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*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)4277-4281
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume2022-September
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022 - Incheon, Korea, Republic of
Duration: 2022 Sep 182022 Sep 22

Keywords

  • end-to-end
  • open-source toolkit
  • Singing voice synthesis

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

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