
Omnivoice: Lifelike AI Voices
in 646 Languages
Omnivoice reads any script naturally, copies a voice from a short clip and invents new speakers from plain text — one model, 646 languages.
Every Tool Your Voice Projects Need
Text to Speech in 646 Languages
Paste a script and Omnivoice returns clear, natural audio within seconds. One shared model covers every language — nothing to switch.
Instant Voice Cloning
Drop in a 3–25 second recording. Tone, accent and pacing are captured, then reused in any language without training.
Design a Voice With Words
Skip the microphone. Write down age, pitch, accent and delivery, and a matching speaker appears from that description.
Emotion and Non-Verbal Cues
Type tags like [laughter] or [sigh] into the text. Omnivoice performs them in place, the way real people talk.
Text to Speech With Omnivoice
One engine. Every tongue.
- ✓646 languages handled by a single model
- ✓Natural rhythm in major and low-resource languages
- ✓Pronunciation control for English and Japanese
- ✓Speaking rate adjustable from 0.5× to 2.0×
Copy Any Voice — No Training Step
Sample in, speaker out.
- ✓Reference audio from just 3 seconds
- ✓Whisper ASR transcribes the clip automatically
- ✓Cross-lingual cloning across all 646 languages
- ✓Stays stable on noisy, imperfect takes
Skip the Mic. Write the Voice You Want.
Where Teams Put Omnivoice to Work
Why Omnivoice Stands Apart
646 Languages, One Model
ElevenLabs offers 32 languages and PlayHT 132. Omnivoice reaches 646, including hundreds of low-resource languages others skip.
Fewer Word Errors
On a 24-language benchmark the model scored 2.85% word error rate against 10.95% for ElevenLabs — fewer retakes, cleaner listening.
Ref. arXiv 2604.00688 (Table 3)
Closer Speaker Match
Speaker similarity (SIM-o) hits 0.830 versus 0.655 for ElevenLabs, so a cloned voice sounds like the real person.
Ref. arXiv 2604.00688 (Table 3)
Speed Built for Production
Batch inference runs at RTF 0.022 — a minute of audio in about 1.3 seconds. Quick for live apps, scalable for bulk jobs.
Cross-Lingual Cloning
Record a voice in English, then hear it speak Japanese, Arabic or Swahili. No extra samples per language.
Single-Pass Architecture
Many TTS stacks chain text → semantic → audio and pile up errors. Omnivoice goes from text to audio in one step.
Omnivoice Side by Side
| Feature | Omnivoice | ElevenLabs | PlayHT |
|---|---|---|---|
| Languages | 646 | 32 | 132 |
| Multilingual WER | 2.85% | 10.95% | — |
| Speaker similarity | 0.830 | 0.655 | — |
| Open weights | Yes | No | No |
| Self-hosting | Apache 2.0 | No | No |
| Text-only voice design | Yes | No | No |
| Cross-lingual cloning | Yes | Limited | No |
| Inference speed | ~45× real-time | — | — |
* WER and SIM-o figures: arXiv 2604.00688, Table 3, 24 languages.
Questions, Answered
What is Omnivoice?
Omnivoice is an open-source AI voice model for 646 languages. It reads text aloud, clones voices from short clips and builds speakers from descriptions. The k2-fsa team trained it on roughly 581,000 hours of open speech data.
Can I run the model myself?
Yes. Weights ship under Apache 2.0, so you can host Omnivoice on your own hardware or use a hosted tool to skip setup.
How many languages are covered?
646 — big ones like English, Japanese, Spanish and Arabic, plus hundreds of low-resource languages most TTS tools ignore.
How does cloning work?
Supply a 3–25 second reference and the speaker profile is extracted at once, with no training. That voice can then speak any supported language.
How does it compare with ElevenLabs?
In a 24-language test Omnivoice posted 2.85% WER versus 10.95% and 0.830 similarity versus 0.655, across 646 languages instead of 32.
What does Voice Design do?
It creates a voice with no audio input. Describe it — "male, warm, Irish accent, upbeat" — and a matching speaker is generated.
Is commercial use allowed?
Apache 2.0 permits it, and training used only open-source datasets, which keeps licensing simple.
Do I need a GPU?
It runs on NVIDIA (CUDA 12.8), Apple Silicon and CPU. A GPU is advised for production — an H20 reaches about 45× real-time.




