PTify Model V1

PTify model v1 — room-robust piano transcription

ByteDance's high-resolution piano CRNN, fine-tuned with continuous room and
detune augmentation. It beats the stock model by +5.3 onset F1 on MAPS, a
corpus neither model trained on.

	ByteDance	PTify v1
MAPS (14 paired tracks)	0.7866	0.8395	+5.3 — improved 14 of 14
MAESTRO (12 tracks)	0.9693	0.9633	−0.6 (the bounded price)

MAPS is the honest target. MAESTRO is ByteDance's own training distribution, so
its 0.969 there is a home-field score — beating it would not mean what it
appears to mean.

Why this is room robustness, not a general uplift

The MAPS paired subset is the same performances captured at two microphone
distances, so everything except the room is held constant:

mic distance	ByteDance	PTify v1	delta
ENSTDkCl close, ~50cm	0.851	0.878	+2.7
ENSTDkAm ambient, 3–4m	0.722	0.801	+7.9
room penalty	0.129	0.077	−0.052

The hard, reverberant condition gained 2.9x what the easy one did. A model
that had merely got generally better would have lifted both equally. The
generalisation gap — 18.3 points between MAESTRO and MAPS — is now 12.4, so
roughly a third of it is closed.

What is in the file

ptify-16b-step6555.pth — 172,037,521 bytes

sha256  17286ad93c5806e02a59caf0333769d9bea9f4f3e53abd7360be8cabe9d4accd

A deployable checkpoint: {'model': {'note_model': ..., 'pedal_model': ...}},
loadable directly by piano_transcription_inference. The note weights are
fine-tuned; the pedal weights are ByteDance's, unmodified, because MAPS
carries no pedal ground truth and a change there could not be scored either way.

Verify the digest before using it. The inference library validates a
checkpoint by size alone, so a different ~172MB .pth loads without complaint
and scores a model you cannot identify.

Use it

python -m transcriber --fetch-ptify          # downloads and verifies
python -m transcriber song.wav --engine ptify
python -m transcriber --doctor               # reports present / absent / wrong

Or point at the file directly with PTIFY_CHECKPOINT=/path/to/file.pth, or
drop it in checkpoints/. The engine raises when the weights are missing
rather than falling back to the pretrained model — a silent fallback would
report the baseline's score under this model's name.

Same architecture as ByteDance, so identical speed (~1.1x real time on CPU),
sustain pedal, and real velocity.

Training

base	ByteDance CRNN_note_F1=0.9677_pedal_F1=0.9186
data	MAESTRO v3.0.0 train split, 962 tracks
steps	6,555 at effective batch 8 (fp32, batch 2 × accum 4)
lr	5e-5
augmentation	rt60 log-uniform 0.2–1.6, detune triangular ±50 cents, 20% clean passthrough
hardware	one free-tier Kaggle T4, a single ~10-hour session

That is 15% of one epoch. The augmentation ranges came from a measured
degradation curve: a quarter-semitone detune cost 14.1 F1 points and a concert
hall 9.3, while 15dB-SNR noise cost 1.5 — so the augmentation targets pitch and
reverb, and does not bother with noise injection.

Run log: benchmarks/training/16b-step6555.jsonl.

Known issue — do not quote the offset metric

MAESTRO +offset rose 0.381 → 0.520 while MAPS +offset fell 0.607 →
0.431. Nothing in the training targeted offsets, and two corpora disagreeing in
direction on the same metric means something systematic is unaccounted for.
Onset F1 is the number this release stands behind. The offset movement is
under investigation and should not be cited as a result.

Reproducing the numbers

python -m evaluation.maps --out recordings/maps_disklavier
python -m evaluation --audio-dir recordings/maps_paired \
    --engine ptify --preset clean \
    --json my-run.json

Then diff against the committed baseline. Scores are not bit-identical across
machines — thread count and device change floating-point reduction order — so
record inference_threads and device alongside any number you publish.

Licence

CC BY-NC-SA 4.0 — research and non-commercial use.

The weights derive from ByteDance's checkpoint (Apache 2.0) and were fine-tuned
on MAESTRO v3.0.0, which is CC BY-NC-SA 4.0. The share-alike term is taken to
propagate to a model trained on that data, so this is the conservative reading
rather than a settled legal one. No MAESTRO or MAPS audio is redistributed here
or in the repository.