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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.

0/500 Ụdị · Nweta 5,000 kwa afọ →
Akaụntụ maka 5,000 akara oghe

Kpọchie ngwe gị n'ime SSML táàbụ̀ maka nlekọta ziri ezi:

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

Táàbụ̀ nke móòdù ahụ a họọrọ na-aghọta - pịa ka ịkpụga otu n'ime ngwe gị ebe ọ na-eme:

Móòdù a na-agụ ngwe nkịtị, yabụ na a na-ewepụta inline táàbụ̀. Maka táàbụ̀-n'okpuru n'émóòdù, gbanwee ka móòdù na-egosi ihe dịka Orpheus mọọbụ Bark.

Ndesịta okwu emeredịkachọrọ:

-12 +12
0.5x 2.0x
Free na Piper, VITS, MeloTTS
Ọdịdị gị ga-egosipụta ebe a. Họrọ móòdù, tinye ngwe, ma pịa Kewapụta.
Ọdịdị a mepụtala nke ọma
0:00
Bubata ụda Bubata.srt Ndesịta njikọ ahụ ga-agwụ n'ime 24h
Free tier: ojiji onwe onye. Commercial license site na $5/mo
Mee ka ọ bụrụ ụda gị Kloo ụda n'ime sekọnd 30
Ị hụrụ TTS.ai? Kpọtụrụ enyi gị!

_N'ihe banyere 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.

Ọkachasị maka: Edge deployment, browser-based TTS, low-resource environments

Nlegharịa niile OuteTTS ụda

N'ime nlele

Ńkwádò
OuteAI
Ikikere
Apache 2.0
Tier
free
Nhazi
slow
Nhazi ụda
Ọ bụghị
Asụsụ ndị ahụ
English
Ụhara Max
1000

OuteTTS ụda

Female 1 (Neutral)

English
Free Female

OuteTTS TTS - Ajụjụ ndị na-emekarị

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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