metadata

library_name: transformers
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: ast-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: 0.94
          - name: Precision
            type: precision
            value: 0.946171802054155
          - name: Recall
            type: recall
            value: 0.9379426129426129
          - name: F1
            type: f1
            value: 0.9379839011750775

ast-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: 0.3551
Accuracy: 0.94
Precision: 0.9462
Recall: 0.9379
F1: 0.9380

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: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
0.9185	1.0	113	0.6489	0.78	0.8099	0.7976	0.7743
0.473	2.0	226	0.6660	0.8	0.8284	0.8208	0.7963
0.4124	3.0	339	0.6544	0.8	0.8237	0.8002	0.7880
0.1625	4.0	452	0.4139	0.86	0.8519	0.8603	0.8454
0.2298	5.0	565	0.5540	0.88	0.8689	0.8694	0.8618
0.1091	6.0	678	0.4291	0.89	0.8933	0.8935	0.8855
0.0208	7.0	791	0.4161	0.91	0.9200	0.9000	0.8977
0.0181	8.0	904	0.3769	0.92	0.9133	0.9202	0.9127
0.0035	9.0	1017	0.3431	0.94	0.9353	0.9424	0.9371
0.013	10.0	1130	0.3551	0.94	0.9462	0.9379	0.9380

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1