---
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: 0.91
---

<!-- 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: 0.3414
- 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 24
- 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6009        | 0.99  | 37   | 0.6286          | 0.8      |
| 0.2809        | 2.0   | 75   | 0.5013          | 0.85     |
| 0.0913        | 2.99  | 112  | 0.3566          | 0.88     |
| 0.0217        | 4.0   | 150  | 0.3274          | 0.89     |
| 0.0401        | 4.99  | 187  | 0.3379          | 0.91     |
| 0.0016        | 6.0   | 225  | 0.3839          | 0.9      |
| 0.0006        | 6.99  | 262  | 0.3449          | 0.9      |
| 0.0027        | 8.0   | 300  | 0.4207          | 0.9      |
| 0.0007        | 8.99  | 337  | 0.3600          | 0.92     |
| 0.0003        | 9.87  | 370  | 0.3414          | 0.91     |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3