Sesame CSM

Sesame CSM TTS

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

Pierakstīties 5000 rakstzīmju limitam

Aplauzt savu tekstu SSML tagus precīzai kontrolei:

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

Tags izvēlētais modelis saprot — noklikšķiniet, lai iemestu vienu jūsu tekstā, kur tas notiek:

Šis modelis lasa vienkāršu tekstu, tāpēc tiek ignorēti inline tagi. Uz tag-based emocijas, pāriet uz izteiksmīgu modeli, piemēram, Orpheus vai Bark.

Definēt pielāgotu izrunas (vārds = izruna):

-12 +12
0.5x 2.0x
Bez piper, VITS, MeloTTS
Šeit parādīsies jūsu ģenerētais audio. Izvēlieties modeli, ievadiet tekstu un noklikšķiniet ģenerējiet.
Audio veiksmīgi ģenerēts
0:00
Lejupielādēt audio Lejupielādēt.srt Saite beidzas 24h
Bezmaksas līmenis: personīgai lietošanai. Komerclicenci no $5/mo
Mīlestība TTS.ai? Stāsti saviem draugiem!

Par 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.

Labākais: AI assistants, chatbots, conversational AI applications

Pārlūkot visu Sesame CSM balsis

Īsumā

Izstrādātājs
Sesame
Licence
Apache 2.0
Līmeņrādis
premium
Ātrums
slow
Balss klonēšana
Valodas
English
Maks. rakstzīmes
500

Sesame CSM balsis

Speaker 0

English
Prēmija Neutral

Speaker 1

English
Prēmija 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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