OuteTTS

OuteTTS TTS

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

Kugadzwa for 5,000 character limit

Wrap yako tenzi mu SSML tags kuti zvive nyore kudzora:

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

Tags iyo yakasarudzwa model inonzwisiswa — tinya kuti utore imwe muchinyorwa chako apo inoitika:

Iyi modhi inoverenga chete mazita ezvinyorwa, saka zvinyorwa zvinonyorwa mumitsara hazvina kutariswa. Kuti uwane pfungwa dziri mumitsara, chinja kune imwe modhi inoratidza pfungwa seOrpheus kana Bark.

Define custom pronunciations (word = pronunciation):

-12 +12
0.5x 2.0x
Free with Piper, VITS, MeloTTS
Yako yakagadzirwa audio ichaonekwa pano. Choose a model, enter text, and click Generate.
Audio Yakagadzirwa Nekubudirira
0:00
Download Audio Dhawunirodha.srt Link inotanga kushanda mu 24h
Free tier: personal usage. Commercial license from $5/mo
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Chii 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.

Yakanaka kune: Edge deployment, browser-based TTS, low-resource environments

Tarisa zvese OuteTTS mazwi

Mufananidzo

Developer
OuteAI
License
Apache 2.0
Tier
free
Speed
slow
Kutaura
Hapana
Zvinhu
English
Max characters
1000

OuteTTS mazwi

Female 1 (Neutral)

English
Free Female

OuteTTS TTS — Zvinyorwa

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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