---
license: bsd-3-clause
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: 0.3548
- 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9569        | 1.0   | 112  | 0.6467          | 0.77     |
| 0.5441        | 2.0   | 225  | 0.5895          | 0.8      |
| 0.4536        | 3.0   | 337  | 0.4070          | 0.82     |
| 0.1096        | 4.0   | 450  | 0.3812          | 0.89     |
| 0.0116        | 5.0   | 562  | 1.1661          | 0.78     |
| 0.0165        | 6.0   | 675  | 0.4822          | 0.91     |
| 0.1206        | 7.0   | 787  | 0.5000          | 0.88     |
| 0.0001        | 8.0   | 900  | 0.4074          | 0.89     |
| 0.2068        | 9.0   | 1012 | 0.4769          | 0.87     |
| 0.0001        | 10.0  | 1125 | 0.3743          | 0.89     |
| 0.0001        | 11.0  | 1237 | 0.3673          | 0.89     |
| 0.0001        | 12.0  | 1350 | 0.3952          | 0.91     |
| 0.0001        | 13.0  | 1462 | 0.3710          | 0.91     |
| 0.0001        | 14.0  | 1575 | 0.3460          | 0.92     |
| 0.0           | 15.0  | 1687 | 0.3481          | 0.92     |
| 0.0           | 16.0  | 1800 | 0.3473          | 0.92     |
| 0.0           | 17.0  | 1912 | 0.3491          | 0.91     |
| 0.0           | 18.0  | 2025 | 0.3507          | 0.91     |
| 0.0           | 19.0  | 2137 | 0.3548          | 0.9      |
| 0.0001        | 19.91 | 2240 | 0.3548          | 0.9      |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1