---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-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: 0.9
---

<!-- 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. -->

# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan

This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4718
- Accuracy: 0.9

## Model description

This model was generated as part of the [HF Audio course](https://huggingface.co/learn/audio-course/), I enjoyed it and currently this architecture achieves an amazing accuracy of 0.9 on music-genre classification task.

The Audio Spectrogram Transformer is equivalent to [ViT](https://huggingface.co/docs/transformers/model_doc/vit), but applied on audio. Audio is first turned into an image (as a spectrogram), after which a Vision Transformer is applied. The model gets state-of-the-art results on several audio classification benchmarks.

## 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: 4
- eval_batch_size: 4
- 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: 10
- mixed_precision_training: Native AMP
- global_step: 2250
- training_loss: 0.23970948094350752
- train_runtime: 1982.7909
- train_samples_per_second: 4.534
- train_steps_per_second: 1.135
- total_flos: 6.094112254328832e+17
- train_loss: 0.23970948094350752

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9734        | 1.0   | 225  | 0.6194          | 0.82     |
| 0.7734        | 2.0   | 450  | 0.4650          | 0.86     |
| 0.7703        | 3.0   | 675  | 0.8101          | 0.78     |
| 0.0052        | 4.0   | 900  | 0.5021          | 0.89     |
| 0.2316        | 5.0   | 1125 | 0.4968          | 0.9      |
| 0.0001        | 6.0   | 1350 | 0.5484          | 0.87     |
| 0.5337        | 7.0   | 1575 | 0.4673          | 0.89     |
| 0.0           | 8.0   | 1800 | 0.4868          | 0.89     |
| 0.0           | 9.0   | 2025 | 0.4709          | 0.9      |
| 0.0           | 10.0  | 2250 | 0.4718          | 0.9      |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0