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distilhubert-finetuned-gtzan

Audio Classification
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PyTorch
Safetensors
hubert
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metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: distilhubert-finetuned-gtzan-30-epochs
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: GTZAN
          type: marsyas/gtzan
          config: all
          split: train
          args: all
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.81

distilhubert-finetuned-gtzan-30-epochs

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: 1.1939
  • Accuracy: 0.81

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: 5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.1804 1.0 113 2.1756 0.46
1.7271 2.0 226 1.6973 0.53
1.2703 3.0 339 1.2950 0.51
0.9446 4.0 452 0.9433 0.68
0.6192 5.0 565 0.7885 0.73
0.3628 6.0 678 0.8338 0.76
0.2871 7.0 791 0.8125 0.74
0.0587 8.0 904 0.7500 0.8
0.1316 9.0 1017 0.8711 0.79
0.0175 10.0 1130 0.7429 0.82
0.0818 11.0 1243 0.9848 0.81
0.0049 12.0 1356 1.0498 0.76
0.0034 13.0 1469 1.0422 0.84
0.0028 14.0 1582 1.0919 0.83
0.0023 15.0 1695 1.0565 0.82
0.0019 16.0 1808 1.0797 0.84
0.0769 17.0 1921 1.1430 0.82
0.104 18.0 2034 1.1482 0.8
0.0014 19.0 2147 1.0972 0.83
0.0012 20.0 2260 1.1867 0.82
0.0012 21.0 2373 1.1914 0.82
0.0012 22.0 2486 1.1461 0.84
0.0009 23.0 2599 1.1401 0.82
0.0009 24.0 2712 1.1686 0.84
0.0009 25.0 2825 1.1824 0.85
0.0009 26.0 2938 1.1815 0.81
0.0008 27.0 3051 1.1808 0.82
0.0008 28.0 3164 1.1904 0.81
0.0008 29.0 3277 1.1990 0.82
0.0008 30.0 3390 1.1939 0.81

Framework versions

  • Transformers 4.33.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.4
  • Tokenizers 0.13.3