CogNetX

CogNetX is an advanced, multimodal neural network architecture inspired by human cognition. It integrates speech, vision, and video processing into one unified framework. Built with PyTorch, CogNetX leverages cutting-edge neural networks such as Transformers, Conformers, and CNNs to handle complex multimodal tasks. The architecture is designed to process inputs like speech, images, and video, and output coherent, human-like text.

Key Features

• Speech Processing: Uses a Conformer network to handle speech inputs with extreme efficiency and accuracy.
• Vision Processing: Employs a ResNet-based Convolutional Neural Network (CNN) for robust image understanding.
• Video Processing: Utilizes a 3D CNN architecture for real-time video analysis and feature extraction.
• Text Generation: Integrates a Transformer model to process and generate human-readable text, combining the features from speech, vision, and video.
• Multimodal Fusion: Combines multiple input streams into a unified architecture, mimicking how humans process various types of sensory information.

Architecture Overview

CogNetX brings together several cutting-edge neural networks:

• Conformer for high-quality speech recognition.
• Transformer for text generation and processing.
• ResNet for vision and image recognition tasks.
• 3D CNN for video stream processing.

The architecture is designed to be highly modular, allowing easy extension and integration of additional modalities.

Neural Networks Used

• Speech: Conformer
• Vision: ResNet50
• Video: 3D CNN (R3D-18)
• Text: Transformer

Installation

To set up and use CogNetX, first clone the repository:

git clone https://github.com/kyegomez/CogNetX
cd CogNetX
pip install -r requirements.txt

Requirements

• Python 3.8+
• PyTorch 1.10+
• Torchvision
• Torchaudio

Install the required packages with:

pip install torch torchvision torchaudio

Usage

Model Architecture

import torch
from cognetx.model import CogNetX

if __name__ == "__main__":
    # Example configuration and usage
    config = {
        "speech_input_dim": 80,  # For example, 80 Mel-filterbank features
        "speech_num_layers": 4,
        "speech_num_heads": 8,
        "encoder_dim": 256,
        "decoder_dim": 512,
        "vocab_size": 10000,
        "embedding_dim": 512,
        "decoder_num_layers": 6,
        "decoder_num_heads": 8,
        "dropout": 0.1,
        "depthwise_conv_kernel_size": 31,
    }

    model = CogNetX(config)

    # Dummy inputs
    batch_size = 2
    speech_input = torch.randn(
        batch_size, 500, config["speech_input_dim"]
    )  # (batch_size, time_steps, feature_dim)
    vision_input = torch.randn(
        batch_size, 3, 224, 224
    )  # (batch_size, 3, H, W)
    video_input = torch.randn(
        batch_size, 3, 16, 112, 112
    )  # (batch_size, 3, time_steps, H, W)
    tgt_input = torch.randint(
        0, config["vocab_size"], (20, batch_size)
    )  # (tgt_seq_len, batch_size)

    # Forward pass
    output = model(speech_input, vision_input, video_input, tgt_input)
    print(
        output.shape
    )  # Expected: (tgt_seq_len, batch_size, vocab_size)

Example Pipeline

1. Speech Input: Provide raw speech data or features extracted via an MFCC filter.
2. Vision Input: Use images or frame snapshots from video.
3. Video Input: Feed the network with video sequences.
4. Text Output: The model will generate a text output based on the combined multimodal input.

Running the Example

To test CogNetX with some example data, run:

python example.py

Code Structure

• cognetx/: Contains the core neural network classes.
  • model: The entire model model architecture.
• example.py: Example script to test the architecture with dummy data.

Future Work

• Add support for additional modalities such as EEG signals or tactile data.
• Optimize the model for real-time performance across edge devices.
• Implement transfer learning and fine-tuning on various datasets.

Contributing

Contributions are welcome! Please submit a pull request or open an issue if you want to suggest an improvement.

Steps to Contribute

1. Fork the repository
2. Create a feature branch (git checkout -b feature/awesome-feature)
3. Commit your changes (git commit -am 'Add awesome feature')
4. Push to the branch (git push origin feature/awesome-feature)
5. Open a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.