voice-antispoofing

Voice Anti-Spoofing System 🎙️

This repository contains a complete deep learning project for detecting AI-generated (spoofed) voices. The system is built on a robust workflow that includes data preprocessing, comparative model training, advanced fine-tuning, and deployment into interactive full-stack web applications.

The core of the project is a comparative study between multiple deep learning architectures to determine the most effective approach for audio anti-spoofing.

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Features

• Comparative Model Analysis: Systematically trains and evaluates three distinct deep learning architectures:
  1. Baseline CNN: A simple convolutional neural network to establish a performance baseline.
  2. Deeper CNN: An enhanced CNN with more layers and batch normalization for improved stability.
  3. Vision Transformer (ViT): A state-of-the-art, pre-trained model fine-tuned for spectrogram analysis.
• Efficient Two-Stage Training: Implements a robust workflow to first train models from scratch on a large dataset and then fine-tune them with data augmentation to combat overfitting and improve generalization.
• Interactive Web Applications: Deploys the trained models using two separate full-stack applications, allowing users to upload audio files and receive real-time predictions.
• High-Performance Backend: Built with FastAPI for an asynchronous, fast, and reliable API.
• Modern Frontend: A responsive and user-friendly interface built with React.js.

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Project Workflow & Notebooks

The project's development is documented across four Jupyter notebooks, each serving a specific purpose in the workflow.

1. FakeVsReal_1.ipynb: The initial exploratory notebook. This was used for early-stage development, prototyping the basic data loading pipeline, and training a simple CNN on a smaller subset of the data (for-rerec dataset) to ensure the core concepts were viable.

2. SamplingDataset.ipynb: A utility notebook created to solve a practical problem. Its purpose is to create a smaller, representative subset of the massive for-original dataset. This allows for rapid prototyping and debugging of models without waiting hours for training on the full dataset.

3. FakeVsReal_2.ipynb: The main initial training script. This notebook scales up the process by training all three models (Baseline CNN, Deeper CNN, ViT) from scratch on the full, large for-original dataset. The trained models from this stage are saved (..._model.pth) for the next step.

4. FakeVsReal_2_FineTunning.ipynb: The final and most important stage. This notebook loads the initially trained models and fine-tunes them for a few more epochs using advanced techniques like data augmentation and a lower learning rate. This step is crucial for combating overfitting and significantly improving the models' final performance. The final, production-ready models (..._finetuned.pth) are saved from this notebook.

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Model Performance

The final test accuracies for the fine-tuned models on the unseen test set are as follows:

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🌐 Web Application

To make the trained AI model accessible to everyone, a full-stack web application was developed and deployed.

🔗 Live Web App

https://voiceantispoofing.netlify.app/

💻 Web App GitHub Repository

https://github.com/Laabh-Gupta/Voice-Anti-Spoofing-Web-App

⚙️ Web App Features

• Upload audio directly from the browser
• Real-time prediction: REAL vs FAKE voice
• FastAPI backend deployed on Railway
• React frontend deployed on Netlify
• Clean UI & responsive design for users
• Uses the final fine-tuned AI model for inference

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Setup and Installation

Follow these steps to set up the project and run the applications locally.

1. Clone the Repository

git clone [https://github.com/Laabh-Gupta/Fake_Vs_Real_Voice_Detection.git](https://github.com/Laabh-Gupta/Fake_Vs_Real_Voice_Detection.git)
cd Fake_Vs_Real_Voice_Detection

2. Download the Dataset

This project uses "The Fake or Real Dataset" from Kaggle.

• Download Link: https://www.kaggle.com/datasets/mohammedabdeldayem/the-fake-or-real-dataset
• Download the for-original.zip file.
• Unzip it and place the resulting FakeVsRealDataset(for-original) folder inside the root of your cloned project directory.

3. Set Up the Python Backend Environment

This project uses Conda to manage backend dependencies.

A. Create the Conda Environment: This command creates a new environment named fastapi_env with all the necessary libraries.

conda create -n fastapi_env python=3.10
conda activate fastapi_env

B. Install Dependencies: Install all required Python packages into your new environment.

pip install "fastapi[all]" uvicorn torch torchaudio torchvision python-multipart SoundFile

Note: If you have a CUDA-enabled GPU, you may want to install a specific PyTorch version that supports it. Visit the PyTorch website for the correct command.

4. Set Up the React Frontend Environment

The frontend requires Node.js and npm.

A. Navigate to a Frontend Directory: Choose one of the frontend applications to set up first.

# Example for the Baseline CNN frontend
cd "Baseline Web App/audio_classifier_app_baseline"

B. Install Dependencies: This command installs all the necessary React packages.

npm install

Repeat this step for the "ViT Web App/audio-classifier-frontend" directory as well.

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Running the Web Applications

To run an application, you must start both its backend and frontend servers simultaneously in two separate terminals.

Example: Running the Baseline CNN Application

Terminal 1: Start the Backend Server

1. Activate the Conda environment:

   conda activate fastapi_env
   

2. Navigate to the backend directory:

   cd "Baseline Web App/audio_app_baseline"
   

3. Start the Uvicorn server:

   uvicorn main:app --reload
   

   Your AI backend is now running at http://127.0.0.1:8000.

Terminal 2: Start the Frontend Server

1. Navigate to the frontend directory:

   cd "Baseline Web App/audio_classifier_app_baseline"
   

2. Start the React development server:

   npm start
   

   Your browser will automatically open to http://localhost:3000, where you can use the application.

Note on Model Choice: While both applications are fully functional, the Baseline Model Application is recommended as it is the most accurate model practically.

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License

This project is licensed under the MIT License. See the LICENCE file for details.