MeloTTS is a high-quality multi-lingual text-to-speech library by MyShell.ai. Supported languages include:
| Language | Example |
|---|---|
| English | Link |
| English (American) | Link |
| English (British) | Link |
| English (Indian) | Link |
| English (Australian) | Link |
| Spanish | Link |
| French | Link |
| Chinese (mix EN) | Link |
| Japanese | Link |
| Korean | Link |
Some other features include:
- The Chinese speaker supports
mixed Chinese and English. - Fast enough for
CPU real-time inference.
git clone [email protected]:myshell-ai/MeloTTS.git
cd MeloTTS
pip install -e .
python -m unidic downloadWe welcome the open-source community to make this repo Mac and Windows compatible. If you find this repo useful, please consider contributing to the repo.
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'
# 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
# Default accent
output_path = 'en-default.wav'
model.tts_to_file(text, speaker_ids['EN-Default'], output_path, speed=speed)
# 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)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)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)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)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)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)This library is under MIT License. Free for both commercial and non-commercial use.
This implementation is based on several excellent projects, TTS, VITS, VITS2 and Bert-VITS2. We appreciate their awesome work!
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