A tiny CNN that scores candidate (string, fret) placements for a sequence of
note-columns, so a guitar-tab arranger (Viterbi over hand positions) fingers
more like a human than a hand-tuned heuristic. The symbolic arm of a
two-model tab stack — the score/MIDI→tab counterpart to the audio→tab
cstr/tabcnn-onnx. It shares TabCNN's
exact per-string output contract, so the same decoder consumes both.
The model never emits tab — it only scores positions the arranger enumerated;
the arranger's transition cost + hard span cap stay the arbiter, so nothing
unplayable is produced. Missing → the heuristic is the fallback.
Versions
file
training data
agreement
notes
tab-labeler.onnx (default, v2)
GuitarSet
82.7%
shipped baseline
tab-labeler-v1-lowmove.onnx (v1)
GuitarSet
78.59%
lower-movement fallback
tab-labeler-v3-egset.onnx (v3)
GuitarSet + EGSet12
82.5% (val 0.829)
more data; better position (6.23 vs human 6.20). GuitarSet-validated; OOD (IDMT) comparison pending
tab-labeler-v3c-egset-spanreg.onnx (v3c)
GuitarSet + EGSet12, span-regularized
val 0.829
tighter emission (span-proxy 1.66); full benchmark pending
Agreement = per-note (string,fret) match vs held-out human (GuitarSet player 05,
60-song arranged benchmark). All models carry top-2 checkpoints (.best.pt).
★ Integration: hitting span<1.5 at agreement>82% (no retraining)
Quality is more than agreement. Measured against the human on all axes
(GuitarSet, 60 songs; human = agreement 100% / movement 5325 / span 1.43 /
position 6.20):
agreement movement span position
heuristic (no model) 57.0% 4095 1.34 4.93 (under-moves, under-reaches)
this model (mv=1.0) 82.7% 4372 1.68 6.43 (good move+pos; OVER-spans)
The model fingers at about the right position and moves about the right amount,
but picks wider shapes than a human. The fix is an arranger-side knob,
not a new model: because the arranger lets the model replace its local cost,
the span penalty is dropped — re-apply a span penalty on top of the emission
(modelSpanCost in CometBeat's arrangeTab). Sweep on v3 (GuitarSet):
modelSpanCost
agreement
span
0.00
82.5%
1.77
0.20
83.5%
1.59
0.50
84.0%
1.47
Span and agreement are NOT a tradeoff — they align: since the human fingers
compact (1.43), preferring compact shapes makes the model match the human more.
So modelSpanCost≈0.5 gives span 1.47 (<1.5) at agreement 84.0% (>82%) with
movement still under the human — on any of these weights, no retraining.
IO contract
Inputinput : float32[N, 49, 9, 1] — per column, a 9-column window
(centred, zero-padded) of multi-hot pitch-presence over MIDI 40..88
(49 bins).
Outputoutput : float32[N, 6, 21] — per-string LogSoftmax log-probs.
class 0 = string silent, class k = fret k-1. String index 0 = high e
… 5 = low E. Emission for (string,fret) = output[string][fret+1].
Data:GuitarSet (CC BY 4.0) — exact
(pitch → string, fret) labels; held out by guitarist (player 05 = val),
±2-semitone transposition aug. v3/v3c additionally use
EGSet12 (CC BY 4.0, original electric
compositions — a 7th player). (A Guitar-TECHS-augmented variant, CC BY 4.0, was
trained too — val 0.823, slightly OOD; not published here.)
Objective: sum of 6 per-string softmax cross-entropies (v3c adds a span
regularizer on the predicted distribution).
Provenance / license
CC BY 4.0. Derived weights redistributable with attribution. Trained on
GuitarSet (Xi et al., ISMIR 2018, CC BY 4.0); v3/v3c also on EGSet12 (CC BY 4.0).
No DadaGP / no request-gated data.
Provenance — authored here, and outside the GPAI definition
Added 2026-08-02 during an account-wide provenance review.
These weights were trained by this repository's maintainer, not converted
from someone else's model. Most cstr/* repositories are GGUF/ONNX conversions
where the upstream research team remains the provider; this one is not, and the
distinction matters because the two attract different obligations. The training
data, hyperparameters and licence inheritance are documented above.
EU AI Act Art. 53 does not apply. Art. 53 binds providers of general-purpose
AI models, which Art. 3(63) defines as models displaying "significant generality"
and capable of "competently performing a wide range of distinct tasks". A CNN scoring candidate (string, fret) placements for guitar tablature
is a narrow, single-task model and does not meet that definition, so the
Art. 53(1)(c) copyright-policy and 53(1)(d) training-content duties are not
engaged. This is recorded explicitly because "trained here" and "subject to
Art. 53" are easy to conflate, and only the first is true.
The training-data documentation above is published because it is useful and
because the licence inheritance depends on it — not because Art. 53 compels it.
