This is an Implementation of MidiNet by pytorch.

MidiNet paper : https://arxiv.org/abs/1703.10847

MidiNet code : https://github.com/RichardYang40148/MidiNet

dataset is from theorytab : https://www.hooktheory.com/theorytab

You can find crawler here : https://github.com/wayne391/Symbolic-Musical-Datasets

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Prepare the data

get_data.py | get melody and chord matrix from xml

get_train_and_test_data.py | seperate the melody data into training set and testing set (chord preparation not included)

Or get the processed data from the author.

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After you have the data,

1. Make sure you have toolkits in the requirement.py

2. Run main.py ,
   is_train = 1 for training, is_draw = 1 for drawing loss, is_sample = 1 for generating music after finishing training.

3. If you would like to turn the output into real midi for listening Run demo.py

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requirement.py | toolkits used in the whole work

main.py | training setting, drawing setting, generation setting.

ops.py | some functions used in model

model.py | Generator and Discriminator. (Based on model 3 in the MidiNet paper)

demo.py | transform matrix into midi. (input : melody and chord matrix, output : midi)