Testutik hizketara Agenteak Musika Audioa testura Ahots klonaketa Ahots-diseinua Merkatua Gehiago
Txostendu programa- errorea / Eskaera
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 karaktereak · 5.000 belaunaldiko. →
Izena eman 5.000 karaktereko muga

Itzulbiratu zure testua SSML etiketetan kontrol zehatzagoa lortzeko:

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

Hautatutako modeloak ulertzen dituen etiketak — egin klik testuan jartzeko:

Eredu honek testu arrunta irakurtzen du, beraz, lerro-barneko etiketei ez zaie jaramonik egiten. Etiketetan oinarritutako emozioetarako, aldatu Orpheus edo Bark bezalako adierazpen-modelo batera.

Definitu ahoskera pertsonalizatuak (hitza = ahoskera):

-12 +12
0.5x 2.0x
Librea Piper, VITS, MeloTTS-ekin
Zure sortutako audioa hemen agertuko da. Aukeratu modelo bat, idatzi testua eta egin klik Sortu botoian.
Audioa behar bezala sortu da
0:00
Deskargatu audioa Deskargatu.srt Esteka 24 ordutan iraungiko da
Librea: erabiltzaile pribatuentzat. Lizentzia komertziala $5/mo-tik
Maite TTS.ai? Esan zure lagunei!

Honi buruz 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.

Honako hauentzako onena: Edge deployment, browser-based TTS, low-resource environments

Arakatu dena OuteTTS ahotsak

Begirada batean

Garatzailea
OuteAI
Lizentzia
Apache 2.0
Tier
free
Abiadura
slow
Ahots klonaketa
Ez
Hizkuntzak
English
Gehienezko karaktereak
1000

OuteTTS ahotsak

Female 1 (Neutral)

English
Libre Female

OuteTTS TTS — Galdera ohikoenak

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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