Tag kang dipahami model kang dipilih - klik kanggo ngethok siji ing teks sampeyan ing ngendi iku kedadeyan:
Model iki maca teks biasa, mula tag ing baris diabaikan. Kanggo emosi berbasis tag, ganti menyang model ekspresif kaya Orpheus utawa Bark.
Cues pangiriman basa alami. Dihormati déning Qwen3-TTS saiki; modél ekspresif liyane bakal teka.
Model iki ora nyokong instruksi gaya — ganti menyang model sing nyokong (kayata Qwen3-TTS).
Nyathet tembung-tembung standar (kata = tembung):
-12
+12
Kontrol ekspresifitas (0 = netral, 1 = banget ekspresif)
Klasifikasi-gratis bobot guide (luwih dhuwur = luwih prompt-following)
Nyathet gaya swara ing basa alami (Parler nggunakake iki tinimbang swara kang wis ditetepake)
Dialog Format: Gunake tag [S1] lan [S2] kanggo nyathet panyatur kang béda. Conto: [S1] Halo! [S2] Halo, apa kabarmu?
Iki ya iku model swara premium, kang kasedhiya ing rencana bayar apa wae. Sampeyan bisa uga ndeleng prabédan swara kanthi gratis nganggo tombol main ing sisihé pemilih swara.
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.
Paling apik kanggo: Edge deployment, browser-based TTS, low-resource environments
CC BY-NC-SA 4.0 (weights, non-commercial) + Llama 3.2 Community (base)
Mung kanggo panggunaan pribadi lan ora komersial
Tanggal
free
Kecepatan
slow
Kloning swara
Ora
Basa
English
Aksara paling akèh
1000
OuteTTS swara
Female 1 (Neutral)
English
BebasFemaleMung kanggo panggunaan pribadi lan ora komersial
OuteTTS 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.
Ing taun 2000, dhèwèké diundang kanggo main ing filem The 1500 Days of Christmas.
Ora perlu sandi — kita bakal ngirim link menyang email kanggo nyetel sandi sabanjuré.
Batas Lapisan Bebas Ditempuh
Sampeyan wis nggunakake 5,000 karakter gratis saben dina. Cipta akun gratis kanggo entuk 15,000 karakter bonus ditambah 10,000 karakter gratis saben wulan.
