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wav2vec2-base-finetuned-gtzan

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metadata
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: music-genre-classifer-20-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: 0.81

music-genre-classifer-20-finetuned-gtzan

This model is a fine-tuned version of facebook/wav2vec2-base on the GTZAN dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1602
  • 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: 2e-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
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Accuracy Validation Loss
2.0297 1.0 113 0.46 2.0056
1.6252 2.0 226 0.61 1.5821
1.4001 3.0 339 0.62 1.3967
1.0201 4.0 452 0.77 1.1288
1.0074 5.0 565 0.69 1.0933
0.8466 6.0 678 0.76 0.9162
0.6966 7.0 791 0.79 0.9122
0.5324 8.0 904 0.82 0.7715
0.6692 9.0 1017 1.0549 0.71
0.7181 10.0 1130 0.8758 0.76
0.5585 11.0 1243 1.0753 0.7
0.4479 12.0 1356 1.1517 0.7
0.3145 13.0 1469 0.8475 0.79
0.8197 14.0 1582 0.8852 0.78
0.4665 15.0 1695 1.0134 0.77
0.2371 16.0 1808 1.0250 0.75
0.3823 17.0 1921 0.9516 0.81
0.5478 18.0 2034 1.2008 0.77
0.3165 19.0 2147 1.0737 0.8
0.1403 20.0 2260 0.9801 0.83
0.2754 21.0 2373 1.0137 0.82
0.2649 22.0 2486 1.2249 0.77
0.0686 23.0 2599 1.3234 0.77
0.0073 24.0 2712 1.2360 0.8
0.0068 25.0 2825 1.1338 0.81
0.0058 26.0 2938 1.2976 0.79
0.0054 27.0 3051 1.1782 0.83
0.0047 28.0 3164 1.0677 0.84
0.0045 29.0 3277 1.1128 0.83
0.0036 30.0 3390 1.1602 0.81

Framework versions

  • Transformers 4.38.2
  • Pytorch 2.2.1+cu121
  • Datasets 2.18.0
  • Tokenizers 0.15.2