---
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: None
      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: 0.5071
- 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6259        | 1.0   | 112  | 0.7596          | 0.72     |
| 0.9466        | 2.0   | 225  | 0.6318          | 0.79     |
| 0.349         | 3.0   | 337  | 0.6183          | 0.84     |
| 0.1775        | 4.0   | 450  | 0.6978          | 0.83     |
| 0.0076        | 5.0   | 562  | 0.6868          | 0.85     |
| 0.0062        | 6.0   | 675  | 0.4879          | 0.88     |
| 0.0102        | 7.0   | 787  | 0.7735          | 0.85     |
| 0.0002        | 8.0   | 900  | 0.6003          | 0.9      |
| 0.0001        | 9.0   | 1012 | 0.4694          | 0.92     |
| 0.0001        | 10.0  | 1125 | 0.4915          | 0.91     |
| 0.0001        | 11.0  | 1237 | 0.4791          | 0.91     |
| 0.0001        | 12.0  | 1350 | 0.5129          | 0.91     |
| 0.0001        | 13.0  | 1462 | 0.5055          | 0.91     |
| 0.0001        | 14.0  | 1575 | 0.4978          | 0.91     |
| 0.0           | 14.93 | 1680 | 0.5071          | 0.91     |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.1