PaddleSpeech/demos/speech_recognition

ASR(Automatic Speech Recognition)

Introduction

ASR, or Automatic Speech Recognition, refers to the problem of getting a program to automatically transcribe spoken language (speech-to-text).

This demo is an implementation to recognize text from a specific audio file. It can be done by a single command or a few lines in python using PaddleSpeech.

Usage

1. Installation

pip install paddlespeech

2. Prepare Input File

Input of this demo should be a WAV file(.wav), and the sample rate must be same as the model's.

Here are sample files for this demo that can be downloaded:

wget https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav

3. Usage

• Command Line(Recommended)

  paddlespeech asr --input ./zh.wav
  

  Usage:

  paddlespeech asr --help
  

  Arguments:

  • input(required): Audio file to recognize.
  • model: Model type of asr task. Default: conformer_wenetspeech.
  • lang: Model language. Default: zh.
  • sample_rate: Sample rate of the model. Default: 16000.
  • config: Config of asr task. Use pretrained model when it is None. Default: None.
  • ckpt_path: Model checkpoint. Use pretrained model when it is None. Default: None.
  • device: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.

  Output:

  [2021-12-08 13:12:34,063] [    INFO] [utils.py] [L225] - ASR Result: 我认为跑步最重要的就是给我带来了身体健康
  

• Python API

  import paddle
  from paddlespeech.cli import ASRExecutor
  
  asr_executor = ASRExecutor()
  text = asr_executor(
      model='conformer_wenetspeech',
      lang='zh',
      sample_rate=16000,
      config=None,  # Set `config` and `ckpt_path` to None to use pretrained model.
      ckpt_path=None,
      audio_file='./zh.wav',
      device=paddle.get_device())
  print('ASR Result: \n{}'.format(text))
  

  Output:

  ASR Result:
  我认为跑步最重要的就是给我带来了身体健康
  

4.Pretrained Models

Here is a list of pretrained models released by PaddleSpeech that can be used by command and python api:

Model	Language	Sample Rate
conformer_wenetspeech	zh	16000
transformer_aishell	zh	16000