PaddleSpeech/demos/text_to_speech

(简体中文|English)

TTS (Text To Speech)

Introduction

Text-to-speech (TTS) is a natural language modeling process that requires changing units of text into units of speech for audio presentation.

This demo is an implementation to generate audio from the given text. 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

The input of this demo should be a text of the specific language that can be passed via argument.

3. Usage

• Command Line (Recommended)

  • Chinese

    The default acoustic model is Fastspeech2, and the default vocoder is Parallel WaveGAN.

    paddlespeech tts --input "你好，欢迎使用百度飞桨深度学习框架！"
    

  • Chinese, use SpeedySpeech as the acoustic model

    paddlespeech tts --am speedyspeech_csmsc --input "你好，欢迎使用百度飞桨深度学习框架！"
    

  • Chinese, multi-speaker

    You can change spk_id here.

    paddlespeech tts --am fastspeech2_aishell3 --voc pwgan_aishell3 --input "你好，欢迎使用百度飞桨深度学习框架！" --spk_id 0
    

  • English

    paddlespeech tts --am fastspeech2_ljspeech --voc pwgan_ljspeech --lang en --input "hello world"
    

  • English, multi-speaker

    You can change spk_id here.

    paddlespeech tts --am fastspeech2_vctk --voc pwgan_vctk --input "hello, boys" --lang en --spk_id 0
    

  Usage:

  paddlespeech tts --help
  

  Arguments:

  • input(required): Input text to generate..
  • am: Acoustic model type of tts task. Default: fastspeech2_csmsc.
  • am_config: Config of acoustic model. Use deault config when it is None. Default: None.
  • am_ckpt: Acoustic model checkpoint. Use pretrained model when it is None. Default: None.
  • am_stat: Mean and standard deviation used to normalize spectrogram when training acoustic model. Default: None.
  • phones_dict: Phone vocabulary file. Default: None.
  • tones_dict: Tone vocabulary file. Default: None.
  • speaker_dict: speaker id map file. Default: None.
  • spk_id: Speaker id for multi speaker acoustic model. Default: 0.
  • voc: Vocoder type of tts task. Default: pwgan_csmsc.
  • voc_config: Config of vocoder. Use deault config when it is None. Default: None.
  • voc_ckpt: Vocoder checkpoint. Use pretrained model when it is None. Default: None.
  • voc_stat: Mean and standard deviation used to normalize spectrogram when training vocoder. Default: None.
  • lang: Language of tts task. Default: zh.
  • device: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.
  • output: Output wave filepath. Default: output.wav.

  Output:

  [2021-12-09 20:49:58,955] [    INFO] [log.py] [L57] - Wave file has been generated: output.wav
  

• Python API

  import paddle
  from paddlespeech.cli import TTSExecutor
  
  tts_executor = TTSExecutor()
  wav_file = tts_executor(
      text='今天的天气不错啊',
      output='output.wav',
      am='fastspeech2_csmsc',
      am_config=None,
      am_ckpt=None,
      am_stat=None,
      spk_id=0,
      phones_dict=None,
      tones_dict=None,
      speaker_dict=None,
      voc='pwgan_csmsc',
      voc_config=None,
      voc_ckpt=None,
      voc_stat=None,
      lang='zh',
      device=paddle.get_device())
  print('Wave file has been generated: {}'.format(wav_file))
  

  Output:

  Wave file has been generated: output.wav
  

4. Pretrained Models

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

• Acoustic model

  Model	Language
  speedyspeech_csmsc	zh
  fastspeech2_csmsc	zh
  fastspeech2_aishell3	zh
  fastspeech2_ljspeech	en
  fastspeech2_vctk	en

• Vocoder

  Model	Language
  pwgan_csmsc	zh
  pwgan_aishell3	zh
  pwgan_ljspeech	en
  pwgan_vctk	en
  mb_melgan_csmsc	zh