OuteTTS

OuteTTS TTS

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

Aliĝi for 5, 000 character limit

Envolvu vian tekston en SSML- etikedojn por preciza kontrolo:

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

Etikedoj kiujn la elektita modelo komprenas - klaku por meti unu en vian tekston kie ĝi okazas:

This model reads plain text, so inline tags are ignored. For tag-based emotion, switch to an expressive model like Orpheus or Bark.

Difini proprajn elparolojn (vorto = elparolo):

-12 +12
0.5x 2.0x
Libera kun Piper, VITS, MeloTTS
Via generita sono aperos tie ĉi. Elektu modelon, entajpu tekston, kaj alklaku Generi.
Sondosiero sukcese generita
0:00
Elŝuti sonon Elŝuti.srt Ligo eksvalidiĝas post 24 horoj
Libera programaro: persona uzo. Komerca licenco ekde $5/mo
Ĉu vi ŝatas TTS.ai? Diru al viaj amikoj!

Pri 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.

Plej bona por: Edge deployment, browser-based TTS, low-resource environments

Foliumi ĉiujn OuteTTS voĉoj

Unu rigardo

Programisto
OuteAI
Licenco
Apache 2.0
Tamuz
free
Rapideco
slow
Voĉo- klonado
Ne
Lingvoj
English
Maksimuma nombro da signoj
1000

OuteTTS voĉoj

Female 1 (Neutral)

English
Libera 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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