speech_recognition/FunASR
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Merge branch 'alibaba-damo-academy:main' into main

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Daniel 2023-03-16 11:44:53 +08:00 committed by GitHub
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61 changed files with 126 additions and 104 deletions
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2
egs/aishell/conformer/run.sh
View File

@ -52,7 +52,7 @@ asr_config=conf/train_asr_conformer.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer.yaml
inference_asr_model=valid.acc.ave_10best.pth
inference_asr_model=valid.acc.ave_10best.pb
# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default

2
egs/aishell/data2vec_paraformer_finetune/run.sh
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@ -55,7 +55,7 @@ asr_config=conf/train_asr_paraformer_transformer_12e_6d_3072_768.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer_noctc_1best.yaml
inference_asr_model=valid.acc.ave_10best.pth
inference_asr_model=valid.acc.ave_10best.pb
# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default

2
egs/aishell/data2vec_transformer_finetune/run.sh
View File

@ -55,7 +55,7 @@ asr_config=conf/train_asr_transformer_12e_6d_3072_768.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer.yaml
inference_asr_model=valid.cer_ctc.ave_10best.pth
inference_asr_model=valid.cer_ctc.ave_10best.pb
# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default

2
egs/aishell/paraformer/run.sh
View File

@ -52,7 +52,7 @@ asr_config=conf/train_asr_paraformer_conformer_12e_6d_2048_256.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer_noctc_1best.yaml
inference_asr_model=valid.acc.ave_10best.pth
inference_asr_model=valid.acc.ave_10best.pb
# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default

2
egs/aishell/paraformerbert/run.sh
View File

@ -56,7 +56,7 @@ asr_config=conf/train_asr_paraformerbert_conformer_12e_6d_2048_256.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer_noctc_1best.yaml
inference_asr_model=valid.acc.ave_10best.pth
inference_asr_model=valid.acc.ave_10best.pb
# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default

2
egs/aishell/transformer/run.sh
View File

@ -52,7 +52,7 @@ asr_config=conf/train_asr_conformer.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer.yaml
inference_asr_model=valid.acc.ave_10best.pth
inference_asr_model=valid.acc.ave_10best.pb
# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default

2
egs/aishell2/conformer/run.sh
View File

@ -54,7 +54,7 @@ asr_config=conf/train_asr_conformer.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer.yaml
inference_asr_model=valid.acc.ave_10best.pth
inference_asr_model=valid.acc.ave_10best.pb
# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, e.g., gpuid_list=2,3, the same as training stage by default

2
egs/aishell2/paraformer/run.sh
View File

@ -54,7 +54,7 @@ asr_config=conf/train_asr_paraformer_conformer_20e_1280_320_6d_1280_320.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer_noctc_1best.yaml
inference_asr_model=valid.acc.ave_10best.pth
inference_asr_model=valid.acc.ave_10best.pb
# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, e.g., gpuid_list=2,3, the same as training stage by default

2
egs/aishell2/paraformerbert/run.sh
View File

@ -58,7 +58,7 @@ asr_config=conf/train_asr_paraformerbert_conformer_20e_6d_1280_320.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer_noctc_1best.yaml
inference_asr_model=valid.acc.ave_10best.pth
inference_asr_model=valid.acc.ave_10best.pb
# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, e.g., gpuid_list=2,3, the same as training stage by default

2
egs/aishell2/transformer/run.sh
View File

@ -54,7 +54,7 @@ asr_config=conf/train_asr_transformer.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer.yaml
inference_asr_model=valid.acc.ave_10best.pth
inference_asr_model=valid.acc.ave_10best.pb
# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, e.g., gpuid_list=2,3, the same as training stage by default

2
egs/aishell2/transformerLM/run.sh
View File

@ -34,7 +34,7 @@ exp_dir=./data
tag=exp1
model_dir="baseline_$(basename "${lm_config}" .yaml)_${lang}_${token_type}_${tag}"
lm_exp=${exp_dir}/exp/${model_dir}
inference_lm=valid.loss.ave.pth # Language model path for decoding.
inference_lm=valid.loss.ave.pb # Language model path for decoding.
stage=0
stop_stage=3

2
egs/alimeeting/diarization/sond/infer_alimeeting_test.py
View File

@ -4,7 +4,7 @@ import sys
def main():
diar_config_path = sys.argv[1] if len(sys.argv) > 1 else "sond_fbank.yaml"
diar_model_path = sys.argv[2] if len(sys.argv) > 2 else "sond.pth"
diar_model_path = sys.argv[2] if len(sys.argv) > 2 else "sond.pb"
output_dir = sys.argv[3] if len(sys.argv) > 3 else "./outputs"
data_path_and_name_and_type = [
("data/test_rmsil/feats.scp", "speech", "kaldi_ark"),

