---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- 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: 0.7337
- 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: 6e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1397        | 1.0   | 75   | 2.0011          | 0.45     |
| 1.4889        | 2.0   | 150  | 1.3599          | 0.66     |
| 1.0109        | 3.0   | 225  | 1.0052          | 0.74     |
| 0.7499        | 4.0   | 300  | 0.8884          | 0.77     |
| 0.5627        | 5.0   | 375  | 0.6333          | 0.86     |
| 0.4138        | 6.0   | 450  | 0.5492          | 0.81     |
| 0.2909        | 7.0   | 525  | 0.6417          | 0.81     |
| 0.1475        | 8.0   | 600  | 0.5900          | 0.84     |
| 0.0845        | 9.0   | 675  | 0.6959          | 0.84     |
| 0.0619        | 10.0  | 750  | 0.6587          | 0.86     |
| 0.0233        | 11.0  | 825  | 0.7675          | 0.82     |
| 0.0168        | 12.0  | 900  | 0.7352          | 0.83     |
| 0.0152        | 13.0  | 975  | 0.7293          | 0.87     |
| 0.0136        | 14.0  | 1050 | 0.7490          | 0.86     |
| 0.0123        | 15.0  | 1125 | 0.7337          | 0.86     |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3