hubert-base-960h-itw-deepfake
This model is a fine-tuned version of 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
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