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Sesame CSM

Sesame CSM TTS

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

0/500 Ụdị · Nweta 5,000 kwa afọ →
Akaụntụ maka 5,000 akara oghe

Kpọchie ngwe gị n'ime SSML táàbụ̀ maka nlekọta ziri ezi:

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

Táàbụ̀ nke móòdù ahụ a họọrọ na-aghọta - pịa ka ịkpụga otu n'ime ngwe gị ebe ọ na-eme:

Móòdù a na-agụ ngwe nkịtị, yabụ na a na-ewepụta inline táàbụ̀. Maka táàbụ̀-n'okpuru n'émóòdù, gbanwee ka móòdù na-egosi ihe dịka Orpheus mọọbụ Bark.

Ndesịta okwu emeredịkachọrọ:

-12 +12
0.5x 2.0x
Free na Piper, VITS, MeloTTS
Ọdịdị gị ga-egosipụta ebe a. Họrọ móòdù, tinye ngwe, ma pịa Kewapụta.
Ọdịdị a mepụtala nke ọma
0:00
Bubata ụda Bubata.srt Ndesịta njikọ ahụ ga-agwụ n'ime 24h
Free tier: ojiji onwe onye. Commercial license site na $5/mo
Mee ka ọ bụrụ ụda gị Kloo ụda n'ime sekọnd 30
Ị hụrụ TTS.ai? Kpọtụrụ enyi gị!

_N'ihe banyere 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.

Ọkachasị maka: AI assistants, chatbots, conversational AI applications

Nlegharịa niile Sesame CSM ụda

N'ime nlele

Ńkwádò
Sesame
Ikikere
Apache 2.0
Tier
premium
Nhazi
slow
Nhazi ụda
Ọ bụghị
Asụsụ ndị ahụ
English
Ụhara Max
500

Sesame CSM ụda

Speaker 0

English
Premium Neutral

Speaker 1

English
Premium Neutral

Sesame CSM TTS - Ajụjụ ndị na-emekarị

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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