---
library_name: transformers
license: apache-2.0
base_model: facebook/hubert-large-ls960-ft
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: hubert-large-ls960-ft-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: 0.76
---

<!-- 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-large-ls960-ft-finetuned-gtzan

This model is a fine-tuned version of [facebook/hubert-large-ls960-ft](https://huggingface.co/facebook/hubert-large-ls960-ft) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4835
- Accuracy: 0.76

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2311        | 1.0   | 100  | 2.2236          | 0.24     |
| 1.9497        | 2.0   | 200  | 1.7922          | 0.375    |
| 1.4897        | 3.0   | 300  | 1.5512          | 0.4      |
| 1.3977        | 4.0   | 400  | 1.5379          | 0.455    |
| 1.0858        | 5.0   | 500  | 1.4778          | 0.535    |
| 1.3193        | 6.0   | 600  | 1.1541          | 0.59     |
| 0.9246        | 7.0   | 700  | 1.3068          | 0.595    |
| 0.8115        | 8.0   | 800  | 1.0093          | 0.67     |
| 0.7293        | 9.0   | 900  | 1.1365          | 0.67     |
| 0.7645        | 10.0  | 1000 | 1.0879          | 0.69     |
| 0.6447        | 11.0  | 1100 | 1.1747          | 0.69     |
| 0.2322        | 12.0  | 1200 | 1.0627          | 0.73     |
| 0.2428        | 13.0  | 1300 | 0.9681          | 0.765    |
| 0.2777        | 14.0  | 1400 | 1.3665          | 0.72     |
| 0.2792        | 15.0  | 1500 | 1.3216          | 0.73     |
| 0.2509        | 16.0  | 1600 | 1.2809          | 0.755    |
| 0.7852        | 17.0  | 1700 | 1.3793          | 0.77     |
| 0.3948        | 18.0  | 1800 | 1.4736          | 0.765    |
| 0.3591        | 19.0  | 1900 | 1.5412          | 0.76     |
| 0.0059        | 20.0  | 2000 | 1.4835          | 0.76     |


### Framework versions

- Transformers 4.50.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0