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---
license: apache-2.0
base_model: Bisher/wav2vec2_ASV_deepfake_audio_detection
tags:
- generated_from_trainer
model-index:
- name: short_name
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. -->
# short_name
This model is a fine-tuned version of [Bisher/wav2vec2_ASV_deepfake_audio_detection](https://huggingface.co/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
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