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: 0.84

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: 0.7772
Accuracy: 0.84

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
2.2028	1.0	90	2.1088	0.42
1.7214	2.0	180	1.6669	0.43
1.6141	3.0	270	1.5335	0.54
0.9971	4.0	360	1.1589	0.64
1.0174	5.0	450	0.9587	0.64
0.7295	6.0	540	0.8286	0.69
0.8034	7.0	630	0.8001	0.76
0.5709	8.0	720	0.9846	0.73
0.4724	9.0	810	0.6829	0.79
0.5161	10.0	900	0.9728	0.72
0.4247	11.0	990	0.7745	0.78
0.2696	12.0	1080	0.5330	0.87
0.1403	13.0	1170	0.7202	0.83
0.3434	14.0	1260	0.8506	0.82
0.2754	15.0	1350	0.6707	0.85
0.152	16.0	1440	0.8752	0.83
0.233	17.0	1530	0.5098	0.9
0.1169	18.0	1620	0.7069	0.86
0.1667	19.0	1710	0.7760	0.84
0.0691	20.0	1800	0.7772	0.84

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.19.1