An automatic beatmap generator using Tensorflow / Deep Learning.

Thank you to Kotritrona who created this amazing project. As an admirer of this project and a player, I will strive to continue this project so that it keeps running well.

The project I am continuing will focus on the osu!mania mode, as I only play that mode myself. I hope this project can help in creating beatmaps for reference or to realize your dream beatmaps.

Pre-requisite:

• Python
• Node.js

How to:

• Install pre-requisite
• Run launcher.bat inside mania-v7.0

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

Kotritrona 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).

• Kotritrona have made a maplist generator under v7.0/ folder. Run node gen_maplist.js under the directory to start.
• The way gen_maplist works has changed; instead of using osu!.db as a reference, the code will now scan the /osu!/songs folder.

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