Papers › Muskits: an End-to-End Music Processing Toolkit for Singing Voice Synthesis
Muskits: an End-to-End Music Processing Toolkit for Singing Voice Synthesis
Jiatong Shi, Shuai Guo, Tao Qian, Nan Huo, Tomoki Hayashi, Yuning Wu, Frank Xu, Xuankai Chang, Huazhe Li, Peter Wu, Shinji Watanabe, Qin Jin
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This paper introduces a new open-source platform named Muskits for end-to-end music processing, which mainly focuses on end-to-end singing voice synthesis (E2E-SVS). Muskits supports state-of-the-art SVS models, including RNN SVS, transformer SVS, and XiaoiceSing. The design of Muskits follows the style of widely-used speech processing toolkits, ESPnet and Kaldi, for data prepossessing, training, and recipe pipelines. To the best of our knowledge, this toolkit is the first platform that allows a fair and highly-reproducible comparison between several published works in SVS. In addition, we also demonstrate several advanced usages based on the toolkit functionalities, including multilingual training and transfer learning. This paper describes the major framework of Muskits, its functionalities, and experimental results in single-singer, multi-singer, multilingual, and transfer learning scenarios. The toolkit is publicly available at https://github.com/SJTMusicTeam/Muskits.
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