Video2Document

Installation

Follow the steps below to install Video2Document (v2d):

1. Install Node.js

Download and install Node.js from https://nodejs.org/en

2. Clone the Repository

Clone the repository using Git:

git clone https://gitlab.rlp.net/proj-wise2526-video2document/video2document.git

Alternatively, you can download the ZIP file and extract it manually.

3. Start the Program

The start scripts automatically install dependencies and launch the program. Choose the appropriate method for your operating system:

Windows:

start.bat

Linux:

./start.sh

macOS:

./start.sh

Alternative: Manual Installation and Start

If the start scripts don't work, you can manually install dependencies and start the program:

cd video2document
npm install
npm start

5. Install FFmpeg

Make sure that ffmpeg is installed on your system, as it may be required for video processing.

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Installation complete! You're now ready to use Video2Document.

###General Despription

Project Overview

V2D (Video to Document Framework) is a student project developed to convert video and audio content into structured, readable documents using AI-based text processing. The tool is designed to help users automatically generate documents such as meeting reports, summaries or structured texts from recorded videos or audio files.

The main problem V2D addresses is the time-consuming manual effort required to listen to recordings and write documents afterwards. By automating transcription, structuring and document generation, the tool improves efficiency and usability for students, teams and small organizations.

The project was created as part of a university software engineering course and is developed using agile methods (Scrum). It focuses on modular design, usability and extensibility, allowing new document types and features to be added easily.

Used Packages and Dependencies

This project uses several Node.js packages to handle video processing, AI-based document generation, backend communication, and the desktop user interface. Below is an overview of the most important dependencies and their purpose.

##Core Dependencies:

Electron: Used to build the desktop application. It allows the project to run as a cross-platform GUI application using web technologies.

Express: Acts as a lightweight backend server that handles internal API requests and communication between different parts of the application.

Axios: Used for making HTTP requests, for example when communicating with external APIs such as AI services.

dotenv: Used to securely load API keys and other sensitive configuration values from environment variables instead of hardcoding them in the source code.

@google/genai: Provides access to Google’s Generative AI models. This package is used to generate structured documents from transcribed text based on configurable prompts.

ffmpeg-static: Provides a static FFmpeg binary so that video and audio processing works without requiring FFmpeg to be installed separately on the system.

fluent-ffmpeg: Used together with FFmpeg to process video and audio files, for example extracting audio tracks from uploaded videos.

puppeteer: Used to render and process HTML content programmatically. This is helpful for document previews and automated content generation.

html-to-docx: Converts generated HTML documents into downloadable DOCX files, which are provided to the user as the final output.

mocha: A testing framework used to run unit tests and ensure that core functionalities work as expected.

##Development Dependencies:

TypeScript: Used to add static typing to the project, improving code quality, readability, and maintainability.

ts-node: Allows running TypeScript files directly without manually compiling them first.

@types/node: Provides TypeScript type definitions for Node.js APIs.

@types/fluent-ffmpeg: Type definitions for fluent-ffmpeg to improve development experience and error checking.

@types/cli-progress: Provides type support for progress bar functionality used during processing tasks.

###Why These Packages Are Needed

Together, these packages enable:

*Processing video and audio files

*Communicating with AI models for document generation

*Secure handling of API keys

*Generating structured documents (DOCX)

*Providing a user-friendly desktop interface

*Ensuring code quality through testing

###API Keys and Configuration

The V2D – Video to Document tool uses external AI and media processing services to convert video and audio content into structured documents. To access these services, several API keys are required. For security reasons, API keys are not stored in the repository and must be provided via environment variables.

##Supported API Keys

The project currently supports the following API keys. Depending on the configuration and selected provider, one or more of these keys may be used.

Google Gemini API

Environment variable: GOOGLE_API_KEY

Usage: Used for AI-based document generation. The Large Language Model processes transcripts and creates structured documents such as meeting reports.

OpenAI (ChatGPT)

Environment variable: OPENAI_API_KEY

Usage: Alternative AI provider for text processing and document generation.

AssemblyAI

Environment variable: ASSEMBLYAI_API_KEY

Usage: Speech-to-text processing for audio and video files.

