---
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: 0.86
---

<!-- 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: 0.6524
- Accuracy: 0.86

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1626        | 1.0   | 90   | 2.0818          | 0.29     |
| 1.6876        | 2.0   | 180  | 1.6356          | 0.46     |
| 1.5907        | 3.0   | 270  | 1.4315          | 0.44     |
| 1.1261        | 4.0   | 360  | 1.1621          | 0.59     |
| 1.2327        | 5.0   | 450  | 1.0259          | 0.7      |
| 0.787         | 6.0   | 540  | 1.0662          | 0.68     |
| 0.9672        | 7.0   | 630  | 0.8381          | 0.77     |
| 0.626         | 8.0   | 720  | 0.7148          | 0.83     |
| 0.4198        | 9.0   | 810  | 0.8384          | 0.77     |
| 0.3601        | 10.0  | 900  | 0.5700          | 0.82     |
| 0.4672        | 11.0  | 990  | 0.8379          | 0.8      |
| 0.3303        | 12.0  | 1080 | 0.5098          | 0.86     |
| 0.2577        | 13.0  | 1170 | 0.8730          | 0.81     |
| 0.3535        | 14.0  | 1260 | 0.8539          | 0.82     |
| 0.2021        | 15.0  | 1350 | 0.8921          | 0.81     |
| 0.1995        | 16.0  | 1440 | 0.4829          | 0.88     |
| 0.3149        | 17.0  | 1530 | 0.6051          | 0.84     |
| 0.0828        | 18.0  | 1620 | 0.5581          | 0.86     |
| 0.0557        | 19.0  | 1710 | 0.5707          | 0.87     |
| 0.1019        | 20.0  | 1800 | 0.6524          | 0.86     |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1