OuteTTS

OuteTTS TTS

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

Ku soo biir 5,000 xaraf xaddid

Wrap qoraalka ku SSML tags si loo hubiyo xakamaynta saxda ah:

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

Tags qaabka la doortay fahmo — riix si aad u hoos mid ka mid ah qoraalka aad halkaas oo uu ka dhacaa:

Model this akhriyo qoraalka caadiga ah, sidaas inline tags waa la iska indho tiri. For tag-ku salaysan dareenka, u dhaqaaqo si ay u muujiyaan qaabka sida Orpheus ama Bark.

Define custom pronunciations (word = dhawaaqa):

-12 +12
0.5x 2.0x
Bilaash ah oo leh Piper, VITS, MeloTTS
Your audio soo saaro halkan ka muuqan doonaa. Dooro qaab, ku qor qoraalka, oo guji soo saaro.
Dhaqdhaqaaqa ayaa la soo saaray
0:00
Soo dejisa Soo dejisan.srt Xidhiidhku wuxuu dhamaaday 24h
Free tier: isticmaalka shakhsiga ah. Liisan ganacsi laga bilaabo $ 5 / mo
Jecel TTS.ai? Ka warran saaxiibadaa!

About 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.

Ugu Fiican: Edge deployment, browser-based TTS, low-resource environments

Taabo oo kala soo bax OuteTTS cod

Eeg

Soo-saarayaasha
OuteAI
Liisan
Apache 2.0
Qiyaamaha
free
Xawaaraha
slow
Duubista Codka
Ha
Afaf
English
Noocyada ugu badan
1000

OuteTTS cod

Female 1 (Neutral)

English
Bilaash Female

OuteTTS Su'aalaha La Weydiiyo

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.
← Codadka oo dhan