SaMoye: Zero-shot Singing Voice Conversion Model Based on Feature Disentanglement and Enhancement

Zihao Wang, Le Ma, Yongsheng Feng, Xin pan, Yuhang Jin, Kejun Zhang

Keywords: singing voice conversion, zero-shot, feature disentanglement

TL;DR: We proposes a zero-shot high quality singing voice conversion method, where we improve the feature disentanglement by enhancing the timbre features and experiment various content representation methods to reduce the timbre leakage.

Abstract: Singing voice conversion (SVC) aims to convert a singer's voice to another singer's from a reference audio while keeping the original semantics. However, existing SVC methods can hardly perform zero-shot due to incomplete feature disentanglement or dependence on the speaker look-up table. We propose the first open-source high-quality zero-shot SVC model SaMoye that can convert singing to human and non-human timbre. SaMoye disentangles the singing voice's features into content, timbre, and pitch features, where we combine multiple ASR models and compress the content features to reduce timbre leakages. Besides, we enhance the timbre features by unfreezing the speaker encoder and mixing the speaker embedding with top-3 similar speakers. We also establish an unparalleled large-scale dataset to guarantee zero-shot performance, which comprises more than 1,815 hours of pure singing voice and 6,367 speakers. We conduct objective and subjective experiments to find that SaMoye outperforms other models in zero-shot SVC tasks even under extreme conditions like converting singing to animals' timbre.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 349