OuteTTS

OuteTTS TTS

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

Daftar untuk batas 5,000 karakter

Bungkus teks Anda dalam tag SSML untuk kendali yang tepat:

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

Tag yang dipilih mengerti klik °C untuk memasukkan satu ke dalam teks Anda di mana hal itu terjadi:

Model ini membaca teks biasa, sehingga tag inline diabaikan. Untuk tag berbasis emosi, beralih ke model ekspresif seperti Orpheus atau Bark.

Definisikan pengucapan ubahan (kata = pelafalan):

-12 +12
0.5x 2.0x
Free with Piper, VITS, Melotts
Audio yang Anda buat akan muncul di sini. Pilih model, masukkan teks, dan klik Generate.
Hasil Audio Berhasil
0:00
Unduh Audio Unduh.srt Sambungan berakhir dalam 24 jam
Tingkatan bebas: penggunaan pribadi. Ijin komersial dari $5/mo
Buatlah ini suara Anda sendiri Kloning suara dalam 30 detik
Beritahu teman-temanmu!

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

Jelajahi semua OuteTTS suara

Pada sekilas

Pengembang
OuteAI
Lisensi
Apache 2.0
Tier
free
Kecepatan
slow
Penklonan Suara
Tidak
Bahasa
English
Karakter maksimal
1000

OuteTTS suara

Female 1 (Neutral)

English
Bebas Female

OuteTTS TTS °F 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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