[![Multi-Modality](agorabanner.png)](https://discord.com/servers/agora-999382051935506503)

# CogNetX

[![Join our Discord](https://img.shields.io/badge/Discord-Join%20our%20server-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/agora-999382051935506503) [![Subscribe on YouTube](https://img.shields.io/badge/YouTube-Subscribe-red?style=for-the-badge&logo=youtube&logoColor=white)](https://www.youtube.com/@kyegomez3242) [![Connect on LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/kye-g-38759a207/) [![Follow on X.com](https://img.shields.io/badge/X.com-Follow-1DA1F2?style=for-the-badge&logo=x&logoColor=white)](https://x.com/kyegomezb)



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](https://arxiv.org/abs/2005.08100)
- **Vision**: [ResNet50](https://arxiv.org/abs/1512.03385)
- **Video**: [3D CNN (R3D-18)](https://arxiv.org/abs/1711.11248)
- **Text**: [Transformer](https://arxiv.org/abs/1706.03762)

## Installation

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

```bash
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:

```bash
pip install torch torchvision torchaudio
```

## Usage

### Model Architecture

```python
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:

```bash
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](LICENSE) file for details.
