E mōhio ana ngā tohu ki te tauira i kōwhiria - ka kōwhiria kia whakawātea tētahi ki roto i tōna kupu i reira ka puta ai:
Ka pānui tēnei tauira i te kupu noa, nā reira ka whakakāhoretia ngā tohu ā-waitara. Mō te āhua o te tohu-taihi, ka huri ki tētahi tauira whakamārama pēnei i a Orpheus, Bark rānei.
Ko ngā tohu whakawhiwhinga reo māori. I whakawhiwhia e Qwen3-TTS i tēnei rā; Ka tae mai ētahi atu tauira whakamāramatanga.
Kāore tēnei tauira i te tautoko i ngā tohutohu kāhua — ka huri ki tētahi tauira e mahi ana (hei tauira, Qwen3-TTS).
Ka tautuhia ngā tohutohu ā-ringa (wāhi = tohutohu):
-12
+12
Ka whakahaeretia te whakahuahua (0 = pūmau, 1 = tino whakahuahua)
Te taumahatanga tohu whakawātea-whakahaere (nui ake = nui ake te whakahau-whakahaere)
E whakaahua ana i te kāhua reo i roto i te reo māori (ka whakamahia e te Pāpāho tēnei i te wāhi o ngā reo i whakaritea i mua)
He pūāhua taupānga te taupānga taupānga: Ka whakamahia ngā tohu [S1] me [S2] hei tohu i ngā kaikōrero rerekē. Hei tauira: [S1] Hei hei! [S2] Hei, he pēhea koe?
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.
Pai mo: Edge deployment, browser-based TTS, low-resource environments
CC BY-NC-SA 4.0 (weights, non-commercial) + Llama 3.2 Community (base)
Ko te whakamahinga whaiaro me te kore-whakaahua anake
Karaka
free
Āhuatanga
slow
Whakakōrero reo
Kāore
reo
English
Kāri nui rawa
1000
OuteTTS ngā oro
Female 1 (Neutral)
English
WaihokiFemaleKo te whakamahinga whaiaro me te kore-whakaahua anake
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.
E tāuru ana i tōna mairā hei haere tonu i te whakaputa kōrero mō te wāteatanga, hei whakaingoatanga rānei mō ngā pūāhua tāpiri 15,000.
Kāore he kupu whakarongo e hiahiatia ana — ka tukuna mai e tātau he pātahitanga ki te tautuhi i tētahi i muri ake nei.
Kua tae te tepe taumata wātea
Kua whakamahia e koe ōna pūāhua wātea 5,000 i ia rā. Whakatū i tētahi kāwanatanga wātea kia whiwhi ai ki ngā pūāhua tāpiri 15,000 tae atu ki ngā pūāhua wātea 10,000 i ia marama.
