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OuteTTS

OuteTTS TTS

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

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Vpišite se. za 5000 mejnih vrednosti znakov

Za natančen nadzor zavijte svoje besedilo v oznake SSML:

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

Oznake izbranega modela razume – kliknite, da spustite enega v svoje besedilo, kjer se zgodi:

Ta model bere navadno besedilo, zato se v vrstici ignorirajo. Za čustva, ki temeljijo na tag, preklopite na izražen model, kot je Orfeus ali Bark.

Opredelitev posebnih izgovorov (beseda = izgovor):

-12 +12
0.5x 2.0x
Brez Piper, VITS, Melotts
Tukaj se bo pojavil vaš ustvarjeni zvok. Izberite model, vnesite besedilo in kliknite Generiraj.
Uspešno ustvarjen zvok
0:00
Prenesi zvok Prenesi.rt Povezava poteče čez 24h
Brezplačna stopnja: osebna uporaba. Trgovska licenca od 5 $/mo
Ljubi TTS.ai, povej prijateljem!

O projektu 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.

Najboljše za: Edge deployment, browser-based TTS, low-resource environments

Brskaj vse OuteTTS glasovi

Na pogled

Razvijalec
OuteAI
Licenca
Apache 2.0
Stopnja
free
Hitrost
slow
kloniranje glasu
Ne
Jeziki
English
Največ znakov
1000

OuteTTS glasovi

Female 1 (Neutral)

English
Prosto Female

OuteTTS TTS – Pogosta vprašanja

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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