---
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