OuteTTS

OuteTTS TTS

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

Bhalisa Uluhlu lwezinto zobumnini Zolwaleko...

Ulawulo oluchanekileyo:

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

Ii-tags imodeli ekhethiweyo iqonda - nqakraza ukushiya enye kumbhalo wakho apho isenza khona:

Le modeli ifunda umbhalo oqhelekileyo, ngoko ke i-inline tags ilahleka. Uphawu olusekelwe kwi-emotions, tshintshela kwimodeli ebonisa umbono njenge-Orpheus okanye i-Bark.

Chaza ubeko lwephepha

-12 +12
0.5x 2.0x
Ikhululekile nge Piper, VITS, MeloTTS
Isandi sakho esivelisweyo siza kuvela apha. Khetha imodeli, ngenisa umbhalo, kwaye unqakraze Yenza.
Isandi Sizaliswe Ngempumelelo
0:00
Layisha ezantsi Layisha ezantsi Ikhonkco liphelelwe lixesha kwiyure ezi-24
Inqanaba elikhululekileyo: ukusetyenziswa komuntu siqu. Ilayisensi yezorhwebo ukusuka kwi- $5/inyanga
Uthando TTS.ai? Nceda utshele abalandeli bakho!

I-About 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.

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

Khangela konke OuteTTS iilizwi

Kwingxelo

Umbhekisi phambili
OuteAI
Ilayisensi
Apache 2.0
I-Tier
free
Isantya
slow
Ukuphinda usebenzise ilizwi
Akukho nanye
Iilwimi
English
Ubukhulu bamagama
1000

OuteTTS iilizwi

Female 1 (Neutral)

English
Iinketho zelizwe Female

OuteTTS TTS - Imibuzo ebuzwa rhoqo

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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