---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.450
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.450-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
---

<!-- 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-10-10-0.450-finetuned-gtzan

This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.450](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.450) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5513
- Accuracy: 0.89

## 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: 42
- 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4812        | 1.0   | 100  | 0.4780          | 0.86     |
| 0.4555        | 2.0   | 200  | 0.6969          | 0.795    |
| 0.106         | 3.0   | 300  | 0.6725          | 0.85     |
| 0.0063        | 4.0   | 400  | 0.5885          | 0.875    |
| 0.0004        | 5.0   | 500  | 0.5513          | 0.89     |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.2
- Datasets 2.14.7
- Tokenizers 0.15.0