Text to Speech Mawakili wa AI Muziki Usemi kwa Maandiko Sauti Yaungana Ubuni wa Sauti Soko Na zaidi
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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 wahusika · Tia sahihi kwa 5,000 kwa kila kizazi →
Tia sahihi kwa kiwango cha tabia 5,000

Pakua maandishi yako katika tovuti ya SSML kwa ajili ya udhibiti sahihi:

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

Tag anaelewa mfano unaochaguliwa na unajibu ujumbe huu:

Mfano huu unasomeka maandishi rahisi, kwa hiyo alama za vidole hupuuzwa. Kwa hisia za ndani za watu, geukia kigezo kinachoonesha hisia kama Orfeus au Bark.

Matamshi ya desturi (neno = matamshi):

-12 +12
0.5x 2.0x
Nikiwa huru na Piper, VITS, MelloTTTS
Unaweza kuchagua mfano, maandishi, na kidofo kinachoitwa Genete.
Edio Iliyorekebishwa kwa Mafanikio
0:00
Paketi ya Audio Paketisha.srt Kiungo kinakufa mnamo 24
Safu huru: matumizi ya kibinafsi. Hati ya biashara kutoka dola 5/mo
Fanya hii sauti yako mwenyewe Chokoa sauti kwa sekunde 30
Waeleze rafiki zako kuhusu mapenzi ya TTS.ai?

Habari 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.

Bora kwa: Edge deployment, browser-based TTS, low-resource environments

Ng'ombe wote OuteTTS sauti

Kutupia jicho

Mbuni
OuteAI
Lenzi
Apache 2.0
Tier
free
Mwendo
slow
Kufanyizwa kwa Sauti
Hapana
Lugha
English
Wahusika wa Max
1000

OuteTTS sauti

Female 1 (Neutral)

English
Huru Female

OuteTTS TTS ngumuSTEGAQ

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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