VITS TTS
The end-to-end TTS architecture that combines a variational autoencoder, normalizing flows, and adversarial training.
Ang yuta palibot sa Ssm kay medyo kabukiran.
<speak><prosody rate="slow">Slow speech</prosody></speak>
Ang mga tag sa gipili nga modelo makasabut - i-klik aron ihulog ang usa sa imong teksto diin kini mahitabo:
Ang modelong kini mobasa sa yano nga teksto, busa ang mga inline tags gi-ignore. Alang sa mga tag-based nga emosyon, i-usab sa usa ka ekspresyonal nga modelo sama sa Orpheus o Bark.
Ang yuta palibot sa Cerro La Pronunciación kay lain-lain.
Sa palibot sa Aïn el Aïd. 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.
Sa palibot sa Best.: General-purpose text-to-speech with natural prosody
Lawak ang lahat VITS TingogSa palibot sa Glance.
- Pag-uswag
- Jaehyeon Kim et al.
- Lisensiya
- MIT
- Tigre
- free
- Katulin
- fast
- Sa palibot sa Klondike.
- Wala
- Linguistics
- English, German, Spanish, French, Portuguese, Dutch, Finnish, Hungarian, Bulgarian, Japanese, Polish
- Maksimum nga mga karakter
- 2000