OuteTTS TTS
An LLM-based TTS that runs on CPU, GPU, or even in the browser via llama.cpp and Transformers.js.
Whāriki i tōna kupu i roto i ngā tohu SSML mō te whakahaere tika:
<speak><prosody rate="slow">Slow speech</prosody></speak>
E mōhio ana ngā tohu ki te tauira i kōwhiria - ka kōwhiria kia whakawātea tētahi ki roto i tōna kupu i reira ka puta ai:
Ka pānui tēnei tauira i te kupu noa, nā reira ka whakakāhoretia ngā tohu ā-waitara. Mō te āhua o te tohu-taihi, ka huri ki tētahi tauira whakamārama pēnei i a Orpheus, Bark rānei.
Ka tautuhia ngā tohutohu ā-ringa (wāhi = tohutohu):
Mo 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.
Pai mo: Edge deployment, browser-based TTS, low-resource environments
Ka tirohia katoa OuteTTS ngā oroI te tirohanga
- Ka whakawhanakehia
- OuteAI
- Ka taea te whakawātea
- Apache 2.0
- Karaka
- free
- Āhuatanga
- slow
- Whakakōrero reo
- Kāore
- reo
- English
- Kāri nui rawa
- 1000