6
egs/alimeeting/diarization/sond/run.sh
View File

@ -17,9 +17,9 @@ if [ ${stage} -le 0 ] && [ ${stop_stage} -ge 0 ]; then
echo "Downloading Pre-trained model..."
git clone https://www.modelscope.cn/damo/speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch.git
git clone https://www.modelscope.cn/damo/speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch.git
ln -s speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.pth ./sv.pth
ln -s speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.pb ./sv.pb
cp speech_xvector_sv-zh-cn-cnceleb-16k-spk3465-pytorch/sv.yaml ./sv.yaml
ln -s speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/sond.pth ./sond.pth
ln -s speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/sond.pb ./sond.pb
cp speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/sond_fbank.yaml ./sond_fbank.yaml
cp speech_diarization_sond-zh-cn-alimeeting-16k-n16k4-pytorch/sond.yaml ./sond.yaml
echo "Done."
@ -30,7 +30,7 @@ fi
if [ ${stage} -le 1 ] && [ ${stop_stage} -ge 1 ]; then
echo "Calculating diarization results..."
python infer_alimeeting_test.py sond_fbank.yaml sond.pth outputs
python infer_alimeeting_test.py sond_fbank.yaml sond.pb outputs
python local/convert_label_to_rttm.py \
outputs/labels.txt \
data/test_rmsil/raw_rmsil_map.scp \

8
egs/alimeeting/diarization/sond/unit_test.py
View File

@ -4,7 +4,7 @@ import os
def test_fbank_cpu_infer():
diar_config_path = "config_fbank.yaml"
diar_model_path = "sond.pth"
diar_model_path = "sond.pb"
output_dir = "./outputs"
data_path_and_name_and_type = [
("data/unit_test/test_feats.scp", "speech", "kaldi_ark"),
@ -24,7 +24,7 @@ def test_fbank_cpu_infer():
def test_fbank_gpu_infer():
diar_config_path = "config_fbank.yaml"
diar_model_path = "sond.pth"
diar_model_path = "sond.pb"
output_dir = "./outputs"
data_path_and_name_and_type = [
("data/unit_test/test_feats.scp", "speech", "kaldi_ark"),
@ -45,7 +45,7 @@ def test_fbank_gpu_infer():
def test_wav_gpu_infer():
diar_config_path = "config.yaml"
diar_model_path = "sond.pth"
diar_model_path = "sond.pb"
output_dir = "./outputs"
data_path_and_name_and_type = [
("data/unit_test/test_wav.scp", "speech", "sound"),
@ -66,7 +66,7 @@ def test_wav_gpu_infer():
def test_without_profile_gpu_infer():
diar_config_path = "config.yaml"
diar_model_path = "sond.pth"
diar_model_path = "sond.pb"
output_dir = "./outputs"
raw_inputs = [[
"data/unit_test/raw_inputs/record.wav",

8
egs/callhome/diarization/sond/unit_test.py
View File

@ -4,7 +4,7 @@ import os
def test_fbank_cpu_infer():
diar_config_path = "sond_fbank.yaml"
diar_model_path = "sond.pth"
diar_model_path = "sond.pb"
output_dir = "./outputs"
data_path_and_name_and_type = [
("data/unit_test/test_feats.scp", "speech", "kaldi_ark"),
@ -24,7 +24,7 @@ def test_fbank_cpu_infer():
def test_fbank_gpu_infer():
diar_config_path = "sond_fbank.yaml"
diar_model_path = "sond.pth"
diar_model_path = "sond.pb"
output_dir = "./outputs"
data_path_and_name_and_type = [
("data/unit_test/test_feats.scp", "speech", "kaldi_ark"),
@ -45,7 +45,7 @@ def test_fbank_gpu_infer():
def test_wav_gpu_infer():
diar_config_path = "config.yaml"
diar_model_path = "sond.pth"
diar_model_path = "sond.pb"
output_dir = "./outputs"
data_path_and_name_and_type = [
("data/unit_test/test_wav.scp", "speech", "sound"),
@ -66,7 +66,7 @@ def test_wav_gpu_infer():
def test_without_profile_gpu_infer():
diar_config_path = "config.yaml"
diar_model_path = "sond.pth"
diar_model_path = "sond.pb"
output_dir = "./outputs"
raw_inputs = [[
"data/unit_test/raw_inputs/record.wav",

