Try the generative model here, or MaiMod here. Check out a video showcase here.
BeatHeritage (formerly Mapperatorinator) is a multi-model framework that uses spectrogram inputs to generate fully featured osu! beatmaps for all gamemodes and assist modding beatmaps.
The goal of this project is to automatically generate rankable quality osu! beatmaps from any song with a high degree of customizability.
🚀 What's New in BeatHeritage V1
Enhanced Stability: Optimized sampling parameters (temperature 0.85, top_p 0.92) for more consistent generation
Quality Control: Automatic spacing correction, overlap detection, and flow optimization
Pattern Variety: Advanced pattern generation with diversity enhancement
All Gamemodes: Full support for std, taiko, ctb, and mania with mode-specific optimizations
Performance: Flash attention, mixed precision (BF16), and gradient checkpointing
Custom Postprocessor: Advanced post-processing with flow optimization and style preservation
Benchmark Tools: Compare performance with previous models
Easy Setup: Auto-setup script with model downloading from Hugging Face
This project is built upon osuT5 and osu-diffusion. In developing this, I spent about 2500 hours of GPU compute across 142 runs on my 4060 Ti and rented 4090 instances on vast.ai.
Use this tool responsibly. Always disclose the use of AI in your beatmaps.
Installation
The instruction below allows you to generate beatmaps on your local machine, alternatively you can run it in the cloud with the colab notebook.
Use Python 3.10, later versions will might not be compatible with the dependencies.
sh
1python -m venv .venv
23# In cmd.exe
4.venv\Scripts\activate.bat
5# In PowerShell
6.venv\Scripts\Activate.ps1
7# In Linux or MacOS
8source .venv/bin/activate
PyTorch: Make sure to follow the Get Started guide so you install torch and torchaudio with GPU support.
and the remaining Python dependencies:
pip install -r requirements.txt
Web GUI (Recommended)
For a more user-friendly experience, consider using the Web UI. It provides a graphical interface to configure generation parameters, start the process, and monitor the output.
Launch the GUI
Navigate to the cloned Mapperatorinator directory in your terminal and run:
python web-ui.py
This will start a local web server and automatically open the UI in a new window.
Using the GUI
Configure: Set input/output paths using the form fields and "Browse" buttons. Adjust generation parameters like gamemode, difficulty, style (year, mapper ID, descriptors), timing, specific features (hitsounds, super timing), and more, mirroring the command-line options. (Note: If you provide a beatmap_path, the UI will automatically determine the audio_path and output_path from it, so you can leave those fields blank)
Start: Click the "Start Inference" button to begin the beatmap generation.
Cancel: You can stop the ongoing process using the "Cancel Inference" button.
Open Output: Once finished, use the "Open Output Folder" button for quick access to the generated files.
The Web UI acts as a convenient wrapper around the inference.py script. For advanced options or troubleshooting, refer to the command-line instructions.
python_u3zyW0S3Vs
Command-Line Inference
For users who prefer the command line or need access to advanced configurations, follow the steps below. Note: For a simpler graphical interface, please see the Web UI (Recommended) section above.
Run inference.py and pass in some arguments to generate beatmaps. For this use Hydra override syntax. See configs/inference_v29.yaml for all available parameters.
python inference.py \
audio_path [Path to input audio] \
output_path [Path to output directory] \
beatmap_path [Path to .osu file to autofill metadata, and output_path, or use as reference] \
gamemode [Game mode to generate 0=std, 1=taiko, 2=ctb, 3=mania] \
difficulty [Difficulty star rating to generate] \
mapper_id [Mapper user ID for style] \
year [Upload year to simulate] \
hitsounded [Whether to add hitsounds] \
slider_multiplier [Slider velocity multiplier] \
circle_size [Circle size] \
keycount [Key count for mania] \
hold_note_ratio [Hold note ratio for mania 0-1] \
scroll_speed_ratio [Scroll speed ratio for mania and ctb 0-1] \
descriptors [List of beatmap user tags for style] \
negative_descriptors [List of beatmap user tags for classifier-free guidance] \
add_to_beatmap [Whether to add generated content to the reference beatmap instead of making a new beatmap] \
start_time [Generation start time in milliseconds] \
end_time [Generation end time in milliseconds] \
in_context [List of additional context to provide to the model [NONE,TIMING,KIAI,MAP,GD,NO_HS]] \
output_type [List of content types to generate] \
cfg_scale [Scale of the classifier-free guidance] \
super_timing [Whether to use slow accurate variable BPM timing generator] \
seed [Random seed for generation] \
For those who prefer a terminal-based workflow but want a guided setup, the interactive CLI script is an excellent alternative to the Web UI.
