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egs_modelscope/asr/conformer/speech_conformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/infer.py
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New file @@ -0,0 +1,11 @@ from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks inference_pipeline = pipeline( task=Tasks.auto_speech_recognition, model='NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950', model_revision='v3.0.0' ) rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav') print(rec_result) egs_modelscope/asr/mfcca/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/infer_after_finetune.py
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File was renamed from egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/infer.py @@ -8,7 +8,7 @@ task=Tasks.auto_speech_recognition, model="damo/speech_paraformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch", output_dir=output_dir, batch_size=32, batch_size=1, ) rec_result = inference_pipline(audio_in=audio_in) print(rec_result) egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-aishell1-vocab4234-pytorch/infer.py
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egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/infer.py
New file @@ -0,0 +1 @@ ../TEMPLATE/infer.py egs_modelscope/asr/paraformer/speech_paraformer_asr_nat-zh-cn-16k-aishell2-vocab5212-pytorch/infer.sh
New file @@ -0,0 +1 @@ ../TEMPLATE/infer.sh egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/README.md
@@ -1,46 +1,246 @@ # ModelScope Model # Speech Recognition ## How to finetune and infer using a pretrained Paraformer-large Model > **Note**: > The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetine. Here we take the typic models as examples to demonstrate the usage. ### Finetune ## Inference - Modify finetune training related parameters in `finetune.py` - <strong>output_dir:</strong> # result dir - <strong>data_dir:</strong> # the dataset dir needs to include files: train/wav.scp, train/text; validation/wav.scp, validation/text. - <strong>batch_bins:</strong> # batch size - <strong>max_epoch:</strong> # number of training epoch - <strong>lr:</strong> # learning rate - Then you can run the pipeline to finetune with: ### Quick start #### [Paraformer Model](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) ```python python finetune.py from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks inference_pipeline = pipeline( task=Tasks.auto_speech_recognition, model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch', ) rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav') print(rec_result) ``` #### [Paraformer-online Model](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/summary) ```python inference_pipeline = pipeline( task=Tasks.auto_speech_recognition, model='damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online', ) import soundfile speech, sample_rate = soundfile.read("example/asr_example.wav") param_dict = {"cache": dict(), "is_final": False} chunk_stride = 7680# 480ms # first chunk, 480ms speech_chunk = speech[0:chunk_stride] rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict) print(rec_result) # next chunk, 480ms speech_chunk = speech[chunk_stride:chunk_stride+chunk_stride] rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict) print(rec_result) ``` Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/241) #### [UniASR Model](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary) There are three decoding mode for UniASR model(`fast`、`normal`、`offline`), for more model detailes, please refer to [docs](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary) ```python decoding_model = "fast" # "fast"、"normal"、"offline" inference_pipeline = pipeline( task=Tasks.auto_speech_recognition, model='damo/speech_UniASR_asr_2pass-minnan-16k-common-vocab3825', param_dict={"decoding_model": decoding_model}) rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav') print(rec_result) ``` The decoding mode of `fast` and `normal` is fake streaming, which could be used for evaluating of recognition accuracy. Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/151) #### [RNN-T-online model]() Undo #### [MFCCA Model](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary) For more model detailes, please refer to [docs](https://www.modelscope.cn/models/NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950/summary) ```python from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks inference_pipeline = pipeline( task=Tasks.auto_speech_recognition, model='NPU-ASLP/speech_mfcca_asr-zh-cn-16k-alimeeting-vocab4950', model_revision='v3.0.0' ) rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav') print(rec_result) ``` ### Inference #### API-reference ##### Define pipeline - `task`: `Tasks.auto_speech_recognition` - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk - `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU - `ncpu`: `1` (Default), sets the number of threads used for intraop parallelism on CPU - `output_dir`: `None` (Default), the output path of results if set - `batch_size`: `1` (Default), batch size when decoding ##### Infer pipeline - `audio_in`: the input to decode, which could be: - wav_path, `e.g.