metadata

license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: hubert-base-ls960-v2-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: 0.86

hubert-base-ls960-v2-finetuned-gtzan

This model is a fine-tuned version of facebook/hubert-base-ls960 on the GTZAN dataset. It achieves the following results on the evaluation set:

Loss: 0.6560
Accuracy: 0.86

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: 10
eval_batch_size: 10
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
2.1778	1.0	90	2.1185	0.42
1.7477	2.0	180	1.6950	0.5
1.6626	3.0	270	1.4481	0.49
1.0488	4.0	360	1.2952	0.56
0.9819	5.0	450	1.0239	0.63
0.8553	6.0	540	0.8149	0.75
0.9188	7.0	630	0.9471	0.73
0.5563	8.0	720	0.7414	0.77
0.6793	9.0	810	0.7851	0.78
0.5282	10.0	900	0.6163	0.8
0.3895	11.0	990	0.6667	0.82
0.3037	12.0	1080	0.6157	0.84
0.1647	13.0	1170	0.6485	0.83
0.3331	14.0	1260	0.5609	0.86
0.1695	15.0	1350	0.6393	0.84
0.0968	16.0	1440	0.7537	0.83
0.1928	17.0	1530	0.7043	0.86
0.1281	18.0	1620	0.6077	0.89
0.0482	19.0	1710	0.7178	0.86
0.1215	20.0	1800	0.6560	0.86

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.19.1