metadata

license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
  - f1
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: 0.89
          - name: F1
            type: f1
            value: 0.89
pipeline_tag: audio-classification

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: 0.5658
Accuracy: 0.87
F1: 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: 5e-05
train_batch_size: 2
eval_batch_size: 2
seed: 2024
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.8357	0.9956	56	0.6582	0.82	0.82
0.4742	1.9911	112	0.6527	0.81	0.81
0.3344	2.9867	168	0.9048	0.76	0.76
0.0659	4.0	225	0.6998	0.84	0.8400
0.0966	4.9956	281	0.6737	0.83	0.83
0.0026	5.9911	337	0.5133	0.89	0.89
0.0038	6.9867	393	0.5704	0.86	0.8600
0.0005	8.0	450	0.5722	0.86	0.8600
0.0003	8.9956	506	0.5632	0.87	0.87
0.0003	9.9556	560	0.5658	0.87	0.87

Framework versions

Transformers 4.42.4
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1