2
egs/mars/sd/local_run.sh
View File

@ -49,7 +49,7 @@ asr_config=conf/train_asr_conformer.yaml
model_dir="baseline_$(basename "${asr_config}" .yaml)_${feats_type}_${lang}_${token_type}_${tag}"
inference_config=conf/decode_asr_transformer.yaml
inference_asr_model=valid.acc.ave_10best.pth
inference_asr_model=valid.acc.ave_10best.pb
# you can set gpu num for decoding here
gpuid_list=$CUDA_VISIBLE_DEVICES # set gpus for decoding, the same as training stage by default

2
egs_modelscope/asr/data2vec/speech_data2vec_pretrain-paraformer-zh-cn-aishell2-16k/README.md
View File

@ -41,7 +41,7 @@ The decoding results can be found in `$output_dir/1best_recog/text.cer`, which i
- Modify inference related parameters in `infer_after_finetune.py`
- <strong>output_dir:</strong> # result dir
- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed~~~~
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
- Then you can run the pipeline to finetune with:
```python

2
egs_modelscope/asr/data2vec/speech_data2vec_pretrain-paraformer-zh-cn-aishell2-16k/infer_after_finetune.py
View File

@ -48,5 +48,5 @@ if __name__ == '__main__':
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "valid.cer_ctc.ave.pth"
params["decoding_model_name"] = "valid.cer_ctc.ave.pb"
modelscope_infer_after_finetune(params)

2
egs_modelscope/asr/data2vec/speech_data2vec_pretrain-zh-cn-aishell2-16k-pytorch/README.md
View File

@ -41,7 +41,7 @@ The decoding results can be found in `$output_dir/1best_recog/text.cer`, which i
- Modify inference related parameters in `infer_after_finetune.py`
- <strong>output_dir:</strong> # result dir
- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed~~~~
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
- Then you can run the pipeline to finetune with:
```python

2
egs_modelscope/asr/data2vec/speech_data2vec_pretrain-zh-cn-aishell2-16k-pytorch/infer_after_finetune.py
View File

@ -48,5 +48,5 @@ if __name__ == '__main__':
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "valid.cer_ctc.ave.pth"
params["decoding_model_name"] = "valid.cer_ctc.ave.pb"
modelscope_infer_after_finetune(params)

2
egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/README.md
View File

@ -41,7 +41,7 @@ The decoding results can be found in `$output_dir/1best_recog/text.sp.cer` and `
- Modify inference related parameters in `infer_after_finetune.py`
- <strong>output_dir:</strong> # result dir
- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
- Then you can run the pipeline to finetune with:
```python

2
egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer_after_finetune.py
View File

@ -63,5 +63,5 @@ if __name__ == '__main__':
params["required_files"] = ["feats_stats.npz", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./example_data/validation"
params["decoding_model_name"] = "valid.acc.ave.pth"
params["decoding_model_name"] = "valid.acc.ave.pb"
modelscope_infer_after_finetune(params)

2
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-aishell1-vocab8404-pytorch/infer_after_finetune.py
View File

@ -49,5 +49,5 @@ if __name__ == '__main__':
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "valid.acc.ave_10best.pth"
params["decoding_model_name"] = "valid.acc.ave_10best.pb"
modelscope_infer_after_finetune(params)

2
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-aishell2-vocab8404-pytorch/infer_after_finetune.py
View File

@ -49,5 +49,5 @@ if __name__ == '__main__':
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "valid.acc.ave_10best.pth"
params["decoding_model_name"] = "valid.acc.ave_10best.pb"
modelscope_infer_after_finetune(params)

2
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md
View File

@ -41,7 +41,7 @@ The decoding results can be found in `$output_dir/1best_recog/text.cer`, which i
- Modify inference related parameters in `infer_after_finetune.py`
- <strong>output_dir:</strong> # result dir
- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
- Then you can run the pipeline to finetune with:
```python

2
egs_modelscope/asr/paraformer/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer_after_finetune.py
View File

@ -49,5 +49,5 @@ if __name__ == '__main__':
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "valid.acc.ave_10best.pth"
params["decoding_model_name"] = "valid.acc.ave_10best.pb"
modelscope_infer_after_finetune(params)

2
egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-8k-common-vocab8358-tensorflow1/infer_after_finetune.py
View File

@ -49,5 +49,5 @@ if __name__ == '__main__':
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "valid.acc.ave_10best.pth"
params["decoding_model_name"] = "valid.acc.ave_10best.pb"
modelscope_infer_after_finetune(params)

