OuteTTS

OuteTTS TTS

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

Registrer deg for 5000 tegn- grense

Bryt teksten i SSML- tagger for nøyaktig kontroll:

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

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Definer selvvalgte uttaler (ord = uttale):

-12 +12
0.5x 2.0x
Fri for piper, VITS, MeloTTS
Her vises din genererte lyd. Velg en modell, skriv inn tekst og trykk Generer.
Lydgenerert vellykket
0:00
Last ned lyd Last ned.srt Lenke utløper om 24 timer
Fritt nivå: personlig bruk. Handelslisens fra $5/mo
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Om 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.

Best for: Edge deployment, browser-based TTS, low-resource environments

Bla gjennom alle OuteTTS stemmer

Med et blikk

Utvikler
OuteAI
Lisens
Apache 2.0
Nivå
free
Hastighet
slow
Stemmekloning
Nei
Språk
English
Største antall tegn
1000

OuteTTS stemmer

Female 1 (Neutral)

English
Ledig Female

OuteTTS TTS — OSS

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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