OmniVoice

---
base_model:

• Qwen/Qwen3-0.6B

• aae

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• en

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• id

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• lua

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• mab

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• mai

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• mbo

• mcf

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• meu

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• mfo

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• my

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• nal

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• 'no'

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• odu

• ogo

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• os

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• udl

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• uk

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• wbl

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• xh

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• yaq

• yav

• yay

• ydd

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• yer

• 'yes'

• yi

• yo

• yue

• zga

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• zoc

• zoh

• zor

• zpv

• zpy

• ztg

• ztn

• ztp

• zts

• ztu

• zu

• zza

• zero-shot

• multilingual

• voice-cloning

• voice-design

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>
&nbsp;
<a href="https://colab.research.google.com/github/k2-fsa/OmniVoice/blob/master/docs/OmniVoice.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></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

• Colab: Google Colab Notebook

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.

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

</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 soundfile as sf
import torch
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 np.ndarray with shape (T,) at 24 kHz.
sf.write("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](https://github