Update README.md

README.md

@@ -1,3 +1,80 @@
 ---
 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