cstr / beat-this-GGUF

Beat This! — GGUF

GGUF conversions of Beat This! (Foscarin, Schlüter & Widmer, CPJKU — ISMIR 2024), a transformer beat and downbeat tracker, for use with CrispASR / ggml.

Checkpoint: final0 (trained on all datasets except GTZAN, seed 0).

Why this model

Nearly every published beat tracker post-processes its framewise output with madmom's Dynamic Bayesian Network , which is Böck-patented and licensed for non-commercial use only. Beat This! reaches state-of-the-art without one — its postprocessing is plain peak-picking, and its dependency list (numpy / torch / torchaudio / einops / rotary-embedding-torch / soxr) contains no part of madmom.

Both the upstream code and the published weights are MIT . That combination — SOTA accuracy, no DBN, MIT weights — is why this is usable in a commercial product where most beat trackers are not.

Files

20.25 M parameters, 147 tensors. Both files bake in the [513, 128] mel filterbank the model was exported with, so the front end never re-derives it (slaney-vs-htk and the freq/mel layout are classic silent-drift sources).

Verification

Ported stage by stage against a PyTorch reference driven by the original checkpoint, comparing every sub-block rather than only the final output.

At f32 , every stage is numerically exact:

At f16 the same stages score cos ≥ 0.99999973 with max rel err ~5e-4, flat across all 12 attention/FF sub-blocks rather than compounding — i.e. the residual is weight quantisation, not drift. The log-mel front end matches torchaudio at cos = 1.00000000.

Windowing (1500-frame chunks, 6-frame border, keep_first overlap) and the peak-picking postprocessor reproduce upstream exactly: on a 45 s two-chunk fixture, running the reference's own logits through the ported peak-picker gives identical beat and downbeat times to 1e-6 s.

Usage

# one line per beat: time_sec <TAB> beat|downbeat
crispasr --beats -m beat-this-f16.gguf -f song.wav

# JSON, including a median-interval tempo estimate
crispasr --beats -m beat-this-f16.gguf --beats-format json -f song.wav

Input is decoded to the model's native 22.05 kHz mono automatically, and long files are chunked internally.

Every downbeat is also reported as a beat. The postprocessor snaps each downbeat onto its nearest detected beat, so downbeats are a strict subset and you never have to merge two lists to reconstruct the grid.

Licence and provenance

Code and weights are MIT , per upstream. Note that upstream's own README records that some of the training audio is copyrighted or under restrictive Creative Commons terms; the licence on the released weights is unambiguous, but the provenance of the training corpus is a separate question that upstream, not this conversion, is the authority on.

Citation

@inproceedings{foscarin2024beatthis,
    title     = {Beat this! Accurate beat tracking without DBN postprocessing},
    author    = {Foscarin, Francesco and Schl{\"u}ter, Jan and Widmer, Gerhard},
    booktitle = {Proceedings of the 25th International Society for
                 Music Information Retrieval Conference (ISMIR)},
    year      = {2024}
}