(简体中文|English)
Voice Activity Detection
Note: The modelscope pipeline supports all the models in model zoo to inference and finetune. Here we take the model of FSMN-VAD as example to demonstrate the usage.
Inference
Quick start
FSMN-VAD model
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
inference_pipeline = pipeline(
task=Tasks.voice_activity_detection,
model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
)
segments_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav')
print(segments_result)
FSMN-VAD-online model
inference_pipeline = pipeline(
task=Tasks.voice_activity_detection,
model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch',
)
import soundfile
speech, sample_rate = soundfile.read("example/asr_example.wav")
param_dict = {"in_cache": dict(), "is_final": False}
chunk_stride = 1600# 100ms
# first chunk, 100ms
speech_chunk = speech[0:chunk_stride]
rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict)
print(rec_result)
# next chunk, 100ms
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
API-reference
Define pipeline
task
:Tasks.voice_activity_detection
model
: model name in model zoo, or model path in local diskngpu
:1
(Default), decoding on GPU. If ngpu=0, decoding on CPUncpu
:1
(Default), sets the number of threads used for intraop parallelism on CPUoutput_dir
:None
(Default), the output path of results if setbatch_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 microphoneaudio sample point,
e.g.
:audio, rate = soundfile.read("asr_example_zh.wav")
, the dtype is numpy.ndarray or torch.Tensorwav.scp, kaldi style wav list (
wav_id \t wav_path
),e.g.
:
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 resultsaudio_fs
: audio sampling rate, only set when audio_in is pcm audiooutput_dir
: None (Default), the output path of results if set
Inference with multi-thread CPUs or multi GPUs
FunASR also offer recipes egs_modelscope/vad/TEMPLATE/infer.sh to decode with multi-thread CPUs, or multi GPUs.
Settings of infer.sh
model
: model name in model zoo, or model path in local diskdata_dir
: the dataset dir needs to includewav.scp
output_dir
: output dir of the recognition resultsbatch_size
:64
(Default), batch size of inference on gpugpu_inference
:true
(Default), whether to perform gpu decoding, set false for CPU inferencegpuid_list
:0,1
(Default), which gpu_ids are used to infernjob
: only used for CPU inference (gpu_inference
=false
),64
(Default), the number of jobs for CPU decodingcheckpoint_dir
: only used for infer finetuned models, the path dir of finetuned modelscheckpoint_name
: only used for infer finetuned models,valid.cer_ctc.ave.pb
(Default), which checkpoint is used to infer
Decode with multi GPUs:
bash infer.sh \
--model "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" \
--data_dir "./data/test" \
--output_dir "./results" \
--batch_size 1 \
--gpu_inference true \
--gpuid_list "0,1"
Decode with multi-thread CPUs:
bash infer.sh \
--model "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" \
--data_dir "./data/test" \
--output_dir "./results" \
--gpu_inference false \
--njob 64