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distilhubert-finetuned-gtzan

Audio Classification
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End of training, 20 epochs, 100 batch size, 1000 writer batch size, 1 gradient accumulation steps, learning rate: 4e-05, 30 s
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metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - audio-classification
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: distilhubert-finetuned-gtzan
    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: 0.86

distilhubert-finetuned-gtzan

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: 0.5120
  • Accuracy: 0.86

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: 4e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • 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: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2584 1.0 57 2.2062 0.35
1.8611 2.0 114 1.7924 0.53
1.4492 3.0 171 1.3901 0.65
1.0971 4.0 228 1.1676 0.69
0.9848 5.0 285 0.9750 0.74
0.8434 6.0 342 0.8434 0.74
0.7321 7.0 399 0.7555 0.83
0.5364 8.0 456 0.6995 0.79
0.4557 9.0 513 0.6118 0.84
0.4166 10.0 570 0.5975 0.83
0.2729 11.0 627 0.5576 0.83
0.2491 12.0 684 0.5737 0.82
0.2211 13.0 741 0.5129 0.84
0.1243 14.0 798 0.5710 0.83
0.0904 15.0 855 0.5087 0.86
0.0773 16.0 912 0.5836 0.8
0.0598 17.0 969 0.4871 0.83
0.0551 18.0 1026 0.4865 0.84
0.0467 19.0 1083 0.5043 0.84
0.0364 20.0 1140 0.5120 0.86

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.4.0
  • Datasets 3.0.0
  • Tokenizers 0.19.1