OuteTTS

OuteTTS TTS

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

Anmelden Limit fir 5. 000 Zeichen

Wrap your text in SSML tags for precise control:

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

Tags déi d'gewielt Modell verstinn - klickt fir eng an Ärem Text ze setzen wou se geschitt:

Dëse Modell liest einfache Text, sou datt Inline-Tags ignoréiert ginn. Fir Tag-baséiert Emotiounen, wielt e expressiven Modell wéi Orpheus oder Bark.

Eegen Aussproochen definéieren (Wuert = Aussprooch):

-12 +12
0.5x 2.0x
Free mat Piper, VITS, MeloTTS
Äert generéiert Audio wäert hei erscheinen. Wielt e Modell, gitt Text an a klickt op Generéieren.
Audio gouf erfollegräich generéiert
0:00
Audio erofgelueden Lëscht vu lëtzebuergesche Schrëftsteller Link expires in 24h
Den Haaptuert ass Personnes. Kommerziell Lizenz vun $5/mo
Maacht dat Är eege Stëmm Klonen eng Stëmm an 30 Sekonnen
Liewe TTS.ai? Erzielt Är Frënn!

Iwwer 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.

Bescht fir: Edge deployment, browser-based TTS, low-resource environments

All sichen OuteTTS Stimmen

Op ee Bléck

Entwéckler
OuteAI
Lizenz
Apache 2.0
Tier
free
Geschwindegkeet
slow
Sprooche-Klonen
Nee
Sproochen
English
Maximal Zeichen
1000

OuteTTS Stimmen

Female 1 (Neutral)

English
Fräi Female

OuteTTS Lëscht vun de 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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