StyleTTS 2

StyleTTS 2 TTS

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

Izena eman 5.000 karaktereko muga

Itzulbiratu zure testua SSML etiketetan kontrol zehatzagoa lortzeko:

<speak><prosody rate="slow">Slow speech</prosody></speak>

Hautatutako modeloak ulertzen dituen etiketak — egin klik testuan jartzeko:

Eredu honek testu arrunta irakurtzen du, beraz, lerro-barneko etiketei ez zaie jaramonik egiten. Etiketetan oinarritutako emozioetarako, aldatu Orpheus edo Bark bezalako adierazpen-modelo batera.

Definitu ahoskera pertsonalizatuak (hitza = ahoskera):

-12 +12
0.5x 2.0x
Librea Piper, VITS, MeloTTS-ekin
Zure sortutako audioa hemen agertuko da. Aukeratu modelo bat, idatzi testua eta egin klik Sortu botoian.
Audioa behar bezala sortu da
0:00
Deskargatu audioa Deskargatu.srt Esteka 24 ordutan iraungiko da
Librea: erabiltzaile pribatuentzat. Lizentzia komertziala $5/mo-tik
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Honi buruz 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.

Honako hauentzako onena: Studio-quality single-speaker synthesis, professional narration

Arakatu dena StyleTTS 2 ahotsak

Begirada batean

Garatzailea
Columbia University
Lizentzia
MIT
Tier
premium
Abiadura
medium
Ahots klonaketa
Ez
Hizkuntzak
English
Gehienezko karaktereak
500

StyleTTS 2 ahotsak

Default

English
Premium Neutral

StyleTTS 2 TTS — Galdera ohikoenak

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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