OuteTTS

OuteTTS TTS

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

Registreeru 5000 tähemärgi piir

SSML-i siltidesse teksti segamine täpseks kontrollimiseks:

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

Sildid valitud mudelil mõistavad ~ klõpsa ühe kukutamiseks teksti, kus see juhtub:

See mudel loeb lihtsat teksti, nii et sisemisi silte ignoreeritakse. Sildil põhinevate emotsioonide puhul lülituge ekspressiivsele mudelile nagu Orpheus või Bark.

Kohandatud häälduste määramine (sõna = hääldus):

-12 +12
0.5x 2.0x
Tasuta Piper, VITS, MeloTTS
Siin ilmub sinu loodud heli. Vali mudel, sisesta tekst ja klõpsa Genereeri.
Audio genereeritud edukalt
0:00
Audio allalaadimine Lae alla.srt Link aegub 24 tunni pärast.
Tasuta tase: isiklik kasutamine. Äriline litsents alates $5/mo
Armastus TTS.ai?

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.

Parim: Edge deployment, browser-based TTS, low-resource environments

Kõigi sirvimine OuteTTS hääled

Põgusalt

Arendaja
OuteAI
Litsents
Apache 2.0
Määramistasand
free
Kiirus
slow
Hääle kloonimine
Ei.
Keeled
English
Maks. märgid
1000

OuteTTS hääled

Female 1 (Neutral)

English
Vaba Female

OuteTTS TTS (KKK)

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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