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metadata
license: apache-2.0
base_model: openai/whisper-base.en
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: whisper-base.en-finetuned-gtzan
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: GTZAN
          type: marsyas/gtzan
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.88

whisper-base.en-finetuned-gtzan

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

  • Loss: 0.6266
  • Accuracy: 0.88

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: 12
  • eval_batch_size: 12
  • 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: 18

Training results

Training Loss Epoch Step Validation Loss Accuracy
1.7396 1.0 75 1.6061 0.56
0.8839 2.0 150 0.8286 0.77
0.7631 3.0 225 0.6353 0.81
0.4049 4.0 300 0.5840 0.82
0.3031 5.0 375 0.4069 0.88
0.3031 6.0 450 0.7152 0.81
0.2879 7.0 525 0.7061 0.85
0.0301 8.0 600 0.5691 0.89
0.0311 9.0 675 0.6153 0.88
0.0025 10.0 750 0.5463 0.88
0.0036 11.0 825 0.6017 0.89
0.0016 12.0 900 0.6859 0.85
0.0014 13.0 975 0.5887 0.89
0.0012 14.0 1050 0.6525 0.9
0.0011 15.0 1125 0.6289 0.89
0.0011 16.0 1200 0.6277 0.88
0.001 17.0 1275 0.6274 0.88
0.0611 18.0 1350 0.6266 0.88

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.14.3
  • Tokenizers 0.13.3