VITS TTS
The end-to-end TTS architecture that combines a variational autoencoder, normalizing flows, and adversarial training.
Envolvu vian tekston en SSML- etikedojn por preciza kontrolo:
<speak><prosody rate="slow">Slow speech</prosody></speak>
Etikedoj kiujn la elektita modelo komprenas - klaku por meti unu en vian tekston kie ĝi okazas:
This model reads plain text, so inline tags are ignored. For tag-based emotion, switch to an expressive model like Orpheus or Bark.
Difini proprajn elparolojn (vorto = elparolo):
Pri VITS
VITS — Variational Inference with adversarial learning for end-to-end Text-to-Speech — was introduced by Jaehyeon Kim and collaborators in 2021 and became a foundational architecture for modern neural speech. Rather than the older two-stage pipeline, it synthesizes audio in a single parallel end-to-end pass, pairing a variational autoencoder with normalizing flows and a GAN-style adversarial training process to lift naturalness. At about 25M parameters and trained on ~585 hours, it produces natural prosody at fast inference speeds and supports multiple speakers. It serves as a solid general-purpose, free baseline and underpins many later models such as Piper and MeloTTS.
Plej bona por: General-purpose text-to-speech with natural prosody
Foliumi ĉiujn VITS voĉojUnu rigardo
- Programisto
- Jaehyeon Kim et al.
- Licenco
- MIT
- Tamuz
- free
- Rapideco
- fast
- Voĉo- klonado
- Ne
- Lingvoj
- English, German, Spanish, French, Portuguese, Dutch, Finnish, Hungarian, Bulgarian, Japanese, Polish
- Maksimuma nombro da signoj
- 2000