End of training

README.md

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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