OuteTTS

OuteTTS TTS

An LLM-based TTS that runs on CPU, GPU, or even in the browser via llama.cpp and Transformers.js.

Cláraigh anois! le haghaidh teorainn 5,000 carachtar

Cuir do théacs i gclibeanna SSML le haghaidh rialú beacht:

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

Clibeanna a thuigeann an tsamhail roghnaithe — cliceáil chun ceann a scaoileadh isteach i do théacs nuair a tharlaíonn sé:

Léann an tsamhail seo gnáth- théacs, mar sin déantar neamhaird ar chlibeanna inlíne. Chun mothúchán clibbhunaithe a chruthú, athraigh go samhail léiritheach cosúil le Orpheus nó Bark.

Sainmhínigh fuaimniú saincheaptha (focal = fuaimniú):

-12 +12
0.5x 2.0x
Saor in Aisce le Piper, VITS, MeloTTS
Taispeánfar an fhuaim a ghintear anseo. Roghnaigh samhail, iontráil téacs, agus cliceáil Giniúint.
D' éirigh le giniúint na fuaime
0:00
Íosluchtaigh Fuaim Íoslódáil.srt Téann an nasc in éag i 24h
Ciseal saor in aisce: úsáid phearsanta. Ceadúnas Tráchtála ó $ 5 / mo
Leabaigh an Tweet Ag tabhairt freagra ar friends!

Eolas Faoi OuteTTS

OuteTTS by OuteAI takes a language-model approach to speech: it extends an LLM with text-to-speech capability while keeping the original architecture intact, so it can run through standard LLM tooling. That gives it unusually broad backend support — llama.cpp on CPU or GPU, Hugging Face Transformers, ExLlamaV2, VLLM, and even in-browser inference via Transformers.js. It is a natural fit for edge deployment and low-resource environments where running a model client-side or on CPU matters more than raw speed. On TTS.ai it is offered on the free tier for English. Because the LLM-based pipeline is slow on long inputs, it is best used for shorter regular text rather than long-form cloning.

Is Fearr le haghaidh: Edge deployment, browser-based TTS, low-resource environments

Brabhsáil Uile OuteTTS guthanna

Ag Sracfhéachaint

Forbróir
OuteAI
Ceadúnas
Apache 2.0
Tír
free
Luas
slow
Clónáil gutha
& Ná Sábháil
Teangacha
English
Carachtair Uasta
1000

OuteTTS guthanna

Female 1 (Neutral)

English
Saor Female

OuteTTS TTS - Ceisteanna Coitianta

Across many backends — llama.cpp (CPU or GPU), Hugging Face Transformers, ExLlamaV2, VLLM, and even directly in the browser through Transformers.js — because it preserves the underlying LLM architecture.

Its LLM-based design runs efficiently on CPU and in the browser, so it can operate client-side or on modest hardware without a dedicated GPU.

It works best on shorter inputs. The LLM-based pipeline is slow on long passages, so it is offered for regular short-to-medium TTS rather than long-form generation.
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