---
base_model: microsoft/wavlm-base
tags:
- audio-classification
- deepfake
- audio-spoof
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wavlm-base-960h-asv19-deepfake
  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. -->

# wavlm-base-960h-asv19-deepfake

This model is a fine-tuned version of [microsoft/wavlm-base](https://huggingface.co/microsoft/wavlm-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0332
- Accuracy: 0.9950
- Far: 0.0416
- Frr: 0.0009
- Eer: 0.0212

## 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: 1e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Far    | Frr    | Eer    |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|
| 0.2408        | 0.39  | 2500  | 0.0574          | 0.9889   | 0.0863 | 0.0025 | 0.0444 |
| 0.0372        | 0.79  | 5000  | 0.0524          | 0.9901   | 0.0914 | 0.0006 | 0.0460 |
| 0.0231        | 1.18  | 7500  | 0.0539          | 0.9912   | 0.0824 | 0.0004 | 0.0414 |
| 0.0213        | 1.58  | 10000 | 0.0301          | 0.9951   | 0.0361 | 0.0013 | 0.0187 |
| 0.0191        | 1.97  | 12500 | 0.0332          | 0.9950   | 0.0416 | 0.0009 | 0.0212 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.2