Autoshorts

Abstract

1. Introduction

AutoShorts analyzes your gameplay videos to identify the most engaging moments—action sequences, funny fails, or highlight achievements—then automatically crops, renders, and adds subtitles or AI voiceovers to create ready-to-upload short-form content.

2. Objective

Automatically generate viral-ready vertical short clips from long-form gameplay footage using AI-powered scene analysis, GPU-accelerated rendering, and optional AI voiceovers.

This project demonstrates how Python, OpenAI API, PyTorch can be applied to a real-world Data Science problem.

4. Technology Stack

• NVIDIA GPU with CUDA support (6GB+ VRAM recommended for Qwen3-TTS 1.7B)
• NVIDIA Drivers and System RAM (16GB+ recommended)

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/divyaprakash0426/autoshorts.git
cd autoshorts

1. Download micromamba if conda/mamba is not found
2. Create a Python 3.10 environment with FFmpeg 4.4.2
3. Install NV Codec Headers for NVENC support
4. Build Decord from source with CUDA enabled
5. Install all pip requirements
6. Place source videos in the gameplay/ directory
7. Run the script:
8. Generated clips are saved to generated/

git clone https://github.com/divyaprakash0426/autoshorts.git
cd autoshorts

# Run the installer (uses conda/micromamba automatically)
make install

# Setup environment variables
cp .env.example .env
# Edit .env and add your API keys (Gemini/OpenAI)

# Activate the environment
overlay use .venv/bin/activate.nu    # For Nushell
# OR
source .venv/bin/activate            # For Bash/Zsh

python run.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by divyaprakash0426 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.