6], i)
-
-    # (batch_size, channels, left_pad, freq)
-    embed_states = init_state[-2]
-    name = "embed_states"
-    logging.info(f"{name}.shape: {embed_states.shape}")
-    inputs[name] = {0: "N"}
-    outputs[f"new_{name}"] = {0: "N"}
-    input_names.append(name)
-    output_names.append(f"new_{name}")
-
-    # (batch_size,)
-    processed_lens = init_state[-1]
-    name = "processed_lens"
-    logging.info(f"{name}.shape: {processed_lens.shape}")
-    inputs[name] = {0: "N"}
-    outputs[f"new_{name}"] = {0: "N"}
-    input_names.append(name)
-    output_names.append(f"new_{name}")
-
-    logging.info(inputs)
-    logging.info(outputs)
-    logging.info(input_names)
-    logging.info(output_names)
-
-    torch.onnx.export(
-        encoder_model,
-        (x, init_state),
-        encoder_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=input_names,
-        output_names=output_names,
-        dynamic_axes={
-            "x": {0: "N"},
-            "encoder_out": {0: "N"},
-            **inputs,
-            **outputs,
-        },
-    )
-
-    add_meta_data(filename=encoder_filename, meta_data=meta_data)
-
-
-def export_decoder_model_onnx(
-    decoder_model: OnnxDecoder,
-    decoder_filename: str,
-    opset_version: int = 11,
-) -> None:
-    """Export the decoder model to ONNX format.
-
-    The exported model has one input:
-
-        - y: a torch.int64 tensor of shape (N, decoder_model.context_size)
-
-    and has one output:
-
-        - decoder_out: a torch.float32 tensor of shape (N, joiner_dim)
-
-    Args:
-      decoder_model:
-        The decoder model to be exported.
-      decoder_filename:
-        Filename to save the exported ONNX model.
-      opset_version:
-        The opset version to use.
-    """
-    context_size = decoder_model.decoder.context_size
-    vocab_size = decoder_model.decoder.vocab_size
-
-    y = torch.zeros(10, context_size, dtype=torch.int64)
-    torch.onnx.export(
-        decoder_model,
-        y,
-        decoder_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=["y"],
-        output_names=["decoder_out"],
-        dynamic_axes={
-            "y": {0: "N"},
-            "decoder_out": {0: "N"},
-        },
-    )
-
-    meta_data = {
-        "context_size": str(context_size),
-        "vocab_size": str(vocab_size),
-    }
-    add_meta_data(filename=decoder_filename, meta_data=meta_data)
-
-
-def export_joiner_model_onnx(
-    joiner_model: nn.Module,
-    joiner_filename: str,
-    opset_version: int = 11,
-) -> None:
-    """Export the joiner model to ONNX format.
-    The exported joiner model has two inputs:
-
-        - encoder_out: a tensor of shape (N, joiner_dim)
-        - decoder_out: a tensor of shape (N, joiner_dim)
-
-    and produces one output:
-
-        - logit: a tensor of shape (N, vocab_size)
-    """
-    joiner_dim = joiner_model.output_linear.weight.shape[1]
-    logging.info(f"joiner dim: {joiner_dim}")
-
-    projected_encoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
-    projected_decoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
-
-    torch.onnx.export(
-        joiner_model,
-        (projected_encoder_out, projected_decoder_out),
-        joiner_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=[
-            "encoder_out",
-            "decoder_out",
-        ],
-        output_names=["logit"],
-        dynamic_axes={
-            "encoder_out": {0: "N"},
-            "decoder_out": {0: "N"},
-            "logit": {0: "N"},
-        },
-    )
-    meta_data = {
-        "joiner_dim": str(joiner_dim),
-    }
-    add_meta_data(filename=joiner_filename, meta_data=meta_data)
-
-
-@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")
-    if torch.cuda.is_available():
-        device = torch.device("cuda", 0)
-
-    logging.info(f"device: {device}")
-
-    lexicon = Lexicon(params.lang_dir)
-    params.blank_id = lexicon.token_table["<blk>"]
-    params.vocab_size = max(lexicon.tokens) + 1
-
-    logging.info(params)
-
-    logging.info("About to create model")
-    model = get_transducer_model(params)
-
-    model.to(device)
-
-    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)
-
-    encoder = OnnxEncoder(
-        encoder=model.encoder,
-        encoder_embed=model.encoder_embed,
-        encoder_proj=model.joiner.encoder_proj,
-    )
-
-    decoder = OnnxDecoder(
-        decoder=model.decoder,
-        decoder_proj=model.joiner.decoder_proj,
-    )
-
-    joiner = OnnxJoiner(output_linear=model.joiner.output_linear)
-
-    encoder_num_param = sum([p.numel() for p in encoder.parameters()])
-    decoder_num_param = sum([p.numel() for p in decoder.parameters()])
-    joiner_num_param = sum([p.numel() for p in 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}")
-
-    if params.iter > 0:
-        suffix = f"iter-{params.iter}"
-    else:
-        suffix = f"epoch-{params.epoch}"
-
-    suffix += f"-avg-{params.avg}"
-
-    opset_version = 13
-
-    logging.info("Exporting encoder")
-    encoder_filename = params.exp_dir / f"encoder-{suffix}.onnx"
-    export_encoder_model_onnx(
-        encoder,
-        encoder_filename,
-        opset_version=opset_version,
-    )
-    logging.info(f"Exported encoder to {encoder_filename}")
-
-    logging.info("Exporting decoder")
-    decoder_filename = params.exp_dir / f"decoder-{suffix}.onnx"
-    export_decoder_model_onnx(
-        decoder,
-        decoder_filename,
-        opset_version=opset_version,
-    )
-    logging.info(f"Exported decoder to {decoder_filename}")
-
-    logging.info("Exporting joiner")
-    joiner_filename = params.exp_dir / f"joiner-{suffix}.onnx"
-    export_joiner_model_onnx(
-        joiner,
-        joiner_filename,
-        opset_version=opset_version,
-    )
-    logging.info(f"Exported joiner to {joiner_filename}")
-
-    # Generate int8 quantization models
-    # See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection
-
-    logging.info("Generate int8 quantization models")
-
-    encoder_filename_int8 = params.exp_dir / f"encoder-{suffix}.int8.onnx"
-    quantize_dynamic(
-        model_input=encoder_filename,
-        model_output=encoder_filename_int8,
-        op_types_to_quantize=["MatMul"],
-        weight_type=QuantType.QInt8,
-    )
-
-    decoder_filename_int8 = params.exp_dir / f"decoder-{suffix}.int8.onnx"
-    quantize_dynamic(
-        model_input=decoder_filename,
-        model_output=decoder_filename_int8,
-        op_types_to_quantize=["MatMul"],
-        weight_type=QuantType.QInt8,
-    )
-
-    joiner_filename_int8 = params.exp_dir / f"joiner-{suffix}.int8.onnx"
-    quantize_dynamic(
-        model_input=joiner_filename,
-        model_output=joiner_filename_int8,
-        op_types_to_quantize=["MatMul"],
-        weight_type=QuantType.QInt8,
-    )
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()

egs/wenetspeech/ASR/zipformer/export-onnx-streaming.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/export-onnx-streaming.py

