OMNIZART

Omnizart is a Python library that aims for democratizing automatic music transcription. Given polyphonic music, it is able to transcribe pitched instruments, vocal melody, chords, drum events, and beat. This is powered by the research outcomes from Music and Culture Technology (MCT) Lab. The paper has been published to Journal of Open Source Software (JOSS).

Transcribe your favorite songs now in Colab or

Quick start

Visit the complete document for detailed guidance.

Pip

# Install omnizart
pip install omnizart

# Download the checkpoints
omnizart download-checkpoints

# Transcribe your songs
omnizart drum transcribe <path/to/audio.wav>
omnizart chord transcribe <path/to/audio.wav>
omnizart music transcribe <path/to/audio.wav>

Docker

docker pull mctlab/omnizart:latest
docker run -it mctlab/omnizart:latest bash

Conda (install from source)

git clone https://github.com/Music-and-Culture-Technology-Lab/omnizart
cd omnizart
# Create a new conda environment
conda env create -f environment.yml
conda activate omnizart
# Install omnizart
pip install .
omnizart download-checkpoints

Supported applications

Application	Transcription	Training	Evaluation	Description
music	✔️	✔️		Transcribe musical notes of pitched instruments.
drum	✔️	⁉️		Transcribe events of percussive instruments.
vocal	✔️	✔️		Transcribe note-level vocal melody.
vocal-contour	✔️	✔️		Transcribe frame-level vocal melody (F0).
chord	✔️	✔️		Transcribe chord progressions.
beat	✔️	✔️		Transcribe beat position.

NOTES The current implementation for the drum model has unknown bugs, preventing loss convergence when training from scratch. Fortunately, you can still enjoy drum transcription with the provided checkpoints.

Compatibility Issue

Currently, Omnizart is incompatible for ARM-based MacOS system due to the underlying dependencies. More details can be found in the issue #38.

Citation

If you use this software in your work, please cite:

@article{Wu2021,
  doi = {10.21105/joss.03391},
  url = {https://doi.org/10.21105/joss.03391},
  year = {2021},
  publisher = {The Open Journal},
  volume = {6},
  number = {68},
  pages = {3391},
  author = {Yu-Te Wu and Yin-Jyun Luo and Tsung-Ping Chen and I-Chieh Wei and Jui-Yang Hsu and Yi-Chin Chuang and Li Su},
  title = {Omnizart: A General Toolbox for Automatic Music Transcription},
  journal = {Journal of Open Source Software}
}