*** A newer version of Deepmusic package with more features and better interface is available at the multitrack branch. However, it is still under development ***
DeepMusic is a high level python package with following features:
- supporting different formats like MIDI, REMI, Compound Word and pianoroll. [1, 2]
- representing musical data in a very simple but useful way for high level music theoretic manipulations.
- preprocessing musical data in order to feed them to neural networks (chord extraction, quantization and numericalization).
- supporting metrics used for evaluating generated sequences. [3, 4]
pip install deepmusic
git clone https://github.com/s-omranpour/DeepMusic
cd DeepMusic
pip install .
from deepmusic import MusicRepr
## reading a midi file
seq = MusicRepr.from_file('test.mid')
## displaying first 10 events
print(seq[:10])
## export to remi representation
remi = seq.to_remi(ret='token')
## export to compound word representation
cp = seq.to_cp()
print(cp.shape) ## (num_events, 8)
## splitting song's bars
bars = seq.get_bars()
print(len(bars))for more details please see examples.
[1] Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions, Yu-Siang Huang, Yi-Hsuan Yang
[2] Compound Word Transformer: Learning to Compose Full-Song Musicover Dynamic Directed Hypergraphs, Wen-Yi Hsiao, Jen-Yu Liu, Yin-Cheng Yeh, Yi-Hsuan Yang
[3] The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-composed Music through Quantitative Measures, Shih-Lun Wu, Yi-Hsuan Yang