`: asr_example.wav, - pcm_path, `e.g.`: asr_example.pcm, - audio bytes stream, `e.g.`: bytes data from a microphone - audio sample point，`e.g.`: `audio, rate = soundfile.read("asr_example_zh.wav")`, the dtype is numpy.ndarray or torch.Tensor - wav.scp, kaldi style wav list (`wav_id \t wav_path`), `e.g.`: ```text asr_example1 ./audios/asr_example1.wav asr_example2 ./audios/asr_example2.wav ``` In this case of `wav.scp` input, `output_dir` must be set to save the output results - `audio_fs`: audio sampling rate, only set when audio_in is pcm audio - `output_dir`: None (Default), the output path of results if set Or you can use the finetuned model for inference directly. ### Inference with multi-thread CPUs or multi GPUs FunASR also offer recipes [egs_modelscope/asr/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs. - Setting parameters in `infer.py` - <strong>audio_in:</strong> # support wav, url, bytes, and parsed audio format. - <strong>output_dir:</strong> # If the input format is wav.scp, it needs to be set. - Setting parameters in `infer.sh` - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk - `data_dir`: the dataset dir needs to include `wav.scp`. If `${data_dir}/text` is also exists, CER will be computed - `output_dir`: output dir of the recognition results - `batch_size`: `64` (Default), batch size of inference on gpu - `gpu_inference`: `true` (Default), whether to perform gpu decoding, set false for CPU inference - `gpuid_list`: `0,1` (Default), which gpu_ids are used to infer - `njob`: only used for CPU inference (`gpu_inference`=`false`), `64` (Default), the number of jobs for CPU decoding - `checkpoint_dir`: only used for infer finetuned models, the path dir of finetuned models - `checkpoint_name`: only used for infer finetuned models, `valid.cer_ctc.ave.pb` (Default), which checkpoint is used to infer - `decoding_mode`: `normal` (Default), decoding mode for UniASR model(fast、normal、offline) - `hotword_txt`: `None` (Default), hotword file for contextual paraformer model(the hotword file name ends with .txt") - Then you can run the pipeline to infer with: ```python python infer.py - Decode with multi GPUs: ```shell bash infer.sh \ --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \ --data_dir "./data/test" \ --output_dir "./results" \ --batch_size 64 \ --gpu_inference true \ --gpuid_list "0,1" ``` ### Inference using local finetuned model - Modify inference related parameters in `infer_after_finetune.py` - <strong>output_dir:</strong> # result dir - <strong>data_dir:</strong> # the dataset dir needs to include `test/wav.scp`. If `test/text` is also exists, CER will be computed - <strong>decoding_model_name:</strong> # set the checkpoint name for decoding, e.g., `valid.cer_ctc.ave.pb` - Then you can run the pipeline to finetune with: ```python python infer_after_finetune.py - Decode with multi-thread CPUs: ```shell bash infer.sh \ --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \ --data_dir "./data/test" \ --output_dir "./results" \ --gpu_inference false \ --njob 64 ``` - Results The decoding results can be found in `$output_dir/decoding_results/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set. The decoding results can be found in `$output_dir/1best_recog/text.cer`, which includes recognition results of each sample and the CER metric of the whole test set. If you decode the SpeechIO test sets, you can use textnorm with `stage`=3, and `DETAILS.txt`, `RESULTS.txt` record the results and CER after text normalization. ## Finetune with pipeline ### Quick start [finetune.py](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/finetune.py) ```python import os from modelscope.metainfo import Trainers from modelscope.trainers import build_trainer from modelscope.msdatasets.audio.asr_dataset import ASRDataset def modelscope_finetune(params): if not os.path.exists(params.output_dir): os.makedirs(params.output_dir, exist_ok=True) # dataset split ["train", "validation"] ds_dict = ASRDataset.load(params.data_path, namespace='speech_asr') kwargs = dict( model=params.model, data_dir=ds_dict, dataset_type=params.dataset_type, work_dir=params.output_dir, batch_bins=params.batch_bins, max_epoch=params.max_epoch, lr=params.lr) trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs) trainer.train() if __name__ == '__main__': from funasr.utils.modelscope_param import modelscope_args params = modelscope_args(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch") params.output_dir = "./checkpoint" # 模型保存路径 params.data_path = "speech_asr_aishell1_trainsets" # 数据路径，可以为modelscope中已上传数据，也可以是本地数据 params.dataset_type = "small" # 小数据量设置small，若数据量大于1000小时，请使用large params.batch_bins = 2000 # batch