OuteTTS

OuteTTS TTS-värden

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

Registrera dig för 5 000 teckengräns

Radera din text i SSML-taggar för exakt kontroll:

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

Taggar den valda modellen förstår — klicka för att släppa en i din text där det händer:

Den här modellen läser vanlig text, så inline-taggar ignoreras. För taggbaserade känslor, byt till en uttrycksfull modell som Orpheus eller Bark.

Definiera egna uttal (ord = uttal):

-12 +12
0.5x 2.0x
Gratis med Piper, VITS, Melotts
Ditt genererade ljud visas här. Välj en modell, skriv in text och klicka på Generera.
Ljud genereras framgångsrikt
0:00
Ladda ner ljud Ladda ner.srt Länken går ut i 24 timmar
Fri nivå: personlig användning. Kommersiell licens från $5/mo
Berätta för dina vänner!

Om jag inte kan 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.

Bäst för: Edge deployment, browser-based TTS, low-resource environments

Bläddra alla OuteTTS röster

Med en blick

Utvecklare
OuteAI
Licens
Apache 2.0
Nivå
free
Varvtal
slow
Röstkloning
Ej tillämpligt
Språk
English
Max tecken
1000

OuteTTS röster

Female 1 (Neutral)

English
Avgiftsfri Female

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.
← Alla röster