---
base_model: microsoft/wavlm-base
tags:
- audio-classification
- deepfake
- audio-spoof
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: wavlm-base-960h-itw-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-itw-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: 0.0593
- Accuracy: 0.9896
- FAR: 0.0080
- FRR: 0.0144
- EER: 0.0112

## Model description

### Quick Use

```python
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

  config = AutoConfig.from_pretrained("abhishtagatya/wavlm-base-960h-itw-deepfake")
  feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("abhishtagatya/wavlm-base-960h-itw-deepfake")

  model = WavLMForSequenceClassification.from_pretrained("abhishtagatya/wavlm-base-960h-itw-deepfake", config=config).to(device)

  # Your Logic Here
```

## 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    |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|
| 0.3205        | 0.39  | 2500  | 0.1223          | 0.9699   | 0.0343 | 0.0229 | 0.0286 |
| 0.0752        | 0.79  | 5000  | 0.0822          | 0.9843   | 0.0145 | 0.0178 | 0.0161 |
| 0.0666        | 1.18  | 7500  | 0.0825          | 0.9849   | 0.0158 | 0.0140 | 0.0149 |
| 0.0569        | 1.57  | 10000 | 0.0674          | 0.9884   | 0.0103 | 0.0140 | 0.0121 |
| 0.0567        | 1.97  | 12500 | 0.0593          | 0.9896   | 0.0080 | 0.0144 | 0.0112 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.2.dev0
- Tokenizers 0.15.1