redline-v1

redline-v1

AI timing for osu! — a 45M-parameter frame-level model that turns raw audio into osu! timing (redlines: BPM / offset / meter), built for the songs that are hard to time by hand: live drummers, metal, rubato, accelerandos. Output is a starting point to verify by ear.

Try it in your browser: https://redline-kohl.vercel.app/ Code and solver: https://github.com/Tiger14n/Redline

Model

Encoder-only transformer (rotary attention, conv frontend over log-mel + onset flux), per-frame outputs at 50 fps:

d_model 512, depth 14, 8 heads, FFN 2048. Trained on ranked osu! beatmaps (three-stage lineage; configs in the repo) on a single RTX 3080.

Results (held-out data)

Ranked mapsets excluded from training by song-level dedup, scored against the ranked map's human timing.

Model heads, 1000 held-out mapsets (frame-level, ±40 ms):

End-to-end (audio → redlines), 366 held-out mapsets, ~50+ per genre:

Genre gaps are mostly sample size: on the 1000-map run the bootstrap intervals overlap (metal − anime = +3.2 pt, 95 % CI [−4.1, +10.5]). Read the "all" row.

The typical song comes out on-grid (median 93–100 % per genre; 240 of 366 maps above 90 %). Of the 185 songs a human timed with one redline, the model writes exactly one on 147.

Known failure modes

~7 % of maps score below 50 %. Classifying those by the shape of the timing error gives four distinct causes:

1. The reference is offset, not the model (~27 %). A perfectly constant displacement, ~12 ms, so a 10 ms tolerance scores ~0 % while the grid is ~2 ms from right. Checked against the audio itself (spectral-flux onsets, detector latency calibrated out): on 19 of 20 such maps the model is 2.0 ms from the audio-true beat and the mapper 16.0 ms away — outside tolerance from their own song. Not an MP3-delay artifact.
2. Metrical-level disagreement (~17 %). ×2, ÷2 or ×2⁄3 of the mapper's BPM — the same pulse at a different metrical level, a notation convention rather than an acoustic fact.
3. Genuinely hard audio (~40 %). Live recordings, solo-piano rubato, jazz swing. Beat activation drops to 0.650 vs 0.942 on healthy maps; neither grid sits on strong onsets.
4. Odd tempo ramps (~4 %). Too few maps to conclude anything.

Excluding only the proven-offset maps moves the 1000-map benchmark from 88.5 % to 90.2 % and the failure rate from 7.0 % to 5.1 %. The reference set is not hand-cleaned, so numbers are reported as measured.

Usage

pip install -r requirements.txt   # from the repo
hf download Tiger14n/redline-v1 redline-v1.pt --local-dir models/
python -m redline.generate_timing song.mp3 --osz

The checkpoint contains model (state dict) and args (data representation + architecture), which redline.infer.Model2Beats uses to reconstruct the exact training-time pipeline.