OuteTTS

OuteTTS Mga TNT

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

Mag-sign up para sa 5,000 character na limitasyon

I-wrap ang iyong teksto sa SSML tags para sa tumpak na kontrol:

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

Tags ang napili modelo nauunawaan — i-click upang ihulog ang isa sa iyong teksto kung saan ito ay nangyayari:

Ang modelong ito ay nagbabasa ng karaniwang teksto, kaya inline tags ay hindi pinapansin. Para sa tag-based na damdamin, lumipat sa isang makahulugang modelo tulad ng Orpheus o Bark.

Tukuyin ang mga pasadyang mga panlapi (word = panlapi):

-12 +12
0.5x 2.0x
Libreng may Piper, VITS, MeloTTS
Ang iyong ginawang audio ay lilitaw dito. Pumili ng modelo, ipasok ang teksto, at i-click ang Bumuo.
Audio nabuo Matagumpay
0:00
I-download ang Audio I-download ang.srt Link expires sa 24h
Free tier: personal na paggamit. Komersyal na lisensya mula sa $5/buwan
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Tungkol sa 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.

Pinakamahusay para sa: Edge deployment, browser-based TTS, low-resource environments

Mag-browse ng lahat OuteTTS Mga boses

Sa isang sulyap

Developer
OuteAI
Lisensya
Apache 2.0
Mga hayop
free
Bilis
slow
Pag-clone ng boses
Hindi
Wika
English
Max character
1000

OuteTTS Mga boses

Female 1 (Neutral)

English
Libre Female

OuteTTS Mga katanungan at sagot

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.
← Lahat ng mga boses