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Abhinay45
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ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan

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: 0.92

Visualize in Weights & Biases

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: 0.3597
  • Accuracy: 0.92

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: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 8
  • 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

Training results

Training Loss Epoch Step Validation Loss Accuracy
0.5671 0.9956 112 0.5463 0.85
0.7083 2.0 225 0.6822 0.78
0.2257 2.9956 337 0.5415 0.85
0.028 4.0 450 0.5070 0.9
0.0526 4.9956 562 0.8882 0.82
0.0628 6.0 675 0.9979 0.79
0.0025 6.9956 787 0.5942 0.88
0.0005 8.0 900 0.6327 0.9
0.0005 8.9956 1012 0.4033 0.9
0.0009 10.0 1125 0.4190 0.88
0.0001 10.9956 1237 0.3672 0.93
0.0001 12.0 1350 0.3615 0.91
0.0001 12.9956 1462 0.3631 0.92
0.0001 14.0 1575 0.3597 0.92
0.0001 14.9956 1687 0.3604 0.92
0.0 16.0 1800 0.3589 0.92
0.0 16.9956 1912 0.3597 0.92
0.0434 18.0 2025 0.3590 0.92
0.0 18.9956 2137 0.3594 0.92
0.0 19.9111 2240 0.3597 0.92

Framework versions

  • Transformers 4.41.0.dev0
  • Pytorch 2.2.1+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1