egs/wenetspeech/ASR/zipformer/export-onnx.py

@@ -1,583 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright 2023 Xiaomi Corporation (Author: Fangjun Kuang)
-# Copyright 2023 Danqing Fu (danqing.fu@gmail.com)
-
-"""
-This script exports a transducer model from PyTorch to ONNX.
-
-1. Export the model to ONNX
-
-./zipformer/export-onnx.py \
-  --lang-dir lang_char \
-  --epoch 12 \
-  --avg 4 \
-  --exp-dir zipformer/exp \
-
-It will generate the following 3 files inside $repo/exp:
-
-  - encoder-epoch-12-avg-4.onnx
-  - decoder-epoch-12-avg-4.onnx
-  - joiner-epoch-12-avg-4.onnx
-
-See ./onnx_pretrained.py for how to
-use the exported ONNX models.
-"""
-
-import argparse
-import logging
-from pathlib import Path
-from typing import Dict, Tuple
-
-import onnx
-import torch
-import torch.nn as nn
-from decoder import Decoder
-from onnxruntime.quantization import QuantType, quantize_dynamic
-from scaling_converter import convert_scaled_to_non_scaled
-from train import add_model_arguments, get_params, get_transducer_model
-from zipformer import Zipformer2
-
-from icefall.checkpoint import (
-    average_checkpoints,
-    average_checkpoints_with_averaged_model,
-    find_checkpoints,
-    load_checkpoint,
-)
-from icefall.lexicon import Lexicon
-from icefall.utils import make_pad_mask, 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(
-        "--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="zipformer/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="Path to lang dir(containing lexicon, tokens etc.)",
-    )
-
-    parser.add_argument(
-        "--context-size",
-        type=int,
-        default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
-    )
-
-    add_model_arguments(parser)
-
-    return parser
-
-
-def add_meta_data(filename: str, meta_data: Dict[str, str]):
-    """Add meta data to an ONNX model. It is changed in-place.
-
-    Args:
-      filename:
-        Filename of the ONNX model to be changed.
-      meta_data:
-        Key-value pairs.
-    """
-    model = onnx.load(filename)
-    for key, value in meta_data.items():
-        meta = model.metadata_props.add()
-        meta.key = key
-        meta.value = value
-
-    onnx.save(model, filename)
-
-
-class OnnxEncoder(nn.Module):
-    """A wrapper for Zipformer and the encoder_proj from the joiner"""
-
-    def __init__(
-        self, encoder: Zipformer2, encoder_embed: nn.Module, encoder_proj: nn.Linear
-    ):
-        """
-        Args:
-          encoder:
-            A Zipformer encoder.
-          encoder_proj:
-            The projection layer for encoder from the joiner.
-        """
-        super().__init__()
-        self.encoder = encoder
-        self.encoder_embed = encoder_embed
-        self.encoder_proj = encoder_proj
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        x_lens: torch.Tensor,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Please see the help information of Zipformer.forward
-
-        Args:
-          x:
-            A 3-D tensor of shape (N, T, C)
-          x_lens:
-            A 1-D tensor of shape (N,). Its dtype is torch.int64
-        Returns:
-          Return a tuple containing:
-            - encoder_out, A 3-D tensor of shape (N, T', joiner_dim)
-            - encoder_out_lens, A 1-D tensor of shape (N,)
-        """
-        x, x_lens = self.encoder_embed(x, x_lens)
-        src_key_padding_mask = make_pad_mask(x_lens)
-        x = x.permute(1, 0, 2)
-        encoder_out, encoder_out_lens = self.encoder(x, x_lens, src_key_padding_mask)
-        encoder_out = encoder_out.permute(1, 0, 2)
-        encoder_out = self.encoder_proj(encoder_out)
-        # Now encoder_out is of shape (N, T, joiner_dim)
-
-        return encoder_out, encoder_out_lens
-
-
-class OnnxDecoder(nn.Module):
-    """A wrapper for Decoder and the decoder_proj from the joiner"""
-
-    def __init__(self, decoder: Decoder, decoder_proj: nn.Linear):
-        super().__init__()
-        self.decoder = decoder
-        self.decoder_proj = decoder_proj
-
-    def forward(self, y: torch.Tensor) -> torch.Tensor:
-        """
-        Args:
-          y:
-            A 2-D tensor of shape (N, context_size).
-        Returns
-          Return a 2-D tensor of shape (N, joiner_dim)
-        """
-        need_pad = False
-        decoder_output = self.decoder(y, need_pad=need_pad)
-        decoder_output = decoder_output.squeeze(1)
-        output = self.decoder_proj(decoder_output)
-
-        return output
-
-
-class OnnxJoiner(nn.Module):
-    """A wrapper for the joiner"""
-
-    def __init__(self, output_linear: nn.Linear):
-        super().__init__()
-        self.output_linear = output_linear
-
-    def forward(
-        self,
-        encoder_out: torch.Tensor,
-        decoder_out: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Args:
-          encoder_out:
-            A 2-D tensor of shape (N, joiner_dim)
-          decoder_out:
-            A 2-D tensor of shape (N, joiner_dim)
-        Returns:
-          Return a 2-D tensor of shape (N, vocab_size)
-        """
-        logit = encoder_out + decoder_out
-        logit = self.output_linear(torch.tanh(logit))
-        return logit
-
-
-def export_encoder_model_onnx(
-    encoder_model: OnnxEncoder,
-    encoder_filename: str,
-    opset_version: int = 11,
-) -> None:
-    """Export the given encoder model to ONNX format.
-    The exported model has two inputs:
-
-        - x, a tensor of shape (N, T, C); dtype is torch.float32
-        - x_lens, a tensor of shape (N,); dtype is torch.int64
-
-    and it has two outputs:
-
-        - encoder_out, a tensor of shape (N, T', joiner_dim)
-        - encoder_out_lens, a tensor of shape (N,)
-
-    Args:
-      encoder_model:
-        The input encoder model
-      encoder_filename:
-        The filename to save the exported ONNX model.
-      opset_version:
-        The opset version to use.
-    """
-    x = torch.zeros(1, 100, 80, dtype=torch.float32)
-    x_lens = torch.tensor([100], dtype=torch.int64)
-
-    encoder_model = torch.jit.trace(encoder_model, (x, x_lens))
-
-    torch.onnx.export(
-        encoder_model,
-        (x, x_lens),
-        encoder_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=["x", "x_lens"],
-        output_names=["encoder_out", "encoder_out_lens"],
-        dynamic_axes={
-            "x": {0: "N", 1: "T"},
-            "x_lens": {0: "N"},
-            "encoder_out": {0: "N", 1: "T"},
-            "encoder_out_lens": {0: "N"},
-        },
-    )
-
-    meta_data = {
-        "model_type": "zipformer2",
-        "version": "1",
-        "model_author": "k2-fsa",
-        "comment": "non-streaming zipformer2",
-    }
-    logging.info(f"meta_data: {meta_data}")
-
-    add_meta_data(filename=encoder_filename, meta_data=meta_data)
-
-
-def export_decoder_model_onnx(
-    decoder_model: OnnxDecoder,
-    decoder_filename: str,
-    opset_version: int = 11,
-) -> None:
-    """Export the decoder model to ONNX format.
-
-    The exported model has one input:
-
-        - y: a torch.int64 tensor of shape (N, decoder_model.context_size)
-
-    and has one output:
-
-        - decoder_out: a torch.float32 tensor of shape (N, joiner_dim)
-
-    Args:
-      decoder_model:
-        The decoder model to be exported.
-      decoder_filename:
-        Filename to save the exported ONNX model.
-      opset_version:
-        The opset version to use.
-    """
-    context_size = decoder_model.decoder.context_size
-    vocab_size = decoder_model.decoder.vocab_size
-
-    y = torch.zeros(10, context_size, dtype=torch.int64)
-    torch.onnx.export(
-        decoder_model,
-        y,
-        decoder_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=["y"],
-        output_names=["decoder_out"],
-        dynamic_axes={
-            "y": {0: "N"},
-            "decoder_out": {0: "N"},
-        },
-    )
-
-    meta_data = {
-        "context_size": str(context_size),
-        "vocab_size": str(vocab_size),
-    }
-    add_meta_data(filename=decoder_filename, meta_data=meta_data)
-
-
-def export_joiner_model_onnx(
-    joiner_model: nn.Module,
-    joiner_filename: str,
-    opset_version: int = 11,
-) -> None:
-    """Export the joiner model to ONNX format.
-    The exported joiner model has two inputs:
-
-        - encoder_out: a tensor of shape (N, joiner_dim)
-        - decoder_out: a tensor of shape (N, joiner_dim)
-
-    and produces one output:
-
-        - logit: a tensor of shape (N, vocab_size)
-    """
-    joiner_dim = joiner_model.output_linear.weight.shape[1]
-    logging.info(f"joiner dim: {joiner_dim}")
-
-    projected_encoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
-    projected_decoder_out = torch.rand(11, joiner_dim, dtype=torch.float32)
-
-    torch.onnx.export(
-        joiner_model,
-        (projected_encoder_out, projected_decoder_out),
-        joiner_filename,
-        verbose=False,
-        opset_version=opset_version,
-        input_names=[
-            "encoder_out",
-            "decoder_out",
-        ],
-        output_names=["logit"],
-        dynamic_axes={
-            "encoder_out": {0: "N"},
-            "decoder_out": {0: "N"},
-            "logit": {0: "N"},
-        },
-    )
-    meta_data = {
-        "joiner_dim": str(joiner_dim),
-    }
-    add_meta_data(filename=joiner_filename, meta_data=meta_data)
-
-
-@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")
-    if torch.cuda.is_available():
-        device = torch.device("cuda", 0)
-
-    logging.info(f"device: {device}")
-
-    lexicon = Lexicon(params.lang_dir)
-    params.blank_id = lexicon.token_table["<blk>"]
-    params.vocab_size = max(lexicon.tokens) + 1
-
-    logging.info(params)
-
-    logging.info("About to create model")
-    model = get_transducer_model(params)
-
-    model.to(device)
-
-    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_onnx=True)
-
-    encoder = OnnxEncoder(
-        encoder=model.encoder,
-        encoder_embed=model.encoder_embed,
-        encoder_proj=model.joiner.encoder_proj,
-    )
-
-    decoder = OnnxDecoder(
-        decoder=model.decoder,
-        decoder_proj=model.joiner.decoder_proj,
-    )
-
-    joiner = OnnxJoiner(output_linear=model.joiner.output_linear)
-
-    encoder_num_param = sum([p.numel() for p in encoder.parameters()])
-    decoder_num_param = sum([p.numel() for p in decoder.parameters()])
-    joiner_num_param = sum([p.numel() for p in 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}")
-
-    if params.iter > 0:
-        suffix = f"iter-{params.iter}"
-    else:
-        suffix = f"epoch-{params.epoch}"
-
-    suffix += f"-avg-{params.avg}"
-
-    opset_version = 13
-
-    logging.info("Exporting encoder")
-    encoder_filename = params.exp_dir / f"encoder-{suffix}.onnx"
-    export_encoder_model_onnx(
-        encoder,
-        encoder_filename,
-        opset_version=opset_version,
-    )
-    logging.info(f"Exported encoder to {encoder_filename}")
-
-    logging.info("Exporting decoder")
-    decoder_filename = params.exp_dir / f"decoder-{suffix}.onnx"
-    export_decoder_model_onnx(
-        decoder,
-        decoder_filename,
-        opset_version=opset_version,
-    )
-    logging.info(f"Exported decoder to {decoder_filename}")
-
-    logging.info("Exporting joiner")
-    joiner_filename = params.exp_dir / f"joiner-{suffix}.onnx"
-    export_joiner_model_onnx(
-        joiner,
-        joiner_filename,
-        opset_version=opset_version,
-    )
-    logging.info(f"Exported joiner to {joiner_filename}")
-
-    # Generate int8 quantization models
-    # See https://onnxruntime.ai/docs/performance/model-optimizations/quantization.html#data-type-selection
-
-    logging.info("Generate int8 quantization models")
-
-    encoder_filename_int8 = params.exp_dir / f"encoder-{suffix}.int8.onnx"
-    quantize_dynamic(
-        model_input=encoder_filename,
-        model_output=encoder_filename_int8,
-        op_types_to_quantize=["MatMul"],
-        weight_type=QuantType.QInt8,
-    )
-
-    decoder_filename_int8 = params.exp_dir / f"decoder-{suffix}.int8.onnx"
-    quantize_dynamic(
-        model_input=decoder_filename,
-        model_output=decoder_filename_int8,
-        op_types_to_quantize=["MatMul"],
-        weight_type=QuantType.QInt8,
-    )
-
-    joiner_filename_int8 = params.exp_dir / f"joiner-{suffix}.int8.onnx"
-    quantize_dynamic(
-        model_input=joiner_filename,
-        model_output=joiner_filename_int8,
-        op_types_to_quantize=["MatMul"],
-        weight_type=QuantType.QInt8,
-    )
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()

egs/wenetspeech/ASR/zipformer/export-onnx.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/export-onnx.py

