yt2doc 0.1.1

Transcribe any YouTube video into a structural Markdown document

yt2doc

yt2doc transcribes videos online into structural documents in Markdown format.

Supported video sources:

• YouTube
• Twitter

yt2doc is meant to work fully locally, without invoking any external API. The OpenAI SDK dependency is required solely to interact with Ollama.

Installation

Install with pipx:

pipx install yt2doc

Or install with uv:

uv tool install yt2doc

Usage

Get helping information:

yt2doc --help

To transcribe a video (on YouTube or Twitter) into a document:

yt2doc --video <video-url>

To save your transcription:

yt2doc --video <video-url> -o some_dir/transcription.md

To transcribe all videos in a Youtube playlist:

yt2doc --playlist <playlist-url> -o some_dir

(Ollama Required) If the video is not chaptered, you can chapter it and add headings to each chapter:

yt2doc --video <video-url> --segment-unchaptered --llm-model <model-name>

Among smaller size models, qwen 2.5 7b seems works best.

For MacOS devices running Apple Silicon, (a hacky) support for whisper.cpp is supported:

yt2doc --video --whisper-backend whisper_cpp --whisper-cpp-executable <path-to-whisper-cpp-executable>  --whisper-cpp-model <path-to-whisper-cpp-model>

yt2doc uses Segment Any Text (SaT) to segment the transcript into sentences and paragraphs. You can change the SaT model:

yt2doc --video <video-url> --sat-model <sat-model>

List of available SaT models here.

TODOs

• Tests and evaluation
• CICD
• Better whisper prompting strategy (right now hugely depend on the title and the description of the video).