distilhubert-finetuned-gtzan – AI Model by vadhri | AlphaNeural AI

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vadhri/
distilhubert-finetuned-gtzan

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audio-classification

generated_from_trainer

ntu-spml/distilhubert

endpoints_compatible

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music-genre-classifer-20-finetuned-gtzan

This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set:

Loss: 1.1035
Accuracy: 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: 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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.0544	1.0	225	1.9608	0.47
1.2995	2.0	450	1.3852	0.51
0.8875	3.0	675	0.9288	0.71
0.4092	4.0	900	0.8114	0.76
0.5624	5.0	1125	0.8704	0.77
0.0609	6.0	1350	0.7951	0.82
0.1018	7.0	1575	0.7055	0.86
0.2941	8.0	1800	0.8832	0.83
0.0044	9.0	2025	0.9883	0.83
0.0025	10.0	2250	0.9306	0.88
0.0016	11.0	2475	0.9535	0.86
0.0012	12.0	2700	1.0921	0.85
0.001	13.0	2925	1.0428	0.86
0.0011	14.0	3150	1.2270	0.83
0.0008	15.0	3375	1.1831	0.84
0.0007	16.0	3600	1.2124	0.84
0.0007	17.0	3825	1.0806	0.86
0.2454	18.0	4050	1.1530	0.85
0.0006	19.0	4275	1.1078	0.86
0.0006	20.0	4500	1.1035	0.87

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2