markury / midmid3-19m-0326

midmid3

A slop generator for Guitar Hero.

19M-parameter masked-prediction transformer that generates "playable" Guitar Hero charts from audio. Give it a song, get a chart for all four difficulty levels. The charts are, at best, mid.

Try the live demo

How it works

1. beat_this detects beats and builds a 16th-note grid
2. MERT (frozen) extracts audio embeddings at each grid position
3. This model iteratively fills in the chart over ~12 passes (MaskGIT-style unmasking), deciding which positions get notes, which frets, and sustain lengths. Conditioned on audio features and difficulty level
4. Post-processing applies per-difficulty constraints (fret limits, chord sizes, note spacing) and snaps everything to the grid
5. Output is a MIDI chart + OGG audio + song.ini, ready to drop into GHWT:DE/DATA/MODS/

Architecture

Training data

Trained exclusively on official Guitar Hero setlists (GH1 through Warriors of Rock, Band Hero, and remastered track packs). Charts were extracted from GHWT:DE-format .pak files using GH-Toolkit-NET . Audio stems were decrypted and merged from the corresponding .fsb files.

Player-made Clone Hero community charts were deliberately excluded.

Limitations

The biggest limitation is that it's pretty mid.

• Performs best on rock and guitar-forward tracks (because that's what the official charts are mostly made up of)
• Beat tracking quality directly affects chart quality
• Sustain behavior can be strange, especially on fast passages. Sustains are heavily post-processed with rules that don't always reflect what real charts do
• No HOPOs, Star Power, or slider/tap notes (could be added, would also be mid)
• Other instruments (drums, bass, vocals) would need separate models
• There's a fundamental ceiling to what an itty bitty model trained on a single consumer GPU can do

The charts may be useful for human charters as a starting point, assuming they aren't entirely offended by its mere existence. Press x to doubt.

Format

• model.safetensors - weights only, no pickle
• config.json - architecture hyperparameters
• pytorch/ - contains the trained .bin with optimizer states

Usage

from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
import json

config_path = hf_hub_download("markury/midmid3-19m-0326", "config.json")
weights_path = hf_hub_download("markury/midmid3-19m-0326", "model.safetensors")

with open(config_path) as f:
    config = json.load(f)

state_dict = load_file(weights_path)

The model expects [batch, seq, 771] audio features (MERT + onset/RMS/centroid), [batch, seq] chart token indices (0-33), and a [batch] difficulty ID (0=easy, 3=expert).

Citation

Credit back here, the GitHub repo , or the live demo .