---
library_name: transformers
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-heart-sounds
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9679878048780488
---

<!-- 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/vldmrl-org/HeartDiseaseDetector/runs/n9fzp3eh)
[<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/vldmrl-org/HeartDiseaseDetector/runs/n9fzp3eh)
# ast-finetuned-audioset-10-10-0.4593-heart-sounds

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 audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1499
- Accuracy: 0.9680

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2135        | 1.0    | 83   | 0.1724          | 0.9390   |
| 0.1655        | 2.0    | 166  | 0.2276          | 0.8918   |
| 0.0959        | 3.0    | 249  | 0.1166          | 0.9527   |
| 0.04          | 4.0    | 332  | 0.1106          | 0.9634   |
| 0.0142        | 5.0    | 415  | 0.1317          | 0.9634   |
| 0.0186        | 6.0    | 498  | 0.1424          | 0.9665   |
| 0.0022        | 7.0    | 581  | 0.1433          | 0.9695   |
| 0.0009        | 7.9119 | 656  | 0.1499          | 0.9680   |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.0.1+cu118
- Datasets 3.3.2
- Tokenizers 0.21.0