Thank you for your interest in this project, Vitaliy!

This project took me one week in total: around two days to set up MIDI data loading, conversion to numeric data and training, and five days of experimenting with different architectures on my 2060rtx GPU. I let each architecture train for around 12 hours, so most of this time investment was passive.

Unfortunately, I cannot publish my dataset since it contains derivatives of copyrighted works. However, the works used can be downloaded from musescore as MIDIs if one buys a "pro" subscription. I would encourage you to hand-choose music that speaks to you if you want to go this route.

Most of my dataset consists of my personal favorite modern piano compositions or arrangements of popular-ish music for piano. I could publish a full list with links if anyone is interested. Some notable works used in my dataset include:

• 24 Preludes from Chopin's Opus 28
• River Flows In You by Yiruma
• Flower Dance by DJ Okawari

There are many larger MIDI music datasets available, and I think some of these would be a nice addition to the Wolfram Data Repository. My original idea was to pre-train a network on such datasets then fine tune on my hand-selected favorites, but I have not invested the time yet. Maybe I can do this in the future.

Training seems to take such a long time with RNNs (even on my comparatively tiny dataset) that I would really like to experiment with transformer architectures before investing too many more gpu-days into training! I would love to see a GPT2-Music net in the Wolfram Neural Net repo some day.

I was surprised how pleasant these music tracks actually are. Thank you for sharing! How much time did this project take? Also I am curious about data -- did you publish your dataset, maybe on that GitHub repo? Is this all modern-classic composers based or there is some old classical music too for training? Any famous composers?

I built my dataset from a collection of my favorite piano scores from musescore. You want a lot of different .mid files so the network you train cannot simply memorize all of your training data and instead has to perform some amount of information generalization. Our data augmentation strategy helps with this, but it will not completely solve the issue of having too little data. I have found that 150 songs is acceptable when using smaller RNNs.

BTW about repos. You might consider for your work Wolfram's Neural Net and Data Repositores:

• https://resources.wolframcloud.com/NeuralNetRepository
• https://datarepository.wolframcloud.com