OuteTTS

OuteTTS TTS

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

Jijjiirama 5,000 character limit

Daangeessii kitaaba keessan keessaa tag SSML akka itti fayyadamtan:

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

Tag'oota mo'ellaa filatamee beekuu - cuqaasi akka tokkotti galchiin gara teekstaatti yoo ta'e:

Mo'ellaan kun kitaaba salphaa baraa, kan akka taggaa inniin linjii hin beekkamne. Akka taggaa-based emooshiniitti, mo'ellaa akka Orpheus ykn Bark.

Haalli fuula

-12 +12
0.5x 2.0x
Birrii fi Piper, VITS, MeloTTS
Oduu kee kan uumame yooka'u yooka'u. Suuraa moolaa, galchi kitaaba, fi bu'u Jijjiira.
Audion itti fufuu
0:00
Fuula Oduu Fuula Liqii dhumaa 24 sa'a keessatti
Tarree hin-ga'iin: fayyadama namaatiif. Liiziinii Kominikeeshinii irraa $5/mo
TTS.ai jaallatan? Sochii keessanitti hiika!

Fuulaa 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.

Fakkeenyaaf: Edge deployment, browser-based TTS, low-resource environments

Fuulaa OuteTTS Dhaamsa

Akkasumas

Deebi'aa
OuteAI
Lizenz
Apache 2.0
Daandiin
free
Jijjiiramni
slow
Dhaabbilee
Haata'u
Afaan Oromoo
English
Akkasumas
1000

OuteTTS Dhaamsa

Female 1 (Neutral)

English
Birrii Female

OuteTTS TTS — FAQ

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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