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--- |
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license: apache-2.0 |
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base_model: motheecreator/Deepfake-audio-detection |
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tags: |
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- generated_from_trainer |
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datasets: |
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- audiofolder |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: Deepfake-audio-detection |
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results: |
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- task: |
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name: Audio Classification |
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type: audio-classification |
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dataset: |
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name: audiofolder |
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type: audiofolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9963791074499864 |
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- name: Precision |
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type: precision |
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value: 0.9943959123125103 |
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- name: Recall |
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type: recall |
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value: 0.9990064580228515 |
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- name: F1 |
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type: f1 |
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value: 0.9966958532958864 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Deepfake-audio-detection |
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This model is a fine-tuned version of [motheecreator/Deepfake-audio-detection](https://huggingface.co/motheecreator/Deepfake-audio-detection) on the audiofolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0192 |
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- Accuracy: 0.9964 |
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- Precision: 0.9944 |
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- Recall: 0.9990 |
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- F1: 0.9967 |
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- Auc Roc: 1.0000 |
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- Confusion Matrix: [[4974, 34], [6, 6033]] |
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- 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}} |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 3e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auc Roc | Confusion Matrix | Classification Report | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|:-------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| 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}} | |
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| 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}} | |
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| 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}} | |
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| 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}} | |
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| 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}} | |
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### Framework versions |
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- Transformers 4.41.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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