egs/wenetspeech/ASR/zipformer/export.py

@@ -1,517 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright 2021-2023 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.
-
-# This script converts several saved checkpoints
-# to a single one using model averaging.
-"""
-
-Usage:
-
-(1) Export to torchscript model using torch.jit.script()
-
-- For non-streaming model:
-
-./zipformer/export.py \
-  --exp-dir ./zipformer/exp \
-  --lang-dir data/lang_char \
-  --epoch 30 \
-  --avg 9 \
-  --jit 1
-
-It will generate a file `jit_script.pt` in the given `exp_dir`. You can later
-load it by `torch.jit.load("jit_script.pt")`.
-
-Check ./jit_pretrained.py for its usage.
-
-Check https://github.com/k2-fsa/sherpa
-for how to use the exported models outside of icefall.
-
-- For streaming model:
-
-./zipformer/export.py \
-  --exp-dir ./zipformer/exp \
-  --causal 1 \
-  --chunk-size 16 \
-  --left-context-frames 128 \
-  --lang-dir data/lang_char \
-  --epoch 30 \
-  --avg 9 \
-  --jit 1
-
-It will generate a file `jit_script_chunk_16_left_128.pt` in the given `exp_dir`.
-You can later load it by `torch.jit.load("jit_script_chunk_16_left_128.pt")`.
-
-Check ./jit_pretrained_streaming.py for its usage.
-
-Check https://github.com/k2-fsa/sherpa
-for how to use the exported models outside of icefall.
-
-(2) Export `model.state_dict()`
-
-- For non-streaming model:
-
-./zipformer/export.py \
-  --exp-dir ./zipformer/exp \
-  --lang-dir data/lang_char \
-  --epoch 30 \
-  --avg 9
-
-- For streaming model:
-
-./zipformer/export.py \
-  --exp-dir ./zipformer/exp \
-  --causal 1 \
-  --lang-dir data/lang_char \
-  --epoch 30 \
-  --avg 9
-
-It will generate a file `pretrained.pt` in the given `exp_dir`. You can later
-load it by `icefall.checkpoint.load_checkpoint()`.
-
-- For non-streaming model:
-
-To use the generated file with `zipformer/decode.py`,
-you can do:
-
-    cd /path/to/exp_dir
-    ln -s pretrained.pt epoch-9999.pt
-
-    cd /path/to/egs/wenetspeech/ASR
-    ./zipformer/decode.py \
-        --exp-dir ./zipformer/exp \
-        --epoch 9999 \
-        --avg 1 \
-        --max-duration 600 \
-        --decoding-method greedy_search
-
-- For streaming model:
-
-To use the generated file with `zipformer/decode.py` and `zipformer/streaming_decode.py`, you can do:
-
-    cd /path/to/exp_dir
-    ln -s pretrained.pt epoch-9999.pt
-
-    cd /path/to/egs/wenetspeech/ASR
-
-    # simulated streaming decoding
-    ./zipformer/decode.py \
-        --exp-dir ./zipformer/exp \
-        --epoch 9999 \
-        --avg 1 \
-        --max-duration 600 \
-        --causal 1 \
-        --chunk-size 16 \
-        --left-context-frames 128 \
-        --decoding-method greedy_search
-
-    # chunk-wise streaming decoding
-    ./zipformer/streaming_decode.py \
-        --exp-dir ./zipformer/exp \
-        --epoch 9999 \
-        --avg 1 \
-        --max-duration 600 \
-        --causal 1 \
-        --chunk-size 16 \
-        --left-context-frames 128 \
-        --decoding-method greedy_search
-
-Check ./pretrained.py for its usage.
-
-Note: If you don't want to train a model from scratch, we have
-provided one for you. You can get it at
-
-- non-streaming model:
-https://huggingface.co/pkufool/icefall-asr-zipformer-wenetspeech-20230615
-
-- streaming model:
-https://huggingface.co/pkufool/icefall-asr-zipformer-streaming-wenetspeech-20230615
-
-with the following commands:
-
-    sudo apt-get install git-lfs
-    git lfs install
-    git clone https://huggingface.co/pkufool/icefall-asr-zipformer-wenetspeech-20230615
-    git clone https://huggingface.co/pkufool/icefall-asr-zipformer-streaming-wenetspeech-20230615
-    # You will find the pre-trained models in exp dir
-"""
-
-import argparse
-import logging
-from pathlib import Path
-from typing import List, Tuple
-
-import torch
-from scaling_converter import convert_scaled_to_non_scaled
-from torch import Tensor, nn
-from train 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 make_pad_mask, str2bool
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    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="zipformer/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(
-        "--jit",
-        type=str2bool,
-        default=False,
-        help="""True to save a model after applying torch.jit.script.
-        It will generate a file named jit_script.pt.
-        Check ./jit_pretrained.py for how to use it.
-        """,
-    )
-
-    parser.add_argument(
-        "--context-size",
-        type=int,
-        default=2,
-        help="The context size in the decoder. 1 means bigram; 2 means tri-gram",
-    )
-
-    add_model_arguments(parser)
-
-    return parser
-
-
-class EncoderModel(nn.Module):
-    """A wrapper for encoder and encoder_embed"""
-
-    def __init__(self, encoder: nn.Module, encoder_embed: nn.Module) -> None:
-        super().__init__()
-        self.encoder = encoder
-        self.encoder_embed = encoder_embed
-
-    def forward(
-        self, features: Tensor, feature_lengths: Tensor
-    ) -> Tuple[Tensor, Tensor]:
-        """
-        Args:
-            features: (N, T, C)
-            feature_lengths: (N,)
-        """
-        x, x_lens = self.encoder_embed(features, feature_lengths)
-
-        src_key_padding_mask = make_pad_mask(x_lens)
-        x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
-
-        encoder_out, encoder_out_lens = self.encoder(x, x_lens, src_key_padding_mask)
-        encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
-
-        return encoder_out, encoder_out_lens
-
-
-class StreamingEncoderModel(nn.Module):
-    """A wrapper for encoder and encoder_embed"""
-
-    def __init__(self, encoder: nn.Module, encoder_embed: nn.Module) -> None:
-        super().__init__()
-        assert len(encoder.chunk_size) == 1, encoder.chunk_size
-        assert len(encoder.left_context_frames) == 1, encoder.left_context_frames
-        self.chunk_size = encoder.chunk_size[0]
-        self.left_context_len = encoder.left_context_frames[0]
-
-        # The encoder_embed subsample features (T - 7) // 2
-        # The ConvNeXt module needs (7 - 1) // 2 = 3 frames of right padding after subsampling
-        self.pad_length = 7 + 2 * 3
-
-        self.encoder = encoder
-        self.encoder_embed = encoder_embed
-
-    def forward(
-        self, features: Tensor, feature_lengths: Tensor, states: List[Tensor]
-    ) -> Tuple[Tensor, Tensor, List[Tensor]]:
-        """Streaming forward for encoder_embed and encoder.
-
-        Args:
-            features: (N, T, C)
-            feature_lengths: (N,)
-            states: a list of Tensors
-
-        Returns encoder outputs, output lengths, and updated states.
-        """
-        chunk_size = self.chunk_size
-        left_context_len = self.left_context_len
-
-        cached_embed_left_pad = states[-2]
-        x, x_lens, new_cached_embed_left_pad = self.encoder_embed.streaming_forward(
-            x=features,
-            x_lens=feature_lengths,
-            cached_left_pad=cached_embed_left_pad,
-        )
-        assert x.size(1) == chunk_size, (x.size(1), chunk_size)
-
-        src_key_padding_mask = make_pad_mask(x_lens)
-
-        # processed_mask is used to mask out initial states
-        processed_mask = torch.arange(left_context_len, device=x.device).expand(
-            x.size(0), left_context_len
-        )
-        processed_lens = states[-1]  # (batch,)
-        # (batch, left_context_size)
-        processed_mask = (processed_lens.unsqueeze(1) <= processed_mask).flip(1)
-        # Update processed lengths
-        new_processed_lens = processed_lens + x_lens
-
-        # (batch, left_context_size + chunk_size)
-        src_key_padding_mask = torch.cat([processed_mask, src_key_padding_mask], dim=1)
-
-        x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
-        encoder_states = states[:-2]
-
-        (
-            encoder_out,
-            encoder_out_lens,
-            new_encoder_states,
-        ) = self.encoder.streaming_forward(
-            x=x,
-            x_lens=x_lens,
-            states=encoder_states,
-            src_key_padding_mask=src_key_padding_mask,
-        )
-        encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
-
-        new_states = new_encoder_states + [
-            new_cached_embed_left_pad,
-            new_processed_lens,
-        ]
-        return encoder_out, encoder_out_lens, new_states
-
-    @torch.jit.export
-    def get_init_states(
-        self,
-        batch_size: int = 1,
-        device: torch.device = torch.device("cpu"),
-    ) -> List[torch.Tensor]:
-        """
-        Returns a list of cached tensors of all encoder layers. For layer-i, states[i*6:(i+1)*6]
-        is (cached_key, cached_nonlin_attn, cached_val1, cached_val2, cached_conv1, cached_conv2).
-        states[-2] is the cached left padding for ConvNeXt module,
-        of shape (batch_size, num_channels, left_pad, num_freqs)
-        states[-1] is processed_lens of shape (batch,), which records the number
-        of processed frames (at 50hz frame rate, after encoder_embed) for each sample in batch.
-        """
-        states = self.encoder.get_init_states(batch_size, device)
-
-        embed_states = self.encoder_embed.get_init_states(batch_size, device)
-        states.append(embed_states)
-
-        processed_lens = torch.zeros(batch_size, dtype=torch.int32, device=device)
-        states.append(processed_lens)
-
-        return states
-
-
-@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")
-    # if torch.cuda.is_available():
-    #     device = torch.device("cuda", 0)
-
-    logging.info(f"device: {device}")
-
-    lexicon = Lexicon(params.lang_dir)
-
-    params.blank_id = lexicon.token_table["<blk>"]
-    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.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.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.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.load_state_dict(
-                average_checkpoints_with_averaged_model(
-                    filename_start=filename_start,
-                    filename_end=filename_end,
-                    device=device,
-                )
-            )
-
-    model.eval()
-
-    if params.jit is True:
-        convert_scaled_to_non_scaled(model, inplace=True)
-        # We won't use the forward() method of the model in C++, so just ignore
-        # it here.
-        # Otherwise, one of its arguments is a ragged tensor and is not
-        # torch scriptabe.
-        model.__class__.forward = torch.jit.ignore(model.__class__.forward)
-
-        # Wrap encoder and encoder_embed as a module
-        if params.causal:
-            model.encoder = StreamingEncoderModel(model.encoder, model.encoder_embed)
-            chunk_size = model.encoder.chunk_size
-            left_context_len = model.encoder.left_context_len
-            filename = f"jit_script_chunk_{chunk_size}_left_{left_context_len}.pt"
-        else:
-            model.encoder = EncoderModel(model.encoder, model.encoder_embed)
-            filename = "jit_script.pt"
-
-        logging.info("Using torch.jit.script")
-        model = torch.jit.script(model)
-        model.save(str(params.exp_dir / filename))
-        logging.info(f"Saved to {filename}")
-    else:
-        logging.info("Not using torchscript. Export model.state_dict()")
-        # Save it using a format so that it can be loaded
-        # by :func:`load_checkpoint`
-        filename = params.exp_dir / "pretrained.pt"
-        torch.save({"model": model.state_dict()}, str(filename))
-        logging.info(f"Saved to {filename}")
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()

egs/wenetspeech/ASR/zipformer/export.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/export.py

