| --- |
| license: apache-2.0 |
| pipeline_tag: audio-classification |
| tags: |
| - decodex |
| - audio-classification |
| - real-time-firewall |
| metrics: |
| - accuracy |
| model-index: |
| - name: DecodeX-Audio-Detection-Base |
| results: [] |
| --- |
| |
| # DecodeX Base: Real-Time Neural Audio Engine |
|
|
| This is the primary foundational model for the DecodeX audio verification platform, running in parallel with V2 to provide highly accurate, real-time deepfake and synthetic voice detection. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0829 |
| - Accuracy: 0.9882 |
|
|
| ## Model description |
|
|
| DecodeX Base operates as the core Wav2Vec 2.0 layer in our dual-engine cross-verification architecture. It is optimized for high-speed inference on raw PCM audio streams, specifically designed to identify zero-day synthetic voices and voice cloning attempts before they breach communication networks. |
|
|
| ## Intended uses & limitations |
|
|
| Intended for deployment in enterprise PBX systems, SIP trunks, and local edge hardware to act as a first line of defense against AI-generated social engineering and spoofing attacks. |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 3e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 32 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Accuracy | Validation Loss | |
| |:-------------:|:-----:|:----:|:--------:|:---------------:| |
| | 0.1448 | 1.0 | 1900 | 0.9601 | 0.1447 | |
| | 0.0673 | 2.0 | 3800 | 0.9824 | 0.0817 | |
| | 0.0178 | 3.0 | 5700 | 0.9796 | 0.1054 | |
| | 0.0002 | 4.0 | 7600 | 0.9824 | 0.1074 | |
| | 0.0108 | 5.0 | 9500 | 0.9882 | 0.0829 | |
|
|
| ### Framework versions |
|
|
| - Transformers 4.39.3 |
| - Pytorch 2.1.2 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
