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logs/rmvpe_ft/rmvpe_ft_v2_20260329_213550.log
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| 1 |
+
=== Fine-tune RMVPE — Sun Mar 29 21:35:50 HKT 2026 ===
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+
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+
=== Run 1/5 — Sun Mar 29 21:35:50 HKT 2026 ===
|
| 4 |
+
Resuming from outputs/rmvpe_ft_v2/last.pt
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| 5 |
+
/userhome/cs/u3571824/fyp/muse/backend/ml_models/dev/vocal/finetune_rmvpe.py:547: FutureWarning: `torch.cuda.amp.GradScaler(args...)` is deprecated. Please use `torch.amp.GradScaler('cuda', args...)` instead.
|
| 6 |
+
scaler = GradScaler(enabled=args.fp16 and device.type == 'cuda')
|
| 7 |
+
Datasets: ['mir_st500', 'medleydb']
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+
Train: 318 tracks, Val: 39 tracks
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+
Loading pretrained RMVPE from outputs/rmvpe_ft_v2/last.pt
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+
RMVPE: 92.0M params
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| 11 |
+
Frozen 230/417 parameter groups (encoder + intermediate)
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| 12 |
+
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| 13 |
+
=== Fine-tuning RMVPE: 30 epochs, lr=5e-05 ===
|
| 14 |
+
/userhome/cs/u3571824/fyp/muse/backend/ml_models/dev/vocal/finetune_rmvpe.py:576: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.
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| 15 |
+
with cuda_autocast(enabled=args.fp16 and device.type == 'cuda'):
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| 16 |
+
Epoch 1/30 (7s) lr=9.97e-07 | loss=0.1831 | val_RPA=0.6782
|
| 17 |
+
★ New best RPA: 0.6782
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| 18 |
+
Epoch 2/30 (6s) lr=9.89e-07 | loss=0.1829 | val_RPA=0.6859
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| 19 |
+
★ New best RPA: 0.6859
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| 20 |
+
Epoch 3/30 (6s) lr=9.76e-07 | loss=0.1832 | val_RPA=0.6744
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| 21 |
+
Epoch 4/30 (6s) lr=9.57e-07 | loss=0.1830 | val_RPA=0.6705
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| 22 |
+
Epoch 5/30 (6s) lr=9.33e-07 | loss=0.1836 | val_RPA=0.6782
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Epoch 6/30 (6s) lr=9.05e-07 | loss=0.1836 | val_RPA=0.6840
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Epoch 7/30 (6s) lr=8.72e-07 | loss=0.1824 | val_RPA=0.6859
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Epoch 8/30 (6s) lr=8.35e-07 | loss=0.1832 | val_RPA=0.6840
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| 26 |
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Epoch 9/30 (6s) lr=7.94e-07 | loss=0.1833 | val_RPA=0.6879
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| 27 |
+
★ New best RPA: 0.6879
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| 28 |
+
Epoch 10/30 (6s) lr=7.50e-07 | loss=0.1828 | val_RPA=0.6917
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| 29 |
+
★ New best RPA: 0.6917
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| 30 |
+
Epoch 11/30 (6s) lr=7.03e-07 | loss=0.1824 | val_RPA=0.6956
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| 31 |
+
★ New best RPA: 0.6956
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| 32 |
+
Epoch 12/30 (6s) lr=6.55e-07 | loss=0.1829 | val_RPA=0.7013
|
| 33 |
+
★ New best RPA: 0.7013
|
| 34 |
+
Epoch 13/30 (6s) lr=6.04e-07 | loss=0.1828 | val_RPA=0.7033
|
| 35 |
+
★ New best RPA: 0.7033
|
| 36 |
+
Epoch 14/30 (6s) lr=5.52e-07 | loss=0.1829 | val_RPA=0.6975
|
| 37 |
+
Epoch 15/30 (6s) lr=5.00e-07 | loss=0.1832 | val_RPA=0.6994
|
| 38 |
+
Epoch 16/30 (6s) lr=4.48e-07 | loss=0.1830 | val_RPA=0.6956
|
| 39 |
+
Epoch 17/30 (6s) lr=3.96e-07 | loss=0.1821 | val_RPA=0.7013
|
| 40 |
+
Epoch 18/30 (6s) lr=3.45e-07 | loss=0.1830 | val_RPA=0.6994
|
| 41 |
+
Epoch 19/30 (6s) lr=2.97e-07 | loss=0.1832 | val_RPA=0.6936
|
| 42 |
+
Epoch 20/30 (6s) lr=2.50e-07 | loss=0.1827 | val_RPA=0.6975
|
| 43 |
+
Epoch 21/30 (6s) lr=2.06e-07 | loss=0.1826 | val_RPA=0.6994
|
| 44 |
+
Early stopping at epoch 21
|
| 45 |
+
|
| 46 |
+
Fine-tuning complete. Best RPA: 0.7033
|
| 47 |
+
Training finished at Sun Mar 29 21:39:02 HKT 2026
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| 48 |
+
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| 49 |
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=== Upload to HuggingFace ===
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Uploading last.pt...
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