PaddleSpeech/demos/speech_translation

(简体中文|English)

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 it to the target language. It can be done by a single command or a few lines in python using PaddleSpeech.

Usage

1. Installation

see installation.

You can choose one way from easy, meduim and hard to install paddlespeech.

2. Prepare Input File

The input of this demo should be a WAV file(.wav).

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

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

3. Usage (not support for Windows now)

• 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