---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
- f1
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
    - name: F1
      type: f1
      value: 0.89
pipeline_tag: audio-classification
---

<!-- 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: 0.5658
- Accuracy: 0.87
- F1: 0.87

## 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: 2024
- gradient_accumulation_steps: 8
- 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| 0.8357        | 0.9956 | 56   | 0.6582          | 0.82     | 0.82   |
| 0.4742        | 1.9911 | 112  | 0.6527          | 0.81     | 0.81   |
| 0.3344        | 2.9867 | 168  | 0.9048          | 0.76     | 0.76   |
| 0.0659        | 4.0    | 225  | 0.6998          | 0.84     | 0.8400 |
| 0.0966        | 4.9956 | 281  | 0.6737          | 0.83     | 0.83   |
| 0.0026        | 5.9911 | 337  | 0.5133          | 0.89     | 0.89   |
| 0.0038        | 6.9867 | 393  | 0.5704          | 0.86     | 0.8600 |
| 0.0005        | 8.0    | 450  | 0.5722          | 0.86     | 0.8600 |
| 0.0003        | 8.9956 | 506  | 0.5632          | 0.87     | 0.87   |
| 0.0003        | 9.9556 | 560  | 0.5658          | 0.87     | 0.87   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1