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ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan-2

Audio Classification
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Safetensors
audio-spectrogram-transformer
generated_from_trainer
Eval Results
Inference Endpoints
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metadata
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: ast-finetuned-audioset-10-10-0.4593-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: 0.85

ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan

This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.4593 on the GTZAN dataset. It achieves the following results on the evaluation set:

  • Loss: 2.3496
  • Accuracy: 0.85

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: 4
  • eval_batch_size: 4
  • 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: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.9076 1.0 225 0.8234 0.75
0.3522 2.0 450 0.4291 0.9
0.4656 3.0 675 0.4656 0.83
0.2739 4.0 900 0.6314 0.9
0.575 5.0 1125 0.7786 0.85
0.4433 6.0 1350 1.1706 0.88
1.4075 7.0 1575 2.5171 0.83
0.1059 8.0 1800 1.5907 0.84
0.1521 9.0 2025 3.7424 0.72
0.5736 10.0 2250 2.0911 0.82
0.7552 11.0 2475 3.2042 0.81
0.166 12.0 2700 1.8762 0.86
0.0 13.0 2925 1.0614 0.91
1.2229 14.0 3150 3.0105 0.78
1.0135 15.0 3375 2.2024 0.85
0.098 16.0 3600 1.6070 0.87
0.0 17.0 3825 2.5323 0.82
0.0 18.0 4050 2.2202 0.86
0.0 19.0 4275 2.2681 0.85
0.0 20.0 4500 2.2394 0.86
1.8867 21.0 4725 4.2168 0.74
0.6094 22.0 4950 4.7781 0.72
0.3684 23.0 5175 2.6412 0.81
0.0 24.0 5400 2.8745 0.82
0.0 25.0 5625 2.9487 0.79
0.0 26.0 5850 2.5325 0.82
0.1597 27.0 6075 2.0327 0.85
0.0 28.0 6300 3.0062 0.84
0.0 29.0 6525 2.3104 0.8
0.0 30.0 6750 2.9985 0.83
0.0 31.0 6975 2.9385 0.82
0.0 32.0 7200 2.1102 0.87
0.0 33.0 7425 2.0060 0.86
1.1173 34.0 7650 1.9131 0.87
0.0 35.0 7875 2.4819 0.84
0.0 36.0 8100 2.0951 0.87
0.0 37.0 8325 1.9796 0.85
0.0 38.0 8550 2.0940 0.85
0.9059 39.0 8775 2.0714 0.85
0.0 40.0 9000 4.0729 0.75
0.0 41.0 9225 2.8627 0.83
0.0 42.0 9450 4.0389 0.76
0.0 43.0 9675 2.3248 0.85
0.6586 44.0 9900 4.9549 0.75
0.0 45.0 10125 3.3910 0.81
0.0 46.0 10350 3.9627 0.77
0.0 47.0 10575 3.4481 0.83
0.0 48.0 10800 2.7042 0.85
0.0 49.0 11025 2.8337 0.85
0.0 50.0 11250 2.4333 0.85
0.0 51.0 11475 2.6346 0.84
0.0 52.0 11700 1.8957 0.88
1.7162 53.0 11925 2.7006 0.85
0.0 54.0 12150 2.2261 0.86
0.0 55.0 12375 1.5562 0.89
0.0 56.0 12600 1.4557 0.91
0.0 57.0 12825 1.6862 0.89
0.0 58.0 13050 1.6635 0.9
0.0 59.0 13275 2.5130 0.85
0.0 60.0 13500 2.1794 0.84
0.0 61.0 13725 3.1630 0.82
0.0 62.0 13950 2.4938 0.84
0.0 63.0 14175 2.9464 0.82
0.0 64.0 14400 3.0567 0.81
0.0 65.0 14625 3.0951 0.82
0.0 66.0 14850 2.8673 0.82
0.0 67.0 15075 2.9092 0.82
0.0 68.0 15300 2.2521 0.85
0.0 69.0 15525 2.5049 0.82
0.0 70.0 15750 2.4376 0.84
0.0 71.0 15975 2.6660 0.82
0.0 72.0 16200 2.5182 0.86
0.0 73.0 16425 2.3814 0.85
0.0 74.0 16650 2.3093 0.85
0.0 75.0 16875 2.3014 0.85
0.0 76.0 17100 2.3845 0.86
0.0 77.0 17325 2.2978 0.85
0.0 78.0 17550 2.4215 0.85
0.7047 79.0 17775 2.3462 0.84
0.0 80.0 18000 2.3230 0.85
0.0 81.0 18225 2.3648 0.85
0.0 82.0 18450 2.2962 0.85
0.0 83.0 18675 2.4231 0.84
0.0 84.0 18900 2.2588 0.86
0.0 85.0 19125 2.4144 0.84
0.0 86.0 19350 2.4220 0.84
0.0 87.0 19575 2.3860 0.84
0.0 88.0 19800 2.3356 0.84
0.0 89.0 20025 2.3223 0.85
0.0 90.0 20250 2.3554 0.83
0.0 91.0 20475 2.3344 0.84
0.6906 92.0 20700 2.3568 0.85
0.0 93.0 20925 2.3905 0.84
0.0 94.0 21150 2.3920 0.85
0.0 95.0 21375 2.3935 0.85
0.0 96.0 21600 2.3392 0.85
0.0 97.0 21825 2.3437 0.85
0.0 98.0 22050 2.3434 0.85
0.0 99.0 22275 2.3503 0.85
0.0 100.0 22500 2.3496 0.85

Framework versions

  • Transformers 4.35.2
  • Pytorch 2.1.0
  • Datasets 2.15.0
  • Tokenizers 0.15.0