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-offline/README.md
View File

@ -41,7 +41,7 @@ The decoding results can be found in `$output_dir/1best_recog/text.cer`, which i
- Modify inference related parameters in `infer_after_finetune.py`
- <strong>output_dir:</strong> # result dir
- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
- Then you can run the pipeline to finetune with:
```python

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-offline/infer_after_finetune.py
View File

@ -50,5 +50,5 @@ if __name__ == '__main__':
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "20epoch.pth"
params["decoding_model_name"] = "20epoch.pb"
modelscope_infer_after_finetune(params)

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/README.md
View File

@ -41,7 +41,7 @@ The decoding results can be found in `$output_dir/1best_recog/text.cer`, which i
- Modify inference related parameters in `infer_after_finetune.py`
- <strong>output_dir:</strong> # result dir
- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
- Then you can run the pipeline to finetune with:
```python

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-fa-16k-common-vocab1257-pytorch-online/infer_after_finetune.py
View File

@ -50,5 +50,5 @@ if __name__ == '__main__':
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "20epoch.pth"
params["decoding_model_name"] = "20epoch.pb"
modelscope_infer_after_finetune(params)

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/README.md
View File

@ -41,7 +41,7 @@ The decoding results can be found in `$output_dir/1best_recog/text.cer`, which i
- Modify inference related parameters in `infer_after_finetune.py`
- <strong>output_dir:</strong> # result dir
- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
- Then you can run the pipeline to finetune with:
```python

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825/infer_after_finetune.py
View File

@ -50,5 +50,5 @@ if __name__ == '__main__':
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "20epoch.pth"
params["decoding_model_name"] = "20epoch.pb"
modelscope_infer_after_finetune(params)

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/README.md
View File

@ -41,7 +41,7 @@ The decoding results can be found in `$output_dir/1best_recog/text.cer`, which i
- Modify inference related parameters in `infer_after_finetune.py`
- <strong>output_dir:</strong> # result dir
- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
- Then you can run the pipeline to finetune with:
```python

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-offline/infer_after_finetune.py
View File

@ -49,5 +49,5 @@ if __name__ == '__main__':
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "20epoch.pth"
params["decoding_model_name"] = "20epoch.pb"
modelscope_infer_after_finetune(params)

3
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/README.md
View File

@ -41,7 +41,8 @@ The decoding results can be found in `$output_dir/1best_recog/text.cer`, which i
- Modify inference related parameters in `infer_after_finetune.py`
- <strong>output_dir:</strong> # result dir
- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave
.pb`
- Then you can run the pipeline to finetune with:
```python

2
egs_modelscope/asr/uniasr/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/infer_after_finetune.py
View File

@ -49,5 +49,5 @@ if __name__ == '__main__':
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "20epoch.pth"
params["decoding_model_name"] = "20epoch.pb"
modelscope_infer_after_finetune(params)

2
egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md
View File

@ -34,7 +34,7 @@ Or you can use the finetuned model for inference directly.
- Modify inference related parameters in `infer_after_finetune.py`
- <strong>output_dir:</strong> # result dir
- <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pth`
- <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb`
- Then you can run the pipeline to finetune with:
```python

2
egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer_after_finetune.py
View File

@ -53,5 +53,5 @@ if __name__ == '__main__':
params["required_files"] = ["am.mvn", "decoding.yaml", "configuration.json", "punc/punc.pb", "punc/punc.yaml", "vad/vad.mvn", "vad/vad.pb", "vad/vad.yaml"]
params["output_dir"] = "./checkpoint"
params["data_dir"] = "./data/test"
params["decoding_model_name"] = "valid.acc.ave_10best.pth"
params["decoding_model_name"] = "valid.acc.ave_10best.pb"
modelscope_infer_after_finetune(params)

2
funasr/bin/asr_inference.py
View File

@ -52,7 +52,7 @@ class Speech2Text:
Examples:
>>> import soundfile
>>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
>>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2text(audio)
[(text, token, token_int, hypothesis object), ...]

2
funasr/bin/asr_inference_mfcca.py
View File

@ -55,7 +55,7 @@ class Speech2Text:
Examples:
>>> import soundfile
>>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
>>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2text(audio)
[(text, token, token_int, hypothesis object), ...]

2
funasr/bin/asr_inference_paraformer.py
View File

@ -50,7 +50,7 @@ class Speech2Text:
Examples:
>>> import soundfile
>>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
>>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2text(audio)
[(text, token, token_int, hypothesis object), ...]

