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-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

hubert-base-ls960-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: 0.6645
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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 30

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.2685	1.0	56	2.2069	0.44
2.0208	1.99	112	1.8352	0.46
1.7603	2.99	168	1.5275	0.49
1.4843	4.0	225	1.4296	0.52
1.347	5.0	281	1.2222	0.52
1.2364	5.99	337	1.1477	0.62
1.2082	6.99	393	1.0181	0.67
0.9861	8.0	450	0.9598	0.71
0.752	9.0	506	0.7499	0.77
1.006	9.99	562	0.8190	0.79
0.6725	10.99	618	0.8798	0.75
0.7457	12.0	675	0.6276	0.81
0.4605	13.0	731	0.6086	0.85
0.5751	13.99	787	0.6894	0.75
0.4886	14.99	843	0.6109	0.83
0.2429	16.0	900	0.6076	0.85
0.3084	17.0	956	0.4646	0.86
0.3762	17.99	1012	0.8349	0.81
0.2897	18.99	1068	0.4509	0.89
0.1296	20.0	1125	0.6791	0.86
0.1291	21.0	1181	0.6466	0.85
0.3784	21.99	1237	0.6272	0.88
0.1156	22.99	1293	0.7916	0.85
0.2093	24.0	1350	0.6536	0.85
0.2167	25.0	1406	0.7050	0.87
0.1095	25.99	1462	0.6128	0.88
0.1004	26.99	1518	0.6092	0.89
0.0897	28.0	1575	0.6730	0.88
0.083	29.0	1631	0.6396	0.89
0.0343	29.87	1680	0.6645	0.88

Framework versions

Transformers 4.32.0.dev0
Pytorch 1.13.1+cu116
Datasets 2.14.1
Tokenizers 0.13.3