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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 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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| | | ##### Define pipeline |
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| | | - `task`: `Tasks.voice_activity_detection` |
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| | | - `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 |
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| | | - `ngpu`: `1` (Defalut), decoding on GPU. If ngpu=0, decoding on CPU |
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| | | - `ncpu`: `1` (Defalut), sets the number of threads used for intraop parallelism on CPU |
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| | | - `output_dir`: `None` (Defalut), the output path of results if set |
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| | | - `batch_size`: `1` (Defalut), batch size when decoding |
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| | | - `ngpu`: `1` (Default), decoding on GPU. If ngpu=0, decoding on CPU |
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| | | - `ncpu`: `1` (Default), sets the number of threads used for intraop parallelism on CPU |
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| | | - `output_dir`: `None` (Default), the output path of results if set |
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| | | - `batch_size`: `1` (Default), batch size when decoding |
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| | | ##### Infer pipeline |
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| | | - `audio_in`: the input to decode, which could be: |
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| | | - wav_path, `e.g.`: asr_example.wav, |
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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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| | | In this case of `wav.scp` input, `output_dir` must be set to save the output results |
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| | | - `audio_fs`: audio sampling rate, only set when audio_in is pcm audio |
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| | | - `output_dir`: None (Defalut), the output path of results if set |
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| | | - `output_dir`: None (Default), the output path of results if set |
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| | | ### Inference with multi-thread CPUs or multi GPUs |
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| | | FunASR also offer recipes [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. |
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| | | 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. |
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| | | - Setting parameters in `infer.sh` |
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| | | - `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 |
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