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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

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

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

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