---
library_name: transformers
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-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: 0.87
---

<!-- 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-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: 0.3848
- Accuracy: 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.8911        | 1.0   | 113  | 1.7770          | 0.52     |
| 0.9154        | 2.0   | 226  | 0.8861          | 0.77     |
| 0.5408        | 3.0   | 339  | 0.5815          | 0.83     |
| 0.3854        | 4.0   | 452  | 0.5075          | 0.86     |
| 0.4656        | 5.0   | 565  | 0.4716          | 0.87     |
| 0.3679        | 6.0   | 678  | 0.4578          | 0.87     |
| 0.3263        | 7.0   | 791  | 0.4368          | 0.87     |
| 0.4072        | 8.0   | 904  | 0.4078          | 0.88     |
| 0.2734        | 9.0   | 1017 | 0.3847          | 0.88     |
| 0.3517        | 10.0  | 1130 | 0.4185          | 0.88     |
| 0.3147        | 11.0  | 1243 | 0.3946          | 0.86     |
| 0.2572        | 12.0  | 1356 | 0.3899          | 0.88     |
| 0.3696        | 13.0  | 1469 | 0.3843          | 0.87     |
| 0.256         | 14.0  | 1582 | 0.3872          | 0.87     |
| 0.3737        | 15.0  | 1695 | 0.3914          | 0.88     |
| 0.1702        | 16.0  | 1808 | 0.3863          | 0.87     |
| 0.2974        | 17.0  | 1921 | 0.3857          | 0.87     |
| 0.1916        | 18.0  | 2034 | 0.3855          | 0.87     |
| 0.223         | 19.0  | 2147 | 0.3848          | 0.87     |
| 0.1942        | 20.0  | 2260 | 0.3848          | 0.87     |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1