---
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-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: 0.92
---

<!-- 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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/soundofai/huggingface-audio-course/runs/nfwkhlry)
# 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: 0.3597
- Accuracy: 0.92

## 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: 20

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.5671        | 0.9956  | 112  | 0.5463          | 0.85     |
| 0.7083        | 2.0     | 225  | 0.6822          | 0.78     |
| 0.2257        | 2.9956  | 337  | 0.5415          | 0.85     |
| 0.028         | 4.0     | 450  | 0.5070          | 0.9      |
| 0.0526        | 4.9956  | 562  | 0.8882          | 0.82     |
| 0.0628        | 6.0     | 675  | 0.9979          | 0.79     |
| 0.0025        | 6.9956  | 787  | 0.5942          | 0.88     |
| 0.0005        | 8.0     | 900  | 0.6327          | 0.9      |
| 0.0005        | 8.9956  | 1012 | 0.4033          | 0.9      |
| 0.0009        | 10.0    | 1125 | 0.4190          | 0.88     |
| 0.0001        | 10.9956 | 1237 | 0.3672          | 0.93     |
| 0.0001        | 12.0    | 1350 | 0.3615          | 0.91     |
| 0.0001        | 12.9956 | 1462 | 0.3631          | 0.92     |
| 0.0001        | 14.0    | 1575 | 0.3597          | 0.92     |
| 0.0001        | 14.9956 | 1687 | 0.3604          | 0.92     |
| 0.0           | 16.0    | 1800 | 0.3589          | 0.92     |
| 0.0           | 16.9956 | 1912 | 0.3597          | 0.92     |
| 0.0434        | 18.0    | 2025 | 0.3590          | 0.92     |
| 0.0           | 18.9956 | 2137 | 0.3594          | 0.92     |
| 0.0           | 19.9111 | 2240 | 0.3597          | 0.92     |


### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1