OmniVoice

OmniVoice 🌍

<p align="center"> <img width="200" height="200" alt="OmniVoice" src="https://zhu-han.github.io/omnivoice/pics/omnivoice.jpg" /> </p> <p align="center"> <a href="https://huggingface.co/k2-fsa/OmniVoice"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-FFD21E" alt="Hugging Face Model"></a> &nbsp; <a href="https://huggingface.co/spaces/k2-fsa/OmniVoice"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Space-blue" alt="Hugging Face Space"></a> &nbsp; <a href="https://huggingface.co/papers/2604.00688"><img src="https://img.shields.io/badge/arXiv-Paper-B31B1B.svg"></a> &nbsp; <a href="https://github.com/k2-fsa/OmniVoice"><img src="https://img.shields.io/badge/GitHub-Code-181717?logo=GitHub" alt="GitHub Code"></a> &nbsp; <a href="https://zhu-han.github.io/omnivoice"><img src="https://img.shields.io/badge/GitHub.io-Demo_Page-blue?logo=GitHub&style=flat-square"></a> </p>

OmniVoice is a massively multilingual zero-shot text-to-speech (TTS) model supporting over 600 languages. Built on a novel diffusion language model-style architecture, it delivers high-quality speech with superior inference speed, supporting voice cloning and voice design.

• Paper: OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models
• Repository: GitHub
• Demo: Hugging Face Space

Key Features

• 600+ Languages Supported: The broadest language coverage among zero-shot TTS models.
• Voice Cloning: State-of-the-art voice cloning quality from a short reference audio.
• Voice Design: Control voices via assigned speaker attributes (gender, age, pitch, dialect/accent, whisper, etc.).
• Fine-grained Control: Non-verbal symbols (e.g., [laughter]) and pronunciation correction via pinyin or phonemes.
• Fast Inference: RTF as low as 0.025 (40x faster than real-time).
• Diffusion Language Model-style Architecture: A clean, streamlined, and scalable design that delivers both quality and speed.

Sample Usage

To get started, install the omnivoice library:

We recommend using a fresh virtual environment (e.g., conda, venv, etc.) to avoid conflicts.

Step 1: Install PyTorch

<details> <summary>NVIDIA GPU</summary>

# Install pytorch with your CUDA version, e.g.
pip install torch==2.8.0+cu128 torchaudio==2.8.0+cu128 --extra-index-url https://download.pytorch.org/whl/cu128

See PyTorch official site for other versions installation.

</details> <details> <summary>Apple Silicon</summary>

pip install torch==2.8.0 torchaudio==2.8.0

</details>

Step 2: Install OmniVoice

pip install omnivoice

Python API

You can use OmniVoice for zero-shot voice cloning as follows:

from omnivoice import OmniVoice
import torch
import torchaudio

# Load the model
model = OmniVoice.from_pretrained(
    "k2-fsa/OmniVoice",
    device_map="cuda:0",
    dtype=torch.float16
)

# Generate audio
audio = model.generate(
    text="Hello, this is a test of zero-shot voice cloning.",
    ref_audio="ref.wav",
    ref_text="Transcription of the reference audio.",
) # audio is a list of `torch.Tensor` with shape (1, T) at 24 kHz.

torchaudio.save("out.wav", audio[0], 24000)

For more generation modes (e.g., voice design), functions (e.g., non-verbal symbols, pronunciation correction) and comprehensive usage instructions, see our GitHub Repository.

Discussion & Communication

You can directly discuss on GitHub Issues.

You can also scan the QR code to join our wechat group or follow our wechat official account.

Citation

@article{zhu2026omnivoice,
      title={OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models},
      author={Zhu, Han and Ye, Lingxuan and Kang, Wei and Yao, Zengwei and Guo, Liyong and Kuang, Fangjun and Han, Zhifeng and Zhuang, Weiji and Lin, Long and Povey, Daniel},
      journal={arXiv preprint arXiv:2604.00688},
      year={2026}
}