metadata

library_name: transformers
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
  - generated_from_trainer
datasets:
  - gtzan
metrics:
  - accuracy
  - precision
  - recall
  - f1
model-index:
  - name: hubert-model-v2
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: gtzan
          type: gtzan
          config: default
          split: train
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.82
          - name: Precision
            type: precision
            value: 0.8391562316626255
          - name: Recall
            type: recall
            value: 0.82
          - name: F1
            type: f1
            value: 0.822405644289722

hubert-model-v2

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: 0.9612
Accuracy: 0.82
Precision: 0.8392
Recall: 0.82
F1: 0.8224

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: Use OptimizerNames.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: 12
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1
2.2086	1.0	200	2.0834	0.265	0.1987	0.265	0.1723
1.8657	2.0	400	1.7149	0.385	0.3228	0.385	0.3226
1.5242	3.0	600	1.4365	0.49	0.4463	0.49	0.4280
1.2542	4.0	800	1.2907	0.57	0.6029	0.57	0.5477
1.0119	5.0	1000	1.0524	0.69	0.7249	0.69	0.6745
0.8835	6.0	1200	1.1677	0.69	0.7156	0.69	0.6718
0.7271	7.0	1400	0.9158	0.745	0.7569	0.745	0.7309
0.5453	8.0	1600	0.7592	0.82	0.8296	0.82	0.8185
0.4503	9.0	1800	0.9936	0.775	0.8138	0.775	0.7799
0.3625	10.0	2000	0.9192	0.815	0.8258	0.815	0.8146
0.2773	11.0	2200	0.9768	0.82	0.8362	0.82	0.8201
0.2309	12.0	2400	0.9612	0.82	0.8392	0.82	0.8224

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0