Merge branch 'alibaba-damo-academy:main' into main

egs/aishell/conformer/run.sh

@@ -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

egs/aishell/data2vec_paraformer_finetune/run.sh

@@ -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

egs/aishell/data2vec_transformer_finetune/run.sh

@@ -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

egs/aishell/paraformer/run.sh

@@ -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

egs/aishell/paraformerbert/run.sh

@@ -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

egs/aishell/transformer/run.sh

@@ -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

egs/aishell2/conformer/run.sh

@@ -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

egs/aishell2/paraformer/run.sh

@@ -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

egs/aishell2/paraformerbert/run.sh

@@ -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

egs/aishell2/transformer/run.sh

@@ -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

egs/aishell2/transformerLM/run.sh

@@ -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

egs/alimeeting/diarization/sond/infer_alimeeting_test.py

@@ -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"),

egs/alimeeting/diarization/sond/run.sh

@@ -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 \

egs/alimeeting/diarization/sond/unit_test.py

@@ -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",

egs/callhome/diarization/sond/unit_test.py

@@ -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",

egs/mars/sd/local_run.sh

@@ -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

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

@@ -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

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

@@ -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)

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

@@ -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

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

@@ -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)

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

@@ -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

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

@@ -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)

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

@@ -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)

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

@@ -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)

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

@@ -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

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

@@ -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)

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

@@ -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)

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

@@ -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

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

@@ -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)

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

@@ -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

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

@@ -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)

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

@@ -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

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

@@ -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)

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

@@ -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

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

@@ -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)

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

@@ -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

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

@@ -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)

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

@@ -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

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

@@ -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)

funasr/bin/asr_inference.py

@@ -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), ...]

funasr/bin/asr_inference_mfcca.py

@@ -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), ...]

funasr/bin/asr_inference_paraformer.py

@@ -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), ...]

funasr/bin/asr_inference_paraformer_vad_punc.py

@@ -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), ...]

funasr/bin/asr_inference_rnnt.py

@@ -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), ...]

funasr/bin/asr_inference_uniasr.py

@@ -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), ...]

funasr/bin/asr_inference_uniasr_vad.py

@@ -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), ...]

funasr/bin/diar_inference_launch.py

@@ -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:

funasr/bin/eend_ola_inference.py

@@ -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,

funasr/bin/sond_inference.py

@@ -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)

funasr/bin/sv_inference.py

@@ -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,

funasr/main_funcs/average_nbest_models.py

@@ -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)

funasr/main_funcs/pack_funcs.py

@@ -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)

funasr/modules/eend_ola/encoder.py

@@ -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__()

funasr/runtime/python/README.md

@@ -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     |
+

funasr/tasks/abs_task.py

@@ -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",

funasr/tasks/asr.py

@@ -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)

funasr/tasks/diar.py

@@ -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)

funasr/tasks/sv.py

@@ -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)

funasr/torch_utils/load_pretrained_model.py

@@ -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:

funasr/train/trainer.py

@@ -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))

setup.py

@@ -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",