FunASR/docs/modescope_pipeline/asr_pipeline.md

Speech Recognition

Inference

Quick start

Paraformer model

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)

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

Inference with you data

Inference with multi-threads on CPU

Inference with multi GPU

Finetune with pipeline

Quick start

Finetune with your data

Inference with your finetuned model