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metadata
license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: whisper-tiny-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: 0.87
pipeline_tag: audio-classification

whisper-tiny-finetuned-gtzan

This model is a fine-tuned version of openai/whisper-tiny on the GTZAN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7650
  • Accuracy: 0.87

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: 0.0001
  • 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_steps: 10
  • num_epochs: 10
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.0205 0.3274 37 1.6041 0.41
1.3349 0.6549 74 0.9462 0.67
1.1646 0.9823 111 0.9334 0.72
0.8737 1.3097 148 0.8974 0.64
0.8703 1.6372 185 0.7014 0.78
0.811 1.9646 222 0.8678 0.7
0.6429 2.2920 259 0.9130 0.66
0.6366 2.6195 296 0.7061 0.78
0.5858 2.9469 333 0.5549 0.82
0.3959 3.2743 370 0.5577 0.82
0.3343 3.6018 407 0.6203 0.83
0.3358 3.9292 444 0.8755 0.76
0.2574 4.2566 481 0.7690 0.79
0.1799 4.5841 518 0.7350 0.85
0.212 4.9115 555 0.6767 0.84
0.1553 5.2389 592 0.7819 0.84
0.1065 5.5664 629 0.9823 0.83
0.1151 5.8938 666 0.7709 0.84
0.0107 6.2212 703 0.7156 0.88
0.0564 6.5487 740 0.7283 0.88
0.0501 6.8761 777 0.7763 0.87
0.0846 7.2035 814 0.8221 0.83
0.0372 7.5310 851 0.7526 0.87
0.0015 7.8584 888 0.7705 0.87
0.0209 8.1858 925 0.7020 0.86
0.0114 8.5133 962 0.8043 0.86
0.0011 8.8407 999 0.7608 0.88
0.0018 9.1681 1036 0.7623 0.88
0.0009 9.4956 1073 0.7708 0.87
0.0219 9.8230 1110 0.7650 0.87

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1