OuteTTS

OuteTTS ТТС

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

Ёзиш 5000 белги чегараси

Матнни аниқ назорат учун SSML теглар билан ўраб қўйиш:

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

Танланган модел тушунадиган тегилар — уларни матнга тушириш учун босинг:

Бу модел оддий матн ўқийди, шунинг учун тегишли теги эътиборга олинмайди. Эмоционал теги асосида, Orpheus ёки Bark каби ифодали моделга ўтинг.

Ўз нутқини белгилаш (сўз = нутқ):

-12 +12
0.5x 2.0x
Piper, VITS, MeloTTS билан бепул
Сизнинг яратилган аудионгиз бу ерда намоён бўлади. Модельни танланг, матнни киритинг ва Юклаш тугмасини босинг.
Аудио муваффақиятли яратилди
0:00
Аудио юклаб олиш .srt юклаб олиш Уланиш муддати 24 соатдан сўнг тугайди
Бепул даража: шахсий фойдаланиш. $5/mo дан бошланувчи коммерциявий лицензия
Буни ўз овозингизга айлантиринг 30 сония ичида овозни клонлаш
TTS.ai'ни севасанми? Дўстларингга айт!

Маълумот 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.

Энг яхшиси: Edge deployment, browser-based TTS, low-resource environments

Ҳаммасини кўриш OuteTTS овозлар

Бир қарашда

Ижодкор
OuteAI
Лицензия
Apache 2.0
Тир
free
Тезлик
slow
Овозни клонлаш
Йўқ
Тиллар
English
Максимум ҳарфлар
1000

OuteTTS овозлар

Female 1 (Neutral)

English
Оқ Female

OuteTTS ТТС — кўп бериладиган саволлар

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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