StyleTTS 2

StyleTTS 2 TTS

Reaches human-level single-speaker synthesis through style diffusion and adversarial training.

Aliĝi for 5, 000 character limit

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):

-12 +12
0.5x 2.0x
Libera kun Piper, VITS, MeloTTS
Via generita sono aperos tie ĉi. Elektu modelon, entajpu tekston, kaj alklaku Generi.
Sondosiero sukcese generita
0:00
Elŝuti sonon Elŝuti.srt Ligo eksvalidiĝas post 24 horoj
Libera programaro: persona uzo. Komerca licenco ekde $5/mo
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Pri StyleTTS 2

StyleTTS 2, developed at Columbia University, achieves human-level text-to-speech for single-speaker synthesis by combining style diffusion with adversarial training guided by large speech language models. Its diffusion-based style modeling captures the full natural variation of human speech — subtle shifts in rhythm, emphasis, and tone — so output can rival real recordings. It is widely regarded as one of the most natural-sounding open single-speaker models, which makes it a strong choice for studio-quality narration and professional voiceover where polish matters more than cloning or multilingual range. StyleTTS 2 is English-focused and released under the permissive MIT license.

Plej bona por: Studio-quality single-speaker synthesis, professional narration

Foliumi ĉiujn StyleTTS 2 voĉoj

Unu rigardo

Programisto
Columbia University
Licenco
MIT
Tamuz
premium
Rapideco
medium
Voĉo- klonado
Ne
Lingvoj
English
Maksimuma nombro da signoj
500

StyleTTS 2 voĉoj

Default

English
PremiumLanguage Neutral

StyleTTS 2 TTS - FAQ

It combines style diffusion with adversarial training using large speech language models. The diffusion-based style modeling captures the full range of human speech variation, producing output that can rival real recordings.

No. It is focused on producing the most natural single-speaker synthesis rather than cloning a specific voice. For cloning, use a model like Chatterbox or GPT-SoVITS.

Studio-quality single-speaker work — professional narration and voiceover — where naturalness and polish are the priority. It is English-focused and MIT-licensed.
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