2
funasr/bin/asr_inference_paraformer_vad_punc.py
View File

@ -58,7 +58,7 @@ class Speech2Text:
Examples:
>>> import soundfile
>>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
>>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2text(audio)
[(text, token, token_int, hypothesis object), ...]

2
funasr/bin/asr_inference_rnnt.py
View File

@ -49,7 +49,7 @@ class Speech2Text:
Examples:
>>> import soundfile
>>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
>>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2text(audio)
[(text, token, token_int, hypothesis object), ...]

2
funasr/bin/asr_inference_uniasr.py
View File

@ -46,7 +46,7 @@ class Speech2Text:
Examples:
>>> import soundfile
>>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
>>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2text(audio)
[(text, token, token_int, hypothesis object), ...]

2
funasr/bin/asr_inference_uniasr_vad.py
View File

@ -46,7 +46,7 @@ class Speech2Text:
Examples:
>>> import soundfile
>>> speech2text = Speech2Text("asr_config.yml", "asr.pth")
>>> speech2text = Speech2Text("asr_config.yml", "asr.pb")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2text(audio)
[(text, token, token_int, hypothesis object), ...]

2
funasr/bin/diar_inference_launch.py
View File

@ -133,7 +133,7 @@ def inference_launch(mode, **kwargs):
param_dict = {
"extract_profile": True,
"sv_train_config": "sv.yaml",
"sv_model_file": "sv.pth",
"sv_model_file": "sv.pb",
}
if "param_dict" in kwargs and kwargs["param_dict"] is not None:
for key in param_dict:

4
funasr/bin/eend_ola_inference.py
View File

@ -35,7 +35,7 @@ class Speech2Diarization:
Examples:
>>> import soundfile
>>> import numpy as np
>>> speech2diar = Speech2Diarization("diar_sond_config.yml", "diar_sond.pth")
>>> speech2diar = Speech2Diarization("diar_sond_config.yml", "diar_sond.pb")
>>> profile = np.load("profiles.npy")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2diar(audio, profile)
@ -209,7 +209,7 @@ def inference_modelscope(
if data_path_and_name_and_type is None and raw_inputs is not None:
if isinstance(raw_inputs, torch.Tensor):
raw_inputs = raw_inputs.numpy()
data_path_and_name_and_type = [raw_inputs[0], "speech", "bytes"]
data_path_and_name_and_type = [raw_inputs[0], "speech", "sound"]
loader = EENDOLADiarTask.build_streaming_iterator(
data_path_and_name_and_type,
dtype=dtype,

2
funasr/bin/sond_inference.py
View File

@ -42,7 +42,7 @@ class Speech2Diarization:
Examples:
>>> import soundfile
>>> import numpy as np
>>> speech2diar = Speech2Diarization("diar_sond_config.yml", "diar_sond.pth")
>>> speech2diar = Speech2Diarization("diar_sond_config.yml", "diar_sond.pb")
>>> profile = np.load("profiles.npy")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2diar(audio, profile)

4
funasr/bin/sv_inference.py
View File

@ -36,7 +36,7 @@ class Speech2Xvector:
Examples:
>>> import soundfile
>>> speech2xvector = Speech2Xvector("sv_config.yml", "sv.pth")
>>> speech2xvector = Speech2Xvector("sv_config.yml", "sv.pb")
>>> audio, rate = soundfile.read("speech.wav")
>>> speech2xvector(audio)
[(text, token, token_int, hypothesis object), ...]
@ -169,7 +169,7 @@ def inference_modelscope(
log_level: Union[int, str] = "INFO",
key_file: Optional[str] = None,
sv_train_config: Optional[str] = "sv.yaml",
sv_model_file: Optional[str] = "sv.pth",
sv_model_file: Optional[str] = "sv.pb",
model_tag: Optional[str] = None,
allow_variable_data_keys: bool = True,
streaming: bool = False,

