Àkọlé àwòrán

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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Àwọn Ìṣàmúlò-ètò (Àwọn Àwọn Àkọ́kọ́ Àwọn Àkọ́kọ́ Àwọn Àkọ́kọ́)

Fi àkọlé rẹ pamọ́ sí àwọn àmì-ìwé SSML fún ìdáràn:

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

Àwọn Àmì-ìwé Ìjánu-ìró / Ìyárà

Àwọn Àmì-ìwé tí àwọn ìṣàmúlò-ètò tí a yàn gbọ́ - tẹ̀ láti fi ọkan sínú àkọ́lé rẹ̀ nínú àwọn ààyè-iṣẹ́ tí o bá jẹ́:

Àwọn Àwọn Àkọlé

Àwọn àwọn ìṣàfarawé àwọn àwọn ìṣàfarawé àwọn (ọrọ = ìṣàfàlì):

Ìjánu-ìsún 0

-12 +12

Àwọn ìṣàmúlò-ètò

Àwọn àmì-ìwé

Àwọn

Ìgúnrégé àwọn ààtòjọ-ẹ̀yàn

Ìjánu-ìṣàmúlò-ètò 1.0x

0.5x 2.0x

Free pẹlu Piper, VITS, MeloTTS

Àwọn àwòrán tí o ti ṣẹ̀dà tí o bá han níbẹ̀. Yan àwọn àwòrán, tẹ̀lẹ̀ àkọlé, ki o si tẹ̀ Ṣẹ̀dà.

Ààyè-iṣẹ́ 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.

Tí o dara jù fún: Edge deployment, browser-based TTS, low-resource environments

Wá Gbogbo àwòrán OuteTTS Àwọn àwòrán

Nínú àwọn ìṣàfarawé

Àwọn Àkọlé: OuteAI
Àwọn Ààyè-iṣẹ́: Apache 2.0
Àwọn àwọn ààyè-iṣẹ́: free
Ìjánu-ìṣàmúlò-ètò: slow
Ìṣàfarawé àwọn àmì-ìwé: Àwọn àwọn àgbéwọlé
Àwọn: English
Àwọn àyọkà ìpele: 1000

OuteTTS Àwọn àwòrán

Female 1 (Neutral)

English

Àìfẹ́ Female

OuteTTS Àwọn Àtòjọ-ẹ̀yàn

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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OuteTTS TTS

O fẹ́ TTS.ai? Fì sọ̀kalẹ̀ fún àwọn ọrẹ̀ rẹ̀!

Ààyè-iṣẹ́ OuteTTS

Nínú àwọn ìṣàfarawé

OuteTTS Àwọn àwòrán

Female 1 (Neutral)

OuteTTS Àwọn Àtòjọ-ẹ̀yàn

Where can OuteTTS run?

Why is OuteTTS suited to edge and low-resource use?

Is OuteTTS good for long text?