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Sesame CSM

Sesame CSM TTS

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

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Wrap test tiegħek fil-tags SSML għall-kontroll preċiż:

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

Tags li l-mudell magħżul jifhem — ikklikkja biex tqiegħed waħda fit-test tiegħek fejn jiġri:

Dan il-mudell jaqra test sempliċi, għalhekk it-tags inline huma injorati.Għal emozzjoni bbażata fuq it-tag, aqleb għal mudell espressiv bħal Orpheus jew Bark.

Iddefinixxi pronunzji tad-dwana (kelma = pronunzja):

-12 +12
0.5x 2.0x
Ħieles ma Piper, VITS, MeloTTS
L-awdjo iġġenerat tiegħek se jidher hawnhekk. Agħżel mudell, daħħal it-test, u kklikkja Iġġenera.
Awdjo Iġġenerat b'suċċess
0:00
Niżżel l-awdjo Niżżel.srt Il-link tiskadi f'24 siegħa
Livell Ħieles: użu personali. Liċenzja Kummerċjali minn $5/mo
Agħmel dan il-vuċi tiegħek stess Klona vuċi f'30 sekonda
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Dwar 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.

L-aħjar għal: AI assistants, chatbots, conversational AI applications

Ibbrawżja kollox Sesame CSM vuċijiet

Daqqa t'għajn

Żviluppatur
Sesame
Liċenzja
Apache 2.0
Annimali
premium
Veloċità
slow
Klonazzjoni tal-vuċi
Nru
Lingwi
English
Karattri massimi
500

Sesame CSM vuċijiet

Speaker 0

English
Primjum Neutral

Speaker 1

English
Primjum Neutral

Sesame CSM TTS — Mistoqsijiet Frekwenti

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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