clipify

# Clipify Find the funniest moments in a video, cut them as standalone clips, optionally reformat 16:9 → 9:16 (face-pan or split-screen), and burn opus-style word-by-word captions. ## Inputs - A video file path (the user will provide it; otherwise ask) - Optional: requested format (9:16, 16:9, 1:1) — if not given, ask after candidates are picked - Optional: subtitle style preference — if not given, ask before captioning ## Tooling (use only the fastest path) - **Whisper:** `whisper --model tiny.en --word_timestamps True --output_format json` (≈10× faster than `small.en`; quality fine for English). For non-English: `--model base` (drop `--language`). - **ffmpeg:** hardware decode is optional and platform-specific — use `-hwaccel auto`, or omit it (macOS: `videotoolbox`; Linux: `vaapi`/`cuda`/none). Add `-preset ultrafast` for renders. Use `-c:v libx264 -crf 20` for the final master. - **Numpy** for audio alignment (FFT cross-correlation). No scipy/cv2 needed. - **Scripts:** `<skill-dir>/scripts/` (where `<skill-dir>` is the directory containing this SKILL.md — typically `~/.claude/skills/clipify/`) - `analyze.py` — speaker timeline from two ROI motion files - `build_pan.py` — ffmpeg crop x-expression with hard cuts - `build_ass.py` — opus-style ASS captions from whisper JSON - `audio_align.py` — find offset of a sub-clip in a longer source Working dir: `/tmp/clipify/` (mkdir at start, leave artifacts for debugging). --- ## Workflow ### Step 1 — Find the funniest parts ```bash mkdir -p /tmp/clipify ffmpeg -y -i "$VIDEO" -vn -ac 1 -ar 16000 /tmp/clipify/audio.wav whisper /tmp/clipify/audio.wav --model tiny.en --word_timestamps True --output_format json --output_dir /tmp/clipify --language en ``` Read the resulting JSON (or `.txt`) and pick 3–5 candidate clips. Funny signals to scan for: - **Punchlines and reactions:** words like "what", "wait", "no way", laughter, "haha", swearing - **Reversal moments:** setup question → unexpected answer - **Awkward pauses:** Whisper segment with long gap, or filler ("uh", "um") - **Self-roast / quotable one-liners:** short declarative sentences that stand alone - **Audio peaks:** detect via `ffmpeg -af volumedetect` or look for rapid back-and-forth (alternating short Whisper segments) For each candidate, propose: `[start, end, why-it's-funny, suggested title]`. Aim for 10–25s clips. Show the list and let the user confirm/pick. ### Step 2 — Trim each chosen clip ```bash ffmpeg -y -ss "$START" -t "$DURATION" -i "$VIDEO" -c copy /tmp/clipify/clip_$N.mp4 ``` (Use `-c copy` for instant trim. Re-encode only if cuts must be frame-accurate.) ### Step 3 — Decide the output format Ask the user (skip if they already specified): "9:16 (TikTok / Reels), 16:9 (YouTube), or 1:1 (Insta feed)?" ### Step 4 — If 16:9 → 9:16: pan-between-faces vs split-screen Detect source aspect with `ffprobe`. If source is 16:9 and target is 9:16, ask: > "Two options: **(a) hard-cut pan** that follows whoever is speaking (single face on screen at a time), or **(b) split-screen** stack with both faces visible. Which do you want?" Skip the question if there's only one face (single-talker clip). For single-talker, just center-crop. #### Step 4a — Pan-between-faces (recommended for fast-cut talking-head dialogue) 1. **Locate the two face ROIs.** Sample one frame: `ffmpeg -ss <middle> -i <clip> -frames:v 1 /tmp/clipify/probe.jpg`. Read it. Eyeball each face's mouth+chin area as `x,y,w,h` in the source's pixel space. (No cv2 needed — camera is static within a clip; one frame is enough.) Verify by drawing boxes: ```bash ffmpeg -i probe.jpg -vf "drawbox=x=$LX:y=$LY:w=$LW:h=$LH:color=cyan@0.9:t=4,drawbox=x=$RX:y=$RY:w=$RW:h=$RH:color=magenta@0.9:t=4" verify.jpg ``` Iterate **at most twice**. Boxes should cover mouth + chin and avoid hands/mics. Don't over-tune — frame differencing is forgiving. 