---
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
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# hubert-model-v2

This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/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