This is the source code of AutoHarmonizer, a harmonic density-controllable melody harmonization system with flexible harmonic rhythm, trained/validated on Wikifonia.org's lead sheet dataset.
The input melodies and harmonized samples are in the inputs and outputs folders respectively.
The musical discrimination test is available at https://sander-wood.github.io/autoharmonizer/test.
For more information, see our paper: arXiv paper.
Python: 3.7.9
Keras: 2.3.0
keras-metrics: 1.1.0
tensorflow-gpu: 2.2.0
music21: 6.7.1
tqdm: 4.62.3
samplings: 0.1.7
PS: Third party libraries can be installed using the pip install command.
1. Put the melodies (could be parsed by music21) in the inputs folder;
2. Simply run harmonizer.py;
3. Wait a while and the harmonized melodies will be saved in the outputs folder.
You can set the parameter RHYTHM_DENSITY∈[0, 1] in config.py to adjust the density of the generated chord progression. The higher the value of RHYTHM_DENSITY, the more chords will be generated, and vice versa.
1. Store all the lead sheets (MusicXML) in the dataset folder;
2. Run loader.py, which will generate data_corpus.bin;
3. Run model.py, which will generate weights.hdf5.
After that, you can use harmonizer.py to harmonize music with chord progressions that fit the musical style of the new dataset.
If you need to finetune the parameters, you can do so in config.py. It is not recommended to change the parameters in other files.