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PaddleSpeech/demos/text_to_speech
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[doc] add README_cn for demos/text_to_speech (#1134)
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README.md [doc] add README_cn for demos/text_to_speech (#1134) 3 years ago
README_cn.md [doc] add README_cn for demos/text_to_speech (#1134) 3 years ago
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README.md

(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 an audio from the giving 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

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 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