EZ-VC: Easy Zero-shot Any-to-Any Voice Conversion
Advait Joglekar, Divyanshu Singh, Rooshil Rohit Bhatia, Srinivasan Umesh
Abstract
Voice Conversion research in recent times has increasingly focused on improving the zero-shot capabilities of existing methods. Despite remarkable advancements, current architectures still tend to struggle in zero-shot cross-lingual settings. They are also often unable to generalize for speakers of unseen languages and accents. In this paper, we adopt a simple yet effective approach that combines discrete speech representations from self-supervised models with a non-autoregressive Diffusion-Transformer based conditional flow matching speech decoder. We show that this architecture allows us to train a voice-conversion model in a purely textless, self-supervised fashion. Our technique works without requiring multiple encoders to disentangle speech features. Our model also manages to excel in zero-shot cross-lingual settings even for unseen languages. We provide our code, model checkpoint and demo samples here: https://github.com/ez-vc/ez-vc
BibTeX
@inproceedings{emnlp2025_ezvceasyzeroshot,
title = {EZ-VC: Easy Zero-shot Any-to-Any Voice Conversion},
author = {Advait Joglekar and Divyanshu Singh and Rooshil Rohit Bhatia and Srinivasan Umesh},
booktitle = {EMNLP 2025},
year = {2025}
}