A Unified System for Voice Cloning and Voice Conversion through Diffusion Probabilistic Modeling
Abstract
Text-to-speech and voice conversion are two common speech generation tasks typically solved using different models. In this paper, we present a novel approach to voice cloning and any-to-any voice conversion relying on a single diffusion probabilistic model with two encoders each operating on its input domain and a shared decoder. Extensive human evaluation shows that the proposed model can copy a target speaker's voice by means of speaker adaptation better than other known multimodal systems of such kind and the quality of the speech synthesized by our system in both voice cloning and voice conversion modes is comparable with that of recently proposed algorithms for the corresponding single tasks. Besides, it takes as few as 3 minutes of GPU time to adapt our model to a new speaker with only 15 seconds of untranscribed audio which makes it attractive for practical applications.