OuteTTS

OuteTTS TTS

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

Enskri Limit pou 5,000 karaktè

Wrap ou tèks nan SSML tags pou presizyon kontwòl:

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

Tags ke modèl la chwazi konprann — klike pou mete yon nan tèks ou kote li rive:

Modèl sa a li tèks senp, se poutèt sa atik ki nan liy yo pa pran an kont. Pou efè ki baze sou atik, chanje pou yon modèl ekspresyon tankou Orpheus oswa Bark.

Define prononciations Custom (mot = prononciation):

-12 +12
0.5x 2.0x
Gratis ak Piper, VITS, MeloTTS
Son ou kreye a ap parèt isit la. Chwazi yon modèl, antre tèks la, epi klike Kreye.
Audio Generated Successfully
0:00
Telechaje son Telechaje.srt Link expires in 24h
Free tier: itilize pèsonèl. Lisans Komèsyal soti nan $5/mo
Love TTS.ai? Di zanmi ou yo!

Atik 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.

Pi bon pou: Edge deployment, browser-based TTS, low-resource environments

Navigue tout OuteTTS Voy

Yon ti gade

Pwogramè
OuteAI
Lisans
Apache 2.0
Nivo
free
Vitès
slow
Klonaj vwa
Non
Lang
English
Karakteris maksimòm
1000

OuteTTS Voy

Female 1 (Neutral)

English
Gratis 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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