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2025-04-02 21:44:17 -07:00
2025-04-02 21:44:17 -07:00
2025-04-02 21:44:17 -07:00
2025-04-02 21:44:17 -07:00
2025-04-02 21:44:17 -07:00
2025-04-02 21:44:17 -07:00
2025-04-02 21:44:17 -07:00
2025-04-02 21:44:17 -07:00
2025-04-02 21:44:17 -07:00
2025-04-02 21:44:17 -07:00
2025-04-02 21:44:17 -07:00

YouTube Transcription with Whisper

A web application that allows you to transcribe YouTube videos with OpenAI's Whisper speech-to-text model.

Features

  • Paste a YouTube URL and get a full transcription
  • Support for individual videos and playlists
  • Queue management with video thumbnails
  • Real-time progress updates
  • Download transcriptions in TXT and SRT formats
  • Clean, responsive user interface

Installation

Prerequisites

  • Python 3.7 or higher
  • FFmpeg (required for audio processing)
  • Git (optional, for cloning the repository)

Install FFmpeg

macOS:

brew install ffmpeg

Ubuntu/Debian:

sudo apt update
sudo apt install ffmpeg

Windows:

Download from FFmpeg's official website or install via Chocolatey:

choco install ffmpeg

Setup Steps

  1. Clone the repository:
git clone https://github.com/yourusername/youtube-whisper-app.git
cd youtube-whisper-app
  1. Create a virtual environment:
python -m venv venv
  1. Activate the virtual environment:

    • Windows: venv\Scripts\activate
    • macOS/Linux: source venv/bin/activate
  2. Install dependencies:

pip install -r requirements.txt
  1. Run the application:
python app.py
  1. Open your browser and navigate to http://127.0.0.1:5000

Usage

  1. Paste a YouTube URL into the input field (supports individual videos or playlists).
  2. Click "Transcribe" to start the process.
  3. Watch the progress in real-time with the queue display.
  4. Click on any queued video to see detailed status.
  5. Once completed, preview the transcription and download in your preferred format.

Note on YouTube Restrictions

YouTube employs anti-bot measures that may block automated downloads. For best results:

  • Run the application locally on your personal computer
  • Use your browser's cookies with the --cookies-from-browser option
  • Access from a residential IP address

Technical Details

  • Backend: Flask with Flask-SocketIO for real-time updates
  • Audio download: yt-dlp for efficient YouTube download
  • Transcription: OpenAI's Whisper speech recognition model
  • Frontend: Vanilla JavaScript, CSS, and HTML

License

MIT

Description
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Readme 2.7 GiB
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Python 74.4%
C++ 15.6%
C 5.3%
CMake 3.5%
Cuda 0.4%
Other 0.5%