OuteTTS

OuteTTS TTS

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

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Avvolgi il tuo testo nei tag SSML per un controllo preciso:

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

Tags il modello selezionato comprende clic su

Questo modello legge testo semplice, quindi i tag inline vengono ignorati. Per le emozioni basate sui tag, passare a un modello espressivo come Orpheus o Bark.

Definire le pronunciazioni personalizzate (parola = pronuncia):

-12 +12
0.5x 2.0x
Gratis con Piper, VITS, MeloTTS
L'audio generato apparirà qui. Scegli un modello, inserisci testo e fai clic su Genera.
Audio generato con successo
0:00
Scarica audio Scarica.srt Link scade in 24 ore
Livello libero: uso personale. Licenza commerciale da $5/mo
Fai di questo la tua voce Clona una voce in 30 secondi
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Informazioni 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.

Meglio per: Edge deployment, browser-based TTS, low-resource environments

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A colpo d'occhio

Sviluppatore
OuteAI
Licenza
Apache 2.0
Livello
free
Velocità
slow
Clonazione vocale
No.
Lingue
English
Caratteri massimi
1000

OuteTTS voci

Female 1 (Neutral)

English
Libero Female

OuteTTS FAQ del TTS

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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