metadata
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: []
wavlm-base-960h-itw-deepfake
This model is a fine-tuned version of 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
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