GOCT_ISMIR2023

This repo contains code for the extended abstract, "BEAT-ALIGNED SPECTROGRAM-TO-SEQUENCE GENERATION OF RHYTHM-GAME CHARTS", accepted to ISMIR2023 LBD.

You may see the demo page here, but this page is currently under construction as well. Alternatively, here is a folder that has videos of generated charts for a wide variety of genres and difficulties.

Pretrained models

We bundled all of the models used to generate the tables in the paper, into one .zip file of size ~300MB. You can download it here. If you'd like to replicate the tables or generate your own charts, unzip it to goct/ckpts, as many scripts are written with this arrangement implied.

Instructions

dependencies

pip install -U h5py scipy tqdm numpy soundfile librosa hydra-core wandb
pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117

wandb is only used to log, so it's optional (but you'd have to remove relevant code)

Downloading Data

Enclosed within the data directory are the list of songs and charts used for training/validation/testing. An entry from the list may look like this:

"BIGFOLDER/1003217/nekodex - circles! (FAMoss) [easy!].osu.json": [
        105,
        421
    ],

That means it's from beatmapset number 1003217, with the file name nekodex - circles! (FAMoss) [easy!].osu.

Due to the copyright laws of my country, I most probably can't distribute the raw data directly. Moreover, the means to download data either:

• requires you to log in to osu.ppy.sh, the official website of osu!, or
• are "mirrors", run by individuals and rarely continued for years, sometimes switching their APIs.

Therefore, it would be meaningless to provide a script for downloading the script. Please somehow obtain the beatmaps yourself.

Some tips on the file format:

• .osz files are actually .zip files. Just rename them and you will be able to unzip them. even after unzipping, it is recommended to keep the contents of the folder in the same folder.
• Likewise, .osu files are plaintext. There's even an wiki article for this online, which is fairly detailed.
• Fortunately, there are actually some parsers in this repo!

Preprocessing Data for osu!mania 4k dataset

If you cannot go through the steps below, I can provide you with the .h5 files. However, I honestly cannot think of a stable way to transport three files that sum up to ~40GB, so you'll have to provide me with a good way to do so. (EDIT: on second thought, I am not entirely sure if this is even legal to do in my nation. I do not expect Korean laws to exhibit any sort of decency nor leniency, not anymore. Do we even have "fair use"?)

Prerequisite. We strongly recommend that the data be organized like this.

(BIGFOLDER)
ㄴ136881
ㄴ153199
    ㄴaudio.mp3
    ㄴSHK - Couple Breaking (Sky_Demon) [MX].osu
    ㄴSHK - Couple Breaking (Sky_Demon) [NM].osu
    ㄴSHK - Couple Breaking (Sky_Demon) [Sakura's HD].osu
    ㄴ...

remember which directory the folders are located in. We will call this (BIGFOLDER). This is not to be confused with OSUFOLDER, which is literally the length-9 text "OSUFOLDER".

We strongly recommend that the steps below be taken in a non-Windows operating system.

1. Wipe all data from the beatmapsets(.oszs) except for the .osu files referenced by data/train.json, data/valid.json, and data/test.json. Also leave the audio files referenced by these .osu files as a background music.
2. Navigate to osu-to-ddc/osutoddc/converter and run python converter.py (BIGFOLDER) (BIGFOLDER).
3. Navigate to 1_preindex_similarity_matrix. Run python cache_similar_beat_index.py (BIGFOLDER) && python cleanup.py (BIGFOLDER). This may take very, very long. Please return tomorrow.
4. Navigate to the top directory and run python replace_text.py BIGFOLDER (BIGFOLDER). Then, cp data/* (BIGFOLDER). (This was primarily generated with 2_generate_dataset/generate_dataset_peripherals.py, but the dataset splits were modified to filter out some beatmaps later, to combat a upstream problem that arose much later into the process. As a result the script does not yield the same outputs anymore.)
5. Navigate to 2_generate_dataset and run python h5pyize_dataset.py (BIGFOLDER)/test.json. Do the same for valid.json and train.json. This will take less time than the previous step, but will take extremely long nonetheless (~30 hours). The generated .h5 files will be ~40GB total.
6. Now the folder should look like this.

(BIGFOLDER)
ㄴ136881
ㄴ153199
    ㄴaudio.mp3
    ㄴSHK - Couple Breaking (Sky_Demon) [MX].osu
    ㄴSHK - Couple Breaking (Sky_Demon) [MX].osu.json
    ㄴSHK - Couple Breaking (Sky_Demon) [MX].osu.json.beat.json
    ㄴ...
...
ㄴtrain.h5
ㄴtrain.json
ㄴvalid.h5
ㄴvalid.json
ㄴtest.h5
ㄴtest.json
ㄴsummary.json

Preprocessing data for Fraxtil and ITG datasets.

Navigate to 0_ddc and follow instructions there.

Henceforth we will call the big folder for the beatmania data, where dirs such as json_filt are, as (SMALLFOLDER).

Preprocessing data for DDC model training.

Clone my fork of ddc_onset, and and please run

python h5pyize_tree.py do (BIGFOLDER) (BIGFOLDER)/all_ddc.h5
python h5pyize_tree.py onset (BIGFOLDER) (BIGFOLDER)/all_ddc.h5 (BIGFOLDER)/all_onset.h5

This will also generate a ~40GB file.

Running Training

navigate to the top directory, and run

python text_replacer.py OSUFOLDER (BIGFOLDER)
python text_replacer.py STEPMANIAFOLDER (SMALLFOLDER)

This is a script that replaces all certain instance of a text within the folder to another, for files with certain extensions. The files modified with this are mainly in the folder conf/.

now, run any script in scripts, from the top directory. mel_timing.sh is used to train models without action tokens; these models are later used to generate the numbers on Table 2. mel.sh is used to train models with action tokens; these models are used for all other experiments on the paper.

On my system, with a RTX 3060, one epoch of mel.sh takes two hours. With a RTX 3090, it should take one for each epoch.

Metrics in Table 2 can be genererated using metrics_timing_AR.py and ddc_eval.py. Please see these files for notes on how to run the evaluation, and which model to use.

(Unorganized) Generating charts

unzip generated.zip to generate on the highest directory of this repo, if you need this feature or would like to try replicating Table 3.

metrics_cond_centered_AR.py is used to generate notes for the models trained on osu!. For the commands and the numbers please see the comments in metrics_timing_AR.py. which is also used in a similar way. Moreover, generated/millin_and_anmillin.ipynb was used to generate stats for Table 3. Also in the generated folder are the files output from metrics_timing_AR.py and metrics_cond_centered_AR.py from the beat-aligned and non-beat-aligned models.

The generated intermediate is output both on the console, and on the outputs folder that will be generated. Either route the output to a folder of your liking or copy the logged output from outputs folder, as well as the ground truth, generated with generate_ref.py.

(WIP) Generating charts for the test set from the notes generated

gen_to_beatmap_all.ipynb has the code, but I would not dare make this into a script, since I the code is very unrefined. The scripts here read in the log files generated, parse them, and translate them into the .osu format with the help of preexisting .osu files.

I have not implemented making stepmania charts at the moment, nor I have implemented making osu!mania charts from scratch. hopefully I will have the time to work on it in the future and release it to both communities.