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