---
base_model: MIT/ast-finetuned-audioset-16-16-0.442
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-16-16-0.442-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: 0.93
---

<!-- 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-16-16-0.442-finetuned-gtzan

This model is a fine-tuned version of [ast-finetuned-audioset-16-16-0.442](https://huggingface.co/ast-finetuned-audioset-16-16-0.442) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3315
- Accuracy: 0.93

## 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: 20
- eval_batch_size: 20
- seed: 42
- optimizer: Adam-8bits with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8802        | 1.0   | 45   | 0.5267          | 0.85     |
| 0.3183        | 2.0   | 90   | 0.5893          | 0.81     |
| 0.1094        | 3.0   | 135  | 0.4421          | 0.89     |
| 0.0259        | 4.0   | 180  | 0.4100          | 0.88     |
| 0.0291        | 5.0   | 225  | 0.3695          | 0.9      |
| 0.0409        | 6.0   | 270  | 0.3071          | 0.91     |
| 0.0152        | 7.0   | 315  | 0.3482          | 0.92     |
| 0.0003        | 8.0   | 360  | 0.3187          | 0.94     |
| 0.0003        | 9.0   | 405  | 0.3258          | 0.93     |
| 0.0004        | 10.0  | 450  | 0.3315          | 0.93     |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1