Tag fantatry ny modely voafaritra — tsindrio mba hametrahana iray ao anatin'ny lahatsoratrao izay misy azy:
Mamakiana lahabolana tsotra io modely io, ka tsy raharahaina ny tag anatin'ny andalana. Raha mila fihetseham-po mifototra amin'ny tag ianao, dia miova ho modely maneho fihetseham-po toy ny Orpheus na Bark.
Fanomezana ny teny natoraly. Nanome voninahitra ny Qwen3-TTS androany; modely hafa hanehoana ny tena ho avy tsy ho ela.
Tsy mandray an-tànana ny toro-làlan'ny endriky ny teny ity modely ity — ampio ilay modely izay mandray an-tànana izany (ohatra: Qwen3-TTS).
Mamaritra ny fanononana safidy (teny = fanononana):
-12
+12
Manara-maso ny fanehoana (0 = tsy misy ifandraisany, 1 = tena fanehoana)
Ny lanjany amin'ny famaritana tsy misy mpizara (ny avo kokoa = manaraka ny toro-hevitra kokoa)
Mamaritra ny endriky ny feo amin'ny teny natoraly (ity no ampiasaina amin'ny Parler fa tsy ny feo efa voafaritra mialoha)
Lamin'ny takila Dia: Ampiasao ny tag [S1] sy [S2] mba hamantarana mpiteny samihafa. Ohatra: [S1] Misaotra! [S2] Misaotra, ahoana ny toe-draharaha?
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.
Tsara indrindra ho an'ny: Edge deployment, browser-based TTS, low-resource environments
CC BY-NC-SA 4.0 (weights, non-commercial) + Llama 3.2 Community (base)
Ho an'ny tena manokana sy tsy ho an'ny varotra ihany
Taona
free
Hafainganan'ny fanovana
slow
Fandraisana feo
Tsy misy
Teny
English
Marika betsaka indrindra
1000
OuteTTS feo
Female 1 (Neutral)
English
FreeFemaleHo an'ny tena manokana sy tsy ho an'ny varotra ihany
OuteTTS TTS - Fanontaniana matetika
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.
Efa nahatratra ny fetra amin'ny modely maimaimpoana ianao. Ampitomboy ny isa mba hahazoana marika fanampiny, na mividy fonosana marika mba hampiasana modely premium.
