---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
- precision
- recall
- f1
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: 0.93
    - name: Precision
      type: precision
      value: 0.9386363636363637
    - name: Recall
      type: recall
      value: 0.93
    - name: F1
      type: f1
      value: 0.9311080732133363
pipeline_tag: audio-classification
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/raspuntinov_ai/huggingface/runs/igv2809l)
# 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: 0.3715
- Accuracy: 0.93
- Precision: 0.9386
- Recall: 0.93
- F1: 0.9311

## 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: 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 | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8552        | 1.0   | 57   | 0.5962          | 0.83     | 0.8693    | 0.83   | 0.8207 |
| 0.448         | 2.0   | 114  | 0.5167          | 0.85     | 0.8736    | 0.85   | 0.8534 |
| 0.1634        | 3.0   | 171  | 0.5433          | 0.86     | 0.8780    | 0.86   | 0.8570 |
| 0.1673        | 4.0   | 228  | 0.4743          | 0.88     | 0.8836    | 0.88   | 0.8769 |
| 0.0065        | 5.0   | 285  | 0.4956          | 0.91     | 0.9212    | 0.91   | 0.9060 |
| 0.0279        | 6.0   | 342  | 0.5635          | 0.89     | 0.8971    | 0.89   | 0.8879 |
| 0.104         | 7.0   | 399  | 0.6799          | 0.86     | 0.8832    | 0.86   | 0.8564 |
| 0.001         | 8.0   | 456  | 0.4927          | 0.91     | 0.9246    | 0.91   | 0.9109 |
| 0.0002        | 9.0   | 513  | 0.3899          | 0.92     | 0.9245    | 0.92   | 0.9187 |
| 0.0002        | 10.0  | 570  | 0.3715          | 0.93     | 0.9386    | 0.93   | 0.9311 |
| 0.0002        | 11.0  | 627  | 0.4695          | 0.92     | 0.9245    | 0.92   | 0.9180 |
| 0.0001        | 12.0  | 684  | 0.4150          | 0.93     | 0.9370    | 0.93   | 0.9291 |
| 0.0468        | 13.0  | 741  | 0.4483          | 0.92     | 0.9294    | 0.92   | 0.9182 |
| 0.0001        | 14.0  | 798  | 0.3852          | 0.93     | 0.9334    | 0.93   | 0.9288 |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1