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Install and Use Locally

Table of Content

Linux and macOS Install

The repo is developed and tested on Ubuntu 20.04 and Python 3.9.

git clone https://github.com/myshell-ai/MeloTTS.git
cd MeloTTS
pip install -e .
python -m unidic download

If you encountered issues in macOS install, try the Docker Install

Docker Install

To avoid compatibility issues, for Windows users and some macOS users, we suggest to run via Docker. Ensure that you have Docker installed.

Build Docker

This could take a few minutes.

git clone https://github.com/myshell-ai/MeloTTS.git
cd MeloTTS
docker build -t melotts .

Run Docker

docker run -it -p 8888:8888 melotts

If your local machine has GPU, then you can choose to run:

docker run --gpus all -it -p 8888:8888 melotts

Then open http://localhost:8888 in your browser to use the app.

Usage

WebUI

The WebUI supports muliple languages and voices. First, follow the installation steps. Then, simply run:

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:

melo "Text to read" output.wav

Specify a language:

melo "Text to read" output.wav --language EN

Specify a speaker:

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:

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:

melo "text-to-speech 领域近年来发展迅速" zh.wav -l ZH

Load from a file:

melo file.txt out.wav --file

The full API documentation may be found using:

melo --help

Python API

English with Multiple Accents

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

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

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

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

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

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)