OuteTTS

OuteTTS TTS

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

Aanmelden voor 5.000 tekenlimiet

Wrap uw tekst in SSML-tags voor nauwkeurige controle:

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

Tags het geselecteerde model begrijpt

Dit model leest platte tekst, dus inline tags worden genegeerd. Voor emotie op basis van tags, schakel naar een expressief model zoals Orpheus of Bark.

Definieer aangepaste uitspraaken (woord = uitspraak):

-12 +12
0.5x 2.0x
Gratis met Piper, VITS, MeloTTS
Uw gegenereerde audio zal hier verschijnen. Kies een model, voer tekst in en klik op Genereren.
Audio Generated Succesvol
0:00
Audio downloaden Download.srt Link verloopt in 24 uur
Gratis niveau: persoonlijk gebruik. Commerciële licentie van $5/mo
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Info 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.

Beste voor: Edge deployment, browser-based TTS, low-resource environments

Alles doorbladeren OuteTTS stemmen

In een oogopslag

Ontwikkelaar
OuteAI
Licentie
Apache 2.0
Niveau
free
Snelheid
slow
Klonen van stemmen
Nee
Talen
English
Max. tekens
1000

OuteTTS stemmen

Female 1 (Neutral)

English
Vrij Female

OuteTTS Veelgestelde vragen

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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