Downloads · 30 days
0
sheet-music-medium-d5
kennethge123/sheet-music-medium-d5
sheet-music-medium-d5 is a machine learning model from kennethge123. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pytorch. The card lists the license as other.
Whisper-style encoder-decoder for piano audio → restdur-tokenized notation. "Medium" size counterpart to sheet-music-small-d5; best overall run in the May 2026 sweep when judged on the mean across synthesized + real-p…
Access
Public
Updated Jul 27, 2026
Repo size
140 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.pt140 MB · 100%
At a glance
- Library
- pytorch
- License
- other
- Access
- Public
- Created
- May 16, 2026
- Updated
- Jul 27, 2026
- SHA
- 72635a24
Model card
From the Hugging Face model README
sheet-music-medium-d5
Whisper-style encoder-decoder for piano audio → restdur-tokenized notation.
"Medium" size counterpart to
sheet-music-small-d5;
best overall run in the May 2026 sweep when judged on the mean across
synthesized + real-piano evaluation clips
(see project progress_log/5-15-2026.md).
Model summary
- Architecture: Whisper-style encoder-decoder, "medium" size
- Tokenizer: restdur — fused REST_DUR_X tokens for rests; pitch + duration tokens for notes. Vocab size 6244.
- Audio input: CQT, 216 bins, 10ms hop, 16 kHz mono.
- Training: CE loss only, AdamW peak LR 1.5e-4, dropout 0.2, grad clip 1.0, cosine schedule with 0.2-epoch warmup. SpecAugment on train split. 198 epochs on dataset 5 (noised synthesized piano).
Headline metrics (teacher-forced, real-audio clips)
| Clip | note_acc | rest_kind_acc | note_dur_mae_10ms | rest_dur_mae_10ms |
|---|---|---|---|---|
| Alkaline | 0.96 | 0.00 | 4.2 | 3000 |
| VivaLaVida (synth) | 0.95 | 0.56 | 9.5 | 1312 |
| UsedToBeMine | 0.33 | 0.00 | 15.1 | 0 |
| VivaLaVidaReal (real singing) | 0.44 | 0.50 | 19.5 | 1521 |
Best mean note_acc across the four real-audio clips in the sweep (0.67). Trades a little real-piano note_acc vs. the small_d5 baseline (0.44 vs. 0.56) for stronger note_acc on every synthetic real-music clip.
Files
best.pt— model state_dict, ~134 MBeval_results.json— full eval metrics across all OOD + real-audio clips
Loading
import torch
# model.py + data.py from the sheet-music repo (matching commit) are
# required to instantiate WhisperLite with the restdur vocab.
import model
model.set_size('medium')
from model import WhisperLite
net = WhisperLite()
net.load_state_dict(torch.load('best.pt', map_location='cpu'), strict=False)
net.eval()
- Library
- pytorch
- License
- other
- Created
- May 16, 2026
- Updated
- Jul 27, 2026