Launch the CLI
Navigate to the cloned directory. You may need to make the script executable first.
sh
1# Make the script executable (only needs to be done once)
2chmod +x cli_inference.sh
sh
1# Run the script
2./cli_inference.sh
Using the CLI
The script will walk you through a series of prompts to configure all generation parameters, just like the Web UI.
It uses a color-coded interface for clarity.
It provides an advanced multi-select menu for choosing style descriptors using your arrow keys and spacebar.
After you've answered all the questions, it will display the final command for your review.
You can then confirm to execute it directly or cancel and copy the command for manual use.
Generation Tips
You can edit configs/inference_v29.yaml and add your arguments there instead of typing them in the terminal every time.
Always provide a year argument between 2007 and 2023. If you leave it unknown, the model might generate with an inconsistent style.
Always provide a difficulty argument. If you leave it unknown, the model might generate with an inconsistent difficulty.
Increase the cfg_scale parameter to increase the effectiveness of the mapper_id and descriptors arguments.
You can use the negative_descriptors argument to guide the model away from certain styles. This only works when cfg_scale > 1. Make sure the number of negative descriptors is equal to the number of descriptors.
If your song style and desired beatmap style don't match well, the model might not follow your directions. For example, its hard to generate a high SR, high SV beatmap for a calm song.
If you already have timing and kiai times done for a song, then you can give this to the model to greatly increase inference speed and accuracy: Use the beatmap_path and in_context=[TIMING,KIAI] arguments.
To remap just a part of your beatmap, use the beatmap_path, start_time, end_time, and add_to_beatmap=true arguments.
To generate a guest difficulty for a beatmap, use the beatmap_path and in_context=[GD,TIMING,KIAI] arguments.
To generate hitsounds for a beatmap, use the beatmap_path and in_context=[NO_HS,TIMING,KIAI] arguments.
To generate only timing for a song, use the super_timing=true and output_type=[TIMING] arguments.
MaiMod: The AI-driven Modding Tool
MaiMod is a modding tool for osu! beatmaps that uses Mapperatorinator predictions to find potential faults and inconsistencies which can't be detected by other automatic modding tools like Mapset Verifier.
It can detect issues like:
Incorrect snapping or rhythmic patterns
Inaccurate timing points
Inconsistent hit object positions or new combo placements
Weird slider shapes
Inconsistent hitsounds or volumes
You can try MaiMod here, or run it locally:
To run MaiMod locally, you'll need to install Mapperatorinator. Then, run the mai_mod.py script, specifying your beatmap's path with the beatmap_path argument.
This will print the modding suggestions to the console, which you can then apply to your beatmap manually.
Suggestions are ordered chronologically and grouped into categories.
The first value in the circle indicates the 'surprisal' which is a measure of how unexpected the model found the issue to be, so you can prioritize the most important issues.
The model can make mistakes, especially on low surprisal issues, so always double-check the suggestions before applying them to your beatmap.
The main goal is to help you narrow down the search space for potential issues, so you don't have to manually check every single hit object in your beatmap.
MaiMod GUI
To run the MaiMod Web UI, you'll need to install Mapperatorinator.
