[](https://discord.com/servers/agora-999382051935506503)
# CogNetX
[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](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.
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"description": "[](https://discord.com/servers/agora-999382051935506503)\n\n# CogNetX\n\n[](https://discord.gg/agora-999382051935506503) [](https://www.youtube.com/@kyegomez3242) [](https://www.linkedin.com/in/kye-g-38759a207/) [](https://x.com/kyegomezb)\n\n\n\nCogNetX 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.\n\n## Key Features\n- **Speech Processing**: Uses a Conformer network to handle speech inputs with extreme efficiency and accuracy.\n- **Vision Processing**: Employs a ResNet-based Convolutional Neural Network (CNN) for robust image understanding.\n- **Video Processing**: Utilizes a 3D CNN architecture for real-time video analysis and feature extraction.\n- **Text Generation**: Integrates a Transformer model to process and generate human-readable text, combining the features from speech, vision, and video.\n- **Multimodal Fusion**: Combines multiple input streams into a unified architecture, mimicking how humans process various types of sensory information.\n\n## Architecture Overview\n\nCogNetX brings together several cutting-edge neural networks:\n- **Conformer** for high-quality speech recognition.\n- **Transformer** for text generation and processing.\n- **ResNet** for vision and image recognition tasks.\n- **3D CNN** for video stream processing.\n\nThe architecture is designed to be highly modular, allowing easy extension and integration of additional modalities.\n\n### Neural Networks Used\n- **Speech**: [Conformer](https://arxiv.org/abs/2005.08100)\n- **Vision**: [ResNet50](https://arxiv.org/abs/1512.03385)\n- **Video**: [3D CNN (R3D-18)](https://arxiv.org/abs/1711.11248)\n- **Text**: [Transformer](https://arxiv.org/abs/1706.03762)\n\n## Installation\n\nTo set up and use CogNetX, first clone the repository:\n\n```bash\ngit clone https://github.com/kyegomez/CogNetX\ncd CogNetX\npip install -r requirements.txt\n```\n\n### Requirements\n- Python 3.8+\n- PyTorch 1.10+\n- Torchvision\n- Torchaudio\n\nInstall the required packages with:\n\n```bash\npip install torch torchvision torchaudio\n```\n\n## Usage\n\n### Model Architecture\n\n```python\nimport torch\nfrom cognetx.model import CogNetX\n\nif __name__ == \"__main__\":\n # Example configuration and usage\n config = {\n \"speech_input_dim\": 80, # For example, 80 Mel-filterbank features\n \"speech_num_layers\": 4,\n \"speech_num_heads\": 8,\n \"encoder_dim\": 256,\n \"decoder_dim\": 512,\n \"vocab_size\": 10000,\n \"embedding_dim\": 512,\n \"decoder_num_layers\": 6,\n \"decoder_num_heads\": 8,\n \"dropout\": 0.1,\n \"depthwise_conv_kernel_size\": 31,\n }\n\n model = CogNetX(config)\n\n # Dummy inputs\n batch_size = 2\n speech_input = torch.randn(\n batch_size, 500, config[\"speech_input_dim\"]\n ) # (batch_size, time_steps, feature_dim)\n vision_input = torch.randn(\n batch_size, 3, 224, 224\n ) # (batch_size, 3, H, W)\n video_input = torch.randn(\n batch_size, 3, 16, 112, 112\n ) # (batch_size, 3, time_steps, H, W)\n tgt_input = torch.randint(\n 0, config[\"vocab_size\"], (20, batch_size)\n ) # (tgt_seq_len, batch_size)\n\n # Forward pass\n output = model(speech_input, vision_input, video_input, tgt_input)\n print(\n output.shape\n ) # Expected: (tgt_seq_len, batch_size, vocab_size)\n\n```\n\n### Example Pipeline\n\n1. **Speech Input**: Provide raw speech data or features extracted via an MFCC filter.\n2. **Vision Input**: Use images or frame snapshots from video.\n3. **Video Input**: Feed the network with video sequences.\n4. **Text Output**: The model will generate a text output based on the combined multimodal input.\n\n### Running the Example\n\nTo test CogNetX with some example data, run:\n\n```bash\npython example.py\n```\n\n## Code Structure\n\n- `cognetx/`: Contains the core neural network classes.\n - `model`: The entire model model architecture.\n- `example.py`: Example script to test the architecture with dummy data.\n\n## Future Work\n- Add support for additional modalities such as EEG signals or tactile data.\n- Optimize the model for real-time performance across edge devices.\n- Implement transfer learning and fine-tuning on various datasets.\n\n## Contributing\nContributions are welcome! 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