metadata

license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
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: 0.9

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: 0.4793
Accuracy: 0.9

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: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 25
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.6559	1.0	112	0.5081	0.86
0.5141	2.0	225	0.5618	0.77
0.5517	3.0	337	0.5009	0.84
0.6651	4.0	450	0.7811	0.82
0.0057	5.0	562	0.3074	0.93
0.0018	6.0	675	0.4843	0.87
0.0007	7.0	787	0.6949	0.85
0.0007	8.0	900	0.6981	0.88
0.0007	9.0	1012	0.8356	0.87
0.0001	10.0	1125	0.6164	0.89
0.1709	11.0	1237	0.5464	0.89
0.0001	12.0	1350	0.4885	0.88
0.0003	13.0	1462	0.4970	0.91
0.0	14.0	1575	0.5346	0.88
0.0001	15.0	1687	0.5526	0.89
0.0	16.0	1800	0.4808	0.91
0.0	17.0	1912	0.4999	0.9
0.0	18.0	2025	0.4909	0.89
0.0	19.0	2137	0.4953	0.89
0.0	20.0	2250	0.4883	0.9
0.0543	21.0	2362	0.4830	0.91
0.0	22.0	2475	0.4811	0.9
0.0	23.0	2587	0.4805	0.9
0.0	24.0	2700	0.4785	0.91
0.0	24.89	2800	0.4793	0.9

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.0.1
Datasets 2.14.5
Tokenizers 0.15.0