egs/wenetspeech/ASR/zipformer/jit_pretrained.py

@@ -1,281 +0,0 @@
-#!/usr/bin/env python3
-# Copyright 2021-2023 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.
-"""
-This script loads torchscript models, exported by `torch.jit.script()`
-and uses them to decode waves.
-You can use the following command to get the exported models:
-
-./zipformer/export.py \
-  --exp-dir ./zipformer/exp \
-  --lang-dir data/lang_char \
-  --epoch 30 \
-  --avg 9 \
-  --jit 1
-
-Usage of this script:
-
-./zipformer/jit_pretrained.py \
-  --nn-model-filename ./zipformer/exp/cpu_jit.pt \
-  --lang-dir data/lang_char \
-  /path/to/foo.wav \
-  /path/to/bar.wav
-"""
-
-import argparse
-import logging
-import math
-from typing import List
-
-import kaldifeat
-import sentencepiece as spm
-import torch
-import torchaudio
-from torch.nn.utils.rnn import pad_sequence
-
-from icefall.lexicon import Lexicon
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--nn-model-filename",
-        type=str,
-        required=True,
-        help="Path to the torchscript model jit_script.pt",
-    )
-
-    parser.add_argument(
-        "--lang-dir",
-        type=str,
-        default="data/lang_char",
-        help="""Path to lang(containing lexicon, tokens).""",
-    )
-
-    parser.add_argument(
-        "sound_files",
-        type=str,
-        nargs="+",
-        help="The input sound file(s) to transcribe. "
-        "Supported formats are those supported by torchaudio.load(). "
-        "For example, wav and flac are supported. "
-        "The sample rate has to be 16kHz.",
-    )
-
-    return parser
-
-
-def read_sound_files(
-    filenames: List[str], expected_sample_rate: float = 16000
-) -> 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 greedy_search(
-    model: torch.jit.ScriptModule,
-    encoder_out: torch.Tensor,
-    encoder_out_lens: torch.Tensor,
-) -> List[List[int]]:
-    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
-    Args:
-      model:
-        The transducer model.
-      encoder_out:
-        A 3-D tensor of shape (N, T, C)
-      encoder_out_lens:
-        A 1-D tensor of shape (N,).
-    Returns:
-      Return the decoded results for each utterance.
-    """
-    assert encoder_out.ndim == 3
-    assert encoder_out.size(0) >= 1, encoder_out.size(0)
-
-    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
-        input=encoder_out,
-        lengths=encoder_out_lens.cpu(),
-        batch_first=True,
-        enforce_sorted=False,
-    )
-
-    device = encoder_out.device
-    blank_id = 0  # hard-code to 0
-
-    batch_size_list = packed_encoder_out.batch_sizes.tolist()
-    N = encoder_out.size(0)
-
-    assert torch.all(encoder_out_lens > 0), encoder_out_lens
-    assert N == batch_size_list[0], (N, batch_size_list)
-
-    context_size = model.decoder.context_size
-    hyps = [[blank_id] * context_size for _ in range(N)]
-
-    decoder_input = torch.tensor(
-        hyps,
-        device=device,
-        dtype=torch.int64,
-    )  # (N, context_size)
-
-    decoder_out = model.decoder(
-        decoder_input,
-        need_pad=torch.tensor([False]),
-    ).squeeze(1)
-
-    offset = 0
-    for batch_size in batch_size_list:
-        start = offset
-        end = offset + batch_size
-        current_encoder_out = packed_encoder_out.data[start:end]
-        current_encoder_out = current_encoder_out
-        # current_encoder_out's shape: (batch_size, encoder_out_dim)
-        offset = end
-
-        decoder_out = decoder_out[:batch_size]
-
-        logits = model.joiner(
-            current_encoder_out,
-            decoder_out,
-        )
-        # logits'shape (batch_size, vocab_size)
-
-        assert logits.ndim == 2, logits.shape
-        y = logits.argmax(dim=1).tolist()
-        emitted = False
-        for i, v in enumerate(y):
-            if v != blank_id:
-                hyps[i].append(v)
-                emitted = True
-        if emitted:
-            # update decoder output
-            decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
-            decoder_input = torch.tensor(
-                decoder_input,
-                device=device,
-                dtype=torch.int64,
-            )
-            decoder_out = model.decoder(
-                decoder_input,
-                need_pad=torch.tensor([False]),
-            )
-            decoder_out = decoder_out.squeeze(1)
-
-    sorted_ans = [h[context_size:] for h in hyps]
-    ans = []
-    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
-    for i in range(N):
-        ans.append(sorted_ans[unsorted_indices[i]])
-
-    return ans
-
-
-@torch.no_grad()
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-    logging.info(vars(args))
-
-    device = torch.device("cpu")
-    if torch.cuda.is_available():
-        device = torch.device("cuda", 0)
-
-    logging.info(f"device: {device}")
-
-    model = torch.jit.load(args.nn_model_filename)
-
-    model.eval()
-
-    model.to(device)
-
-    lexicon = Lexicon(args.lang_dir)
-
-    logging.info("Constructing Fbank computer")
-    opts = kaldifeat.FbankOptions()
-    opts.device = device
-    opts.frame_opts.dither = 0
-    opts.frame_opts.snip_edges = False
-    opts.frame_opts.samp_freq = 16000
-    opts.mel_opts.num_bins = 80
-
-    fbank = kaldifeat.Fbank(opts)
-
-    logging.info(f"Reading sound files: {args.sound_files}")
-    waves = read_sound_files(
-        filenames=args.sound_files,
-    )
-    waves = [w.to(device) for w in waves]
-
-    logging.info("Decoding started")
-    features = fbank(waves)
-    feature_lengths = [f.size(0) for f in features]
-
-    features = pad_sequence(
-        features,
-        batch_first=True,
-        padding_value=math.log(1e-10),
-    )
-
-    feature_lengths = torch.tensor(feature_lengths, device=device)
-
-    encoder_out, encoder_out_lens = model.encoder(
-        features=features,
-        feature_lengths=feature_lengths,
-    )
-
-    hyps = greedy_search(
-        model=model,
-        encoder_out=encoder_out,
-        encoder_out_lens=encoder_out_lens,
-    )
-
-    def token_ids_to_words(token_ids: List[int]) -> str:
-        text = ""
-        for i in token_ids:
-            text += lexicon.token_table[i]
-        return text.replace("▁", " ").strip()
-
-    s = "\n"
-    for filename, hyp in zip(args.sound_files, hyps):
-        words = token_ids_to_words(hyp)
-        s += f"{filename}:\n{words}\n\n"
-    logging.info(s)
-
-    logging.info("Decoding Done")
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()

egs/wenetspeech/ASR/zipformer/jit_pretrained.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/jit_pretrained.py

egs/wenetspeech/ASR/zipformer/jit_pretrained_streaming.py

@@ -1,277 +0,0 @@
-#!/usr/bin/env python3
-# flake8: noqa
-# Copyright      2022-2023  Xiaomi Corp.        (authors: Fangjun Kuang, Zengwei Yao)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-This script loads torchscript models exported by `torch.jit.script()`
-and uses them to decode waves.
-You can use the following command to get the exported models:
-
-./zipformer/export.py \
-  --exp-dir ./zipformer/exp \
-  --causal 1 \
-  --chunk-size 16 \
-  --left-context-frames 128 \
-  --lang-dir data/lang_char \
-  --epoch 30 \
-  --avg 9 \
-  --jit 1
-
-Usage of this script:
-
-./zipformer/jit_pretrained_streaming.py \
-  --nn-model-filename ./zipformer/exp-causal/jit_script_chunk_16_left_128.pt \
-  --lang-dir data/lang_char \
-  /path/to/foo.wav \
-"""
-
-import argparse
-import logging
-import math
-from typing import List, Optional
-
-import kaldifeat
-import sentencepiece as spm
-import torch
-import torchaudio
-from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature
-from torch.nn.utils.rnn import pad_sequence
-
-from icefall.lexicon import Lexicon
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--nn-model-filename",
-        type=str,
-        required=True,
-        help="Path to the torchscript model cpu_jit.pt",
-    )
-
-    parser.add_argument(
-        "--lang-dir",
-        type=str,
-        default="data/lang_char",
-        help="""Path to lang(containing lexicon, tokens).""",
-    )
-
-    parser.add_argument(
-        "--sample-rate",
-        type=int,
-        default=16000,
-        help="The sample rate of the input sound file",
-    )
-
-    parser.add_argument(
-        "sound_file",
-        type=str,
-        help="The input sound file(s) to transcribe. "
-        "Supported formats are those supported by torchaudio.load(). "
-        "For example, wav and flac are supported. "
-        "The sample rate has to be 16kHz.",
-    )
-
-    return parser
-
-
-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 greedy_search(
-    decoder: torch.jit.ScriptModule,
-    joiner: torch.jit.ScriptModule,
-    encoder_out: torch.Tensor,
-    decoder_out: Optional[torch.Tensor] = None,
-    hyp: Optional[List[int]] = None,
-    device: torch.device = torch.device("cpu"),
-):
-    assert encoder_out.ndim == 2
-    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, device=device).unsqueeze(0)
-        # decoder_input.shape (1,, 1 context_size)
-        decoder_out = decoder(decoder_input, torch.tensor([False])).squeeze(1)
-    else:
-        assert decoder_out.ndim == 2
-        assert hyp is not None, hyp
-
-    T = encoder_out.size(0)
-    for i in range(T):
-        cur_encoder_out = encoder_out[i : i + 1]
-        joiner_out = joiner(cur_encoder_out, decoder_out).squeeze(0)
-        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, device=device
-            ).unsqueeze(0)
-            decoder_out = decoder(decoder_input, torch.tensor([False])).squeeze(1)
-
-    return hyp, decoder_out
-
-
-def create_streaming_feature_extractor(sample_rate) -> 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 = sample_rate
-    opts.mel_opts.num_bins = 80
-    return OnlineFbank(opts)
-
-
-@torch.no_grad()
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-    logging.info(vars(args))
-
-    device = torch.device("cpu")
-    if torch.cuda.is_available():
-        device = torch.device("cuda", 0)
-
-    logging.info(f"device: {device}")
-
-    model = torch.jit.load(args.nn_model_filename)
-    model.eval()
-    model.to(device)
-
-    encoder = model.encoder
-    decoder = model.decoder
-    joiner = model.joiner
-
-    lexicon = Lexicon(args.lang_dir)
-
-    logging.info("Constructing Fbank computer")
-    online_fbank = create_streaming_feature_extractor(args.sample_rate)
-
-    logging.info(f"Reading sound files: {args.sound_file}")
-    wave_samples = read_sound_files(
-        filenames=[args.sound_file],
-        expected_sample_rate=args.sample_rate,
-    )[0]
-    logging.info(wave_samples.shape)
-
-    logging.info("Decoding started")
-
-    chunk_length = encoder.chunk_size * 2
-    T = chunk_length + encoder.pad_length
-
-    logging.info(f"chunk_length: {chunk_length}")
-    logging.info(f"T: {T}")
-
-    states = encoder.get_init_states(device=device)
-
-    tail_padding = torch.zeros(int(0.3 * args.sample_rate), dtype=torch.float32)
-
-    wave_samples = torch.cat([wave_samples, tail_padding])
-
-    chunk = int(0.25 * args.sample_rate)  # 0.2 second
-    num_processed_frames = 0
-
-    hyp = None
-    decoder_out = None
-
-    start = 0
-    while start < wave_samples.numel():
-        logging.info(f"{start}/{wave_samples.numel()}")
-        end = min(start + chunk, wave_samples.numel())
-        samples = wave_samples[start:end]
-        start += chunk
-        online_fbank.accept_waveform(
-            sampling_rate=args.sample_rate,
-            waveform=samples,
-        )
-        while online_fbank.num_frames_ready - num_processed_frames >= T:
-            frames = []
-            for i in range(T):
-                frames.append(online_fbank.get_frame(num_processed_frames + i))
-            frames = torch.cat(frames, dim=0).to(device).unsqueeze(0)
-            x_lens = torch.tensor([T], dtype=torch.int32, device=device)
-            encoder_out, out_lens, states = encoder(
-                features=frames,
-                feature_lengths=x_lens,
-                states=states,
-            )
-            num_processed_frames += chunk_length
-
-            hyp, decoder_out = greedy_search(
-                decoder, joiner, encoder_out.squeeze(0), decoder_out, hyp, device=device
-            )
-
-    context_size = 2
-
-    text = ""
-    for i in hyp[context_size:]:
-        text += lexicon.token_table[i]
-    text = text.replace("▁", " ").strip()
-
-    logging.info(args.sound_file)
-    logging.info(text)
-
-    logging.info("Decoding Done")
-
-
-torch.set_num_threads(4)
-torch.set_num_interop_threads(1)
-torch._C._jit_set_profiling_executor(False)
-torch._C._jit_set_profiling_mode(False)
-torch._C._set_graph_executor_optimize(False)
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()

