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metadata
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

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