size，如果dataset_type="small"，batch_bins单位为fbank特征帧数，如果dataset_type="large"，batch_bins单位为毫秒， params.max_epoch = 50 # 最大训练轮数 params.lr = 0.00005 # 设置学习率 modelscope_finetune(params) ``` ```shell python finetune.py &> log.txt & ``` ### Finetune with your data - Modify finetune training related parameters in [finetune.py](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/finetune.py) - `output_dir`: result dir - `data_dir`: the dataset dir needs to include files: `train/wav.scp`, `train/text`; `validation/wav.scp`, `validation/text` - `dataset_type`: for dataset larger than 1000 hours, set as `large`, otherwise set as `small` - `batch_bins`: batch size. For dataset_type is `small`, `batch_bins` indicates the feature frames. For dataset_type is `large`, `batch_bins` indicates the duration in ms - `max_epoch`: number of training epoch - `lr`: learning rate - Training data formats： ```sh cat ./example_data/text BAC009S0002W0122 而 对 楼 市 成 交 抑 制 作 用 最 大 的 限 购 BAC009S0002W0123 也 成 为 地 方 政 府 的 眼 中 钉 english_example_1 hello world english_example_2 go swim 去 游 泳 cat ./example_data/wav.scp BAC009S0002W0122 /mnt/data/wav/train/S0002/BAC009S0002W0122.wav BAC009S0002W0123 /mnt/data/wav/train/S0002/BAC009S0002W0123.wav english_example_1 /mnt/data/wav/train/S0002/english_example_1.wav english_example_2 /mnt/data/wav/train/S0002/english_example_2.wav ``` - Then you can run the pipeline to finetune with: ```shell python finetune.py ``` If you want finetune with multi-GPUs, you could: ```shell CUDA_VISIBLE_DEVICES=1,2 python -m torch.distributed.launch --nproc_per_node 2 finetune.py > log.txt 2>&1 ``` ## Inference with your finetuned model - Setting parameters in [egs_modelscope/asr/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/asr/TEMPLATE/infer.sh) is the same with [docs](https://github.com/alibaba-damo-academy/FunASR/tree/main/egs_modelscope/asr/TEMPLATE#inference-with-multi-thread-cpus-or-multi-gpus), `model` is the model name from modelscope, which you finetuned. - Decode with multi GPUs: ```shell bash infer.sh \ --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \ --data_dir "./data/test" \ --output_dir "./results" \ --batch_size 64 \ --gpu_inference true \ --gpuid_list "0,1" \ --checkpoint_dir "./checkpoint" \ --checkpoint_name "valid.cer_ctc.ave.pb" ``` - Decode with multi-thread CPUs: ```shell bash infer.sh \ --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \ --data_dir "./data/test" \ --output_dir "./results" \ --gpu_inference false \ --njob 64 \ --checkpoint_dir "./checkpoint" \ --checkpoint_name "valid.cer_ctc.ave.pb" ``` egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/demo.py
New file @@ -0,0 +1,15 @@ from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks if __name__ == '__main__': audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav' output_dir = None inference_pipeline = pipeline( task=Tasks.auto_speech_recognition, model='damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch', vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch', punc_model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch', ) rec_result = inference_pipeline(audio_in=audio_in) print(rec_result)
@@ -1,16 +1,28 @@ import os import shutil import argparse from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks if __name__ == '__main__': audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav' output_dir = None def modelscope_infer(args): os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpuid) inference_pipeline = pipeline( task=Tasks.auto_speech_recognition, model='damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch', vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch', punc_model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch', ngpu=1, model=args.model, output_dir=args.output_dir, batch_size=args.batch_size, param_dict={"decoding_model": args.decoding_mode, "hotword": args.hotword_txt} ) rec_result = inference_pipeline(audio_in=audio_in) print(rec_result) inference_pipeline(audio_in=args.audio_in) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument('--model', type=str, default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch") parser.add_argument('--audio_in', type=str, default="./data/test/wav.scp") parser.add_argument('--output_dir', type=str, default="./results/") parser.add_argument('--decoding_mode', type=str, default="normal") parser.add_argument('--hotword_txt', type=str, default=None) parser.add_argument('--batch_size', type=int, default=64) parser.add_argument('--gpuid', type=str, default="0") args = parser.parse_args() modelscope_infer(args) egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer.sh
egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/infer_after_finetune.py
File was deleted egs_modelscope/asr_vad_punc/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch/utils
