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## Introduction
MeloTTS is a **high-quality multi-lingual** text-to-speech library by [MIT](https://www.mit.edu/) and [MyShell.ai](https://myshell.ai). Supported languages include:
- The Chinese speaker supports
mixed Chinese and English. - Fast enough for
CPU real-time inference. - Many thanks to @fakerybakery for adding the Web UI and CLI part.
- Wenliang Zhao at Tsinghua University
- Xumin Yu at Tsinghua University
- Zengyi Qin at MIT and MyShell
| Language | Example |
|---|---|
| English (American) | Link |
| English (British) | Link |
| English (Indian) | Link |
| English (Australian) | Link |
| English (Default) | Link |
| Spanish | Link |
| French | Link |
| Chinese (mix EN) | Link |
| Japanese | Link |
| Korean | Link |
Some other features include:
The Python API and model cards can be found in this repo or on HuggingFace.
Discord
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Contributing
If you find this work useful, please consider contributing to this repo.
Citation
@software{zhao2024melo,
author={Zhao, Wenliang and Yu, Xumin and Qin, Zengyi},
title = {MeloTTS: High-quality Multi-lingual Multi-accent Text-to-Speech},
url = {https://github.com/myshell-ai/MeloTTS},
year = {2023}
}
This library is under MIT License, which means it is free for both commercial and non-commercial use.
This implementation is based on TTS, VITS, VITS2 and Bert-VITS2. We appreciate their awesome work.
- MeloTTS model supports using openvino to accelerate the inference process. Currently only verified on Linux system.
- TTS and Bert model support int8 quantize.
pip install -r requirements.txt
pip install openvino nncf
python setup.py develop # or pip install -e .
python -m unidic download
python3 test_tts.py- Now the input will be split and processed serially. This can be optimized to use openvino asynchronous inference, like this:
...
self.tts_model = self.core.read_model(Path(ov_model_path))
self.tts_compiled_model = self.core.compile_model(self.tts_model, 'CPU')
self.tts_request_0 = self.tts_compiled_model.create_infer_request()
self.tts_request_1 = self.tts_compiled_model.create_infer_request()
...
for index, t in enumerate(texts):
...
if index == 0:
self.tts_request_0.start_async(inputs_dict, share_inputs=True)
elif index ==1 :
self.tts_request_1.start_async(inputs_dict, share_inputs=True)
...
self.tts_request_0.wait()
self.tts_request_1.wait()
...