---
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- audio-classification
- deepfake
- audio-spoof
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: hubert-base-960h-itw-deepfake
  results: []
language:
- en
---

<!-- 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. -->

# hubert-base-960h-itw-deepfake

This model is a fine-tuned version of [facebook/hubert-base-ls960](https://huggingface.co/facebook/hubert-base-ls960) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0756
- Accuracy: 0.9873
- FAR: 0.0083
- FRR: 0.0203
- EER: 0.0143

## Model description

### Quick Use

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

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

  model = HubertForSequenceClassification.from_pretrained("abhishtagatya/hubert-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    |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:|
| 0.4081        | 0.39  | 2500  | 0.1152          | 0.9722   | 0.0285 | 0.0267 | 0.0276 |
| 0.1168        | 0.79  | 5000  | 0.0822          | 0.9844   | 0.0120 | 0.0216 | 0.0168 |
| 0.0979        | 1.18  | 7500  | 0.0896          | 0.9846   | 0.0130 | 0.0195 | 0.0162 |
| 0.0983        | 1.57  | 10000 | 0.1007          | 0.9833   | 0.0155 | 0.0186 | 0.0171 |
| 0.0901        | 1.97  | 12500 | 0.0756          | 0.9873   | 0.0083 | 0.0203 | 0.0143 |


### Framework versions

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