---
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: 0.4793
- Accuracy: 0.9

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6559        | 1.0   | 112  | 0.5081          | 0.86     |
| 0.5141        | 2.0   | 225  | 0.5618          | 0.77     |
| 0.5517        | 3.0   | 337  | 0.5009          | 0.84     |
| 0.6651        | 4.0   | 450  | 0.7811          | 0.82     |
| 0.0057        | 5.0   | 562  | 0.3074          | 0.93     |
| 0.0018        | 6.0   | 675  | 0.4843          | 0.87     |
| 0.0007        | 7.0   | 787  | 0.6949          | 0.85     |
| 0.0007        | 8.0   | 900  | 0.6981          | 0.88     |
| 0.0007        | 9.0   | 1012 | 0.8356          | 0.87     |
| 0.0001        | 10.0  | 1125 | 0.6164          | 0.89     |
| 0.1709        | 11.0  | 1237 | 0.5464          | 0.89     |
| 0.0001        | 12.0  | 1350 | 0.4885          | 0.88     |
| 0.0003        | 13.0  | 1462 | 0.4970          | 0.91     |
| 0.0           | 14.0  | 1575 | 0.5346          | 0.88     |
| 0.0001        | 15.0  | 1687 | 0.5526          | 0.89     |
| 0.0           | 16.0  | 1800 | 0.4808          | 0.91     |
| 0.0           | 17.0  | 1912 | 0.4999          | 0.9      |
| 0.0           | 18.0  | 2025 | 0.4909          | 0.89     |
| 0.0           | 19.0  | 2137 | 0.4953          | 0.89     |
| 0.0           | 20.0  | 2250 | 0.4883          | 0.9      |
| 0.0543        | 21.0  | 2362 | 0.4830          | 0.91     |
| 0.0           | 22.0  | 2475 | 0.4811          | 0.9      |
| 0.0           | 23.0  | 2587 | 0.4805          | 0.9      |
| 0.0           | 24.0  | 2700 | 0.4785          | 0.91     |
| 0.0           | 24.89 | 2800 | 0.4793          | 0.9      |


### Framework versions

- Transformers 4.36.0.dev0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.15.0