egs/wenetspeech/ASR/zipformer/jit_pretrained_streaming.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/jit_pretrained_streaming.py

egs/wenetspeech/ASR/zipformer/onnx_check.py

@@ -1,235 +0,0 @@
-#!/usr/bin/env python3
-#
-# Copyright 2022 Xiaomi Corporation (Author: Fangjun Kuang)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-"""
-This script checks that exported onnx models produce the same output
-with the given torchscript model for the same input.
-
-We use the pre-trained model from
-https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless5_offline/
-as an example to show how to use this file.
-
-1. Download the pre-trained model
-
-cd egs/wenetspeech/ASR
-
-repo_url=https://huggingface.co/luomingshuang/icefall_asr_wenetspeech_pruned_transducer_stateless5_offline/
-GIT_LFS_SKIP_SMUDGE=1 git clone $repo_url
-repo=$(basename $repo_url)
-
-pushd $repo
-git lfs pull --include "data/lang_char/Linv.pt"
-git lfs pull --include "exp/pretrained_epoch_4_avg_1.pt"
-git lfs pull --include "exp/cpu_jit_epoch_4_avg_1_torch.1.7.1.pt"
-
-cd exp
-ln -s pretrained_epoch_9_avg_1_torch.1.7.1.pt epoch-99.pt
-popd
-
-2. Export the model to ONNX
-
-./pruned_transducer_stateless5/export-onnx.py \
-  --lang-dir $repo/data/lang_char \
-  --epoch 99 \
-  --avg 1 \
-  --use-averaged-model 0 \
-  --exp-dir $repo/exp \
-  --num-encoder-layers 24 \
-  --dim-feedforward 1536 \
-  --nhead 8 \
-  --encoder-dim 384 \
-  --decoder-dim 512 \
-  --joiner-dim 512
-
-It will generate the following 3 files inside $repo/exp:
-
-  - encoder-epoch-99-avg-1.onnx
-  - decoder-epoch-99-avg-1.onnx
-  - joiner-epoch-99-avg-1.onnx
-
-4. Run this file
-
-./pruned_transducer_stateless5/onnx_check.py \
-  --jit-filename $repo/exp/cpu_jit_epoch_4_avg_1_torch.1.7.1.pt \
-  --onnx-encoder-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
-  --onnx-decoder-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
-  --onnx-joiner-filename $repo/exp/joiner-epoch-99-avg-1.onnx
-"""
-
-import argparse
-import logging
-
-import torch
-from onnx_pretrained import OnnxModel
-
-from icefall import is_module_available
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--jit-filename",
-        required=True,
-        type=str,
-        help="Path to the torchscript model",
-    )
-
-    parser.add_argument(
-        "--onnx-encoder-filename",
-        required=True,
-        type=str,
-        help="Path to the onnx encoder model",
-    )
-
-    parser.add_argument(
-        "--onnx-decoder-filename",
-        required=True,
-        type=str,
-        help="Path to the onnx decoder model",
-    )
-
-    parser.add_argument(
-        "--onnx-joiner-filename",
-        required=True,
-        type=str,
-        help="Path to the onnx joiner model",
-    )
-
-    return parser
-
-
-def test_encoder(
-    torch_model: torch.jit.ScriptModule,
-    onnx_model: OnnxModel,
-):
-    C = 80
-    for i in range(3):
-        N = torch.randint(low=1, high=20, size=(1,)).item()
-        T = torch.randint(low=30, high=50, size=(1,)).item()
-        logging.info(f"test_encoder: iter {i}, N={N}, T={T}")
-
-        x = torch.rand(N, T, C)
-        x_lens = torch.randint(low=30, high=T + 1, size=(N,))
-        x_lens[0] = T
-
-        torch_encoder_out, torch_encoder_out_lens = torch_model.encoder(x, x_lens)
-        torch_encoder_out = torch_model.joiner.encoder_proj(torch_encoder_out)
-
-        onnx_encoder_out, onnx_encoder_out_lens = onnx_model.run_encoder(x, x_lens)
-
-        assert torch.allclose(torch_encoder_out, onnx_encoder_out, atol=1e-05), (
-            (torch_encoder_out - onnx_encoder_out).abs().max()
-        )
-
-
-def test_decoder(
-    torch_model: torch.jit.ScriptModule,
-    onnx_model: OnnxModel,
-):
-    context_size = onnx_model.context_size
-    vocab_size = onnx_model.vocab_size
-    for i in range(10):
-        N = torch.randint(1, 100, size=(1,)).item()
-        logging.info(f"test_decoder: iter {i}, N={N}")
-        x = torch.randint(
-            low=1,
-            high=vocab_size,
-            size=(N, context_size),
-            dtype=torch.int64,
-        )
-        torch_decoder_out = torch_model.decoder(x, need_pad=torch.tensor([False]))
-        torch_decoder_out = torch_model.joiner.decoder_proj(torch_decoder_out)
-        torch_decoder_out = torch_decoder_out.squeeze(1)
-
-        onnx_decoder_out = onnx_model.run_decoder(x)
-        assert torch.allclose(torch_decoder_out, onnx_decoder_out, atol=1e-4), (
-            (torch_decoder_out - onnx_decoder_out).abs().max()
-        )
-
-
-def test_joiner(
-    torch_model: torch.jit.ScriptModule,
-    onnx_model: OnnxModel,
-):
-    encoder_dim = torch_model.joiner.encoder_proj.weight.shape[1]
-    decoder_dim = torch_model.joiner.decoder_proj.weight.shape[1]
-    for i in range(10):
-        N = torch.randint(1, 100, size=(1,)).item()
-        logging.info(f"test_joiner: iter {i}, N={N}")
-        encoder_out = torch.rand(N, encoder_dim)
-        decoder_out = torch.rand(N, decoder_dim)
-
-        projected_encoder_out = torch_model.joiner.encoder_proj(encoder_out)
-        projected_decoder_out = torch_model.joiner.decoder_proj(decoder_out)
-
-        torch_joiner_out = torch_model.joiner(encoder_out, decoder_out)
-        onnx_joiner_out = onnx_model.run_joiner(
-            projected_encoder_out, projected_decoder_out
-        )
-
-        assert torch.allclose(torch_joiner_out, onnx_joiner_out, atol=1e-4), (
-            (torch_joiner_out - onnx_joiner_out).abs().max()
-        )
-
-
-@torch.no_grad()
-def main():
-    args = get_parser().parse_args()
-    logging.info(vars(args))
-
-    torch_model = torch.jit.load(args.jit_filename)
-
-    onnx_model = OnnxModel(
-        encoder_model_filename=args.onnx_encoder_filename,
-        decoder_model_filename=args.onnx_decoder_filename,
-        joiner_model_filename=args.onnx_joiner_filename,
-    )
-
-    logging.info("Test encoder")
-    test_encoder(torch_model, onnx_model)
-
-    logging.info("Test decoder")
-    test_decoder(torch_model, onnx_model)
-
-    logging.info("Test joiner")
-    test_joiner(torch_model, onnx_model)
-    logging.info("Finished checking ONNX models")
-
-
-torch.set_num_threads(1)
-torch.set_num_interop_threads(1)
-
-# See https://github.com/pytorch/pytorch/issues/38342
-# and https://github.com/pytorch/pytorch/issues/33354
-#
-# If we don't do this, the delay increases whenever there is
-# a new request that changes the actual batch size.
-# If you use `py-spy dump --pid <server-pid> --native`, you will
-# see a lot of time is spent in re-compiling the torch script model.
-torch._C._jit_set_profiling_executor(False)
-torch._C._jit_set_profiling_mode(False)
-torch._C._set_graph_executor_optimize(False)
-if __name__ == "__main__":
-    torch.manual_seed(20220727)
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()

egs/wenetspeech/ASR/zipformer/onnx_check.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/onnx_check.py

