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| | | # Voice Activity Detection |
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| | | |
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| | | > **Note**: |
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| | | > 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 model of FSMN-VAD as example to demonstrate the usage. |
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| | | > 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 FSMN-VAD as example to demonstrate the usage. |
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| | | |
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| | | ## Inference |
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| | | |
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| | |
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| | | - pcm_path, `e.g.`: asr_example.pcm, |
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| | | - audio bytes stream, `e.g.`: bytes data from a microphone |
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| | | - audio sample point，`e.g.`: `audio, rate = soundfile.read("asr_example_zh.wav")`, the dtype is numpy.ndarray or torch.Tensor |
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| | | - wav.scp, kaldi style wav list (`wav_id \t wav_path``), `e.g.`: |
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| | | - wav.scp, kaldi style wav list (`wav_id \t wav_path`), `e.g.`: |
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| | | ```text |
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| | | asr_example1 ./audios/asr_example1.wav |
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| | | asr_example2 ./audios/asr_example2.wav |
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| | |
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| | | --model "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \ |
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| | | --data_dir "./data/test" \ |
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| | | --output_dir "./results" \ |
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| | | --batch_size 64 \ |
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| | | --batch_size 1 \ |
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| | | --gpu_inference true \ |
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| | | --gpuid_list "0,1" |
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| | | ``` |
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| | |
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| | | --data_dir "./data/test" \ |
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| | | --output_dir "./results" \ |
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| | | --gpu_inference false \ |
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| | | --njob 64 |
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| | | --njob 1 |
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| | | ``` |
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| | | |
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| | | ## Finetune with pipeline |
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