YouTube Transcripts Machine: Extract Timestamps and Transcripts from Videos

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Introduction

The YouTube Transcripts Machine (YTM) is a powerful web application designed to automatically extract detailed transcripts and corresponding timestamps from any YouTube video. Built with Next.js and leveraging browser automation tools like Stagehand and BrowserBase, YTM offers a seamless experience for users looking to access video content in text format. Key features include easy URL input, interactive timestamped transcripts, and convenient export options to copy or download the text.

Installation

To get started with YouTube Transcripts Machine, follow these steps:

Prerequisites: Ensure you have Node.js and npm installed. You will also need an OpenAI API key and BrowserBase API key and project ID for cloud execution.

Clone the repository:

git clone https://github.com/zaidmukaddam/youtube-transcripts-machine.git
cd youtube-transcripts-machine

Install dependencies:
```
npm install
```
Set up environment variables:
```
cp .example.env .env.local
```
Then, add your OpenAI API key and BrowserBase credentials to the .env.local file.
Running Locally:
```
npm run dev
```

Examples

YTM simplifies the process of getting video transcripts. Here's how it works:

Input: A user pastes a YouTube video URL into the application.
Processing: The application uses Stagehand to automate a browser session, navigating to the specified YouTube video. It then opens the transcript panel, extracts all transcript entries along with their precise timestamps.
Output: The extracted data is displayed as a formatted transcript with clickable timestamps, allowing users to jump to specific points in the video directly from the text.

Why Use It

YouTube Transcripts Machine is an invaluable tool for various use cases:

Accessibility: Provides text alternatives for video content, making it accessible to a wider audience.
Content Analysis: Easily review and analyze video content without rewatching, useful for researchers, journalists, or content creators.
Learning and Reference: Quickly find specific information within long videos or create study notes.
Productivity: Save time by extracting key information or quotes directly from video transcripts.
Developer-Friendly: The project's open-source nature and clear technology stack make it easy for developers to contribute or adapt for their own needs.