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---
license: apache-2.0
base_model: motheecreator/Deepfake-audio-detection
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Deepfake-audio-detection
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: 0.9963791074499864
- name: Precision
type: precision
value: 0.9943959123125103
- name: Recall
type: recall
value: 0.9990064580228515
- name: F1
type: f1
value: 0.9966958532958864
---
<!-- 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. -->
# Deepfake-audio-detection
This model is a fine-tuned version of [motheecreator/Deepfake-audio-detection](https://huggingface.co/motheecreator/Deepfake-audio-detection) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0192
- Accuracy: 0.9964
- Precision: 0.9944
- Recall: 0.9990
- F1: 0.9967
- Auc Roc: 1.0000
- Confusion Matrix: [[4974, 34], [6, 6033]]
- Classification Report: {'0': {'precision': 0.9987951807228915, 'recall': 0.9932108626198083, 'f1-score': 0.9959951942330797, 'support': 5008}, '1': {'precision': 0.9943959123125103, 'recall': 0.9990064580228515, 'f1-score': 0.9966958532958864, 'support': 6039}, 'accuracy': 0.9963791074499864, 'macro avg': {'precision': 0.996595546517701, 'recall': 0.9961086603213298, 'f1-score': 0.996345523764483, 'support': 11047}, 'weighted avg': {'precision': 0.9963902579447351, 'recall': 0.9963791074499864, 'f1-score': 0.9963782194960733, 'support': 11047}}
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- 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 | Auc Roc | Confusion Matrix | Classification Report |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|:-------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.1006 | 0.3621 | 1000 | 0.1897 | 0.9651 | 0.9424 | 0.9972 | 0.9690 | 0.9989 | [[4640, 368], [17, 6022]] | {'0': {'precision': 0.9963495812754992, 'recall': 0.9265175718849841, 'f1-score': 0.9601655457837558, 'support': 5008}, '1': {'precision': 0.9424100156494523, 'recall': 0.9971849643980791, 'f1-score': 0.969024056641725, 'support': 6039}, 'accuracy': 0.9651489092061193, 'macro avg': {'precision': 0.9693797984624757, 'recall': 0.9618512681415317, 'f1-score': 0.9645948012127403, 'support': 11047}, 'weighted avg': {'precision': 0.9668627489395077, 'recall': 0.9651489092061193, 'f1-score': 0.9650081770023017, 'support': 11047}} |
| 0.07 | 0.7241 | 2000 | 0.0333 | 0.9916 | 0.9914 | 0.9932 | 0.9923 | 0.9997 | [[4956, 52], [41, 5998]] | {'0': {'precision': 0.9917950770462277, 'recall': 0.9896166134185304, 'f1-score': 0.9907046476761618, 'support': 5008}, '1': {'precision': 0.991404958677686, 'recall': 0.993210796489485, 'f1-score': 0.9923070560013236, 'support': 6039}, 'accuracy': 0.9915814248212185, 'macro avg': {'precision': 0.9916000178619568, 'recall': 0.9914137049540077, 'f1-score': 0.9915058518387427, 'support': 11047}, 'weighted avg': {'precision': 0.9915818132798093, 'recall': 0.9915814248212185, 'f1-score': 0.9915806270258181, 'support': 11047}} |
| 0.016 | 1.0862 | 3000 | 0.1018 | 0.9841 | 0.9727 | 0.9988 | 0.9856 | 0.9998 | [[4839, 169], [7, 6032]] | {'0': {'precision': 0.9985555096987206, 'recall': 0.9662539936102237, 'f1-score': 0.9821392327988635, 'support': 5008}, '1': {'precision': 0.9727463312368972, 'recall': 0.9988408676933267, 'f1-score': 0.9856209150326798, 'support': 6039}, 'accuracy': 0.9840680727799402, 'macro avg': {'precision': 0.985650920467809, 'recall': 0.9825474306517752, 'f1-score': 0.9838800739157716, 'support': 11047}, 'weighted avg': {'precision': 0.9844465544410985, 'recall': 0.9840680727799402, 'f1-score': 0.9840425440154849, 'support': 11047}} |
| 0.0209 | 1.4482 | 4000 | 0.0212 | 0.9957 | 0.9950 | 0.9972 | 0.9961 | 0.9999 | [[4978, 30], [17, 6022]] | {'0': {'precision': 0.9965965965965966, 'recall': 0.9940095846645367, 'f1-score': 0.9953014095771269, 'support': 5008}, '1': {'precision': 0.9950429610046265, 'recall': 0.9971849643980791, 'f1-score': 0.9961128111818707, 'support': 6039}, 'accuracy': 0.995745451253734, 'macro avg': {'precision': 0.9958197788006116, 'recall': 0.995597274531308, 'f1-score': 0.9957071103794988, 'support': 11047}, 'weighted avg': {'precision': 0.9957472795566846, 'recall': 0.995745451253734, 'f1-score': 0.9957449738290548, 'support': 11047}} |
| 0.0233 | 1.8103 | 5000 | 0.0192 | 0.9964 | 0.9944 | 0.9990 | 0.9967 | 1.0000 | [[4974, 34], [6, 6033]] | {'0': {'precision': 0.9987951807228915, 'recall': 0.9932108626198083, 'f1-score': 0.9959951942330797, 'support': 5008}, '1': {'precision': 0.9943959123125103, 'recall': 0.9990064580228515, 'f1-score': 0.9966958532958864, 'support': 6039}, 'accuracy': 0.9963791074499864, 'macro avg': {'precision': 0.996595546517701, 'recall': 0.9961086603213298, 'f1-score': 0.996345523764483, 'support': 11047}, 'weighted avg': {'precision': 0.9963902579447351, 'recall': 0.9963791074499864, 'f1-score': 0.9963782194960733, 'support': 11047}} |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
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