---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: wav2vec2-base-960h-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: 0.8673780487804879
---

<!-- 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/7tntog3e)
[<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/7tntog3e)
# wav2vec2-base-960h-heart-sounds

This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3595
- Accuracy: 0.8674

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.9791        | 1.0    | 83   | 0.9290          | 0.5442   |
| 0.6532        | 2.0    | 166  | 0.5495          | 0.8186   |
| 0.5202        | 3.0    | 249  | 0.4569          | 0.8216   |
| 0.4421        | 4.0    | 332  | 0.4378          | 0.8399   |
| 0.4144        | 5.0    | 415  | 0.3853          | 0.8765   |
| 0.4213        | 6.0    | 498  | 0.3835          | 0.8537   |
| 0.3819        | 7.0    | 581  | 0.3647          | 0.8674   |
| 0.3994        | 7.9119 | 656  | 0.3595          | 0.8674   |


### Framework versions

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