| --- |
| license: apache-2.0 |
| tags: |
| - decodex |
| - audio-classification |
| - real-time-firewall |
| datasets: |
| - audiofolder |
| metrics: |
| - accuracy |
| model-index: |
| - name: DecodeX-Audio-Detection-V2 |
| results: |
| - task: |
| name: Audio Classification |
| type: audio-classification |
| dataset: |
| name: audiofolder |
| type: audiofolder |
| config: default |
| split: train |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 0.9972843305874898 |
| --- |
| |
| # DecodeX V2: Real-Time Neural Edge Firewall |
|
|
| This is the core neural engine for the DecodeX audio verification platform, optimized for high-speed edge inference and deepfake voice detection. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0141 |
| - Accuracy: 0.9973 |
|
|
| ## Model description |
|
|
| DecodeX V2 is a highly optimized audio classification model designed to detect synthetic, spoofed, and AI-generated voices in real-time. It is built to operate within a dual-model cross-verification architecture, ingesting raw PCM audio streams normalized via Z-Score mathematical optimization to output high-confidence threat vectors. |
|
|
| ## Intended uses & limitations |
|
|
| Intended for enterprise PBX, SIP trunk integrations, and local edge hardware as a real-time firewall against voice spoofing and deepfake social engineering attacks. |
|
|
| ## Training and evaluation data |
|
|
| Aggregated high-fidelity authentic and synthetic voice samples, resampled and normalized at 16 kHz. |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 3e-05 |
| - train_batch_size: 32 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - gradient_accumulation_steps: 4 |
| - total_train_batch_size: 128 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 5 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:| |
| | 0.0503 | 1.0 | 1381 | 0.0514 | 0.9858 | |
| | 0.0327 | 2.0 | 2762 | 0.0174 | 0.9956 | |
| | 0.0064 | 3.0 | 4143 | 0.0221 | 0.9950 | |
| | 0.0003 | 4.0 | 5524 | 0.0174 | 0.9965 | |
| | 0.0115 | 5.0 | 6905 | 0.0141 | 0.9973 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.41.2 |
| - Pytorch 2.1.2 |
| - Datasets 2.19.2 |
| - Tokenizers 0.19.1 |
