---
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: 0.89
---

<!-- 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: 0.3030
- Accuracy: 0.89

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9203        | 1.0   | 112  | 0.6901          | 0.82     |
| 0.2906        | 1.99  | 224  | 0.4069          | 0.86     |
| 0.3416        | 3.0   | 337  | 0.3651          | 0.84     |
| 0.2143        | 4.0   | 449  | 0.3208          | 0.89     |
| 0.0052        | 4.99  | 561  | 0.3180          | 0.88     |
| 0.0037        | 6.0   | 674  | 0.3233          | 0.88     |
| 0.0011        | 6.99  | 786  | 0.2975          | 0.9      |
| 0.0011        | 8.0   | 899  | 0.3200          | 0.88     |
| 0.0325        | 9.0   | 1011 | 0.3028          | 0.89     |
| 0.0008        | 9.97  | 1120 | 0.3030          | 0.89     |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2