FunASR/docs/modescope_pipeline/vad_pipeline.md

Voice Activity Detection

Note

: The modelscope pipeline supports all the models in model zoo to inference and finetine. Here we take 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.auto_speech_recognition,
    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, 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

API-reference

define pipeline

• task: Tasks.auto_speech_recognition
• model: model name in model zoo, or model path in local disk
• ngpu: 1 (Defalut), decoding on GPU. If ngpu=0, decoding on CPU
• ncpu: 1 (Defalut), sets the number of threads used for intraop parallelism on CPU
• output_dir: None (Defalut), the output path of results if set
• batch_size: 1 (Defalut), 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.`:

  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 (Defalut), the output path of results if set

Inference with multi-thread CPUs or multi GPUs

FunASR also offer recipes infer.sh to decode with multi-thread CPUs, or multi GPUs.

• Setting parameters in infer.sh

  • model: # model name on ModelScope
  • data_dir: # the dataset dir needs to include ${data_dir}/wav.scp. If ${data_dir}/text is also exists, CER will be computed
  • output_dir: # result dir
  • batch_size: # batchsize of inference
  • gpu_inference: # whether to perform gpu decoding, set false for cpu decoding
  • gpuid_list: # set gpus, e.g., gpuid_list="0,1"
  • njob: # the number of jobs for CPU decoding, if gpu_inference=false, use CPU decoding, please set njob

• Decode with multi GPUs:

    bash infer.sh \
    --model "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" \
    --data_dir "./data/test" \
    --output_dir "./results" \
    --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

• Results

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 with your data

Inference with your finetuned model