---
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: 0.85
---

<!-- 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-base-ls960-finetuned-gtzan

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: 0.7461
- Accuracy: 0.85

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0275        | 1.0   | 225  | 1.8624          | 0.36     |
| 1.3649        | 2.0   | 450  | 1.4155          | 0.51     |
| 1.1545        | 3.0   | 675  | 1.2385          | 0.6      |
| 0.9293        | 4.0   | 900  | 0.9788          | 0.67     |
| 0.5855        | 5.0   | 1125 | 0.8809          | 0.7      |
| 0.2652        | 6.0   | 1350 | 0.9386          | 0.73     |
| 1.2178        | 7.0   | 1575 | 0.7286          | 0.81     |
| 0.1843        | 8.0   | 1800 | 1.2881          | 0.7      |
| 0.089         | 9.0   | 2025 | 0.4900          | 0.9      |
| 0.0928        | 10.0  | 2250 | 0.7461          | 0.85     |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1