Merge pull request #240 from alibaba-damo-academy/dev_wjm

zhifu gao

2023-03-16 1cca7562552ac99f381903b1bee1cac21e39efeb

Merge pull request #240 from alibaba-damo-academy/dev_wjm

update

 egs/aishell/conformer/run.sh

@@ -52,7 +52,7 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@

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 @@
  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 @@

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 @@

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_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_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_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 @@

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_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_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_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 @@
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 @@
- 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 @@
    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 @@
- 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 @@
    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 @@
- 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 @@
    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 @@
    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 @@
    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 @@
- 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 @@
    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 @@
    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 @@
- 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 @@
    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 @@
- 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 @@
    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 @@
- 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 @@
    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 @@
- 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 @@
    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 @@
- 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 @@
    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 @@
- 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 @@
    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 @@

    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 @@

    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 @@

    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 @@

    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 @@

    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 @@

    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 @@

    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 @@
        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 @@
    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/sond_inference.py

@@ -42,7 +42,7 @@
    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 @@

    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 @@
        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 @@
            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 @@
                    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 @@
                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 @@

    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/tasks/abs_task.py

@@ -639,12 +639,12 @@
                 "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 @@
            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 @@
            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 @@
                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 @@
                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 @@
        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 @@
        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 @@
                        },
                        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 @@
                            ],
                            "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 @@
                    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 @@
                        # 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 @@
                        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 @@

                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))