---
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: 0.89
---

<!-- 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: 0.4867
- Accuracy: 0.89

## 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: 20
- eval_batch_size: 20
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2324        | 1.0   | 45   | 2.1551          | 0.32     |
| 1.858         | 2.0   | 90   | 1.7637          | 0.43     |
| 1.6808        | 3.0   | 135  | 1.5373          | 0.5      |
| 1.4424        | 4.0   | 180  | 1.3738          | 0.59     |
| 1.2715        | 5.0   | 225  | 1.1840          | 0.61     |
| 1.1501        | 6.0   | 270  | 1.0517          | 0.63     |
| 1.0187        | 7.0   | 315  | 0.8796          | 0.72     |
| 0.9446        | 8.0   | 360  | 0.8616          | 0.66     |
| 0.9266        | 9.0   | 405  | 0.8598          | 0.68     |
| 0.7204        | 10.0  | 450  | 0.7464          | 0.72     |
| 0.5817        | 11.0  | 495  | 0.7511          | 0.79     |
| 0.6758        | 12.0  | 540  | 0.8287          | 0.75     |
| 0.5383        | 13.0  | 585  | 0.6391          | 0.8      |
| 0.659         | 14.0  | 630  | 0.5670          | 0.84     |
| 0.4272        | 15.0  | 675  | 0.6181          | 0.85     |
| 0.4661        | 16.0  | 720  | 0.4935          | 0.86     |
| 0.4798        | 17.0  | 765  | 0.5827          | 0.85     |
| 0.3895        | 18.0  | 810  | 0.4870          | 0.88     |
| 0.3039        | 19.0  | 855  | 0.4571          | 0.9      |
| 0.2401        | 20.0  | 900  | 0.4867          | 0.89     |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.0+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1