SAVRN
Search Contact SAVRN

Open-weight model · Text to speech

MeloTTS-Chinese

by MyShell.ai myshell-ai/MeloTTS-Chinese

MeloTTS is a high-quality multi-lingual text-to-speech library by MyShell.ai. Supported languages include: - The Chinese speaker supports mixed Chinese and English. - Fast enough for CPU real-time inference.

Parameters
Context
Weights207.8 MB
Licensemit
AccessOpen weights
Monthly Downloads51.8k

Model Card

By MyShell.ai, published under mit, revision af5d207a364e.

MeloTTS is a high-quality multi-lingual text-to-speech library by MyShell.ai. Supported languages include: - The Chinese speaker supports mixed Chinese and English. - Fast enough for CPU real-time inference. An unofficial live demo is hosted on Hugging Face Spaces. There are hundreds of TTS models on MyShell, much more than MeloTTS. See examples here. More can be found at the widget center of MyShell.ai. Follow the installation steps here before using the following snippet: Open Source AI Grant We are actively sponsoring open-source AI projects. The sponsorship includes GPU resources, fundings and intellectual support (collaboration with top research labs). We welcome both reseach and…

Read MyShell.ai's full model card

MeloTTS

MeloTTS is a high-quality multi-lingual text-to-speech library by MyShell.ai. Supported languages include:

Model card 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 Chinese speaker supports mixed Chinese and English. - Fast enough for CPU real-time inference.

Usage

Without Installation

An unofficial live demo is hosted on Hugging Face Spaces.

Use it on MyShell

There are hundreds of TTS models on MyShell, much more than MeloTTS. See examples here. More can be found at the widget center of MyShell.ai.

Install and Use Locally

Follow the installation steps here before using the following snippet:

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)

Join the Community

Open Source AI Grant

We are actively sponsoring open-source AI projects. The sponsorship includes GPU resources, fundings and intellectual support (collaboration with top research labs). We welcome both reseach and engineering projects, as long as the open-source community needs them. Please contact Zengyi Qin if you are interested.

Contributing

If you find this work useful, please consider contributing to the GitHub repo.

  • Many thanks to @fakerybakery for adding the Web UI and CLI part.

License

This library is under MIT License, which means it is free for both commercial and non-commercial use.

Acknowledgements

This implementation is based on TTS, VITS, VITS2 and Bert-VITS2. We appreciate their awesome work.

Identity and Version

Repository
myshell-ai/MeloTTS-Chinese
Publisher
MyShell.ai
Task
Text to speech
Modality
Audio
Library
transformers
Parameters
Not stated by the source
Languages
ko
Revision
af5d207a364ea4208c6f589c89f57f88414bdd16
First published
2024-02-29
Last updated
2024-03-01

Files and Weights

4 files, 207.8 MB in total. The weights are 1 file totalling 207.8 MB in pth.

Weights1 file · 207.8 MB
Configuration1 file · 2.3 KB
Documentation1 file · 4.4 KB
Repository1 file · 1.5 KB
Every file
FileTypeSizeSHA-256
checkpoint.pthWeights207.8 MB a74e9eadffff
config.jsonConfiguration2.3 KB
README.mdDocumentation4.4 KB
.gitattributesRepository1.5 KB

License and Download

License
mit
Access
Open weights, no gate
Download size
207.8 MB
Download from MyShell.ai

Released by MyShell.ai through its official repository on Hugging Face. Read the license.

Memory Requirements

PrecisionWeights in memory
As published207.8 MB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About MeloTTS-Chinese

Can I use MeloTTS-Chinese commercially?

Yes. MeloTTS-Chinese is released under MIT License. The MIT License is a short permissive license. It permits commercial use, modification and redistribution, provided the copyright notice and permission notice are included.

Similar Models

Model · Text to speech

Kokoro-82M

Hexgrad

Kokoro is an open-weight TTS model with 82 million parameters. Despite its lightweight architecture, it delivers comparable quality to larger models while being significantly faster and more cost-efficient. With Apache-licensed weights, Kokoro can be deployed anywhere from production environments to personal projects. You can run this basic cell on Google Colab. Listen to samples. For more languages and details, see Advanced Usage. Under the hood, kokoro uses misaki, a G2P library at https://github.com/hexgrad/misaki Model SHA256 Hash: 496dba118d1a58f5f3db2efc88dbdc216e0483fc89fe6e47ee1f2c53f18ad1e4 Data: Kokoro was trained exclusively on permissive/non-copyrighted audio data and IPA…

Open weights apache-2.0

Model · Text to speech

XTTS-v2

Coqui.ai

ⓍTTS is a Voice generation model that lets you clone voices into different languages by using just a quick 6-second audio clip. There is no need for an excessive amount of training data that spans countless hours. This is the same or similar model to what powers Coqui Studio and Coqui API. - Supports 17 languages. - Voice cloning with just a 6-second audio clip. - Emotion and style transfer by cloning. - Cross-language voice cloning. - Multi-lingual speech generation. - 24khz sampling rate. - 2 new languages; Hungarian and Korean - Architectural improvements for speaker conditioning. - Enables the use of multiple speaker references and interpolation between speakers. - Stability…

Open weights other coqui

Model · Text to speech

audio.cpp-gguf

Audio.cpp

This directory contains audio.cpp-native GGUF conversions of multiple speech models. These files are intended for use with audio.cpp. If you enjoy the project, please star audio.cpp on GitHub and this Hugging Face repository. For conversion details, supported layouts, direct-file loading, sidecar embedding, and the latest compatibility notes, see the audio.cpp GGUF guide: - https://github.com/0xShug0/audio.cpp/blob/main/docs/gguf.md!!! Converted and quantized packages are checked with automated metrics, but perceived quality can still differ for human listeners. Please validate the exact package, backend, and route to confirm the output is acceptable for your use case. The table lists the…

Open weights other audio.cpp

Model · Text to speech

chatterbox

Resemble AI

Chatterbox Multilingual V3 is the latest general-purpose multilingual TTS model in the Chatterbox family. It keeps the same 0.5B model size while improving speaker similarity, reducing hallucinations, and producing more natural, conversational speech across languages. V3 is designed for broad language coverage like V2, but with stronger stability and more expressive generation. It is the recommended multilingual model for users who want one voice cloning model that works across many languages. Try it in the Chatterbox Multilingual TTS V3 Space. Alongside V3, we are releasing the Single Language Pack: dedicated finetunes for priority languages where tighter quality control, stronger…

Open weights mit chatterbox

Model · Text to speech

F5-TTS

Yushen CHEN

Download F5-TTS or E2 TTS and place under ckpts/ Paper: F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching

Open weights cc-by-nc-4.0 f5-tts

Model · Text to speech

Kokoro-82M-v1.0-ONNX

ONNX Community

Kokoro is a frontier TTS model for its size of 82 million parameters (text in/audio out). First, install the kokoro-js library from NPM using: You can then generate speech as follows: Optionally, save the audio to a file: The model is resilient to quantization, enabling efficient high-quality speech synthesis at a fraction of the original model size.

Open weights apache-2.0 transformers.js