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([简体中文](./README_cn.md)|English)
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# TTS (Text To Speech)
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## Introduction
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Text-to-speech (TTS) is a natural language modeling process that requires changing units of text into units of speech for audio presentation.
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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`.
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## Usage
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### 1. Installation
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see [installation](https://github.com/PaddlePaddle/PaddleSpeech/blob/develop/docs/source/install.md).
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You can choose one way from easy, meduim and hard to install paddlespeech.
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### 2. Prepare Input
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The input of this demo should be a text of the specific language that can be passed via argument.
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### 3. Usage
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- Command Line (Recommended)
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- Chinese
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The default acoustic model is `Fastspeech2`, and the default vocoder is `Parallel WaveGAN`.
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```bash
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paddlespeech tts --input "你好，欢迎使用百度飞桨深度学习框架！"
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```
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- Batch Process
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```bash
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echo -e "1 欢迎光临。\n2 谢谢惠顾。" | paddlespeech tts
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```
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- Chinese, use `SpeedySpeech` as the acoustic model
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```bash
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paddlespeech tts --am speedyspeech_csmsc --input "你好，欢迎使用百度飞桨深度学习框架！"
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```
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- Chinese, multi-speaker
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You can change `spk_id` here.
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```bash
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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
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```
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- English
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```bash
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paddlespeech tts --am fastspeech2_ljspeech --voc pwgan_ljspeech --lang en --input "hello world"
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```
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- English, multi-speaker
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You can change `spk_id` here.
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```bash
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paddlespeech tts --am fastspeech2_vctk --voc pwgan_vctk --input "hello, boys" --lang en --spk_id 0
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```
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- Chinese English Mixed, multi-speaker
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You can change `spk_id` here.
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```bash
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# The `am` must be `fastspeech2_mix`!
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# The `lang` must be `mix`!
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# The voc must be chinese datasets' voc now!
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# spk 174 is csmcc, spk 175 is ljspeech
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paddlespeech tts --am fastspeech2_mix --voc hifigan_csmsc --lang mix --input "热烈欢迎您在 Discussions 中提交问题，并在 Issues 中指出发现的 bug。此外，我们非常希望您参与到 Paddle Speech 的开发中！" --spk_id 174 --output mix_spk174.wav
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paddlespeech tts --am fastspeech2_mix --voc hifigan_aishell3 --lang mix --input "热烈欢迎您在 Discussions 中提交问题，并在 Issues 中指出发现的 bug。此外，我们非常希望您参与到 Paddle Speech 的开发中！" --spk_id 174 --output mix_spk174_aishell3.wav
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paddlespeech tts --am fastspeech2_mix --voc pwgan_csmsc --lang mix --input "我们的声学模型使用了 Fast Speech Two, 声码器使用了 Parallel Wave GAN and Hifi GAN." --spk_id 175 --output mix_spk175_pwgan.wav
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paddlespeech tts --am fastspeech2_mix --voc hifigan_csmsc --lang mix --input "我们的声学模型使用了 Fast Speech Two, 声码器使用了 Parallel Wave GAN and Hifi GAN." --spk_id 175 --output mix_spk175.wav
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```
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Usage:
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```bash
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paddlespeech tts --help
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```
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Arguments:
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- `input`(required): Input text to generate..
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- `am`: Acoustic model type of tts task. Default: `fastspeech2_csmsc`.
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- `am_config`: Config of acoustic model. Use deault config when it is None. Default: `None`.
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- `am_ckpt`: Acoustic model checkpoint. Use pretrained model when it is None. Default: `None`.
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- `am_stat`: Mean and standard deviation used to normalize spectrogram when training acoustic model. Default: `None`.
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- `phones_dict`: Phone vocabulary file. Default: `None`.
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- `tones_dict`: Tone vocabulary file. Default: `None`.
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- `speaker_dict`: speaker id map file. Default: `None`.
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- `spk_id`: Speaker id for multi speaker acoustic model. Default: `0`.
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- `voc`: Vocoder type of tts task. Default: `pwgan_csmsc`.
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- `voc_config`: Config of vocoder. Use deault config when it is None. Default: `None`.
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- `voc_ckpt`: Vocoder checkpoint. Use pretrained model when it is None. Default: `None`.
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- `voc_stat`: Mean and standard deviation used to normalize spectrogram when training vocoder. Default: `None`.
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- `lang`: Language of tts task. Default: `zh`.
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- `device`: Choose device to execute model inference. Default: default device of paddlepaddle in current environment.
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- `output`: Output wave filepath. Default: `output.wav`.
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Output:
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```bash
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[2021-12-09 20:49:58,955] [ INFO] [log.py] [L57] - Wave file has been generated: output.wav
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```
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- Python API
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```python
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import paddle
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from paddlespeech.cli.tts import TTSExecutor
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tts_executor = TTSExecutor()
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wav_file = tts_executor(
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text='今天的天气不错啊',
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output='output.wav',
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am='fastspeech2_csmsc',
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am_config=None,
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am_ckpt=None,
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am_stat=None,
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spk_id=0,
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phones_dict=None,
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tones_dict=None,
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speaker_dict=None,
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voc='pwgan_csmsc',
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voc_config=None,
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voc_ckpt=None,
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voc_stat=None,
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lang='zh',
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device=paddle.get_device())
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print('Wave file has been generated: {}'.format(wav_file))
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```
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Output:
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```bash
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Wave file has been generated: output.wav
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```
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### 4. Pretrained Models
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Here is a list of pretrained models released by PaddleSpeech that can be used by command and python API:
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- Acoustic model
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| Model | Language |
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| :--- | :---: |
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| speedyspeech_csmsc | zh |
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| fastspeech2_csmsc | zh |
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| fastspeech2_ljspeech | en |
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| fastspeech2_aishell3 | zh |
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| fastspeech2_vctk | en |
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| fastspeech2_cnndecoder_csmsc | zh |
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| fastspeech2_mix | mix |
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| tacotron2_csmsc | zh |
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| tacotron2_ljspeech | en |
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- Vocoder
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| Model | Language |
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| :--- | :---: |
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| pwgan_csmsc | zh |
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| pwgan_ljspeech | en |
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| pwgan_aishell3 | zh |
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| pwgan_vctk | en |
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| mb_melgan_csmsc | zh |
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| style_melgan_csmsc | zh |
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| hifigan_csmsc | zh |
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| hifigan_ljspeech | en |
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| hifigan_aishell3 | zh |
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| hifigan_vctk | en |
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| wavernn_csmsc | zh |
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