claude-real-video 0.4.0

Let Claude (or any LLM) actually watch a video — scene-aware, deduplicated frames + transcript, from a URL or local file.

claude-real-video

Let Claude — or any LLM — actually watch a video.

Most AI tools don't really see a video. Paste a YouTube link into ChatGPT and it reads the transcript, not the picture. Claude won't take a video file at all. Even Gemini, which can read video natively, has to send it up to Google and samples frames at a fixed interval (1 fps by default), so fast cuts slip past.

claude-real-video does it differently, and locally: point it at a URL or a file, and it pulls the frames that actually matter (every scene change, not a fixed quota), throws away the near-duplicates, transcribes the audio, and hands you a clean folder any LLM can read. All the processing happens on your own machine — what gets sent anywhere is only the frames/text you choose to paste into an LLM afterwards.

crv "https://www.youtube.com/watch?v=..."
# → crv-out/frames/*.jpg  +  crv-out/transcript.txt  +  crv-out/MANIFEST.txt

Then drop the frames + MANIFEST.txt into Claude / ChatGPT / Gemini and ask away.

New in 0.3.0 — tell it why you're watching, and keep what it finds:

crv "https://youtu.be/..." --why "find the pricing strategy" --kb ~/notes

--why makes the analysis focus on what you care about instead of a generic summary; --kb saves the result as a dated note in your own notes folder, so it doesn't die in crv-out.

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Why not just sample frames?

Most "let an LLM watch a video" scripts (and Gemini's own pipeline) grab frames at a fixed interval — e.g. one per second. That over-samples a static screencast and under-samples a fast-cut reel. claude-real-video is smarter:

	fixed-interval sampling	claude-real-video
Frame selection	every N seconds	scene-change detection + density floor
Repeated shots (A-B-A cuts)	sent again every time	sliding-window dedup sends each shot once
Static slide (10 min)	~600 near-identical frames	collapses to 1 (dedup)
Fast-cut reel	misses frames between samples	catches each visual change
Audio	often ignored	Whisper transcript w/ language detect
Where the processing happens	often in someone's cloud	on your machine (you choose what to share with an LLM afterwards)
Input	usually local file only	URL (yt-dlp) or local file

You feed the model fewer, more meaningful frames — cheaper context, better understanding.

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Install

pip install claude-real-video              # core (frames + dedup)
pip install "claude-real-video[whisper]"   # + audio transcription

System requirement: ffmpeg

ffmpeg / ffprobe are used for frame extraction and audio, and aren't pip-installable. Install them once:

OS	command
macOS	brew install ffmpeg
Linux	sudo apt install ffmpeg (or your distro's package manager)
Windows	winget install Gyan.FFmpeg — or choco install ffmpeg — or download a build and add its bin\ folder to your PATH

Verify it's on your PATH:

ffmpeg -version

Transcription uses the whisper CLI (installed by the [whisper] extra, or pip install openai-whisper). Whisper also relies on ffmpeg.

Works on macOS, Windows, and Linux — Python 3.10+.

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Usage

# A YouTube / Instagram / TikTok / ... link
crv "https://www.instagram.com/reel/XXXX/"

# A local file, English transcript, output to ./out
crv lecture.mp4 -o out --lang en

# Frames only, no transcription
crv clip.mp4 --no-transcribe

# A login-gated video (your own / authorised use): pass a Netscape cookie file
crv "https://..." --cookies cookies.txt

python -m claude_real_video ... works as an alias for crv too.

Options

flag	default	meaning
-o, --out	crv-out	output directory
--scene	0.30	scene-change sensitivity (lower = more frames)
--fps-floor	1.0	at least one frame every N seconds
--max-frames	150	hard cap on total frames
--lang	auto	Whisper language (en, zh, auto, ...)
--dedup-threshold	8	% of pixels that must change for a frame to count as new; higher = fewer frames
--dedup-window	4	compare against the last N kept frames — a shot the model already saw doesn't come back after a cutaway (1 = consecutive-only)
--report	off	keep dropped frames in ./dropped + write report.html visualising every keep/drop decision
--no-transcribe	off	skip audio
--keep-audio	off	also save the full soundtrack (audio.m4a) so audio models can hear it
--why	–	why you're watching, e.g. --why "find the pricing strategy" — written into MANIFEST.txt so the model analyses with that lens instead of a generic summary
--kb	–	also save the analysis as a dated markdown note into this folder (your Obsidian vault, notes dir, ...) — so it joins your knowledge base instead of dying in crv-out
--cookies	–	Netscape cookie file for login-gated sources

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Use it from Python

from claude_real_video import process

r = process("https://youtu.be/...", "out", lang="en")
print(r.frame_count, r.transcript_path)

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How it works

1. Fetch — yt-dlp for URLs (optional cookies), or copy a local file.
2. Extract — one chronological ffmpeg select pass grabs every scene change plus a density floor (at least one frame every --fps-floor seconds), so fast cuts and slow screencasts are both covered.
3. Dedup — real pixel difference (downscaled RGB, not a perceptual hash — hashes go blind on flat colours and equal-luma hue changes) against a sliding window of the last --dedup-window kept frames, so an A-B-A cutaway doesn't re-send a shot the model has already seen. --report writes report.html showing every keep/drop decision with its diff %, for tuning.
4. Text — if the video already has subtitles (a sidecar .srt/.vtt next to a local file, or an embedded subtitle track), those are used as the transcript — faster and more accurate than re-transcribing. Only when there are no subtitles does it fall back to Whisper on the audio (skipped cleanly if there's no audio).
5. Audio (optional, --keep-audio) — save the full original soundtrack (audio.m4a: music + speech + effects, copied losslessly when possible). The transcript only has the words; the audio file lets a model that can listen (Gemini, GPT-4o, …) actually hear the music and tone.
6. Manifest — MANIFEST.txt summarises everything for the model.

So the model can see (key frames), read (transcript) and — with --keep-audio — hear (full soundtrack) the video. The transcript is plain text any model can read; the tool doesn't burn subtitles into the video — burning is a presentation choice, not something needed to make a video AI-readable.

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Notes

• Only download content you have the right to. The --cookies option is for your own, authorised access — don't ship credentials in a repo.
• Re-running overwrites the output directory.

License

MIT