---
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: 0.85
---

<!-- 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: 0.8372
- Accuracy: 0.85

## 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: 7
- eval_batch_size: 7
- 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: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9696        | 1.0   | 129  | 1.8571          | 0.55     |
| 1.4269        | 2.0   | 258  | 1.2394          | 0.61     |
| 1.0166        | 3.0   | 387  | 1.0173          | 0.74     |
| 0.7446        | 4.0   | 516  | 0.8103          | 0.75     |
| 0.4953        | 5.0   | 645  | 0.7800          | 0.77     |
| 0.3973        | 6.0   | 774  | 0.7359          | 0.81     |
| 0.2831        | 7.0   | 903  | 0.6434          | 0.84     |
| 0.2147        | 8.0   | 1032 | 0.6592          | 0.84     |
| 0.1287        | 9.0   | 1161 | 0.6988          | 0.85     |
| 0.014         | 10.0  | 1290 | 0.7569          | 0.83     |
| 0.0073        | 11.0  | 1419 | 0.8282          | 0.84     |
| 0.0049        | 12.0  | 1548 | 0.8531          | 0.84     |
| 0.0053        | 13.0  | 1677 | 0.8584          | 0.84     |
| 0.0044        | 14.0  | 1806 | 0.8707          | 0.84     |
| 0.0038        | 15.0  | 1935 | 0.8372          | 0.85     |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3