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gigant/distilhubert-audio-course-finetuned-gtzan-v5

🤗 Hugging Face 来源audio-classificationapache-2.04.8 GBother✓ 2 个校验和今天更新
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distilhubert-audio-course-finetuned-gtzan-v5

This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.9236
  • Accuracy: 0.87

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.7
  • num_epochs: 50

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2989 0.99 56 2.2882 0.11
2.2716 2.0 113 2.2469 0.31
2.1919 2.99 169 2.1317 0.4
2.0117 4.0 226 1.9244 0.53
1.7966 4.99 282 1.7315 0.65
1.6379 6.0 339 1.5920 0.59
1.4496 6.99 395 1.3539 0.71
1.3264 8.0 452 1.1879 0.7
1.0601 8.99 508 1.1342 0.7
0.9737 10.0 565 0.9209 0.79
0.7915 10.99 621 0.8768 0.74
0.6432 12.0 678 0.8060 0.8
0.5217 12.99 734 0.6562 0.85
0.3335 14.0 791 0.7744 0.76
0.2866 14.99 847 0.6969 0.82
0.1425 16.0 904 0.6378 0.82
0.1278 16.99 960 0.6972 0.82
0.0706 18.0 1017 0.7328 0.84
0.0301 18.99 1073 0.9245 0.76
0.0379 20.0 1130 0.8437 0.85
0.0147 20.99 1186 0.7489 0.83
0.0067 22.0 1243 0.8975 0.83
0.0049 22.99 1299 1.1788 0.81
0.0038 24.0 1356 1.1146 0.81
0.0028 24.99 1412 1.0270 0.85
0.0027 26.0 1469 1.0634 0.83
0.0024 26.99 1525 1.0220 0.84
0.0023 28.0 1582 1.0282 0.83
0.0487 28.99 1638 1.0735 0.82
0.0458 30.0 1695 1.1198 0.82
0.2453 30.99 1751 1.1154 0.81
0.0552 32.0 1808 1.1630 0.79
0.1202 32.99 1864 1.2746 0.81
0.2709 34.0 1921 1.3797 0.79
0.275 34.99 1977 1.5372 0.75
0.1268 36.0 2034 0.8140 0.86
0.1582 36.99 2090 1.4153 0.77
0.0054 38.0 2147 1.3796 0.79
0.0299 38.99 2203 1.3653 0.78
0.0199 40.0 2260 0.9987 0.87
0.0021 40.99 2316 1.0689 0.84
0.0007 42.0 2373 1.0383 0.85
0.0006 42.99 2429 1.0493 0.84
0.0006 44.0 2486 1.0744 0.85
0.0005 44.99 2542 0.9151 0.86
0.0004 46.0 2599 0.8946 0.87
0.01 46.99 2655 0.8960 0.88
0.0073 48.0 2712 0.9485 0.87
0.0004 48.99 2768 0.9247 0.87
0.0004 49.56 2800 0.9236 0.87

Framework versions

  • Transformers 4.32.1
  • Pytorch 2.0.1+cu117
  • Datasets 2.14.4
  • Tokenizers 0.13.3