StyleTTS2

Why we included this project

StyleTTS2 produces speech that native English listeners rate as natural as human recordings, and it does so without a reference recording for each new voice. The model treats speaking style as a latent variable that a diffusion process shapes from the text alone, which gives the output real prosody instead of a flat, neutral reading. Its zero-shot speaker adaptation is the part most people will care about: with the pre-trained multispeaker models you can clone a voice from a short sample, handy for audiobook or dubbing experiments. The repo includes inference scripts, training code for single- and multi-speaker setups, and a finetuning script for new speakers, so you can go from downloaded checkpoints to audio fairly quickly. It is a research codebase, so expect checkpoints and Python scripts rather than a packaged API.