OuteTTS

OuteTTS TTS

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

Langganan for 5,000 characters limit

Ngresiki teks ing tag SSML kanggo kontrol presisi:

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

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.

Nyathet tembung-tembung standar (kata = tembung):

-12 +12
0.5x 2.0x
Bebas karo Piper, VITS, MeloTTS
Audio sing digawé bakal katon ing kene. Pilih modél, ketik teks, lan pencet Ngembangaké.
Audio Digawé kanthi Sukses
0:00
Unduh Audio Download.srt Link expires in 24h
Ing basa Indonésia, iku tegesé: pribadi. Lisénsi komersial saka $5/mo
TTS.ai? Nyathet kanca-kancamu!

Ngendi 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.

Paling apik kanggo: Edge deployment, browser-based TTS, low-resource environments

Jajal kabeh OuteTTS swara

Ing cetha

Pangembang
OuteAI
Lisénsi
Apache 2.0
Tanggal
free
Kecepatan
slow
Kloning swara
Ora
Basa
English
Aksara paling akèh
1000

OuteTTS swara

Female 1 (Neutral)

English
Bebas Female

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.
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