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arXiv · 2411.18918

CoDiff-VC: A Codec-Assisted Diffusion Model for Zero-shot Voice Conversion

Yuke Li·Xinfa Zhu·Hanzhao Li·JiXun Yao·WenJie Tian·XiPeng Yang·YunLin Chen·Zhifei Li·Lei Xie
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Abstract

Zero-shot voice conversion (VC) aims to convert the original speaker's timbre to any target speaker while keeping the linguistic content. Current mainstream zero-shot voice conversion approaches depend on pre-trained recognition models to disentangle linguistic content and speaker representation. This results in a timbre residue within the decoupled linguistic content and inadequacies in speaker representation modeling. In this study, we propose CoDiff-VC, an end-to-end framework for zero-shot voice conversion that integrates a speech codec and a diffusion model to produce high-fidelity waveforms. Our approach involves employing a single-codebook codec to separate linguistic content from the source speech. To enhance content disentanglement, we introduce Mix-Style layer normalization (MSLN) to perturb the original timbre. Additionally, we incorporate a multi-scale speaker timbre modeling approach to ensure timbre consistency and improve voice detail similarity. To improve speech quality and speaker similarity, we introduce dual classifier-free guidance, providing both content and timbre guidance during the generation process. Objective and subjective experiments affirm that CoDiff-VC significantly improves speaker similarity, generating natural and higher-quality speech.

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Yuke Li, Xinfa Zhu, Hanzhao Li, JiXun Yao, WenJie Tian, XiPeng Yang, YunLin Chen, Zhifei Li, Lei Xie. 2024-11-28. CoDiff-VC: A Codec-Assisted Diffusion Model for Zero-shot Voice Conversion. https://arxiv.org/abs/2411.18918

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