Aryan1245678/DecodeX_V2

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