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@@ -23,216 +23,9 @@ Some other features include:
- The Chinese speaker supports `mixed Chinese and English`.
- Fast enough for `CPU real-time inference`.
## Install on Linux or macOS
**Installation:**
```bash
pip install git+https://github.com/myshell-ai/MeloTTS.git
python -m unidic download
```
**Manual installation:**
```bash
git clone https://github.com/myshell-ai/MeloTTS.git
cd MeloTTS
pip install -e .
python -m unidic download
```
We welcome the open-source community to make this repo `Windows` compatible. If you find this repo useful, please consider contributing to the repo.
## Usage
An unofficial [live demo](https://huggingface.co/spaces/mrfakename/MeloTTS) is hosted on Hugging Face Spaces.
### WebUI
The WebUI supports muliple languages and voices. First, follow the installation steps. Then, simply run:
```bash
melo-ui
# Or: python melo/app.py
```
### CLI
You may use the MeloTTS CLI to interact with MeloTTS. The CLI may be invoked using either `melotts` or `melo`. Here are some examples:
**Read English text:**
```bash
melo "Text to read" output.wav
```
**Specify a language:**
```bash
melo "Text to read" output.wav --language EN
```
**Specify a speaker:**
```bash
melo "Text to read" output.wav --language EN --speaker EN-US
melo "Text to read" output.wav --language EN --speaker EN-AU
```
The available speakers are: `EN-Default`, `EN-US`, `EN-BR`, `EN-INDIA` `EN-AU`.
**Specify a speed:**
```bash
melo "Text to read" output.wav --language EN --speaker EN-US --speed 1.5
melo "Text to read" output.wav --speed 1.5
```
**Use a different language:**
```bash
melo "text-to-speech 领域近年来发展迅速" zh.wav -l ZH
```
**Load from a file:**
```bash
melo file.txt out.wav --file
```
The full API documentation may be found using:
```bash
melo --help
```
### Python API
#### English with Multiple Accents
```python
from melo.api import TTS
# Speed is adjustable
speed = 1.0
# CPU is sufficient for real-time inference.
# You can set it manually to 'cpu' or 'cuda' or 'cuda:0' or 'mps'
device = 'auto' # Will automatically use GPU if available
# English
text = "Did you ever hear a folk tale about a giant turtle?"
model = TTS(language='EN', device=device)
speaker_ids = model.hps.data.spk2id
# American accent
output_path = 'en-us.wav'
model.tts_to_file(text, speaker_ids['EN-US'], output_path, speed=speed)
# British accent
output_path = 'en-br.wav'
model.tts_to_file(text, speaker_ids['EN-BR'], output_path, speed=speed)
# Indian accent
output_path = 'en-india.wav'
model.tts_to_file(text, speaker_ids['EN_INDIA'], output_path, speed=speed)
# Australian accent
output_path = 'en-au.wav'
model.tts_to_file(text, speaker_ids['EN-AU'], output_path, speed=speed)
# Default accent
output_path = 'en-default.wav'
model.tts_to_file(text, speaker_ids['EN-Default'], output_path, speed=speed)
```
### Spanish
```python
from melo.api import TTS
# Speed is adjustable
speed = 1.0
# CPU is sufficient for real-time inference.
# You can also change to cuda:0
device = 'cpu'
text = "El resplandor del sol acaricia las olas, pintando el cielo con una paleta deslumbrante."
model = TTS(language='ES', device=device)
speaker_ids = model.hps.data.spk2id
output_path = 'es.wav'
model.tts_to_file(text, speaker_ids['ES'], output_path, speed=speed)
```
#### French
```python
from melo.api import TTS
# Speed is adjustable
speed = 1.0
device = 'cpu' # or cuda:0
text = "La lueur dorée du soleil caresse les vagues, peignant le ciel d'une palette éblouissante."
model = TTS(language='FR', device=device)
speaker_ids = model.hps.data.spk2id
output_path = 'fr.wav'
model.tts_to_file(text, speaker_ids['FR'], output_path, speed=speed)
```
#### Chinese
```python
from melo.api import TTS
# Speed is adjustable
speed = 1.0
device = 'cpu' # or cuda:0
text = "我最近在学习machine learning，希望能够在未来的artificial intelligence领域有所建树。"
model = TTS(language='ZH', device=device)
speaker_ids = model.hps.data.spk2id
output_path = 'zh.wav'
model.tts_to_file(text, speaker_ids['ZH'], output_path, speed=speed)
```
#### Japanese
```python
from melo.api import TTS
# Speed is adjustable
speed = 1.0
device = 'cpu' # or cuda:0
text = "彼は毎朝ジョギングをして体を健康に保っています。"
model = TTS(language='JP', device=device)
speaker_ids = model.hps.data.spk2id
output_path = 'jp.wav'
model.tts_to_file(text, speaker_ids['JP'], output_path, speed=speed)
```
#### Korean
```python
from melo.api import TTS
# Speed is adjustable
speed = 1.0
device = 'cpu' # or cuda:0
text = "안녕하세요! 오늘은 날씨가 정말 좋네요."
model = TTS(language='KR', device=device)
speaker_ids = model.hps.data.spk2id
output_path = 'kr.wav'
model.tts_to_file(text, speaker_ids['KR'], output_path, speed=speed)
```
- [Use without Installation](docs/quick_use.md)
- [Install and Use Locally](docs/install.md)
## License
@@ -240,4 +33,6 @@ This library is under MIT License, which means it is free for both commercial an
## Acknowledgements
This implementation is based on several excellent projects, [TTS](https://github.com/coqui-ai/TTS), [VITS](https://github.com/jaywalnut310/vits), [VITS2](https://github.com/daniilrobnikov/vits2) and [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2). We appreciate their awesome work!
This implementation is based on [TTS](https://github.com/coqui-ai/TTS), [VITS](https://github.com/jaywalnut310/vits), [VITS2](https://github.com/daniilrobnikov/vits2) and [Bert-VITS2](https://github.com/fishaudio/Bert-VITS2). We appreciate their awesome work.
Many thanks to [@fakerybakery](https://github.com/fakerybakery) for adding the Web UI and CLI part.