G-OmniVoice
G-OmniVoice is a Vietnamese-optimized text-to-speech model with zero-shot voice cloning and voice design, fine-tuned from k2-fsa/OmniVoice (Qwen3-0.6B backbone + Higgs Audio 2 codec) on a large-scale Vietnamese speech corpus.
Its defining strength is doing two hard things at once: it reads Vietnamese accurately and preserves the reference speaker's voice — the lowest WER of any public OmniVoice model, combined with top-tier speaker similarity.
- 🇻🇳 Optimized for natural Vietnamese speech
- 🎯 Accurate pronunciation and faithful voice — best-in-class WER without sacrificing speaker similarity
- 🎙️ Zero-shot voice cloning from a 3–10 second reference clip
Benchmark
Evaluated on a held-out Vietnamese test set. WER ↓ (intelligibility), SIM ↑ (speaker similarity), MOS ↑ (naturalness).
| Model name | WER ↓ | SIM ↑ | MOS ↑ |
|---|---|---|---|
| k2-fsa/OmniVoice | 0.0712 | 0.892 | 7.709 |
| kjanh/KhanhTTS-OmniVoice | 0.0375 | 0.888 | 7.678 |
| VietNeu/v3turbo | 0.0551 | 0.778 | 7.570 |
| g-group-ai-lab/g-omnivoice (ours) | 0.0259 | 0.890 | 7.685 |
The scatter above plots accuracy (WER) against voice fidelity (SIM). Every other model has to compromise: k2-fsa keeps the voice but reads least accurately, VietNeu drifts on both. G-OmniVoice sits alone in the top-left "best of both worlds" corner — it reads the most accurately (lowest WER, ≈31% better than the next model) while staying faithful to the original voice (SIM 0.890, essentially tied with the best). Accurate and faithful, at the same time.
Installation
# 1. PyTorch (NVIDIA GPU, CUDA 12.8)
pip install torch==2.8.0+cu128 torchaudio==2.8.0+cu128 \
--extra-index-url https://download.pytorch.org/whl/cu128
# 2. OmniVoice runtime
pip install omnivoice
Usage
import torch
import soundfile as sf
from omnivoice import OmniVoice
model = OmniVoice.from_pretrained(
"g-group-ai-lab/g-omnivoice",
device_map="cuda:0",
dtype=torch.float16,
)
# --- Voice cloning (from a reference clip) ---
audio = model.generate(
text="Xin chào, đây là giọng nói được nhân bản bằng G-OmniVoice.",
ref_audio="reference.wav",
ref_text="Transcript of the reference audio.",
)
sf.write("clone.wav", audio[0], 24000)
# --- Voice design (no reference audio) ---
audio = model.generate(
text="Đây là một giọng nói được tạo từ mô tả thuộc tính.",
instruct="female, young adult, medium pitch, northern Vietnamese accent",
)
sf.write("design.wav", audio[0], 24000)
Tips
- Apply text normalization (numbers, dates, symbols, abbreviations) before synthesis.
- Split long inputs into sentence-sized chunks for stable prosody.
- Use 3–10 s of clean, single-speaker reference audio for the best clone.
Supported Languages
Optimized for Vietnamese. The underlying OmniVoice base supports 600+ languages in zero-shot mode; performance on other languages follows the base model.
Citation
@misc{gomnivoice2026,
title = {G-OmniVoice: Natural Vietnamese Zero-Shot Text-to-Speech},
author = {G-Group AI Lab},
year = {2026},
url = {https://huggingface.co/g-group-ai-lab/g-omnivoice}
}
@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}
}
License
Apache 2.0, following the base k2-fsa/OmniVoice model. The bundled audio tokenizer is derived from Higgs Audio 2 and remains subject to the Boson Higgs Audio 2 Community License (see audio_tokenizer/LICENSE).
Acknowledgments
- k2-fsa / Next-gen Kaldi — OmniVoice base model and runtime
- Boson AI — Higgs Audio 2 tokenizer
- Qwen Team — Qwen3 backbone
- G-Group AI Lab — Vietnamese fine-tuning and release