PaddleSpeech/demos/speech_translation

Speech Translation

Introduction

Speech translation is the process by which conversational spoken phrases are instantly translated and spoken aloud in a second language.

This demo is an implementation to recognize text from a specific audio file and translate to target language. 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).

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 st --input ~/en.wav
  

  Usage:

  paddlespeech st --help
  

  Arguments:

  • input(required): Audio file to recognize and translate.
  • model: Model type of st task. Default: fat_st_ted.
  • src_lang: Source language. Default: en.
  • tgt_lang: Target language. Default: zh.
  • sample_rate: Sample rate of the model. Default: 16000.
  • config: Config of st 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:

  [2021-12-09 11:13:03,178] [    INFO] [utils.py] [L225] - ST Result: ['我 在 这栋 建筑 的 古老 门上 敲门 。']
  

• Python API

  import paddle
  from paddlespeech.cli import STExecutor
  
  st_executor = STExecutor()
  text = st_executor(
      model='fat_st_ted',
      src_lang='en',
      tgt_lang='zh',
      sample_rate=16000,
      config=None,  # Set `config` and `ckpt_path` to None to use pretrained model.
      ckpt_path=None,
      audio_file='./en.wav',
      device=paddle.get_device())
  print('ST Result: \n{}'.format(text))
  

  Output:

  ST Result:
  ['我 在 这栋 建筑 的 古老 门上 敲门 。'] 
  

4.Pretrained Models

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

Model	Source Language	Target Language
fat_st_ted	en	zh