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103 lines
3.2 KiB
103 lines
3.2 KiB
3 years ago
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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 an audio from the giving 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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```bash
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pip install paddlespeech
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```
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### 2. Prepare Input
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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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```bash
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paddlespeech tts --input 今天的天气不错啊
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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 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_aishell3| zh
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| fastspeech2_ljspeech| en
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| fastspeech2_vctk| 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_aishell3| zh
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| pwgan_ljspeech| en
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| pwgan_vctk| en
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| mb_melgan_csmsc| zh
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