Oznake odabrani model razumije — kliknite da bacite jedan u vaš tekst gdje se to događa:
Ovaj model čita običan tekst, tako da inline oznake se zanemaruju. Za tag-based emocije, prebaci na ekspresivni model kao što su Orfeus ili Bark.
Prirodno-jezični znakovi isporuke. Počašćeni od Qwen3-TTS danas; drugi ekspresivni modeli dolaze uskoro.
Ovaj model ne podržava upute o stilu — prebaci se na model koji radi (npr. Qwen3-TTS).
Definiši vlastite izgovore (riječ = izgovor):
-12
+12
Kontrola ekspresnosti (0 = neutralna, 1 = vrlo ekspresna)
Težina smjera bez klasifikacije (viša = više brza praćenja)
Opišite glasovni stil na prirodnom jeziku (parler koristi ovo umjesto predefiniranih glasova)
Dia dijaloški format: Koristi [S1] i [S2] oznake za različite zvučnike. Primjerice: [S1] Pozdrav tamo! [S2] Zdravo, kako ste?
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.
Najbolje za: Edge deployment, browser-based TTS, low-resource environments
CC BY-NC-SA 4.0 (weights, non-commercial) + Llama 3.2 Community (base)
Samo osobna i netrgovačka upotreba
Nivo
free
Brzina
slow
Kloniranje glasa
Ne.
Jezici
English
Maks. znakova
1000
OuteTTS glasovi
Female 1 (Neutral)
English
SlobodnoFemaleSamo osobna i netrgovačka upotreba
OuteTTS TTS – FAQ
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.