18
funasr/main_funcs/average_nbest_models.py
View File

@ -66,13 +66,13 @@ def average_nbest_models(
elif n == 1:
# The averaged model is same as the best model
e, _ = epoch_and_values[0]
op = output_dir / f"{e}epoch.pth"
sym_op = output_dir / f"{ph}.{cr}.ave_1best.{suffix}pth"
op = output_dir / f"{e}epoch.pb"
sym_op = output_dir / f"{ph}.{cr}.ave_1best.{suffix}pb"
if sym_op.is_symlink() or sym_op.exists():
sym_op.unlink()
sym_op.symlink_to(op.name)
else:
op = output_dir / f"{ph}.{cr}.ave_{n}best.{suffix}pth"
op = output_dir / f"{ph}.{cr}.ave_{n}best.{suffix}pb"
logging.info(
f"Averaging {n}best models: " f'criterion="{ph}.{cr}": {op}'
)
@ -83,12 +83,12 @@ def average_nbest_models(
if e not in _loaded:
if oss_bucket is None:
_loaded[e] = torch.load(
output_dir / f"{e}epoch.pth",
output_dir / f"{e}epoch.pb",
map_location="cpu",
)
else:
buffer = BytesIO(
oss_bucket.get_object(os.path.join(pai_output_dir, f"{e}epoch.pth")).read())
oss_bucket.get_object(os.path.join(pai_output_dir, f"{e}epoch.pb")).read())
_loaded[e] = torch.load(buffer)
states = _loaded[e]
@ -115,13 +115,13 @@ def average_nbest_models(
else:
buffer = BytesIO()
torch.save(avg, buffer)
oss_bucket.put_object(os.path.join(pai_output_dir, f"{ph}.{cr}.ave_{n}best.{suffix}pth"),
oss_bucket.put_object(os.path.join(pai_output_dir, f"{ph}.{cr}.ave_{n}best.{suffix}pb"),
buffer.getvalue())
# 3. *.*.ave.pth is a symlink to the max ave model
# 3. *.*.ave.pb is a symlink to the max ave model
if oss_bucket is None:
op = output_dir / f"{ph}.{cr}.ave_{max(_nbests)}best.{suffix}pth"
sym_op = output_dir / f"{ph}.{cr}.ave.{suffix}pth"
op = output_dir / f"{ph}.{cr}.ave_{max(_nbests)}best.{suffix}pb"
sym_op = output_dir / f"{ph}.{cr}.ave.{suffix}pb"
if sym_op.is_symlink() or sym_op.exists():
sym_op.unlink()
sym_op.symlink_to(op.name)

4
funasr/main_funcs/pack_funcs.py
View File

@ -191,12 +191,12 @@ def unpack(
Examples:
tarfile:
model.pth
model.pb
some1.file
some2.file
>>> unpack("tarfile", "out")
{'asr_model_file': 'out/model.pth'}
{'asr_model_file': 'out/model.pb'}
"""
input_archive = Path(input_archive)
outpath = Path(outpath)

2
funasr/modules/eend_ola/encoder.py
View File

@ -87,7 +87,7 @@ class EENDOLATransformerEncoder(nn.Module):
n_layers: int,
n_units: int,
e_units: int = 2048,
h: int = 8,
h: int = 4,
dropout_rate: float = 0.1,
use_pos_emb: bool = False):
super(EENDOLATransformerEncoder, self).__init__()

21
funasr/runtime/python/README.md Normal file
View File

@ -0,0 +1,21 @@
Benchmark [Paraformer-large](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) based on Aishell1 test set , the total audio duration is 36108.919 seconds.
(Note: The service has been fully warm up.)
Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz 16core-32processor with avx512_vnni
| concurrent-tasks | processing time(s) | RTF | Speedup Rate |
|:----------------:|:------------------:|:------:|:------------:|
| 1 (onnx fp32) | 2806 | 0.0777 | 12.9 |
| 1 (onnx int8) | 1611 | 0.0446 | 22.4 |
| 8 (onnx fp32) | 538 | 0.0149 | 67.1 |
| 8 (onnx int8) | 210 | 0.0058 | 172.4 |
| 16 (onnx fp32) | 288 | 0.0080 | 125.2 |
| 16 (onnx int8) | 117 | 0.0032 | 309.9 |
| 32 (onnx fp32) | 167 | 0.0046 | 216.5 |
| 32 (onnx int8) | 107 | 0.0030 | 338.0 |
| 64 (onnx fp32) | 158 | 0.0044 | 228.1 |
| 64 (onnx int8) | 82 | 0.0023 | 442.8 |
| 96 (onnx fp32) | 151 | 0.0042 | 238.0 |
| 96 (onnx int8) | 80 | 0.0022 | 452.0 |

