Deepfake Spanish Wav2vec2 Linear Noaugmented

--- library_name: transformers license: apache-2.0 base_model: Gustking/wav2vec2-large-xlsr-deepfake-audio-classification tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: deepfake-spanish-wav2vec2-linear-noaugmented results: [] --- <!-- 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-spanish-wav2vec2-linear-noaugmented This model is a fine-tuned version of [Gustking/wav2vec2-large-xlsr-deepfake-audio-classification](https://huggingface.co/Gustking/wav2vec2-large-xlsr-deepfake-audio-classification) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5678 - Accuracy: 0.8906 - F1: 0.8893 - Precision: 0.9102 - Recall: 0.8906 - Roc Auc: 0.9950 - Eer: 0.0171 ## 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: 32 - eval_batch_size: 48 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 75 - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc | Eer | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|:------:| | 1.4707 | 0.4264 | 100 | 0.7530 | 0.8218 | 0.8159 | 0.8686 | 0.8218 | 0.9964 | 0.0309 | | 1.4707 | 0.8529 | 200 | 0.5932 | 0.8394 | 0.8351 | 0.8784 | 0.8394 | 0.9983 | 0.0181 | | 1.4707 | 1.2772 | 300 | 0.8508 | 0.8276 | 0.8224 | 0.8718 | 0.8276 | 0.9981 | 0.0141 | | 1.4707 | 1.7036 | 400 | 0.6778 | 0.8658 | 0.8633 | 0.8942 | 0.8658 | 0.9944 | 0.0211 | | 0.3105 | 2.1279 | 500 | 0.5678 | 0.8906 | 0.8893 | 0.9102 | 0.8906 | 0.9950 | 0.0171 | ### Framework versions - Transformers 5.2.0 - Pytorch 2.10.0+cu130 - Datasets 4.6.1 - Tokenizers 0.22.2