Muskit: Open-source music processing toolkits

Muskit is an open-source music processing toolkit. Currently we mostly focus on benchmarking the end-to-end singing voice synthesis and expect to extend more tasks in the future. Muskit employs pytorch as a deep learning engine and also follows ESPnet and Kaldi style data processing, and recipes to provide a complete setup for various music processing experiments. The main structure and base codes are adapted from ESPnet (we expect to merge the Muskit into ESPnet in later stages)

Key Features

ESPnet style complete recipe

• Support numbers of SVS recipes in several databases (e.g., Kiritan, Oniku_db, Ofuton_db, Natsume database, CSD database)
• On the fly feature extraction and text processing

SVS: Singing Voice Synthesis

• Reproducible results in serveral SVS public domain copora
• Various network architecutres for end-to-end SVS
  • RNN-based non-autoregressive model
  • Xiaoice
  • Sequence-to-sequence Transformer (with GLU-based encoder)
  • MLP singer
  • Tacotron-singing (in progress)
  • DiffSinger (to be published)
• Multi-speaker & Multilingual extention
  • Speaker ID embedding
  • Language ID embedding
  • Global sytle token (GST) embedding
• Various language support
  • Jp / En / Kr / Zh
• Integration with neural vocoders
  • the style matches the PWG repo with supports of various of vocoders

Installation

The full installation guide is available at https://github.com/SJTMusicTeam/Muskits/wiki/Installation-Instructions

Demonstration

• Real-time SVS demo with Muskits

Pretrain models

Acoustic models are available at https://github.com/SJTMusicTeam/Muskits/blob/main/doc/pretrained_models.md Vocoders are available at https://github.com/kan-bayashi/ParallelWaveGAN/blob/master/README.md

Running instructions

The tutorial of how to use Muskits is at https://github.com/SJTMusicTeam/Muskits/blob/main/doc/tutorial.md