---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: deeepfake-audio-555
  results:
  - task:
      name: Audio Classification
      type: audio-classification
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9247311827956989
---

<!-- 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. -->

# deeepfake-audio-555

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4156
- Accuracy: 0.9247

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 16

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6428        | 1.0   | 46   | 0.6271          | 0.7204   |
| 0.4622        | 2.0   | 92   | 0.4054          | 0.8602   |
| 0.3098        | 3.0   | 138  | 0.5667          | 0.8172   |
| 0.2696        | 4.0   | 184  | 0.4179          | 0.8817   |
| 0.2806        | 5.0   | 230  | 0.4129          | 0.8710   |
| 0.2078        | 6.0   | 276  | 0.3541          | 0.9140   |
| 0.1652        | 7.0   | 322  | 0.3338          | 0.9140   |
| 0.0871        | 8.0   | 368  | 0.4072          | 0.9140   |
| 0.1267        | 9.0   | 414  | 0.3649          | 0.9247   |
| 0.0651        | 10.0  | 460  | 0.3436          | 0.9355   |
| 0.0976        | 11.0  | 506  | 0.4163          | 0.9140   |
| 0.0186        | 12.0  | 552  | 0.4164          | 0.9247   |
| 0.0324        | 13.0  | 598  | 0.4156          | 0.9247   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2