egs/wenetspeech/ASR/zipformer/onnx_pretrained-streaming.py

@@ -1,544 +0,0 @@
-#!/usr/bin/env python3
-# Copyright      2023  Xiaomi Corp.        (authors: Fangjun Kuang)
-# Copyright      2023  Danqing Fu (danqing.fu@gmail.com)
-
-"""
-This script loads ONNX models exported by ./export-onnx-streaming.py
-and uses them to decode waves.
-
-We use the pre-trained model from
-https://huggingface.co/Zengwei/icefall-asr-librispeech-streaming-zipformer-2023-05-17
-as an example to show how to use this file.
-
-1. Download the pre-trained model
-
-cd egs/librispeech/ASR
-
-repo_url=https://huggingface.co/Zengwei/icefall-asr-librispeech-streaming-zipformer-2023-05-17
-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
-
-2. Export the model to ONNX
-
-./zipformer/export-onnx-streaming.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
-  --use-averaged-model 0 \
-  --epoch 99 \
-  --avg 1 \
-  --exp-dir $repo/exp \
-  --num-encoder-layers "2,2,3,4,3,2" \
-  --downsampling-factor "1,2,4,8,4,2" \
-  --feedforward-dim "512,768,1024,1536,1024,768" \
-  --num-heads "4,4,4,8,4,4" \
-  --encoder-dim "192,256,384,512,384,256" \
-  --query-head-dim 32 \
-  --value-head-dim 12 \
-  --pos-head-dim 4 \
-  --pos-dim 48 \
-  --encoder-unmasked-dim "192,192,256,256,256,192" \
-  --cnn-module-kernel "31,31,15,15,15,31" \
-  --decoder-dim 512 \
-  --joiner-dim 512 \
-  --causal True \
-  --chunk-size 16 \
-  --left-context-frames 64
-
-It will generate the following 3 files inside $repo/exp:
-
-  - encoder-epoch-99-avg-1.onnx
-  - decoder-epoch-99-avg-1.onnx
-  - joiner-epoch-99-avg-1.onnx
-
-3. Run this file with the exported ONNX models
-
-./zipformer/onnx_pretrained-streaming.py \
-  --encoder-model-filename $repo/exp/encoder-epoch-99-avg-1.onnx \
-  --decoder-model-filename $repo/exp/decoder-epoch-99-avg-1.onnx \
-  --joiner-model-filename $repo/exp/joiner-epoch-99-avg-1.onnx \
-  --tokens $repo/data/lang_bpe_500/tokens.txt \
-  $repo/test_wavs/1089-134686-0001.wav
-
-Note: Even though this script only supports decoding a single file,
-the exported ONNX models do support batch processing.
-"""
-
-import argparse
-import logging
-from typing import Dict, List, Optional, Tuple
-
-import k2
-import numpy as np
-import onnxruntime as ort
-import torch
-import torchaudio
-from kaldifeat import FbankOptions, OnlineFbank, OnlineFeature
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--encoder-model-filename",
-        type=str,
-        required=True,
-        help="Path to the encoder onnx model. ",
-    )
-
-    parser.add_argument(
-        "--decoder-model-filename",
-        type=str,
-        required=True,
-        help="Path to the decoder onnx model. ",
-    )
-
-    parser.add_argument(
-        "--joiner-model-filename",
-        type=str,
-        required=True,
-        help="Path to the joiner onnx model. ",
-    )
-
-    parser.add_argument(
-        "--tokens",
-        type=str,
-        help="""Path to tokens.txt.""",
-    )
-
-    parser.add_argument(
-        "sound_file",
-        type=str,
-        help="The input sound file to transcribe. "
-        "Supported formats are those supported by torchaudio.load(). "
-        "For example, wav and flac are supported. "
-        "The sample rate has to be 16kHz.",
-    )
-
-    return parser
-
-
-class OnnxModel:
-    def __init__(
-        self,
-        encoder_model_filename: str,
-        decoder_model_filename: str,
-        joiner_model_filename: str,
-    ):
-        session_opts = ort.SessionOptions()
-        session_opts.inter_op_num_threads = 1
-        session_opts.intra_op_num_threads = 1
-
-        self.session_opts = session_opts
-
-        self.init_encoder(encoder_model_filename)
-        self.init_decoder(decoder_model_filename)
-        self.init_joiner(joiner_model_filename)
-
-    def init_encoder(self, encoder_model_filename: str):
-        self.encoder = ort.InferenceSession(
-            encoder_model_filename,
-            sess_options=self.session_opts,
-        )
-        self.init_encoder_states()
-
-    def init_encoder_states(self, batch_size: int = 1):
-        encoder_meta = self.encoder.get_modelmeta().custom_metadata_map
-        logging.info(f"encoder_meta={encoder_meta}")
-
-        model_type = encoder_meta["model_type"]
-        assert model_type == "zipformer2", model_type
-
-        decode_chunk_len = int(encoder_meta["decode_chunk_len"])
-        T = int(encoder_meta["T"])
-
-        num_encoder_layers = encoder_meta["num_encoder_layers"]
-        encoder_dims = encoder_meta["encoder_dims"]
-        cnn_module_kernels = encoder_meta["cnn_module_kernels"]
-        left_context_len = encoder_meta["left_context_len"]
-        query_head_dims = encoder_meta["query_head_dims"]
-        value_head_dims = encoder_meta["value_head_dims"]
-        num_heads = encoder_meta["num_heads"]
-
-        def to_int_list(s):
-            return list(map(int, s.split(",")))
-
-        num_encoder_layers = to_int_list(num_encoder_layers)
-        encoder_dims = to_int_list(encoder_dims)
-        cnn_module_kernels = to_int_list(cnn_module_kernels)
-        left_context_len = to_int_list(left_context_len)
-        query_head_dims = to_int_list(query_head_dims)
-        value_head_dims = to_int_list(value_head_dims)
-        num_heads = to_int_list(num_heads)
-
-        logging.info(f"decode_chunk_len: {decode_chunk_len}")
-        logging.info(f"T: {T}")
-        logging.info(f"num_encoder_layers: {num_encoder_layers}")
-        logging.info(f"encoder_dims: {encoder_dims}")
-        logging.info(f"cnn_module_kernels: {cnn_module_kernels}")
-        logging.info(f"left_context_len: {left_context_len}")
-        logging.info(f"query_head_dims: {query_head_dims}")
-        logging.info(f"value_head_dims: {value_head_dims}")
-        logging.info(f"num_heads: {num_heads}")
-
-        num_encoders = len(num_encoder_layers)
-
-        self.states = []
-        for i in range(num_encoders):
-            num_layers = num_encoder_layers[i]
-            key_dim = query_head_dims[i] * num_heads[i]
-            embed_dim = encoder_dims[i]
-            nonlin_attn_head_dim = 3 * embed_dim // 4
-            value_dim = value_head_dims[i] * num_heads[i]
-            conv_left_pad = cnn_module_kernels[i] // 2
-
-            for layer in range(num_layers):
-                cached_key = torch.zeros(
-                    left_context_len[i], batch_size, key_dim
-                ).numpy()
-                cached_nonlin_attn = torch.zeros(
-                    1, batch_size, left_context_len[i], nonlin_attn_head_dim
-                ).numpy()
-                cached_val1 = torch.zeros(
-                    left_context_len[i], batch_size, value_dim
-                ).numpy()
-                cached_val2 = torch.zeros(
-                    left_context_len[i], batch_size, value_dim
-                ).numpy()
-                cached_conv1 = torch.zeros(batch_size, embed_dim, conv_left_pad).numpy()
-                cached_conv2 = torch.zeros(batch_size, embed_dim, conv_left_pad).numpy()
-                self.states += [
-                    cached_key,
-                    cached_nonlin_attn,
-                    cached_val1,
-                    cached_val2,
-                    cached_conv1,
-                    cached_conv2,
-                ]
-        embed_states = torch.zeros(batch_size, 128, 3, 19).numpy()
-        self.states.append(embed_states)
-        processed_lens = torch.zeros(batch_size, dtype=torch.int64).numpy()
-        self.states.append(processed_lens)
-
-        self.num_encoders = num_encoders
-
-        self.segment = T
-        self.offset = decode_chunk_len
-
-    def init_decoder(self, decoder_model_filename: str):
-        self.decoder = ort.InferenceSession(
-            decoder_model_filename,
-            sess_options=self.session_opts,
-        )
-
-        decoder_meta = self.decoder.get_modelmeta().custom_metadata_map
-        self.context_size = int(decoder_meta["context_size"])
-        self.vocab_size = int(decoder_meta["vocab_size"])
-
-        logging.info(f"context_size: {self.context_size}")
-        logging.info(f"vocab_size: {self.vocab_size}")
-
-    def init_joiner(self, joiner_model_filename: str):
-        self.joiner = ort.InferenceSession(
-            joiner_model_filename,
-            sess_options=self.session_opts,
-        )
-
-        joiner_meta = self.joiner.get_modelmeta().custom_metadata_map
-        self.joiner_dim = int(joiner_meta["joiner_dim"])
-
-        logging.info(f"joiner_dim: {self.joiner_dim}")
-
-    def _build_encoder_input_output(
-        self,
-        x: torch.Tensor,
-    ) -> Tuple[Dict[str, np.ndarray], List[str]]:
-        encoder_input = {"x": x.numpy()}
-        encoder_output = ["encoder_out"]
-
-        def build_inputs_outputs(tensors, i):
-            assert len(tensors) == 6, len(tensors)
-
-            # (downsample_left, batch_size, key_dim)
-            name = f"cached_key_{i}"
-            encoder_input[name] = tensors[0]
-            encoder_output.append(f"new_{name}")
-
-            # (1, batch_size, downsample_left, nonlin_attn_head_dim)
-            name = f"cached_nonlin_attn_{i}"
-            encoder_input[name] = tensors[1]
-            encoder_output.append(f"new_{name}")
-
-            # (downsample_left, batch_size, value_dim)
-            name = f"cached_val1_{i}"
-            encoder_input[name] = tensors[2]
-            encoder_output.append(f"new_{name}")
-
-            # (downsample_left, batch_size, value_dim)
-            name = f"cached_val2_{i}"
-            encoder_input[name] = tensors[3]
-            encoder_output.append(f"new_{name}")
-
-            # (batch_size, embed_dim, conv_left_pad)
-            name = f"cached_conv1_{i}"
-            encoder_input[name] = tensors[4]
-            encoder_output.append(f"new_{name}")
-
-            # (batch_size, embed_dim, conv_left_pad)
-            name = f"cached_conv2_{i}"
-            encoder_input[name] = tensors[5]
-            encoder_output.append(f"new_{name}")
-
-        for i in range(len(self.states[:-2]) // 6):
-            build_inputs_outputs(self.states[i * 6 : (i + 1) * 6], i)
-
-        # (batch_size, channels, left_pad, freq)
-        name = "embed_states"
-        embed_states = self.states[-2]
-        encoder_input[name] = embed_states
-        encoder_output.append(f"new_{name}")
-
-        # (batch_size,)
-        name = "processed_lens"
-        processed_lens = self.states[-1]
-        encoder_input[name] = processed_lens
-        encoder_output.append(f"new_{name}")
-
-        return encoder_input, encoder_output
-
-    def _update_states(self, states: List[np.ndarray]):
-        self.states = states
-
-    def run_encoder(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Args:
-          x:
-            A 3-D tensor of shape (N, T, C)
-        Returns:
-          Return a 3-D tensor of shape (N, T', joiner_dim) where
-          T' is usually equal to ((T-7)//2+1)//2
-        """
-        encoder_input, encoder_output_names = self._build_encoder_input_output(x)
-
-        out = self.encoder.run(encoder_output_names, encoder_input)
-
-        self._update_states(out[1:])
-
-        return torch.from_numpy(out[0])
-
-    def run_decoder(self, decoder_input: torch.Tensor) -> torch.Tensor:
-        """
-        Args:
-          decoder_input:
-            A 2-D tensor of shape (N, context_size)
-        Returns:
-          Return a 2-D tensor of shape (N, joiner_dim)
-        """
-        out = self.decoder.run(
-            [self.decoder.get_outputs()[0].name],
-            {self.decoder.get_inputs()[0].name: decoder_input.numpy()},
-        )[0]
-
-        return torch.from_numpy(out)
-
-    def run_joiner(
-        self, encoder_out: torch.Tensor, decoder_out: torch.Tensor
-    ) -> torch.Tensor:
-        """
-        Args:
-          encoder_out:
-            A 2-D tensor of shape (N, joiner_dim)
-          decoder_out:
-            A 2-D tensor of shape (N, joiner_dim)
-        Returns:
-          Return a 2-D tensor of shape (N, vocab_size)
-        """
-        out = self.joiner.run(
-            [self.joiner.get_outputs()[0].name],
-            {
-                self.joiner.get_inputs()[0].name: encoder_out.numpy(),
-                self.joiner.get_inputs()[1].name: decoder_out.numpy(),
-            },
-        )[0]
-
-        return torch.from_numpy(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].contiguous())
-    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: OnnxModel,
-    encoder_out: torch.Tensor,
-    context_size: int,
-    decoder_out: Optional[torch.Tensor] = None,
-    hyp: Optional[List[int]] = None,
-) -> List[int]:
-    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
-    Args:
-      model:
-        The transducer model.
-      encoder_out:
-        A 3-D tensor of shape (1, T, joiner_dim)
-      context_size:
-        The context size of the decoder model.
-      decoder_out:
-        Optional. Decoder output of the previous chunk.
-      hyp:
-        Decoding results for previous chunks.
-    Returns:
-      Return the decoded results so far.
-    """
-
-    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.int64)
-        decoder_out = model.run_decoder(decoder_input)
-    else:
-        assert hyp is not None, hyp
-
-    encoder_out = encoder_out.squeeze(0)
-    T = encoder_out.size(0)
-    for t in range(T):
-        cur_encoder_out = encoder_out[t : t + 1]
-        joiner_out = model.run_joiner(cur_encoder_out, decoder_out).squeeze(0)
-        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.int64)
-            decoder_out = model.run_decoder(decoder_input)
-
-    return hyp, decoder_out
-
-
-@torch.no_grad()
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-    logging.info(vars(args))
-
-    model = OnnxModel(
-        encoder_model_filename=args.encoder_model_filename,
-        decoder_model_filename=args.decoder_model_filename,
-        joiner_model_filename=args.joiner_model_filename,
-    )
-
-    sample_rate = 16000
-
-    logging.info("Constructing Fbank computer")
-    online_fbank = create_streaming_feature_extractor()
-
-    logging.info(f"Reading sound files: {args.sound_file}")
-    waves = read_sound_files(
-        filenames=[args.sound_file],
-        expected_sample_rate=sample_rate,
-    )[0]
-
-    tail_padding = torch.zeros(int(0.3 * sample_rate), dtype=torch.float32)
-    wave_samples = torch.cat([waves, tail_padding])
-
-    num_processed_frames = 0
-    segment = model.segment
-    offset = model.offset
-
-    context_size = model.context_size
-    hyp = None
-    decoder_out = None
-
-    chunk = int(1 * sample_rate)  # 1 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)
-            frames = frames.unsqueeze(0)
-            encoder_out = model.run_encoder(frames)
-            hyp, decoder_out = greedy_search(
-                model,
-                encoder_out,
-                context_size,
-                decoder_out,
-                hyp,
-            )
-
-    symbol_table = k2.SymbolTable.from_file(args.tokens)
-
-    text = ""
-    for i in hyp[context_size:]:
-        text += symbol_table[i]
-    text = text.replace("▁", " ").strip()
-
-    logging.info(args.sound_file)
-    logging.info(text)
-
-    logging.info("Decoding Done")
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()

