Sesame CSM

Sesame CSM TTS

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

Teken op vir 5 000 karakterbeperking

Oorvloei jou teks in SSML etiket vir presiese beheer:

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

Merk die gekose model verstaan ooit die woord ooit om een in jou teks te laat val waar dit gebeur:

Hierdie model lees gewone teks, so inlyn etiket word geignoreer. Vir etiket-gebaseerde emosie, wissel na 'n uitdrukkingende model soos Orpheus of Bark.

Definieer pasmaak uitspraak (woord = uitspraak):

-12 +12
0.5x 2.0x
Vry met Pyper, VITS, MiloTTS
Jou gegenereer oudio sal hier verskyn. Kies 'n model, invoer teks, en kliek Genereer.
Klank Genereer suksesvol
0:00
Aflaai klaar gemaak Aflaai klaar gemaak Skakel verstrek in 24h
Vryvlak: persoonlike gebruik. Kommonsielisensie van R5/m
Liefde TTS.ai, vertel jou vriende!

Aangaande 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.

Beste vir: AI assistants, chatbots, conversational AI applications

Blaai deur almal Sesame CSM stemme

Met'n blik

Ontwikkelingvloeistof is minDeveloper
Sesame
Lisensie
Apache 2.0
Tier
premium
Spoed
slow
Stem kloning
Nee
Tale
English
Voeg- agteraan- by Taal
500

Sesame CSM stemme

Speaker 0

English
Premium Neutral

Speaker 1

English
Premium 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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