OuteTTS

OuteTTS TTS

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

Irreġistra issa għal 5,000 karattru limitu

Wrap test tiegħek fil-tags SSML għall-kontroll preċiż:

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

Tags li l-mudell magħżul jifhem — ikklikkja biex tqiegħed waħda fit-test tiegħek fejn jiġri:

Dan il-mudell jaqra test sempliċi, għalhekk it-tags inline huma injorati.Għal emozzjoni bbażata fuq it-tag, aqleb għal mudell espressiv bħal Orpheus jew Bark.

Iddefinixxi pronunzji tad-dwana (kelma = pronunzja):

-12 +12
0.5x 2.0x
Ħieles ma Piper, VITS, MeloTTS
L-awdjo iġġenerat tiegħek se jidher hawnhekk. Agħżel mudell, daħħal it-test, u kklikkja Iġġenera.
Awdjo Iġġenerat b'suċċess
0:00
Niżżel l-awdjo Niżżel.srt Il-link tiskadi f'24 siegħa
Livell Ħieles: użu personali. Liċenzja Kummerċjali minn $5/mo
Agħmel dan il-vuċi tiegħek stess Klona vuċi f'30 sekonda
Imħabba TTS.ai? Għid lill-ħbieb tiegħek!

Dwar 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.

L-aħjar għal: Edge deployment, browser-based TTS, low-resource environments

Ibbrawżja kollox OuteTTS vuċijiet

Daqqa t'għajn

Żviluppatur
OuteAI
Liċenzja
Apache 2.0
Annimali
free
Veloċità
slow
Klonazzjoni tal-vuċi
Nru
Lingwi
English
Karattri massimi
1000

OuteTTS vuċijiet

Female 1 (Neutral)

English
Ħieles Female

OuteTTS TTS — Mistoqsijiet Frekwenti

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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