🎨 Project Description: Avatarify : An Avatar Generator

🌟 Overview

The Avatarify enables users to create unique and personalized avatars by converting text messages into speech and integrating them into engaging videos. With a user-friendly interface, individuals can complete their profiles, input multiple messages, and select their preferred method of avatar presentation.

🛤️ User Journey

1. 👤 Profile Completion:

   • Users begin by filling out their profiles to set up their accounts.

2. 💬 Message Input:

   • After completing their profiles, users are redirected to a dedicated page where they can input multiple messages.
   • Each message is transformed into speech using the Google Text-to-Speech (gTTS) library, providing an audio representation of their text.

3. 🖼️ Avatar Creation:

   • Following message input, users proceed to the avatar creation page where they can choose between two options:
     • Option 1: Static Image
       • Users upload a static image that serves as their avatar. The audio of their speech is added as a background, creating a simple yet effective presentation.
     • Option 2: Dynamic GIF
       • Users can opt for a more dynamic experience by utilizing a face-swapping technique to create an avatar with an existing GIF. The resulting video synchronizes their speech with the animated visuals.

📽️ Output

• The final output showcases the generated video alongside the latest message.
• Videos are played at specific times designated by the user, allowing for scheduled presentations.
• Users can plan messages for specific dates, with only the most imminent message displayed on the interface.

🛠️ Tech Stack

• Frontend:

  • Framework: Next.js 14
  • Language: TypeScript
  • Key Packages:
    • @clerk/nextjs: For authentication and user management
    • axios: For handling HTTP requests
    • tailwindcss: For modern styling

• Backend:

  • Framework: Node.js with Express.js
  • Language: JavaScript
  • Database: MongoDB (utilizing Mongoose ORM)
  • Key Packages:
    • child_process: To spawn Python processes for multimedia tasks
    • cloudinary: For media storage and management
    • multer: For handling file uploads

• Python Libraries Used:

  • gTTS: To convert text into speech
  • moviepy: For video editing and integrating audio
  • opencv: For image processing tasks
  • insightface and deepface: For advanced face detection and analysis
  • buffalo_l: Leveraged for precise face detection
  • inswapper_128.onnx: Utilized for accurate face-swapping

🚀 Key Features

• 🔊 Text-to-Speech Conversion:

  • Seamlessly converts user-inputted messages into high-quality speech audio, enhancing user engagement.

• 🖼️ Avatar Options:

  • Users can select between a static image paired with audio or an animated GIF featuring face-swapping technology.

• ⏰ Scheduled Messages:

  • Users can easily set specific times for their messages to be played, ensuring timely delivery and engagement.

• 📩 Multiple Message Support:

  • The platform allows users to input multiple messages; however, only the latest or most relevant message is displayed based on the scheduled time.

⚒️ Work Flow

🎯 Conclusion

The Avatar Generation project combines cutting-edge technology with creative expression, enabling users to bring their messages to life through personalized avatars. With an intuitive design and powerful features, this project offers a unique experience for users looking to enhance their communication.