OuteTTS

OuteTTS TTS

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

Daftar masuk had 5,000 aksara

Lilitkan teks anda dalam tag SSML untuk kawalan tepat:

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

Tag model dipilih memahami - klik untuk jatuhkan satu ke dalam teks anda di mana ia berlaku:

Model ini membaca teks biasa, jadi tag dalam baris diabaikan. Untuk emosi berdasar tag, beralih ke model ekspresif seperti Orpheus atau Bark.

Tetapkan sebutan tersendiri (perkataan = sebutan):

-12 +12
0.5x 2.0x
Bebas dengan Piper, VITS, MeloTTS
Audio yang dijana akan muncul di sini. Pilih model, masukkan teks, dan klik Janakan.
Audio Dijana Dengan Berjaya
0:00
Muat turun Audio Muat turun.srt Pautan luput dalam 24 jam
Tahap percuma: penggunaan peribadi. Lesen Komersial dari $5/mo
Cinta TTS.ai? Beritahu kawan-kawan anda!

Tentang 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.

Terbaik untuk: Edge deployment, browser-based TTS, low-resource environments

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Dengan sekejap mata

Pemaju
OuteAI
Lesen
Apache 2.0
Tajuk
free
Kelajuan
slow
Klon suara
Tidak
Bahasa
English
Aksara maksimum
1000

OuteTTS suara

Female 1 (Neutral)

English
Bebas 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.
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