short_name

This model is a fine-tuned version of Bisher/wav2vec2_ASV_deepfake_audio_detection on an unknown dataset. It achieves the following results on the evaluation set:

• eval_loss: 0.5552
• eval_accuracy: 0.895
• eval_precision: 0.9061
• eval_recall: 0.895
• eval_f1: 0.8500
• eval_TP: 1
• eval_TN: 178
• eval_FN: 21
• eval_FP: 0
• eval_EER: 0.2727
• eval_min_tDCF: 0.0281
• eval_auc_roc: 0.7296
• eval_runtime: 66.7052
• eval_samples_per_second: 2.998
• eval_steps_per_second: 2.998
• epoch: 0.04
• step: 1

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: 1
• eval_batch_size: 1
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 4
• 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

Framework versions

• Transformers 4.44.0
• Pytorch 2.4.0+cpu
• Datasets 2.21.0
• Tokenizers 0.19.1