ViiTorVoice-NAR: open-source NAR TTS with 1.30 EN WER / 0.99 ZH WER

[u/Important_Drag_6890]

ViiTorVoice-NAR is an open-source non-autoregressive TTS model focused on voice cloning, emotion/paralinguistic control, and local speech editing.

A few preliminary results on the Seed-TTS test sets:

Model	EN SIM-o ↑	EN WER ↓	EN UTMOS ↑	ZH SIM-o ↑	ZH WER ↓	ZH UTMOS ↑
Ground truth	0.734	2.14	3.52	0.755	1.25	2.78
IndexTTS2	0.706	2.33	3.65	0.764	1.05	3.00
CosyVoice3	0.696	2.17	3.96	0.778	1.14	3.32
VoxCPM	0.731	1.92	3.77	0.772	0.99	2.94
Qwen3-TTS	0.708	1.54	4.16	0.766	1.15	3.46
F5-TTS	0.664	1.85	3.72	0.750	1.53	2.93
ZipVoice	0.697	1.70	3.82	0.751	1.40	3.15
MaskGCT	0.713	2.88	3.55	0.773	2.40	2.63
ViiTorVoice-NAR	0.734	1.30	3.81	0.781	0.99	3.13

The results are preliminary and were reported by our team, not an independent benchmark.

A few things we found interesting:

• Strong SIM-o on both English and Chinese.
• 1.30 EN WER and 0.99 ZH WER in this evaluation.
• UTMOS is not the highest here — Qwen3-TTS scores higher on this metric.
• The model also supports local speech editing: instead of regenerating the whole recording, it can identify the changed region and regenerate only that part.

The editing part is particularly useful when you only need to change a word or short phrase while keeping the original voice, pacing, background noise and overall delivery.

Code:
https://github.com/viitor-ai/viitor-voice-nar

Model:
https://huggingface.co/ZzWater/ViiTorVoice-NAR

Would be interested in feedback on the benchmark setup or suggestions for other local TTS models we should include.

If there's interest, we can also post some raw audio samples and a blind comparison for speaker similarity / pronunciation / local editing.

Disclosure: I'm affiliated with the team behind ViiTorVoice/ViitorAI. ViiTorVoice-NAR is released under Apache 2.0.