---
license: apache-2.0
base_model: Bisher/wav2vec2_ASV_deepfake_audio_detection_DF_finetune_frozen
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ourData_train
  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: 0.9790442116023511
    - name: Precision
      type: precision
      value: 0.9805875236043416
    - name: Recall
      type: recall
      value: 0.9790442116023511
    - name: F1
      type: f1
      value: 0.979465951529853
---

<!-- 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/bishertello-/huggingface/runs/m9t2y29z)
# ourData_train

This model is a fine-tuned version of [Bisher/wav2vec2_ASV_deepfake_audio_detection_DF_finetune_frozen](https://huggingface.co/Bisher/wav2vec2_ASV_deepfake_audio_detection_DF_finetune_frozen) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0655
- Accuracy: 0.9790
- Precision: 0.9806
- Recall: 0.9790
- F1: 0.9795
- Tp: 518
- Tn: 3313
- Fn: 14
- Fp: 68
- Eer: 0.0216
- Min Tdcf: 0.0069
- Auc Roc: 0.9984

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Tp  | Tn   | Fn  | Fp   | Eer    | Min Tdcf | Auc Roc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:---:|:----:|:---:|:----:|:------:|:--------:|:-------:|
| 2.0925        | 0.0816 | 5    | 2.4908          | 0.3095   | 0.7455    | 0.3095 | 0.3596 | 376 | 835  | 156 | 2546 | 0.5167 | 0.0499   | 0.4827  |
| 1.8849        | 0.1633 | 10   | 1.8190          | 0.4613   | 0.7686    | 0.4613 | 0.5381 | 303 | 1502 | 229 | 1879 | 0.4874 | 0.05     | 0.5170  |
| 1.1783        | 0.2449 | 15   | 0.9851          | 0.3330   | 0.8128    | 0.3330 | 0.3755 | 455 | 848  | 77  | 2533 | 0.3856 | 0.0499   | 0.6498  |
| 0.661         | 0.3265 | 20   | 0.5800          | 0.8684   | 0.8702    | 0.8684 | 0.8693 | 283 | 3115 | 249 | 266  | 0.2256 | 0.0446   | 0.8570  |
| 0.5967        | 0.4082 | 25   | 0.4366          | 0.8433   | 0.9017    | 0.8433 | 0.8609 | 448 | 2852 | 84  | 529  | 0.1559 | 0.0338   | 0.9265  |
| 0.3993        | 0.4898 | 30   | 0.2574          | 0.8919   | 0.9281    | 0.8919 | 0.9020 | 488 | 3002 | 44  | 379  | 0.0973 | 0.0228   | 0.9650  |
| 0.25          | 0.5714 | 35   | 0.1732          | 0.9422   | 0.9512    | 0.9422 | 0.9449 | 487 | 3200 | 45  | 181  | 0.0636 | 0.0147   | 0.9811  |
| 0.2353        | 0.6531 | 40   | 0.3041          | 0.9540   | 0.9526    | 0.9540 | 0.9529 | 413 | 3320 | 119 | 61   | 0.0769 | 0.0177   | 0.9787  |
| 0.1748        | 0.7347 | 45   | 0.1481          | 0.9601   | 0.9645    | 0.9601 | 0.9614 | 499 | 3258 | 33  | 123  | 0.0515 | 0.0126   | 0.9904  |
| 0.1273        | 0.8163 | 50   | 0.1373          | 0.9698   | 0.9702    | 0.9698 | 0.9700 | 479 | 3316 | 53  | 65   | 0.0451 | 0.0117   | 0.9939  |
| 0.143         | 0.8980 | 55   | 0.2027          | 0.9640   | 0.9635    | 0.9640 | 0.9637 | 453 | 3319 | 79  | 62   | 0.0545 | 0.0130   | 0.9910  |
| 0.1021        | 0.9796 | 60   | 0.1321          | 0.9701   | 0.9710    | 0.9701 | 0.9704 | 488 | 3308 | 44  | 73   | 0.0494 | 0.0101   | 0.9939  |
| 0.0694        | 1.0612 | 65   | 0.1845          | 0.9612   | 0.9626    | 0.9612 | 0.9617 | 475 | 3286 | 57  | 95   | 0.0564 | 0.0123   | 0.9906  |
| 0.0665        | 1.1429 | 70   | 0.1669          | 0.9681   | 0.9682    | 0.9681 | 0.9681 | 473 | 3315 | 59  | 66   | 0.0447 | 0.0116   | 0.9940  |
| 0.069         | 1.2245 | 75   | 0.1528          | 0.9691   | 0.9698    | 0.9691 | 0.9694 | 483 | 3309 | 49  | 72   | 0.0429 | 0.0114   | 0.9950  |
| 0.042         | 1.3061 | 80   | 0.1797          | 0.9693   | 0.9701    | 0.9693 | 0.9696 | 485 | 3308 | 47  | 73   | 0.0438 | 0.0103   | 0.9946  |
| 0.0689        | 1.3878 | 85   | 0.1625          | 0.9711   | 0.9718    | 0.9711 | 0.9714 | 488 | 3312 | 44  | 69   | 0.0399 | 0.0106   | 0.9956  |
| 0.0739        | 1.4694 | 90   | 0.0861          | 0.9750   | 0.9768    | 0.9750 | 0.9755 | 512 | 3303 | 20  | 78   | 0.0282 | 0.0080   | 0.9976  |
| 0.0556        | 1.5510 | 95   | 0.1952          | 0.9778   | 0.9775    | 0.9778 | 0.9775 | 474 | 3352 | 58  | 29   | 0.0358 | 0.0095   | 0.9963  |
| 0.056         | 1.6327 | 100  | 0.1888          | 0.9767   | 0.9764    | 0.9767 | 0.9765 | 472 | 3350 | 60  | 31   | 0.0364 | 0.0087   | 0.9959  |
| 0.0312        | 1.7143 | 105  | 0.1572          | 0.9783   | 0.9781    | 0.9783 | 0.9781 | 482 | 3346 | 50  | 35   | 0.0376 | 0.0088   | 0.9962  |
| 0.0532        | 1.7959 | 110  | 0.1333          | 0.9775   | 0.9778    | 0.9775 | 0.9776 | 494 | 3331 | 38  | 50   | 0.0338 | 0.0088   | 0.9967  |
| 0.0575        | 1.8776 | 115  | 0.0958          | 0.9798   | 0.9805    | 0.9798 | 0.9800 | 507 | 3327 | 25  | 54   | 0.0301 | 0.0080   | 0.9976  |
| 0.0624        | 1.9592 | 120  | 0.0655          | 0.9790   | 0.9806    | 0.9790 | 0.9795 | 518 | 3313 | 14  | 68   | 0.0216 | 0.0069   | 0.9984  |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1