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An automatic beatmap generator using Tensorflow / Deep Learning.

Demo map 1 (low BPM):https://osu.ppy.sh/beatmapsets/1290030

Demo map 2 (high BPM):https://osu.ppy.sh/beatmapsets/1290026

https://colab.research.google.com/github/kotritrona/osumapper/blob/master/v7.0/Colab.ipynb

For mania mode:mania_Colab.ipynb

https://github.com/kotritrona/osumapper/wiki/Complete-guide:-creating-beatmap-using-osumapper

• Refer tohttps://github.com/kotritrona/osumapper/tree/master/v6.2 for version 6.2
• Refer tohttps://github.com/kotritrona/osumapper/tree/master/v7.0 for version 7.0

Don't train with every single map in your osu!. That's not how machine learning works!

I would suggest you select only maps you think are well made, for instance a mapset that contains all 5.0 ~ 6.5☆ maps mapped by (insert mapper name).

• I have made a maplist generator underv7.0/ folder. Runnode gen_maplist.js under the directory to start.
• the other way to create a maplist.txt file to train the model is by using the maplist creator.py script (found in v6.2 folder). running this should overwrite the maplist.txt in the folder with a new one using the maps from the collection folder you have specified.

Structure diagram

• Rhythm model
  • CNN/LSTM + dense layers
  • input music FFTs (7 time_windows x 32 fft_size x 2 (magnitude, phase))
  • additional input timing (is_1/1, is_1/4, is_1/2, is_the_other_1/4, BPM, tick_length, slider_length)
  • output (is_note, is_circle, is_slider, is_spinner, is_sliding, is_spinning) for 1/-1 classification
• Momentum model
  • Same structure as above
  • output (momentum, angular_momentum) as regression
  • momentum is distance over time. It should be proportional to circle size which I may implement later.
  • angular_momentum is angle over time. currently unused.
  • it's only used in v6.2
• Slider model
  • was designed to classify slider lengths and shapes
  • currently unused
• Flow model
  • uses GAN to generate the flow.
  • takes 10 notes as a group and train them each time
  • Generator: some dense layers, input (randomness x 50), output (cos_list x 20, sin_list x 20)
  • this output is then fed into a map generator to build a map corresponding to the angular values
  • map constructor output: (x_start, y_start, vector_out_x, vector_out_y, x_end, y_end) x 10
  • Discriminator: simpleRNN, some dense layers, input ↑, output (1,) ranging from 0 to 1
  • every big epoch(?), trains generator for 7 epochs and then discriminator 3 epochs
  • trains 6 ~ 25 big epochs each group. mostly 6 epochs unless the generated map is out of the mapping region (0:512, 0:384).
• Beatmap Converter
  • uses node.js to convert map data between JSON and .osu formats

If you want to cite osumapper in a scholarly work, please cite the github page. I'm not going to write a paper for it.