LIMI-VC: A Light Weight Voice Conversion Model with Mutual Information Disentanglement
Liangjie Huang, Tian Yuan, Yunming Liang, Zeyu Chen, Can Wen, Yanlu Xie, Jinsong Zhang, Dengfeng Ke
Abstract
Voice conversion(VC) model aims to convert the source timbre to the target one. Recently, many VC models utilize pre-trained models to enhance the performance and achieve good results. However, pre-trained models could not somehow disentangle the timbre and linguistic information, thus resulting in a redundancy, which may hurt the conversion performance. In this paper we proposed LIMI-VC, reducing the redundancy between the linguistic content and the timbre information with mutual information disentanglement. We design the model in a light weight form, for the sake of parameter and computation efficiency when pre-trained models are commonly used nowadays. Experiments show that the proposed model can still improve the performance, with 15 times smaller size, compared to baseline. An out-of-domain cross-lingual inference also shows that our model greatly outperforms the baseline. Our source code and audio examples will be available at: https://github.com/WongLaw/LIMI-VC.
BibTeX
@inproceedings{icassp2023_limivcalightweig,
title = {LIMI-VC: A Light Weight Voice Conversion Model with Mutual Information Disentanglement},
author = {Liangjie Huang and Tian Yuan and Yunming Liang and Zeyu Chen and Can Wen and Yanlu Xie and Jinsong Zhang and Dengfeng Ke},
booktitle = {ICASSP 2023},
year = {2023}
}