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://arxiv.org/abs/2604.00688"><img src="https://img.shields.io/badge/arXiv-Paper-B31B1B.svg"></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 state-of-the-art massively multilingual zero-shot text-to-speech (TTS) model supporting over 600 languages. Built on a novel diffusion language model-style architecture, it generates high-quality speech with superior inference speed, supporting voice cloning and voice design.

**Contents**: [Key Features](#key-features) | [Installation](#installation) | [Quick Start](#quick-start) | [Python API](#python-api) | [Command-Line Tools](#command-line-tools) | [Training & Evaluation](#training--evaluation) | [Discussion](#discussion--communication) | [Citation](#citation)

## Key Features

- **600+ Languages Supported**: The broadest language coverage among zero-shot TTS models ([full list](docs/languages.md)).
- **Voice Cloning**: State-of-the-art voice cloning quality.
- **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.

---

## Installation

Choose **one** of the following methods: **pip** or **uv**.

### pip

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

**Step 1**: Install PyTorch

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

```bash
# 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](https://pytorch.org/get-started/locally/) for other versions installation.

</details>

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

```bash
pip install torch==2.8.0 torchaudio==2.8.0
```

</details>

<details>
<summary>Intel Arc GPU (XPU)</summary>

Intel Arc GPUs (Alchemist and Battlemage archite