Sesame CSM

Sesame CSM TTS

A 1B conversational speech model that captures natural dialogue timing, turn-taking, and backchannel responses.

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 Sesame CSM

Sesame CSM (Conversational Speech Model) is a 1-billion-parameter model from Sesame designed specifically for the rhythms of human conversation. Built on a Llama backbone paired with an audio codec, it models turn-taking timing, backchannel responses (the small acknowledgements people make while listening), emotional reactions, and overall conversational flow. The result reads less like read-aloud text and more like a real spoken exchange. It is a natural fit for AI assistants, chatbots, and conversational interfaces where the goal is speech that feels responsive and human. CSM is released under Apache 2.0, and access on TTS.ai requires a Hugging Face token at the model level.

Plej bona por: AI assistants, chatbots, conversational AI applications

Foliumi ĉiujn Sesame CSM voĉoj

Unu rigardo

Programisto
Sesame
Licenco
Apache 2.0
Tamuz
premium
Rapideco
slow
Voĉo- klonado
Ne
Lingvoj
English
Maksimuma nombro da signoj
500

Sesame CSM voĉoj

Speaker 0

English
PremiumLanguage Neutral

Speaker 1

English
PremiumLanguage Neutral

Sesame CSM TTS - FAQ

Conversational speech. It models the natural patterns of dialogue — turn-taking timing, backchannel responses, and emotional reactions — so generated audio sounds like a real conversation rather than synthetic narration.

It is a 1-billion-parameter model built on a Llama backbone with an audio codec for waveform generation.

AI assistants, chatbots, and other conversational applications where responsive, human-sounding speech matters more than long-form narration.
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