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arXiv · 2505.16691

EZ-VC: Easy Zero-shot Any-to-Any Voice Conversion

Advait Joglekar·Divyanshu Singh·Rooshil Rohit Bhatia·S. Umesh
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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. For Demo: https://ez-vc.github.io/EZ-VC-Demo/

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Advait Joglekar, Divyanshu Singh, Rooshil Rohit Bhatia, S. Umesh. 2025-05-23. EZ-VC: Easy Zero-shot Any-to-Any Voice Conversion. https://arxiv.org/abs/2505.16691

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