egs/wenetspeech/ASR/zipformer/onnx_pretrained-streaming.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/onnx_pretrained-streaming.py

egs/wenetspeech/ASR/zipformer/onnx_pretrained.py

@@ -1,417 +0,0 @@
-#!/usr/bin/env python3
-# Copyright      2022  Xiaomi Corp.        (authors: Fangjun Kuang)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-This script loads ONNX models and uses them to decode waves.
-You can use the following command to get the exported models:
-
-We use the pre-trained model from
-https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
-as an example to show how to use this file.
-
-1. Download the pre-trained model
-
-cd egs/librispeech/ASR
-
-repo_url=https://huggingface.co/csukuangfj/icefall-asr-librispeech-pruned-transducer-stateless3-2022-05-13
-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-iter-1224000-avg-14.pt"
-
-cd exp
-ln -s pretrained-iter-1224000-avg-14.pt epoch-9999.pt
-popd
-
-2. Export the model to ONNX
-
-./pruned_transducer_stateless3/export-onnx.py \
-  --bpe-model $repo/data/lang_bpe_500/bpe.model \
-  --epoch 9999 \
-  --avg 1 \
-  --exp-dir $repo/exp/
-
-It will generate the following 3 files inside $repo/exp:
-
-  - encoder-epoch-9999-avg-1.onnx
-  - decoder-epoch-9999-avg-1.onnx
-  - joiner-epoch-9999-avg-1.onnx
-
-3. Run this file
-
-./pruned_transducer_stateless3/onnx_pretrained.py \
-  --encoder-model-filename $repo/exp/encoder-epoch-9999-avg-1.onnx \
-  --decoder-model-filename $repo/exp/decoder-epoch-9999-avg-1.onnx \
-  --joiner-model-filename $repo/exp/joiner-epoch-9999-avg-1.onnx \
-  --tokens $repo/data/lang_bpe_500/tokens.txt \
-  $repo/test_wavs/1089-134686-0001.wav \
-  $repo/test_wavs/1221-135766-0001.wav \
-  $repo/test_wavs/1221-135766-0002.wav
-"""
-
-import argparse
-import logging
-import math
-from typing import List, Tuple
-
-import k2
-import kaldifeat
-import onnxruntime as ort
-import torch
-import torchaudio
-from torch.nn.utils.rnn import pad_sequence
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--encoder-model-filename",
-        type=str,
-        required=True,
-        help="Path to the encoder onnx model. ",
-    )
-
-    parser.add_argument(
-        "--decoder-model-filename",
-        type=str,
-        required=True,
-        help="Path to the decoder onnx model. ",
-    )
-
-    parser.add_argument(
-        "--joiner-model-filename",
-        type=str,
-        required=True,
-        help="Path to the joiner onnx model. ",
-    )
-
-    parser.add_argument(
-        "--tokens",
-        type=str,
-        help="""Path to tokens.txt.""",
-    )
-
-    parser.add_argument(
-        "sound_files",
-        type=str,
-        nargs="+",
-        help="The input sound file(s) to transcribe. "
-        "Supported formats are those supported by torchaudio.load(). "
-        "For example, wav and flac are supported. "
-        "The sample rate has to be 16kHz.",
-    )
-
-    parser.add_argument(
-        "--sample-rate",
-        type=int,
-        default=16000,
-        help="The sample rate of the input sound file",
-    )
-
-    return parser
-
-
-class OnnxModel:
-    def __init__(
-        self,
-        encoder_model_filename: str,
-        decoder_model_filename: str,
-        joiner_model_filename: str,
-    ):
-        session_opts = ort.SessionOptions()
-        session_opts.inter_op_num_threads = 1
-        session_opts.intra_op_num_threads = 4
-
-        self.session_opts = session_opts
-
-        self.init_encoder(encoder_model_filename)
-        self.init_decoder(decoder_model_filename)
-        self.init_joiner(joiner_model_filename)
-
-    def init_encoder(self, encoder_model_filename: str):
-        self.encoder = ort.InferenceSession(
-            encoder_model_filename,
-            sess_options=self.session_opts,
-        )
-
-    def init_decoder(self, decoder_model_filename: str):
-        self.decoder = ort.InferenceSession(
-            decoder_model_filename,
-            sess_options=self.session_opts,
-        )
-
-        decoder_meta = self.decoder.get_modelmeta().custom_metadata_map
-        self.context_size = int(decoder_meta["context_size"])
-        self.vocab_size = int(decoder_meta["vocab_size"])
-
-        logging.info(f"context_size: {self.context_size}")
-        logging.info(f"vocab_size: {self.vocab_size}")
-
-    def init_joiner(self, joiner_model_filename: str):
-        self.joiner = ort.InferenceSession(
-            joiner_model_filename,
-            sess_options=self.session_opts,
-        )
-
-        joiner_meta = self.joiner.get_modelmeta().custom_metadata_map
-        self.joiner_dim = int(joiner_meta["joiner_dim"])
-
-        logging.info(f"joiner_dim: {self.joiner_dim}")
-
-    def run_encoder(
-        self,
-        x: torch.Tensor,
-        x_lens: torch.Tensor,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        Args:
-          x:
-            A 3-D tensor of shape (N, T, C)
-          x_lens:
-            A 2-D tensor of shape (N,). Its dtype is torch.int64
-        Returns:
-          Return a tuple containing:
-            - encoder_out, its shape is (N, T', joiner_dim)
-            - encoder_out_lens, its shape is (N,)
-        """
-        out = self.encoder.run(
-            [
-                self.encoder.get_outputs()[0].name,
-                self.encoder.get_outputs()[1].name,
-            ],
-            {
-                self.encoder.get_inputs()[0].name: x.numpy(),
-                self.encoder.get_inputs()[1].name: x_lens.numpy(),
-            },
-        )
-        return torch.from_numpy(out[0]), torch.from_numpy(out[1])
-
-    def run_decoder(self, decoder_input: torch.Tensor) -> torch.Tensor:
-        """
-        Args:
-          decoder_input:
-            A 2-D tensor of shape (N, context_size)
-        Returns:
-          Return a 2-D tensor of shape (N, joiner_dim)
-        """
-        out = self.decoder.run(
-            [self.decoder.get_outputs()[0].name],
-            {self.decoder.get_inputs()[0].name: decoder_input.numpy()},
-        )[0]
-
-        return torch.from_numpy(out)
-
-    def run_joiner(
-        self, encoder_out: torch.Tensor, decoder_out: torch.Tensor
-    ) -> torch.Tensor:
-        """
-        Args:
-          encoder_out:
-            A 2-D tensor of shape (N, joiner_dim)
-          decoder_out:
-            A 2-D tensor of shape (N, joiner_dim)
-        Returns:
-          Return a 2-D tensor of shape (N, vocab_size)
-        """
-        out = self.joiner.run(
-            [self.joiner.get_outputs()[0].name],
-            {
-                self.joiner.get_inputs()[0].name: encoder_out.numpy(),
-                self.joiner.get_inputs()[1].name: decoder_out.numpy(),
-            },
-        )[0]
-
-        return torch.from_numpy(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 greedy_search(
-    model: OnnxModel,
-    encoder_out: torch.Tensor,
-    encoder_out_lens: torch.Tensor,
-) -> List[List[int]]:
-    """Greedy search in batch mode. It hardcodes --max-sym-per-frame=1.
-    Args:
-      model:
-        The transducer model.
-      encoder_out:
-        A 3-D tensor of shape (N, T, joiner_dim)
-      encoder_out_lens:
-        A 1-D tensor of shape (N,).
-    Returns:
-      Return the decoded results for each utterance.
-    """
-    assert encoder_out.ndim == 3, encoder_out.shape
-    assert encoder_out.size(0) >= 1, encoder_out.size(0)
-
-    packed_encoder_out = torch.nn.utils.rnn.pack_padded_sequence(
-        input=encoder_out,
-        lengths=encoder_out_lens.cpu(),
-        batch_first=True,
-        enforce_sorted=False,
-    )
-
-    blank_id = 0  # hard-code to 0
-
-    batch_size_list = packed_encoder_out.batch_sizes.tolist()
-    N = encoder_out.size(0)
-
-    assert torch.all(encoder_out_lens > 0), encoder_out_lens
-    assert N == batch_size_list[0], (N, batch_size_list)
-
-    context_size = model.context_size
-    hyps = [[blank_id] * context_size for _ in range(N)]
-
-    decoder_input = torch.tensor(
-        hyps,
-        dtype=torch.int64,
-    )  # (N, context_size)
-
-    decoder_out = model.run_decoder(decoder_input)
-
-    offset = 0
-    for batch_size in batch_size_list:
-        start = offset
-        end = offset + batch_size
-        current_encoder_out = packed_encoder_out.data[start:end]
-        # current_encoder_out's shape: (batch_size, joiner_dim)
-        offset = end
-
-        decoder_out = decoder_out[:batch_size]
-        logits = model.run_joiner(current_encoder_out, decoder_out)
-
-        # logits'shape (batch_size, vocab_size)
-
-        assert logits.ndim == 2, logits.shape
-        y = logits.argmax(dim=1).tolist()
-        emitted = False
-        for i, v in enumerate(y):
-            if v != blank_id:
-                hyps[i].append(v)
-                emitted = True
-        if emitted:
-            # update decoder output
-            decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
-            decoder_input = torch.tensor(
-                decoder_input,
-                dtype=torch.int64,
-            )
-            decoder_out = model.run_decoder(decoder_input)
-
-    sorted_ans = [h[context_size:] for h in hyps]
-    ans = []
-    unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
-    for i in range(N):
-        ans.append(sorted_ans[unsorted_indices[i]])
-
-    return ans
-
-
-@torch.no_grad()
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-    logging.info(vars(args))
-    model = OnnxModel(
-        encoder_model_filename=args.encoder_model_filename,
-        decoder_model_filename=args.decoder_model_filename,
-        joiner_model_filename=args.joiner_model_filename,
-    )
-
-    logging.info("Constructing Fbank computer")
-    opts = kaldifeat.FbankOptions()
-    opts.device = "cpu"
-    opts.frame_opts.dither = 0
-    opts.frame_opts.snip_edges = False
-    opts.frame_opts.samp_freq = args.sample_rate
-    opts.mel_opts.num_bins = 80
-
-    fbank = kaldifeat.Fbank(opts)
-
-    logging.info(f"Reading sound files: {args.sound_files}")
-    waves = read_sound_files(
-        filenames=args.sound_files,
-        expected_sample_rate=args.sample_rate,
-    )
-
-    logging.info("Decoding started")
-    features = fbank(waves)
-    feature_lengths = [f.size(0) for f in features]
-
-    features = pad_sequence(
-        features,
-        batch_first=True,
-        padding_value=math.log(1e-10),
-    )
-
-    feature_lengths = torch.tensor(feature_lengths, dtype=torch.int64)
-    encoder_out, encoder_out_lens = model.run_encoder(features, feature_lengths)
-
-    hyps = greedy_search(
-        model=model,
-        encoder_out=encoder_out,
-        encoder_out_lens=encoder_out_lens,
-    )
-    s = "\n"
-
-    symbol_table = k2.SymbolTable.from_file(args.tokens)
-
-    def token_ids_to_words(token_ids: List[int]) -> str:
-        text = ""
-        for i in token_ids:
-            text += symbol_table[i]
-        return text.replace("▁", " ").strip()
-
-    for filename, hyp in zip(args.sound_files, hyps):
-        words = token_ids_to_words(hyp)
-        s += f"{filename}:\n{words}\n"
-    logging.info(s)
-
-    logging.info("Decoding Done")
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()