Saya

Environment variable: SAYA_API_KEY

Usage: Additional or experimental AI provider that can be integrated into the document generation pipeline.

##How to Set API Keys

API keys must be configured as environment variables before starting the application.

Linux / macOS export GOOGLE_API_KEY="your_api_key_here" export OPENAI_API_KEY="your_api_key_here" export ASSEMBLYAI_API_KEY="your_api_key_here" export SAYA_API_KEY="your_api_key_here"

Windows (PowerShell) setx GOOGLE_API_KEY "your_api_key_here" setx OPENAI_API_KEY "your_api_key_here" setx ASSEMBLYAI_API_KEY "your_api_key_here" setx SAYA_API_KEY "your_api_key_here"

Alternatively, for local development, a .env file can be used:

GOOGLE_API_KEY=your_api_key_here OPENAI_API_KEY=your_api_key_here ASSEMBLYAI_API_KEY=your_api_key_here SAYA_API_KEY=your_api_key_here

⚠️ Important: The .env file must not be committed to the repository and should be listed in .gitignore.

Security Notes:

*API keys are injected at runtime

*No secrets are stored in the source code

*Prevents accidental exposure of sensitive data

*Supports secure collaboration in GitLab and CI/CD environments

*Follows best practices for secret management

###End-to-End User Guide (Video → Final Document)

This section describes how a user can create a structured document from a video using the V2D – Video to Document tool.

Start the Application:

Ensure all required API keys are configured as environment variables.

Install dependencies:

npm install

Start the application:

npm start

The Electron-based GUI will open.

#Upload a Video File:

In the application interface, select Upload Video.

Choose a supported video file (e.g. .mp4, .mov).

The video is loaded into the system for processing.

#Audio Extraction:

The application automatically extracts audio from the uploaded video.

This is handled internally using FFmpeg.

No user interaction is required for this step.

#Speech-to-Text Transcription:

The extracted audio is sent to the speech-to-text service.

The transcription process converts spoken content into text.

The generated transcript is stored internally and used for further processing.

#Select Document Type:

The user selects a document type (e.g. Meeting Report).

Each document type is based on a predefined prompt template.

The selected template defines the structure and style of the final document.

#Document Generation:

The transcript and selected prompt are sent to the AI service.

The AI model processes the input and generates a structured document.

The output is formatted in Markdown.

#Document Preview:

The generated document is displayed in the application preview.

Users can review the content before exporting.

No manual editing is required, but validation is possible.

#Export the Final Document:

The user exports the document in the desired format.

#Supported formats include:

Markdown (.md)

Word (.docx)

The document is saved locally.

#Completion:

The final document is now ready for use.

The user can repeat the process with another video if needed.

###Resources

This section lists the main technologies, libraries, and external resources used in the V2D (Video to Document) project. These resources are required to understand, run, and further develop the application.

##Project Dependencies

The following packages and tools are used in this project (as defined in package.json):

Application & Backend

Node.js – JavaScript runtime environment used for backend processing

Electron – Framework for building the desktop application

Express – Backend web framework for handling requests and internal APIs

AI & API Communication

@google/genai – Used for AI-based document generation

Axios – HTTP client for communicating with external APIs (LLMs)

Video & Audio Processing

ffmpeg-static – Extracts audio from uploaded video files

fluent-ffmpeg – Controls FFmpeg operations programmatically

Document Generation & Preview

html-to-docx – Converts structured content into Word documents

Puppeteer – Renders HTML content for document preview and processing

Configuration & Security

dotenv – Loads API keys securely from environment variables

Testing & Development

Mocha – Unit testing framework

TypeScript – Improves code quality and type safety

These dependencies enable the complete end-to-end workflow from video input to structured document output.

##Relevant Repositories

V2D Main Repository: https://gitlab.rlp.net/proj-wise2526-video2document/video2document

This repository contains the full source code, configuration files, and documentation for the V2D project.

Downloads & External Resources

The following tools and documentation may be required to run or understand the project:

Node.js: https://nodejs.org

Electron Documentation: https://www.electronjs.org/docs

FFmpeg: https://ffmpeg.org

Google Generative AI: https://ai.google.dev

Puppeteer: https://pptr.dev