YouTube Channel Text Extract

Download all videos from a YouTube channel as audio only (no video), then transcribe them to text locally with OpenAI Whisper. The text is ready for Claude, NotebookLM, or any text-based tool.

Available as both a CLI tool and a desktop GUI application (Windows, Linux, Mac).

Project structure

youtube-channel-text-extract/
├── src/
│   └── ytextract/
│       ├── downloader.py      # download logic (yt-dlp)
│       ├── transcriber.py     # transcription logic (Whisper)
│       └── cli.py             # CLI entry point
├── desktop/                   # Electron desktop app
│   ├── package.json
│   ├── electron.vite.config.mjs
│   └── src/
│       ├── main/index.js      # Electron main process
│       ├── preload/index.js   # contextBridge API
│       └── renderer/src/App.jsx  # React + Radix UI frontend
└── pyproject.toml

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Requirements

• Python 3.8+
• FFmpeg — required for audio conversion and Whisper transcription. Must be on your PATH.
  • FFmpeg download
• Node.js 18+ — required for the desktop app only

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Python CLI setup

pip install -e .

This registers the ytextract command globally.

Usage

ytextract "https://www.youtube.com/@ChannelName"

Accepted URL formats include:

• channel handles: https://www.youtube.com/@ChannelName
• channel/user URLs: https://www.youtube.com/channel/..., .../user/..., .../c/...
• playlists: https://www.youtube.com/playlist?list=...
• single videos: https://www.youtube.com/watch?v=VIDEO_ID, https://youtu.be/VIDEO_ID

Output is written to the user's Desktop by default:

~/Desktop/
└── ChannelName/
    ├── audio/            ← downloaded audio files
    ├── transcriptions/   ← .txt transcripts (one per audio file)
    └── downloaded.txt    ← yt-dlp archive

Options

Option	Description
-o, --output-dir	Base directory for the channel folder (default: Desktop)
-f, --format	Audio format: mp3, m4a, opus, vorbis, wav (default: mp3)
--no-archive	Do not use download archive; re-download all videos
-m, --model	Whisper model: tiny, base, small, medium, large (default: base)
--force	Re-transcribe even if .txt already exists
--with-timestamps	Also write .srt and .segments.json alongside each transcript
-q, --quiet	Less verbose output

CLI vs Desktop app options

The desktop app maps directly to these CLI flags:

Desktop option	CLI flag
Re-download all	--no-archive
Force re-transcribe	--force
Include timestamps (.srt)	--with-timestamps
Audio format	-f, --format
Whisper model	-m, --model
Output directory	-o, --output-dir

Note: -q, --quiet is currently CLI-only.

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Desktop app setup

cd desktop
npm install
npm run dev

Quick setup with Make (Windows)

If you have make available (for example via Git Bash), you can prepare everything from the project root:

make setup
make dev

Useful targets:

• make check — verify Python, pip, Node.js, npm, and FFmpeg
• make setup-python — install Python package/dependencies (pip install -e .)
• make setup-desktop — install desktop dependencies (npm --prefix desktop install)
• make build — build desktop bundles

Quick setup with PowerShell (no make)

If you are using Windows PowerShell and do not have make:

# from project root
python -m pip install -e .
npm --prefix desktop install
npm --prefix desktop run dev

Optional checks:

python --version
node --version
npm --version
ffmpeg -version

The app opens automatically. To build a distributable:

npm run build

Features

• URL field — paste a YouTube channel URL or a single video URL
• Output directory — browse or type a path; defaults to your Desktop
• Format & model selectors — choose audio format and Whisper model size
• Options — re-download all, force re-transcribe, include timestamps
• Live log — real-time output from yt-dlp and Whisper with colour coding
• Stop button — cancel a running extraction at any time
• Open folder — jump to the output folder in your file manager when done

The desktop app spawns the ytextract Python CLI as a subprocess. If ytextract is not installed, it falls back to running python3 -m ytextract.cli automatically (requires Python dependencies installed from pyproject.toml, e.g. pip install -e . in the project root).

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How it works

• Download: yt-dlp with bestaudio format — only the audio stream is fetched. FFmpeg converts to your chosen format.
• Transcription: OpenAI Whisper runs on your machine and writes plain-text transcripts to the transcriptions/ folder.

Contributing

This repository is using Gitflow Workflow and Conventional Commits, so if you want to contribute:

• create a branch from develop branch;
• make your contributions;
• open a Pull Request to develop branch;
• wait for discussion and future approval;

I thank you in advance for any contribution.

Status

Maintaining

License

MIT