metadata

license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.450
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: ast-finetuned-audioset-10-10-0.450-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

ast-finetuned-audioset-10-10-0.450-finetuned-gtzan

This model is a fine-tuned version of MIT/ast-finetuned-audioset-10-10-0.450 on the GTZAN dataset. It achieves the following results on the evaluation set:

Loss: 0.4457
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: 16
eval_batch_size: 16
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: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.5804	1.0	57	0.5356	0.84
0.2655	2.0	114	0.5664	0.76
0.1767	3.0	171	0.3925	0.88
0.4169	4.0	228	0.8874	0.78
0.0685	5.0	285	0.6067	0.83
0.0725	6.0	342	0.5612	0.81
0.1003	7.0	399	0.6928	0.82
0.004	8.0	456	0.4814	0.86
0.0122	9.0	513	0.6141	0.86
0.0009	10.0	570	0.4017	0.91
0.0828	11.0	627	0.4937	0.88
0.0025	12.0	684	0.8455	0.82
0.0005	13.0	741	0.4439	0.89
0.0001	14.0	798	0.4956	0.87
0.0001	15.0	855	0.4362	0.88
0.0001	16.0	912	0.4146	0.89
0.0299	17.0	969	0.4241	0.9
0.0001	18.0	1026	0.4375	0.87
0.0001	19.0	1083	0.4502	0.88
0.0001	20.0	1140	0.4457	0.88

Framework versions

Transformers 4.36.0.dev0
Pytorch 2.1.0+cu118
Datasets 2.15.0
Tokenizers 0.15.0