8
funasr/tasks/abs_task.py
View File

@ -639,12 +639,12 @@ class AbsTask(ABC):
"and exclude_keys excludes keys of model states for the initialization."
"e.g.\n"
" # Load all parameters"
" --init_param some/where/model.pth\n"
" --init_param some/where/model.pb\n"
" # Load only decoder parameters"
" --init_param some/where/model.pth:decoder:decoder\n"
" --init_param some/where/model.pb:decoder:decoder\n"
" # Load only decoder parameters excluding decoder.embed"
" --init_param some/where/model.pth:decoder:decoder:decoder.embed\n"
" --init_param some/where/model.pth:decoder:decoder:decoder.embed\n",
" --init_param some/where/model.pb:decoder:decoder:decoder.embed\n"
" --init_param some/where/model.pb:decoder:decoder:decoder.embed\n",
)
group.add_argument(
"--ignore_init_mismatch",

4
funasr/tasks/asr.py
View File

@ -826,7 +826,7 @@ class ASRTaskUniASR(ASRTask):
if "model.ckpt-" in model_name or ".bin" in model_name:
model_name_pth = os.path.join(model_dir, model_name.replace('.bin',
'.pb')) if ".bin" in model_name else os.path.join(
model_dir, "{}.pth".format(model_name))
model_dir, "{}.pb".format(model_name))
if os.path.exists(model_name_pth):
logging.info("model_file is load from pth: {}".format(model_name_pth))
model_dict = torch.load(model_name_pth, map_location=device)
@ -1073,7 +1073,7 @@ class ASRTaskParaformer(ASRTask):
if "model.ckpt-" in model_name or ".bin" in model_name:
model_name_pth = os.path.join(model_dir, model_name.replace('.bin',
'.pb')) if ".bin" in model_name else os.path.join(
model_dir, "{}.pth".format(model_name))
model_dir, "{}.pb".format(model_name))
if os.path.exists(model_name_pth):
logging.info("model_file is load from pth: {}".format(model_name_pth))
model_dict = torch.load(model_name_pth, map_location=device)

2
funasr/tasks/diar.py
View File

@ -553,7 +553,7 @@ class DiarTask(AbsTask):
if ".bin" in model_name:
model_name_pth = os.path.join(model_dir, model_name.replace('.bin', '.pb'))
else:
model_name_pth = os.path.join(model_dir, "{}.pth".format(model_name))
model_name_pth = os.path.join(model_dir, "{}.pb".format(model_name))
if os.path.exists(model_name_pth):
logging.info("model_file is load from pth: {}".format(model_name_pth))
model_dict = torch.load(model_name_pth, map_location=device)

2
funasr/tasks/sv.py
View File

@ -501,7 +501,7 @@ class SVTask(AbsTask):
if ".bin" in model_name:
model_name_pth = os.path.join(model_dir, model_name.replace('.bin', '.pb'))
else:
model_name_pth = os.path.join(model_dir, "{}.pth".format(model_name))
model_name_pth = os.path.join(model_dir, "{}.pb".format(model_name))
if os.path.exists(model_name_pth):
logging.info("model_file is load from pth: {}".format(model_name_pth))
model_dict = torch.load(model_name_pth, map_location=device)

10
funasr/torch_utils/load_pretrained_model.py
View File

@ -52,13 +52,13 @@ def load_pretrained_model(
init_param: <file_path>:<src_key>:<dst_key>:<exclude_Keys>
Examples:
>>> load_pretrained_model("somewhere/model.pth", model)
>>> load_pretrained_model("somewhere/model.pth:decoder:decoder", model)
>>> load_pretrained_model("somewhere/model.pth:decoder:decoder:", model)
>>> load_pretrained_model("somewhere/model.pb", model)
>>> load_pretrained_model("somewhere/model.pb:decoder:decoder", model)
>>> load_pretrained_model("somewhere/model.pb:decoder:decoder:", model)
>>> load_pretrained_model(
... "somewhere/model.pth:decoder:decoder:decoder.embed", model
... "somewhere/model.pb:decoder:decoder:decoder.embed", model
... )
>>> load_pretrained_model("somewhere/decoder.pth::decoder", model)
>>> load_pretrained_model("somewhere/decoder.pb::decoder", model)
"""
sps = init_param.split(":", 4)
if len(sps) == 4:

