---
license: bsd-3-clause
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: 0.9
---

<!-- 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: 0.4717
- Accuracy: 0.9

## 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: 4
- 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7581        | 1.0   | 56   | 0.7029          | 0.78     |
| 0.3942        | 1.99  | 112  | 0.4646          | 0.86     |
| 0.3298        | 2.99  | 168  | 0.3861          | 0.88     |
| 0.1227        | 4.0   | 225  | 0.4702          | 0.86     |
| 0.0774        | 5.0   | 281  | 0.4492          | 0.9      |
| 0.0039        | 5.99  | 337  | 0.4607          | 0.9      |
| 0.0014        | 6.99  | 393  | 0.5022          | 0.9      |
| 0.0022        | 8.0   | 450  | 0.4711          | 0.9      |
| 0.0193        | 9.0   | 506  | 0.5226          | 0.86     |
| 0.0004        | 9.99  | 562  | 0.6055          | 0.82     |
| 0.0003        | 10.99 | 618  | 0.4793          | 0.89     |
| 0.0002        | 12.0  | 675  | 0.5052          | 0.9      |
| 0.0002        | 13.0  | 731  | 0.4652          | 0.89     |
| 0.0001        | 13.99 | 787  | 0.4617          | 0.9      |
| 0.0001        | 14.99 | 843  | 0.4653          | 0.9      |
| 0.0001        | 16.0  | 900  | 0.4635          | 0.91     |
| 0.0001        | 17.0  | 956  | 0.4693          | 0.9      |
| 0.0001        | 17.99 | 1012 | 0.4697          | 0.9      |
| 0.0001        | 18.99 | 1068 | 0.4715          | 0.9      |
| 0.0025        | 19.91 | 1120 | 0.4717          | 0.9      |


### Framework versions

- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3