2022/05/09 by Jiatong Shi, Shi, Jiatong, Shuai Guo +22 · 8 citations
Computer Science · Engineering · #Music and Audio Processing #Speech Recognition and Synthesis #Speech and Audio Processing #cs.MM #cs.SD #eess.AS
paper · pdf · doi:10.48550/arxiv.2205.04029
Accepted by Interspeech
arxiv created 2022/07/02 · arxiv updated 2022/07/05
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.