Two models, one API key
Text to speech and speech to text, one key, one balance, nothing to wire up between them
Trained on speechyou can’t scrape
Tokay is trained end to end on voice data we record ourselves. That is why it speaks languages the incumbents can’t, and why the quality holds where scraped models fall apart.
It sounds like a person, not a text-to-speech voice, and it starts talking fast enough that nobody waits for the first word.
- Languages
- Kazakh
- Voices
- Six, three female and three male
- Model
- tokay-kk-v1
- Next
- More languages on the same endpoint
It never askswhich languageyou’re speaking
Every other engine makes you pick a language per recording. That breaks the moment someone starts a sentence in one language and finishes it in another, which for bilingual speakers is most sentences.
Seta reads every language it supports in one shared output space, so switching costs it nothing. There is no language setting to get wrong, because there is no language setting.
On the same Kazakh audio, Whisper scores 44.95% word error rate. Seta scores 8.71%.
- Languages
- Kazakh and Russian
- Switching
- Mid-sentence, and inside a single word
- Accuracy
- 8.71% word error rate, 5× better than Whisper
- Speed
- 40× faster than real time
- Model
- seta-kk-ru-v2
- Next
- More languages on the same endpoint
Need a voice that doesn’t exist yet?
Tell us the language, we record the speakers and train the model