GitHub - HUANGCHIHHUNGLeo/claude-real-video: Let Claude (or any LLM) actually watch a video — scene-aware, deduplicated frames + transcript, from a URL or local file. Runs locally, MIT.

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 — on your own machine, nothing uploaded. 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.

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 video goes often uploaded to a cloud stays on your machine

Input usually local file only URL (yt-dlp) or local file

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

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+.

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

--cookies – Netscape cookie file for login-gated sources

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)

How it works

Fetch — yt-dlp for URLs (optional cookies), or copy a local file. 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. 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. 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). 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. 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.

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