Speech Synthesis

TL;DR for operators Morpho-VITS1 improves Kinyarwanda text-to-speech on both automatic and native-listener evaluation. Compared with a VITS baseline, word error rate falls from 13.4% to 9.4%, while perceived intelligibility rises from 92.1% to 96.7%. Listener scores also improve for naturalness and intonation. The architectural change is explicit linguistic structure. Kinyarwanda spelling does not mark all of the tonal information needed for natural speech, so Morpho-VITS separately represents the meaningful grammatical and lexical pieces inside words and feeds that information into phoneme-level processing. ...

Voice demos are easy to fake. Give a model a clean recording, let it read a theatrical sentence, and the result can sound impressive enough for a launch video. That is not the hard part. The hard part is making speech generation behave like an actual product: multilingual, low-latency, emotionally credible, speaker-consistent, and not outrageously expensive to serve. ...

A high-quality multilingual text-to-speech model from ByteDance, capable of generating human-like speech with emotion, prosody, and cross-lingual support.