Chord Generation from Symbolic Melody Using BLSTM Networks

This is the reproduction of 'Chord Generation from Symbolic Melody Using BLSTM Networks'.

The input melodies and harmonized samples are in the inputs and outputs folders respectively.

This reproduced model is the same as the original setup, trained/validated on the Wikifonia Dataset, except that we have added the additional "rest" chord type.

For more information, see their paper: arXiv paper.

Install Dependencies

Python: 3.7.9
Keras: 2.3.0
tensorflow-gpu: 2.2.0
music21: 6.7.1

PS: Third party libraries can be installed using the pip install command.

Melody Harmonization

1.　Put the melodies (MIDI or MusicXML) in the inputs folder;
2.　Simply run harmonizer.py;
3.　Wait a while and the harmonized melodies will be saved in the outputs folder.

Use Your Own Dataset

1.　Store all the lead sheets (MusicXML) in the dataset folder;
2.　Run loader.py, which will generate orpus.bin;
3.　Run train_model.py, which will generate weights.hdf5.

After that, you can use harmonizer.py to harmonize music that 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.