ast-finetuned-gtzan – AI Model by model-man | AlphaNeural AI

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ast-finetuned-gtzan

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audio-spectrogram-transformer

audio-classification

generated_from_trainer

MIT/ast-finetuned-audioset-10-10-0.4593

endpoints_compatible

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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: 0.6261
Accuracy: 0.89

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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.03	1.0	225	0.7218	0.81
0.3004	2.0	450	1.1639	0.8
0.5633	3.0	675	0.8839	0.83
0.0354	4.0	900	0.8839	0.8
0.5666	5.0	1125	1.1155	0.82
0.0001	6.0	1350	0.6813	0.9
0.2482	7.0	1575	0.6845	0.9
0.0001	8.0	1800	1.4196	0.8
0.0	9.0	2025	0.9603	0.84
0.0	10.0	2250	0.7030	0.88
0.0	11.0	2475	0.6363	0.89
0.0	12.0	2700	0.6589	0.89
0.0	13.0	2925	0.6845	0.87
0.0	14.0	3150	0.6061	0.9
0.0	15.0	3375	0.6210	0.89
0.0	16.0	3600	0.6136	0.89
0.0	17.0	3825	0.6104	0.89
0.0	18.0	4050	0.6147	0.89
0.0	19.0	4275	0.6259	0.89
0.0	20.0	4500	0.6261	0.89

Framework versions

Transformers 4.32.0.dev0
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3