finetuned-gtzan / README.md
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metadata
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: hubert-base-ls960-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: 0.88

hubert-base-ls960-finetuned-gtzan

This model is a fine-tuned version of facebook/hubert-base-ls960 on the GTZAN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7650
  • 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: 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: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Accuracy
2.2258 1.0 225 1.9240 0.28
1.6083 2.0 450 1.4887 0.39
1.3983 3.0 675 1.3524 0.56
0.7368 4.0 900 1.3110 0.56
0.6121 5.0 1125 0.9572 0.72
0.1772 6.0 1350 0.8775 0.73
1.8666 7.0 1575 0.6078 0.82
0.091 8.0 1800 0.9999 0.76
0.0458 9.0 2025 0.7169 0.83
0.6817 10.0 2250 0.7614 0.86
0.7023 11.0 2475 0.9348 0.84
0.0047 12.0 2700 0.7222 0.88
0.0363 13.0 2925 0.7027 0.89
0.0073 14.0 3150 0.7440 0.88
0.0055 15.0 3375 0.7650 0.88

Framework versions

  • Transformers 4.38.0.dev0
  • Pytorch 2.1.2+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0