  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.