36
funasr/train/trainer.py
View File

@ -205,9 +205,9 @@ class Trainer:
else:
scaler = None
if trainer_options.resume and (output_dir / "checkpoint.pth").exists():
if trainer_options.resume and (output_dir / "checkpoint.pb").exists():
cls.resume(
checkpoint=output_dir / "checkpoint.pth",
checkpoint=output_dir / "checkpoint.pb",
model=model,
optimizers=optimizers,
schedulers=schedulers,
@ -361,7 +361,7 @@ class Trainer:
},
buffer,
)
trainer_options.oss_bucket.put_object(os.path.join(trainer_options.output_dir, "checkpoint.pth"), buffer.getvalue())
trainer_options.oss_bucket.put_object(os.path.join(trainer_options.output_dir, "checkpoint.pb"), buffer.getvalue())
else:
torch.save(
{
@ -374,7 +374,7 @@ class Trainer:
],
"scaler": scaler.state_dict() if scaler is not None else None,
},
output_dir / "checkpoint.pth",
output_dir / "checkpoint.pb",
)
# 5. Save and log the model and update the link to the best model
@ -382,22 +382,22 @@ class Trainer:
buffer = BytesIO()
torch.save(model.state_dict(), buffer)
trainer_options.oss_bucket.put_object(os.path.join(trainer_options.output_dir,
f"{iepoch}epoch.pth"),buffer.getvalue())
f"{iepoch}epoch.pb"),buffer.getvalue())
else:
torch.save(model.state_dict(), output_dir / f"{iepoch}epoch.pth")
torch.save(model.state_dict(), output_dir / f"{iepoch}epoch.pb")
# Creates a sym link latest.pth -> {iepoch}epoch.pth
# Creates a sym link latest.pb -> {iepoch}epoch.pb
if trainer_options.use_pai:
p = os.path.join(trainer_options.output_dir, "latest.pth")
p = os.path.join(trainer_options.output_dir, "latest.pb")
if trainer_options.oss_bucket.object_exists(p):
trainer_options.oss_bucket.delete_object(p)
trainer_options.oss_bucket.copy_object(trainer_options.oss_bucket.bucket_name,
os.path.join(trainer_options.output_dir, f"{iepoch}epoch.pth"), p)
os.path.join(trainer_options.output_dir, f"{iepoch}epoch.pb"), p)
else:
p = output_dir / "latest.pth"
p = output_dir / "latest.pb"
if p.is_symlink() or p.exists():
p.unlink()
p.symlink_to(f"{iepoch}epoch.pth")
p.symlink_to(f"{iepoch}epoch.pb")
_improved = []
for _phase, k, _mode in trainer_options.best_model_criterion:
@ -407,16 +407,16 @@ class Trainer:
# Creates sym links if it's the best result
if best_epoch == iepoch:
if trainer_options.use_pai:
p = os.path.join(trainer_options.output_dir, f"{_phase}.{k}.best.pth")
p = os.path.join(trainer_options.output_dir, f"{_phase}.{k}.best.pb")
if trainer_options.oss_bucket.object_exists(p):
trainer_options.oss_bucket.delete_object(p)
trainer_options.oss_bucket.copy_object(trainer_options.oss_bucket.bucket_name,
os.path.join(trainer_options.output_dir, f"{iepoch}epoch.pth"),p)
os.path.join(trainer_options.output_dir, f"{iepoch}epoch.pb"),p)
else:
p = output_dir / f"{_phase}.{k}.best.pth"
p = output_dir / f"{_phase}.{k}.best.pb"
if p.is_symlink() or p.exists():
p.unlink()
p.symlink_to(f"{iepoch}epoch.pth")
p.symlink_to(f"{iepoch}epoch.pb")
_improved.append(f"{_phase}.{k}")
if len(_improved) == 0:
logging.info("There are no improvements in this epoch")
@ -438,7 +438,7 @@ class Trainer:
type="model",
metadata={"improved": _improved},
)
artifact.add_file(str(output_dir / f"{iepoch}epoch.pth"))
artifact.add_file(str(output_dir / f"{iepoch}epoch.pb"))
aliases = [
f"epoch-{iepoch}",
"best" if best_epoch == iepoch else "",
@ -473,12 +473,12 @@ class Trainer:
for e in range(1, iepoch):
if trainer_options.use_pai:
p = os.path.join(trainer_options.output_dir, f"{e}epoch.pth")
p = os.path.join(trainer_options.output_dir, f"{e}epoch.pb")
if trainer_options.oss_bucket.object_exists(p) and e not in nbests:
trainer_options.oss_bucket.delete_object(p)
_removed.append(str(p))
else:
p = output_dir / f"{e}epoch.pth"
p = output_dir / f"{e}epoch.pb"
if p.exists() and e not in nbests:
p.unlink()
_removed.append(str(p))

2
setup.py
View File

@ -17,7 +17,7 @@ requirements = {
"humanfriendly",
"scipy>=1.4.1",
# "filelock",
"librosa>=0.8.0",
"librosa==0.8.1",
"jamo==0.4.1", # For kss
"PyYAML>=5.1.2",
"soundfile>=0.10.2",