Hacksync_Error101

Podify-AI: Personalized Podcast Generator Using Generative AI

Project Overview

Podify-AI is a full-stack web application that leverages state-of-the-art Generative AI to create personalized podcast episodes for users. By combining advanced language models and neural text-to-speech, the system generates high-quality, engaging, and contextually relevant podcasts in real time. Users can customize topics, emotions, voices, and more, resulting in a unique listening experience tailored to their preferences.

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Features

• AI-Generated Podcast Scripts: Automatically generate podcast scripts based on user input (topic, emotion, location, etc.) using large language models.
• Text-to-Speech Audio Synthesis: Convert scripts into natural-sounding audio with customizable voices, pitch, and emotion.
• Topic Recommendation: Suggest trending or relevant podcast topics using AI-based recommendation.
• Multi-Speaker Support: Simulate conversations with multiple speakers, each with unique voice and emotion settings.
• Modern UI: Responsive, user-friendly interface built with Next.js and Tailwind CSS.
• Authentication & Protected Routes: Secure user authentication using Clerk.

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Tech Stack

• Frontend: Next.js, React, TypeScript, Tailwind CSS
• Backend: Python (Flask)
• AI/ML:
  • Large Language Models (e.g., GPT, Gemini, Llama)
  • Neural Text-to-Speech (e.g., Tacotron, FastSpeech, or commercial APIs)
• Authentication: Clerk
• Other: REST APIs, Custom UI Components

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Generative AI Techniques Used

• Natural Language Generation (NLG): Large language models (e.g., GPT, Gemini, Llama) for script writing.
• Text-to-Speech (TTS) Synthesis: Neural TTS models for generating podcast audio with customizable voices.
• Topic Recommendation: Embedding-based similarity search or LLM-based suggestion for relevant topics.
• Voice Customization: Deep learning models for voice selection, pitch, and emotion control in audio output.

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How the Project Works

1. User Input: Users enter a podcast topic, select emotion, location, number of speakers, and customize voice settings.
2. Script Generation: The backend uses a large language model to generate a podcast script based on the input.
3. Audio Generation: The script is converted to audio using neural TTS, with user-selected voice and emotion.
4. Playback & Download: Users can listen to or download the generated podcast episode directly from the web app.
5. Topic Recommendation: Users can get AI-powered suggestions for podcast topics.

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How to Run This Project

1. Clone the Repository

git clone <repo-url>
cd Hacksync_Error101

2. Install Frontend Dependencies

cd podcast
npm install

3. Install Backend Dependencies

cd ../backend
pip install -r requirements.txt

4. Start the Backend Server

python app.py

This will start the Flask server (usually at http://127.0.0.1:5000).

5. Start the Frontend (Next.js) Server

Open a new terminal, then:

cd podcast
npm run dev

This will start the Next.js app (usually at http://localhost:3000).

6. Open the App

Visit http://localhost:3000 in your browser.

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Notes

• Make sure your backend .env and API keys are set up for the language model and TTS services you use.
• MongoDB is included in the backend for potential storage of scripts/audio, but is not required for basic generation features.
• For any issues, check the backend and frontend logs for error messages.