---
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: 0.88
---

<!-- 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-10-10-0.4593-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: 0.6087
- 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: 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9526        | 1.0   | 112  | 1.8797          | 0.74     |
| 0.9704        | 2.0   | 225  | 1.0561          | 0.7      |
| 0.7957        | 3.0   | 337  | 0.7362          | 0.77     |
| 0.4428        | 4.0   | 450  | 0.7820          | 0.8      |
| 0.1422        | 5.0   | 562  | 0.6142          | 0.84     |
| 0.3502        | 6.0   | 675  | 0.9189          | 0.82     |
| 0.01          | 7.0   | 787  | 0.7735          | 0.83     |
| 0.0068        | 8.0   | 900  | 1.0699          | 0.81     |
| 0.1751        | 9.0   | 1012 | 0.5360          | 0.88     |
| 0.0045        | 10.0  | 1125 | 0.5377          | 0.89     |
| 0.154         | 11.0  | 1237 | 0.6542          | 0.86     |
| 0.0025        | 12.0  | 1350 | 0.6206          | 0.89     |
| 0.0022        | 13.0  | 1462 | 0.6118          | 0.88     |
| 0.0021        | 14.0  | 1575 | 0.5961          | 0.89     |
| 0.0018        | 15.0  | 1687 | 0.5958          | 0.88     |
| 0.0017        | 16.0  | 1800 | 0.6062          | 0.88     |
| 0.0017        | 17.0  | 1912 | 0.6005          | 0.88     |
| 0.0015        | 18.0  | 2025 | 0.6052          | 0.88     |
| 0.0014        | 19.0  | 2137 | 0.6114          | 0.88     |
| 0.0015        | 19.91 | 2240 | 0.6087          | 0.88     |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0