---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.450
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.450-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: 0.88
---

<!-- 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-10-10-0.450-finetuned-gtzan

This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.450](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.450) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4457
- Accuracy: 0.88

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5804        | 1.0   | 57   | 0.5356          | 0.84     |
| 0.2655        | 2.0   | 114  | 0.5664          | 0.76     |
| 0.1767        | 3.0   | 171  | 0.3925          | 0.88     |
| 0.4169        | 4.0   | 228  | 0.8874          | 0.78     |
| 0.0685        | 5.0   | 285  | 0.6067          | 0.83     |
| 0.0725        | 6.0   | 342  | 0.5612          | 0.81     |
| 0.1003        | 7.0   | 399  | 0.6928          | 0.82     |
| 0.004         | 8.0   | 456  | 0.4814          | 0.86     |
| 0.0122        | 9.0   | 513  | 0.6141          | 0.86     |
| 0.0009        | 10.0  | 570  | 0.4017          | 0.91     |
| 0.0828        | 11.0  | 627  | 0.4937          | 0.88     |
| 0.0025        | 12.0  | 684  | 0.8455          | 0.82     |
| 0.0005        | 13.0  | 741  | 0.4439          | 0.89     |
| 0.0001        | 14.0  | 798  | 0.4956          | 0.87     |
| 0.0001        | 15.0  | 855  | 0.4362          | 0.88     |
| 0.0001        | 16.0  | 912  | 0.4146          | 0.89     |
| 0.0299        | 17.0  | 969  | 0.4241          | 0.9      |
| 0.0001        | 18.0  | 1026 | 0.4375          | 0.87     |
| 0.0001        | 19.0  | 1083 | 0.4502          | 0.88     |
| 0.0001        | 20.0  | 1140 | 0.4457          | 0.88     |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0