VoiceVibes

VoiceVibes is a Flask-based web application that leverages a trained CNN model to perform voice-related predictions. This project demonstrates how to integrate machine learning models with a web interface for interactive user experience.

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📝 Project Structure

VoiceVibes/
├── app.py                 # Flask application
├── model.py               # Model loading and prediction logic
├── requirements.txt       # Python dependencies
├── voicevibes_cnn_model.h5 # Pre-trained CNN model
├── templates/
│   └── index.html         # HTML template for UI
└── static/
    └── style.css          # Style template for UI

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⚡ Features

• Load a pre-trained CNN model for voice prediction.
• User-friendly web interface via Flask.
• Easy to extend and integrate with other voice or ML applications.

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💻 Installation

1. Clone the repository

git clone https://github.com/HariN999/VoiceVibes.git
cd VoiceVibes

2. Create and activate a virtual environment

python -m venv venv
# Windows
venv\Scripts\activate
# macOS/Linux
source venv/bin/activate

3. Install dependencies

pip install -r requirements.txt

4. Download / Generate the Model Before running the Flask app, you need the model file. Run:

python model.py 

💡 Tip: It is recommended to use Google Colab for generating or training the model, especially if your local machine has limited resources. Colab provides free GPU support and ensures faster model creation.

This will either download the pre-trained CNN model or generate it locally, saving it as voicevibes_cnn_model.h5 in the project folder.

🚀 Running the Application

python app.py

• Open your browser and navigate to http://127.0.0.1:5000 to access the web interface.

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📁 Usage

1. Open the app in your browser.
2. Upload or provide input as required by the model.
3. Click Predict to get results based on the CNN model.

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⚙️ Customization

• Modify model.py to load different models or change prediction logic.
• Update templates/index.html for a different UI layout.
• Add new routes in app.py for additional functionality.

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🧰 Dependencies

• Python 3.8+
• Flask
• TensorFlow/Keras
• Numpy

Install all required packages via:

pip install -r requirements.txt

🔗 Author

Hariharan Narlakanti GitHub | LinkedIn