OuteTTS

OuteTTS TTS

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

Signa per 5000 caràcters límit

Ajusta el text a les etiquetes SSML per al control precís:

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

Etiquetes del model seleccionat entenen el clic show clic per a deixar- ne un al text a on succeeix:

Aquest model llegeix text pla, així que les etiquetes inserides s' ignoren. Per a emocions basades en etiquetes, canvieu a un model expressiu com Orfeus o Bark.

Defineix pronúncies personalitzades (word = pronunciació):

-12 +12
0.5x 2.0x
Lliure amb Pipista, VITS, MeloTTS
Aquí apareixerà el vostre àudio generat. Escolliu un model, introduïu text i cliqueu Genera.
L' àudio s' ha generat correctament
0:00
Descarrega àudio Descarrega.srt L' enllaç expirarà el 24h
Correlitzador lliure: ús personal. Llicència de venda de 5/mo
Fes que això sigui la teva pròpia veu Clona una veu en 30 segons
Els teus amics!

Quant a 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.

Millor per: Edge deployment, browser-based TTS, low-resource environments

Navega- ho tot OuteTTS veus

En una mirada

Desenvolupador
OuteAI
Llicència
Apache 2.0
TierCity name (optional, probably does not need a translation)
free
Velocitat
slow
clonació de veu
No
Idiomes
English
Nombre màxim de caràcters
1000

OuteTTS veus

Female 1 (Neutral)

English
Lliure Female

OuteTTS PMF TTS

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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