An automatic beatmap generator using Tensorflow / Deep Learning.
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An automatic beatmap generator using Tensorflow / Deep Learning.

demo map:

Model Specification:

Diagram of structure

  • 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.
  • 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 between map position data and .osu file
    • most of its code is from 3 years ago


  • tensorflow v1.9.0 - v1.10.0
  • common python libs
  • soundfile
  • node.js
  • polynomial
  • ffmpeg

Running the model:

  1. prepare a maplist.txt containing .osu files and run 01_osumap_loader.ipynb
  2. run 02_osurhythm_estimator.ipynb
  3. run 03_osurhythm_momentum_estimator.ipynb
  4. have a rest since #4 is not currently used
  5. prepare a new song with timing and run 05_newsong_importer.ipynb
  6. run 06_osurhythm_evaluator.ipynb
  7. run 07_osuflow_evaluator_from_rhythm.ipynb
  8. find the generated .osu file under the ipynb folder and try it out in osu!

if you don't have a good idea about what map to train with, you can use the default model and start from step #5.

Environments tested:

  • win10, canopy, python3.5, tf1.9.0, no cuda
  • win10, canopy, python3.5, tf1.10.0, no cuda
  • google colaboratory, no GPU
  • anaconda3, python3.6, tf1.10.0, the machine has no graphics card
  • previous environments with GPU enabled - probably needs to set batch_size=(some smaller value) otherwise it will randomly go out of memory!!

Current Progress:

  • stage0 (completed)
  • stage1 (completed)
  • stage2 (completed)
  • stage3 (completed)
  • stage4 (completed)
  • stage5 (completed)
  • stage6 (completed)
  • stage7 (completed)
  • stage8 (?)
  • description 66%
  • more testing 66%
  • tensorflow.js 66%
  • code comments -550%
  • create a map and rank it -99,999,999%


  • stream regularization (done)
  • slider shape classification
  • deal with 1/3 and 1/1 maps (parametrize divisor) (done?)
  • spinner classification (kind of think this is impossible)
  • play with tensorflow.js to make it usable for everyone (seems tfjs itself is not very mature there)