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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-base-960h |
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tags: |
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- audio-classification |
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- deepfake |
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- audio-spoof |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: wav2vec2-base-960h-itw-deepfake |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-base-960h-itw-deepfake |
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This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0917 |
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- Accuracy: 0.9835 |
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- FAR: 0.0068 |
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- FRR: 0.0330 |
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- EER: 0.0199 |
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## Model description |
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### Quick Use |
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```python |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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config = AutoConfig.from_pretrained("abhishtagatya/wav2vec2-base-960h-itw-deepfake") |
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feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("abhishtagatya/wav2vec2-base-960h-itw-deepfake") |
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model = Wav2Vec2ForSequenceClassification.from_pretrained("abhishtagatya/wav2vec2-base-960h-itw-deepfake", config=config).to(device) |
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# Your Logic Here |
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``` |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-06 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | FAR | FRR | EER | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:------:|:------:| |
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| 0.6363 | 0.39 | 2500 | 0.4678 | 0.8652 | 0.0178 | 0.3326 | 0.1752 | |
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| 0.2896 | 0.79 | 5000 | 0.1145 | 0.9744 | 0.0170 | 0.0402 | 0.0286 | |
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| 0.1554 | 1.18 | 7500 | 0.1024 | 0.9797 | 0.0100 | 0.0377 | 0.0238 | |
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| 0.1327 | 1.57 | 10000 | 0.0945 | 0.9825 | 0.0070 | 0.0351 | 0.0211 | |
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| 0.13 | 1.97 | 12500 | 0.0917 | 0.9835 | 0.0068 | 0.0330 | 0.0199 | |
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### Framework versions |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.2.dev0 |
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- Tokenizers 0.15.1 |
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