---
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: 0.91
---

<!-- 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: 0.3273
- Accuracy: 0.91

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5056        | 1.0   | 112  | 0.5669          | 0.85     |
| 0.2324        | 2.0   | 225  | 0.5131          | 0.85     |
| 0.2623        | 3.0   | 337  | 0.6539          | 0.79     |
| 0.4419        | 4.0   | 450  | 0.7401          | 0.83     |
| 0.0177        | 5.0   | 562  | 0.5134          | 0.85     |
| 0.0026        | 6.0   | 675  | 0.3351          | 0.9      |
| 0.0046        | 7.0   | 787  | 0.5120          | 0.88     |
| 0.0005        | 8.0   | 900  | 0.5165          | 0.91     |
| 0.2003        | 9.0   | 1012 | 0.3453          | 0.91     |
| 0.0001        | 10.0  | 1125 | 0.3438          | 0.91     |
| 0.0003        | 11.0  | 1237 | 0.3324          | 0.92     |
| 0.0           | 12.0  | 1350 | 0.3999          | 0.89     |
| 0.0           | 13.0  | 1462 | 0.3152          | 0.91     |
| 0.0001        | 14.0  | 1575 | 0.3212          | 0.92     |
| 0.0           | 15.0  | 1687 | 0.3220          | 0.92     |
| 0.0           | 16.0  | 1800 | 0.3343          | 0.9      |
| 0.0           | 17.0  | 1912 | 0.3324          | 0.91     |
| 0.0           | 18.0  | 2025 | 0.3311          | 0.91     |
| 0.0           | 19.0  | 2137 | 0.3292          | 0.91     |
| 0.0           | 19.91 | 2240 | 0.3273          | 0.91     |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3