Then, run the mai_mod_ui.py script. This will start a local web server and automatically open the UI in a new window:
python mai_mod_ui.py
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Overview
Tokenization
Mapperatorinator converts osu! beatmaps into an intermediate event representation that can be directly converted to and from tokens.
It includes hit objects, hitsounds, slider velocities, new combos, timing points, kiai times, and taiko/mania scroll speeds.
Here is a small examle of the tokenization process:
mapperatorinator_parser
To save on vocabulary size, time events are quantized to 10ms intervals and position coordinates are quantized to 32 pixel grid points.
Model architecture
The model is basically a wrapper around the HF Transformers Whisper model, with custom input embeddings and loss function.
Model size amounts to 219M parameters.
This model was found to be faster and more accurate than T5 for this task.
The high-level overview of the model's input-output is as follows:
Picture2
The model uses Mel spectrogram frames as encoder input, with one frame per input position. The model decoder output at each step is a softmax distribution over a discrete, predefined, vocabulary of events. Outputs are sparse, events are only needed when a hit-object occurs, instead of annotating every single audio frame.
Multitask training format
Multitask training format
Before the SOS token are additional tokens that facilitate conditional generation. These tokens include the gamemode, difficulty, mapper ID, year, and other metadata.
During training, these tokens do not have accompanying labels, so they are never output by the model.
Also during training there is a random chance that a metadata token gets replaced by an 'unknown' token, so during inference we can use these 'unknown' tokens to reduce the amount of metadata we have to give to the model.
Seamless long generation
The context length of the model is 8.192 seconds long. This is obviously not enough to generate a full beatmap, so we have to split the song into multiple windows and generate the beatmap in small parts.
To make sure that the generated beatmap does not have noticeable seams in between windows, we use a 90% overlap and generate the windows sequentially.
Each generation window except the first starts with the decoder pre-filled up to 50% of the generation window with tokens from the previous windows.
We use a logit processor to make sure that the model can't generate time tokens that are in the first 50% of the generation window.
Additionally, the last 40% of the generation window is reserved for the next window. Any generated time tokens in that range are treated as EOS tokens.
This ensures that each generated token is conditioned on at least 4 seconds of previous tokens and 3.3 seconds of future audio to anticipate.
To prevent offset drifting during long generation, random offsets have been added to time events in the decoder during training.
This forces it to correct timing errors by listening to the onsets in the audio instead, and results in a consistently accurate offset.
Refined coordinates with diffusion
Position coordinates generated by the decoder are quantized to 32 pixel grid points, so afterward we use diffusion to denoise the coordinates to the final positions.
For this we trained a modified version of osu-diffusion that is specialized to only the last 10% of the noise schedule, and accepts the more advanced metadata tokens that Mapperatorinator uses for conditional generation.
Since the Mapperatorinator model outputs the SV of sliders, the required length of the slider is fixed regardless of the shape of the control point path.
Therefore, we try to guide the diffusion process to create coordinates that fit the required slider lengths.
We do this by recalculating the slider end positions after every step of the diffusion process based on the required length and the current control point path.
This means that the diffusion process does not have direct control over the slider end positions, but it can still influence them by changing the control point path.
Post-processing
Mapperatorinator does some extra post-processing to improve the quality of the generated beatmap:
Refine position coordinates with diffusion.
Resnap time events to the nearest tick using the snap divisors generated by the model.
Snap near-perfect positional overlaps.
Convert mania column events to X coordinates.
Generate slider paths for taiko drumrolls.
Fix big discrepancies in required slider length and control point path length.
Super timing generator
Super timing generator is an algorithm that improves the precision and accuracy of generated timing by infering timing for the whole song 20 times and averaging the results.
This is useful for songs with variable BPM, or songs with BPM changes. The result is almost perfect with only sometimes a section that needs manual adjustment.
Training
The instruction below creates a training environment on your local machine.
Create your own dataset using the Mapperator console app. It requires an osu! OAuth client token to verify beatmaps and get additional metadata. Place the dataset in a datasets directory next to the Mapperatorinator directory.