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@@ -1,5 +1,4 @@ # Punctuation Restoration # Voice Activity Detection > **Note**: > The modelscope pipeline supports all the models in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope) to inference and finetune. Here we take the model of the punctuation model of CT-Transformer as example to demonstrate the usage. @@ -69,7 +68,7 @@ - `param_dict`: reserving the cache which is necessary in realtime mode. ### Inference with multi-thread CPUs or multi GPUs FunASR also offer recipes [egs_modelscope/punc/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/punc/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs. It is an offline recipe and only support offline model. FunASR also offer recipes [egs_modelscope/punctuation/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/punctuation/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs. It is an offline recipe and only support offline model. - Setting parameters in `infer.sh` - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk @@ -87,7 +86,7 @@ --model "damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch" \ --data_dir "./data/test" \ --output_dir "./results" \ --batch_size 64 \ --batch_size 1 \ --gpu_inference true \ --gpuid_list "0,1" ``` @@ -98,7 +97,7 @@ --data_dir "./data/test" \ --output_dir "./results" \ --gpu_inference false \ --njob 64 --njob 1 ```
New file @@ -0,0 +1 @@ ../../TEMPLATE/README.md egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vadrealtime-vocab272727/demo.py
egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vadrealtime-vocab272727/infer.py
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egs_modelscope/punctuation/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/infer.py
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@@ -59,11 +59,11 @@ ``` ### Inference with multi-thread CPUs or multi GPUs FunASR also offer recipes [egs_modelscope/vad/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/vad/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs. FunASR also offer recipes [egs_modelscope/tp/TEMPLATE/infer.sh](https://github.com/alibaba-damo-academy/FunASR/blob/main/egs_modelscope/tp/TEMPLATE/infer.sh) to decode with multi-thread CPUs, or multi GPUs. - Setting parameters in `infer.sh` - `model`: model name in [model zoo](https://alibaba-damo-academy.github.io/FunASR/en/modelscope_models.html#pretrained-models-on-modelscope), or model path in local disk - `data_dir`: the dataset dir **must** include `wav.scp` and `text.scp` - `data_dir`: the dataset dir **must** include `wav.scp` and `text.txt` - `output_dir`: output dir of the recognition results - `batch_size`: `64` (Default), batch size of inference on gpu - `gpu_inference`: `true` (Default), whether to perform gpu decoding, set false for CPU inference @@ -78,7 +78,7 @@ --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \ --data_dir "./data/test" \ --output_dir "./results" \ --batch_size 64 \ --batch_size 1 \ --gpu_inference true \ --gpuid_list "0,1" ``` @@ -89,7 +89,7 @@ --data_dir "./data/test" \ --output_dir "./results" \ --gpu_inference false \ --njob 64 --njob 1 ``` ## Finetune with pipeline egs_modelscope/tp/TEMPLATE/infer.py
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egs_modelscope/tp/TEMPLATE/infer.sh
@@ -37,7 +37,7 @@ split_texts="$split_texts $output_dir/split/text.$JOB.scp" done perl utils/split_scp.pl ${data_dir}/wav.scp ${split_scps} perl utils/split_scp.pl ${data_dir}/text.scp ${split_texts} perl utils/split_scp.pl ${data_dir}/text.txt ${split_texts} if [ -n "${checkpoint_dir}" ]; then python utils/prepare_checkpoint.py ${model} ${checkpoint_dir} ${checkpoint_name} egs_modelscope/tp/speech_timestamp_prediction-v1-16k-offline/README.md
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New file @@ -0,0 +1,12 @@ from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks inference_pipline = pipeline( task=Tasks.speech_timestamp, model='damo/speech_timestamp_prediction-v1-16k-offline', output_dir=None) rec_result = inference_pipline( audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_timestamps.wav', text_in='一 个 东 太 平 洋 国 家 为 什 么 跑 到 西 太 平 洋 来 了 呢',) print(rec_result) egs_modelscope/tp/speech_timestamp_prediction-v1-16k-offline/infer.py
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@@ -86,7 +86,7 @@ --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \ --data_dir "./data/test" \ --output_dir "./results" \ --batch_size 64 \ --batch_size 1 \ --gpu_inference true \ --gpuid_list "0,1" ``` @@ -97,7 +97,7 @@ --data_dir "./data/test" \ --output_dir "./results" \ --gpu_inference false \ --njob 64 --njob 1 ``` ## Finetune with pipeline egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/README.md
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egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/demo_online.py
egs_modelscope/vad/speech_fsmn_vad_zh-cn-16k-common/infer.py
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egs_modelscope/vad/speech_fsmn_vad_zh-cn-8k-common/demo_online.py
egs_modelscope/vad/speech_fsmn_vad_zh-cn-8k-common/infer.py
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