Muzic is a research project on AI music that empowers music understanding and generation with deep learning and artificial intelligence.
Muzic is pronounced as [ˈmjuːzeik] and ‘谬贼客’ (in Chinese). Besides the logo in image version (see above), Muzic also has a logo in video version (you can click here to watch ). Muzic was started by some researchers from Microsoft Research Asia.
We summarize the scope of our Muzic project in the following figure:
The current work in Muzic include:
- Song Writing: SongMASS
- Lyric Generation: DeepRapper
- Melody Generation: TeleMelody
- Accompaniment Generation: PopMAG
- Singing Voice Synthesis: HiFiSinger
You can find some music samples generated by our systems from this page: https://ai-muzic.github.io/.
What is New!
The operating system is Linux. We test on Ubuntu 16.04.6 LTS, CUDA 10, with Python 3.6.12. The requirements for running Muzic are listed in
requirements.txt. To install the requirements, run:
pip install -r requirements.txt
We initially release the code of 5 research work: MusicBERT, PDAugment, DeepRapper, SongMASS, and TeleMelody. You can find the README in the corresponding folder for detailed instructions on how to use.
If you find the Muzic project useful in your work, you can cite the following papers if there’s a need:
- MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training, Mingliang Zeng, Xu Tan, Rui Wang, Zeqian Ju, Tao Qin, Tie-Yan Liu, ACL 2021.
- PDAugment: Data Augmentation by Pitch and Duration Adjustments for Automatic Lyrics Transcription, Chen Zhang, Jiaxing Yu, Luchin Chang, Xu Tan, Jiawei Chen, Tao Qin, Kejun Zhang, arXiv 2021.
- DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling, Lanqing Xue, Kaitao Song, Duocai Wu, Xu Tan, Nevin L. Zhang, Tao Qin, Wei-Qiang Zhang, Tie-Yan Liu, ACL 2021.
- SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint, Zhonghao Sheng, Kaitao Song, Xu Tan, Yi Ren, Wei Ye, Shikun Zhang, Tao Qin, AAAI 2021.
- TeleMelody: Lyric-to-Melody Generation with a Template-Based Two-Stage Method, Zeqian Ju, Peiling Lu, Xu Tan, Rui Wang, Chen Zhang, Songruoyao Wu, Kejun Zhang, Xiangyang Li, Tao Qin, Tie-Yan Liu, arXiv 2021.
This project welcomes contributions and suggestions. Most contributions require you to agree to a
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