---
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: 0.86
    - name: F1
      type: f1
      value: 0.8599999999999999
---

<!-- 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: 0.7149
- Accuracy: 0.86
- F1: 0.8600

## 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: 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 | F1     |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|
| 0.6891        | 0.9956  | 112  | 0.6422          | 0.76     | 0.76   |
| 0.7267        | 2.0     | 225  | 0.8163          | 0.78     | 0.78   |
| 0.7077        | 2.9956  | 337  | 0.7802          | 0.8      | 0.8000 |
| 0.1884        | 4.0     | 450  | 0.6157          | 0.87     | 0.87   |
| 0.0209        | 4.9956  | 562  | 0.7885          | 0.84     | 0.8400 |
| 0.117         | 6.0     | 675  | 0.6744          | 0.85     | 0.85   |
| 0.0098        | 6.9956  | 787  | 0.6213          | 0.85     | 0.85   |
| 0.0002        | 8.0     | 900  | 1.0599          | 0.82     | 0.82   |
| 0.0001        | 8.9956  | 1012 | 0.7052          | 0.86     | 0.8600 |
| 0.0001        | 10.0    | 1125 | 0.6891          | 0.85     | 0.85   |
| 0.0001        | 10.9956 | 1237 | 0.6718          | 0.86     | 0.8600 |
| 0.0001        | 12.0    | 1350 | 0.6712          | 0.85     | 0.85   |
| 0.0001        | 12.9956 | 1462 | 0.6942          | 0.86     | 0.8600 |
| 0.0001        | 14.0    | 1575 | 0.7002          | 0.86     | 0.8600 |
| 0.0176        | 14.9956 | 1687 | 0.7053          | 0.86     | 0.8600 |
| 0.0001        | 16.0    | 1800 | 0.7140          | 0.86     | 0.8600 |
| 0.0001        | 16.9956 | 1912 | 0.7089          | 0.86     | 0.8600 |
| 0.0           | 18.0    | 2025 | 0.7120          | 0.86     | 0.8600 |
| 0.0628        | 18.9956 | 2137 | 0.7162          | 0.86     | 0.8600 |
| 0.0037        | 19.9111 | 2240 | 0.7149          | 0.86     | 0.8600 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1