2. **Extract per-frame motion energy in each ROI:** ```bash ffmpeg -y -i clip.mp4 -filter_complex " [0:v]split=2[a][b]; [a]crop=$LW:$LH:$LX:$LY,format=gray,tblend=all_mode=difference,signalstats,metadata=mode=print:key=lavfi.signalstats.YAVG:file=/tmp/clipify/L.txt[la]; [b]crop=$RW:$RH:$RX:$RY,format=gray,tblend=all_mode=difference,signalstats,metadata=mode=print:key=lavfi.signalstats.YAVG:file=/tmp/clipify/R.txt[ra] " -map "[la]" -f null - -map "[ra]" -f null - ``` 3. **Build speaker timeline** (min dwell 1.0s — short interjections merge into the prior speaker): ```bash python3 <skill-dir>/scripts/analyze.py /tmp/clipify/L.txt /tmp/clipify/R.txt 1.0 > /tmp/clipify/segments.json ``` 4. **Pick pan x-coordinates** for a 9:16 vertical strip from the source. With source W=1920 and target W=1080, crop strip width = 608. - LEFT_X = `face_left_center_x - 304` (clamp ≥ 0) - RIGHT_X = `face_right_center_x - 304` (clamp ≤ source_W - 608) 5. **Generate the hard-cut x expression and render:** ```bash EXPR=$(python3 <skill-dir>/scripts/build_pan.py /tmp/clipify/segments.json $LEFT_X $RIGHT_X) ffmpeg -y -i clip.mp4 -filter_complex \ "[0:v]crop=608:1080:x='$EXPR':y=0,scale=1080:1920:flags=lanczos[v]" \ -map "[v]" -map 0:a -c:v libx264 -preset fast -crf 20 -pix_fmt yuv420p \ -c:a aac -b:a 192k /tmp/clipify/clip_panned.mp4 ``` Source 1920×1080 assumed; for 4K source either downscale first or double all coordinates. #### Step 4b — Split-screen (both faces always visible) Two stacked tiles, 1080×960 each. The active speaker's tile is on top — overlay flips at speaker changes. ``` [0:v]split=2[a0][a1]; [a0]crop=Wcrop:Hcrop:LX_tile:LY_tile,scale=1080:960,split=2[lt0][lt1]; [a1]crop=Wcrop:Hcrop:RX_tile:RY_tile,scale=1080:960,split=2[rt0][rt1]; [lt0][rt0]vstack[layoutL]; [rt1][lt1]vstack[layoutR]; [layoutL][layoutR]overlay=0:0:enable='<RIGHT_SPEAKER_ENABLE>'[v] ``` Build `<RIGHT_SPEAKER_ENABLE>` from `segments.json` as `between(t,a,b)+between(t,a,b)+...` over the right-speaker segments. Tile crops should target ~720×640 around each face (1.125:1 to match 1080×960). ### Step 5 — Add subtitles Ask once (only if user hasn't already specified a style): > "Three subtitle styles: **opus** (big bold white, yellow active-word highlight), **karaoke** (4-word chunks, green highlight), **minimal** (clean Helvetica, no highlight). Or paste an example you like." If they paste a reference image/example: match the font, size, weight, color, position, and animation as closely as possible — write a custom ASS by hand or extend `build_ass.py`. Else use the preset: ```bash # Re-run whisper on the trimmed clip for accurate timestamps relative to clip start whisper /tmp/clipify/clip_panned.mp4 --model tiny.en --word_timestamps True --output_format json --output_dir /tmp/clipify --language en python3 <skill-dir>/scripts/build_ass.py /tmp/clipify/clip_panned.json /tmp/clipify/captions.ass opus ``` Burn captions: ```bash ffmpeg -y -i /tmp/clipify/clip_panned.mp4 -vf "subtitles=/tmp/clipify/captions.ass" \ -c:v libx264 -preset fast -crf 20 -c:a copy "$OUTPUT.mp4" ``` ### Step 6 — Deliver - Save each output to `<source_dir>/clipify_out/` (mkdir if missing) - Print one line per clip: name, duration, what was funny, output path - Print the first output path (or open it — Linux `xdg-open <path>`, macOS `open <path>`) so the user can check it - Offer to iterate (different style, different ROI, swap to split-screen, retime captions) --- ## Pitfalls (lessons from prior runs — don't repeat) - **Don't over-tune ROIs.** Two iterations max. Motion-diff is forgiving — wider ROIs covering mouth+chin work fine even if not perfectly mouth-centered. - **Watch out for scene cuts inside a clip.** Run `ffmpeg -filter:v "select='gt(scene,0.3)',showinfo" -f null -` to count cuts. If a 16:9→9:16 clip has many cuts, the fixed face ROIs only work for the dominant scene; warn the user, and offer to either pick a single-take clip or accept off-center framing during cuts. - **Source resolution matters.** If source is 4K, either downscale to 1920×1080 first (faster, fine for 9:16 output) or multiply all ROI/pan coordinates by 2. - **Burned-in subtitles in source.** Some "raw" clips still have subtitles. If so, find the no-subs master via audio cross-correlation (`audio_align.py`) and trim from there. - **Don't run whisper on the full feature-length source if a short clip suffices.** Whisper the trimmed clip after Step 2; only whisper the full source in Step 1 if you need a transcript to find funny moments. - **State the plan in one line, then act.** Don't narrate every iteration.