distilhubert-finetuned-gtzan – AI Model by audiosurffer0 | AlphaNeural AI

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distilhubert-finetuned-gtzan

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audio-classification

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ntu-spml/distilhubert

endpoints_compatible

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distilhubert-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: 1.0159
Accuracy: 0.83

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: 0.0003
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10
mixed_precision_training: Native AMP
label_smoothing_factor: 0.1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.7593	1.0	113	1.6563	0.51
1.4751	2.0	226	1.4366	0.61
1.3819	3.0	339	1.2893	0.64
1.1279	4.0	452	0.9950	0.75
0.8815	5.0	565	1.0208	0.79
0.5435	6.0	678	1.0175	0.82
0.5235	7.0	791	1.0193	0.8
0.5427	8.0	904	0.9317	0.86
0.5754	9.0	1017	1.0385	0.83
0.5527	10.0	1130	1.0159	0.83

Framework versions

Transformers 4.48.1
Pytorch 2.6.0+cu126
Datasets 3.3.2
Tokenizers 0.21.0