---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- audio-classification
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-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. -->

# distilhubert-finetuned-gtzan

This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5120
- 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: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2584        | 1.0   | 57   | 2.2062          | 0.35     |
| 1.8611        | 2.0   | 114  | 1.7924          | 0.53     |
| 1.4492        | 3.0   | 171  | 1.3901          | 0.65     |
| 1.0971        | 4.0   | 228  | 1.1676          | 0.69     |
| 0.9848        | 5.0   | 285  | 0.9750          | 0.74     |
| 0.8434        | 6.0   | 342  | 0.8434          | 0.74     |
| 0.7321        | 7.0   | 399  | 0.7555          | 0.83     |
| 0.5364        | 8.0   | 456  | 0.6995          | 0.79     |
| 0.4557        | 9.0   | 513  | 0.6118          | 0.84     |
| 0.4166        | 10.0  | 570  | 0.5975          | 0.83     |
| 0.2729        | 11.0  | 627  | 0.5576          | 0.83     |
| 0.2491        | 12.0  | 684  | 0.5737          | 0.82     |
| 0.2211        | 13.0  | 741  | 0.5129          | 0.84     |
| 0.1243        | 14.0  | 798  | 0.5710          | 0.83     |
| 0.0904        | 15.0  | 855  | 0.5087          | 0.86     |
| 0.0773        | 16.0  | 912  | 0.5836          | 0.8      |
| 0.0598        | 17.0  | 969  | 0.4871          | 0.83     |
| 0.0551        | 18.0  | 1026 | 0.4865          | 0.84     |
| 0.0467        | 19.0  | 1083 | 0.5043          | 0.84     |
| 0.0364        | 20.0  | 1140 | 0.5120          | 0.86     |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1