osumapper

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.

Important tip for model training

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).

Maplist.txt creation:

• 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.

Model Specification

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