Sesame CSM

Sesame CSM TTS

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

Signa per 5000 caràcters límit

Ajusta el text a les etiquetes SSML per al control precís:

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

Etiquetes del model seleccionat entenen el clic show clic per a deixar- ne un al text a on succeeix:

Aquest model llegeix text pla, així que les etiquetes inserides s' ignoren. Per a emocions basades en etiquetes, canvieu a un model expressiu com Orfeus o Bark.

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Lliure amb Pipista, VITS, MeloTTS
Aquí apareixerà el vostre àudio generat. Escolliu un model, introduïu text i cliqueu Genera.
L' àudio s' ha generat correctament
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Descarrega àudio Descarrega.srt L' enllaç expirarà el 24h
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Quant a 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.

Millor per: AI assistants, chatbots, conversational AI applications

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Desenvolupador
Sesame
Llicència
Apache 2.0
TierCity name (optional, probably does not need a translation)
premium
Velocitat
slow
clonació de veu
No
Idiomes
English
Nombre màxim de caràcters
500

Sesame CSM veus

Speaker 0

English
Premium Neutral

Speaker 1

English
Premium Neutral

Sesame CSM PMF TTS

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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