egs/wenetspeech/ASR/zipformer/onnx_pretrained.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/onnx_pretrained.py

egs/wenetspeech/ASR/zipformer/pretrained.py

@@ -1,378 +0,0 @@
-#!/usr/bin/env python3
-# Copyright      2021-2023  Xiaomi Corp.        (authors: Fangjun Kuang, Zengwei Yao)
-#
-# See ../../../../LICENSE for clarification regarding multiple authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-This script loads a checkpoint and uses it to decode waves.
-You can generate the checkpoint with the following command:
-
-- For non-streaming model:
-
-./zipformer/export.py \
-  --exp-dir ./zipformer/exp \
-  --lang-dir data/lang_char \
-  --epoch 30 \
-  --avg 9
-
-- For streaming model:
-
-./zipformer/export.py \
-  --exp-dir ./zipformer/exp \
-  --causal 1 \
-  --lang-dir data/lang_char \
-  --epoch 30 \
-  --avg 9
-
-Usage of this script:
-
-- For non-streaming model:
-
-(1) greedy search
-./zipformer/pretrained.py \
-  --checkpoint ./zipformer/exp/pretrained.pt \
-  --lang-dir data/lang_char \
-  --method greedy_search \
-  /path/to/foo.wav \
-  /path/to/bar.wav
-
-(2) modified beam search
-./zipformer/pretrained.py \
-  --checkpoint ./zipformer/exp/pretrained.pt \
-  --lang-dir data/lang_char \
-  --method modified_beam_search \
-  /path/to/foo.wav \
-  /path/to/bar.wav
-
-(3) fast beam search
-./zipformer/pretrained.py \
-  --checkpoint ./zipformer/exp/pretrained.pt \
-  --lang-dir data/lang_char \
-  --method fast_beam_search \
-  /path/to/foo.wav \
-  /path/to/bar.wav
-
-- For streaming model:
-
-(1) greedy search
-./zipformer/pretrained.py \
-  --checkpoint ./zipformer/exp/pretrained.pt \
-  --causal 1 \
-  --chunk-size 16 \
-  --left-context-frames 128 \
-  --lang-dir data/lang_char \
-  --method greedy_search \
-  /path/to/foo.wav \
-  /path/to/bar.wav
-
-(2) modified beam search
-./zipformer/pretrained.py \
-  --checkpoint ./zipformer/exp/pretrained.pt \
-  --causal 1 \
-  --chunk-size 16 \
-  --left-context-frames 128 \
-  --lang-dir data/lang_char \
-  --method modified_beam_search \
-  /path/to/foo.wav \
-  /path/to/bar.wav
-
-(3) fast beam search
-./zipformer/pretrained.py \
-  --checkpoint ./zipformer/exp/pretrained.pt \
-  --causal 1 \
-  --chunk-size 16 \
-  --left-context-frames 128 \
-  --lang-dir data/lang_char \
-  --method fast_beam_search \
-  /path/to/foo.wav \
-  /path/to/bar.wav
-
-
-You can also use `./zipformer/exp/epoch-xx.pt`.
-
-Note: ./zipformer/exp/pretrained.pt is generated by ./zipformer/export.py
-"""
-
-
-import argparse
-import logging
-import math
-from typing import List
-
-import k2
-import kaldifeat
-import torch
-import torchaudio
-from beam_search import (
-    fast_beam_search_one_best,
-    greedy_search_batch,
-    modified_beam_search,
-)
-from torch.nn.utils.rnn import pad_sequence
-from train import add_model_arguments, get_params, get_transducer_model
-
-from icefall.lexicon import Lexicon
-from icefall.utils import make_pad_mask
-
-
-def get_parser():
-    parser = argparse.ArgumentParser(
-        formatter_class=argparse.ArgumentDefaultsHelpFormatter
-    )
-
-    parser.add_argument(
-        "--checkpoint",
-        type=str,
-        required=True,
-        help="Path to the checkpoint. "
-        "The checkpoint is assumed to be saved by "
-        "icefall.checkpoint.save_checkpoint().",
-    )
-
-    parser.add_argument(
-        "--lang-dir",
-        type=str,
-        default="data/lang_char",
-        help="""Path to lang.
-        """,
-    )
-
-    parser.add_argument(
-        "--method",
-        type=str,
-        default="greedy_search",
-        help="""Possible values are:
-          - greedy_search
-          - modified_beam_search
-          - fast_beam_search
-        """,
-    )
-
-    parser.add_argument(
-        "sound_files",
-        type=str,
-        nargs="+",
-        help="The input sound file(s) to transcribe. "
-        "Supported formats are those supported by torchaudio.load(). "
-        "For example, wav and flac are supported. "
-        "The sample rate has to be 16kHz.",
-    )
-
-    parser.add_argument(
-        "--sample-rate",
-        type=int,
-        default=16000,
-        help="The sample rate of the input sound file",
-    )
-
-    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 --method is beam_search or
-        modified_beam_search.""",
-    )
-
-    parser.add_argument(
-        "--beam",
-        type=float,
-        default=4,
-        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 --method is fast_beam_search""",
-    )
-
-    parser.add_argument(
-        "--max-contexts",
-        type=int,
-        default=4,
-        help="""Used only when --method is fast_beam_search""",
-    )
-
-    parser.add_argument(
-        "--max-states",
-        type=int,
-        default=8,
-        help="""Used only when --method is fast_beam_search""",
-    )
-
-    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
-        --method is greedy_search.
-        """,
-    )
-
-    add_model_arguments(parser)
-
-    return parser
-
-
-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
-
-
-@torch.no_grad()
-def main():
-    parser = get_parser()
-    args = parser.parse_args()
-
-    params = get_params()
-
-    params.update(vars(args))
-
-    lexicon = Lexicon(params.lang_dir)
-    params.blank_id = lexicon.token_table["<blk>"]
-    params.vocab_size = max(lexicon.tokens) + 1
-
-    logging.info(f"{params}")
-
-    device = torch.device("cpu")
-    if torch.cuda.is_available():
-        device = torch.device("cuda", 0)
-
-    logging.info(f"device: {device}")
-
-    if params.causal:
-        assert (
-            "," not in params.chunk_size
-        ), "chunk_size should be one value in decoding."
-        assert (
-            "," not in params.left_context_frames
-        ), "left_context_frames should be one value in decoding."
-
-    logging.info("Creating model")
-    model = get_transducer_model(params)
-
-    num_param = sum([p.numel() for p in model.parameters()])
-    logging.info(f"Number of model parameters: {num_param}")
-
-    checkpoint = torch.load(args.checkpoint, map_location="cpu")
-    model.load_state_dict(checkpoint["model"], strict=False)
-    model.to(device)
-    model.eval()
-
-    logging.info("Constructing Fbank computer")
-    opts = kaldifeat.FbankOptions()
-    opts.device = device
-    opts.frame_opts.dither = 0
-    opts.frame_opts.snip_edges = False
-    opts.frame_opts.samp_freq = params.sample_rate
-    opts.mel_opts.num_bins = params.feature_dim
-
-    fbank = kaldifeat.Fbank(opts)
-
-    logging.info(f"Reading sound files: {params.sound_files}")
-    waves = read_sound_files(
-        filenames=params.sound_files, expected_sample_rate=params.sample_rate
-    )
-    waves = [w.to(device) for w in waves]
-
-    logging.info("Decoding started")
-    features = fbank(waves)
-    feature_lengths = [f.size(0) for f in features]
-
-    features = pad_sequence(features, batch_first=True, padding_value=math.log(1e-10))
-    feature_lengths = torch.tensor(feature_lengths, device=device)
-
-    # model forward
-    x, x_lens = model.encoder_embed(features, feature_lengths)
-
-    src_key_padding_mask = make_pad_mask(x_lens)
-    x = x.permute(1, 0, 2)  # (N, T, C) -> (T, N, C)
-
-    encoder_out, encoder_out_lens = model.encoder(x, x_lens, src_key_padding_mask)
-    encoder_out = encoder_out.permute(1, 0, 2)  # (T, N, C) ->(N, T, C)
-
-    hyps = []
-    msg = f"Using {params.method}"
-    logging.info(msg)
-
-    if params.method == "fast_beam_search":
-        decoding_graph = k2.trivial_graph(params.vocab_size - 1, device=device)
-        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 i in range(encoder_out.size(0)):
-            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
-    elif params.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 i in range(encoder_out.size(0)):
-            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
-    elif params.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 i in range(encoder_out.size(0)):
-            hyps.append([lexicon.token_table[idx] for idx in hyp_tokens[i]])
-    else:
-        raise ValueError(f"Unsupported method: {params.method}")
-
-    s = "\n"
-    for filename, hyp in zip(params.sound_files, hyps):
-        words = " ".join(hyp)
-        s += f"{filename}:\n{words}\n\n"
-    logging.info(s)
-
-    logging.info("Decoding Done")
-
-
-if __name__ == "__main__":
-    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
-
-    logging.basicConfig(format=formatter, level=logging.INFO)
-    main()

egs/wenetspeech/ASR/zipformer/pretrained.py

@@ -0,0 +1 @@
+../../../librispeech/ASR/zipformer/pretrained.py