Skip to content
master
Go to file
Code

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
Nov 14, 2020
Aug 16, 2018

README.md

osumapper

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

Colaboratory

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

For mania mode: mania_Colab.ipynb

Complete guide for a newcomer in osu! mapping

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

Installation & Model Running

Important tip for model training

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).

Maplist.txt creation:

  • I have made a maplist generator under v7.0/ folder. Run node 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.

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

Citing

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.

You can’t perform that action at this time.