TubeStoevsky

Downloads a YouTube video transcript and turns it into literary prose through a LangChain LLM chain — outputs TXT + MD.

Works with any OpenAI-compatible chat completions API: a local gateway, Ollama, vLLM, OpenAI, or anything else speaking that protocol.

Available on PyPI as tubestoevsky.

How it works

URL
 ├─ yt-dlp       subtitles (manual > auto)  -> {title}.srt
 ├─ SRT parser   timecodes and rolling dupes -> raw text
 ├─ LangChain    prose -> title -> Markdown
 └─ output       {title}.txt, {title}.md

Install

# as a CLI tool
uv tool install tubestoevsky   # or: pip install tubestoevsky
tubestoevsky --help

# from a checkout, for development
uv sync

Configuration

Copy .env.example to .env and set LLM_API_KEY. The file is read from the current directory, the project root, or ~/.config/tubestoevsky/.env.

Usage

Example run:

$ tubestoevsky --lang en --model deepseek-flash https://youtu.be/q0aFOxT6TNw
[1/4] Downloading subtitles (en) via yt-dlp ...
      -> iPhone-17-Pro-Review-Paradox-in-a-Box.srt
[2/4] Parsing SRT, deduplicating lines ...
      -> 2349 words (raw text: 12816 chars)
[3/4] LangChain chain (deepseek-flash via http://127.0.0.1:4000/v1) ...
      [LLM 1/3] rewriting as prose (2349 words) ...
      [LLM 1/3] done: 2300 words
      [LLM 2/3] document title ...
      [LLM 2/3] done: "iPhone 17 Pro Review: The Most Pro iPhone for the Masses"
      [LLM 3/3] Markdown layout ...
      [LLM 3/3] done: 2318 words
      -> prose: 2300 words, title: "iPhone 17 Pro Review: The Most Pro iPhone for the Masses"
[4/4] Writing .txt and .md ...
Done:
  ./iPhone-17-Pro-Review-Paradox-in-a-Box.srt
  ./iPhone-17-Pro-Review-Paradox-in-a-Box.txt
  ./iPhone-17-Pro-Review-Paradox-in-a-Box.md

Full generated output is available in examples/: transcript, prose, and Markdown.

Install globally and run with an inline API key:

uv tool install tubestoevsky
LLM_API_KEY='sk-xxxxx' tubestoevsky --lang en --model qwen3.8-27b https://youtu.be/q0aFOxT6TNw

Other examples:

tubestoevsky <URL>
tubestoevsky <URL> --lang en --out ./out
tubestoevsky <URL> --model gpt-4o-mini

# or without installing:
uvx tubestoevsky <URL>

# from a checkout:
uv run python -m transcript_app <URL>

Layout

transcript_app/
  __main__.py    CLI
  config.py      .env + CLI flags
  downloader.py  yt-dlp
  srt.py         subtitle parser
  chain.py       LangChain chain over the API
  prompts.py     step prompts
  outputs.py     .txt/.md writers

Notes

• LLM steps stream: some gateways fail to return monolithic responses.
• Accept-Encoding: identity and use_responses_api=False work around proxy hangs.
• Run long requests in the foreground.