القائمة
Extract and process subtitles from a SOLAR LINE episode video. Supports YouTube VTT download, Whisper STT, video OCR, and dialogue line extraction.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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| name | subtitle-extraction |
| description | Extract and process subtitles from a SOLAR LINE episode video. Supports YouTube VTT download, Whisper STT, video OCR, and dialogue line extraction. |
| argument-hint | <video-id-or-path> --episode <N> |
Extract subtitles from SOLAR LINE episodes using multiple sources.
cd ts
npm run collect-subtitles -- <youtube-video-id> --lang ja --out-dir ../raw_data/subtitles
npm run extract-dialogue -- ../raw_data/subtitles/<vtt-file> --episode <N> --video-id <id>
cd ts
# Step 1: Download audio (run in background — takes ~1 min)
# yt-dlp -x --audio-format wav -o ../raw_data/audio/ep<N>.wav "https://youtu.be/<video-id>"
# Step 2: Run Whisper (run in background — takes 10-26 min CPU-only)
npm run whisper -- ../raw_data/audio/ep<N>.wav --model large-v3-turbo --language ja
# Step 3: Process output
npm run process-whisper -- ../raw_data/whisper/<output>.json --episode <N> --video-id <id>
# Step 4: Extract dialogue lines
npm run extract-dialogue-whisper -- ../raw_data/whisper/<processed>.json --episode <N>
cd ts
# Step 1: Extract keyframes from video
python src/extract-frames.py ../raw_data/<video-file> ../raw_data/frames/ep<N>
# Step 2: Run OCR on extracted frames
python src/video-ocr.py ../raw_data/frames/ep<N> --episode <N>
Output: reports/data/episodes/epNN_ocr.json
npm run compare-transcriptions -- ../raw_data/subtitles/<vtt> ../raw_data/whisper/<whisper> --episode <N>
| Step | Output | Location |
|---|---|---|
| YouTube VTT | Raw subtitle file | raw_data/subtitles/ (gitignored) |
| Whisper | Raw transcription JSON | raw_data/whisper/ (gitignored) |
| Video OCR | OCR data JSON | reports/data/episodes/epNN_ocr.json |
| Phase 1 Extract | epNN_lines.json | reports/data/episodes/ |
| Phase 2 Attribute | epNN_dialogue.json + epNN_speakers.json | reports/data/episodes/ |
run_in_background for yt-dlp and Whisper to avoid inflating session context| Source | Type Code | Accuracy (EP01) | Notes |
|---|---|---|---|
| YouTube auto-generated | youtube-auto | 68.3% | Default VTT download |
| YouTube manual | youtube-manual | — | Rare, higher quality |
| Whisper (large-v3-turbo) | whisper | 91.4% | Local inference, best accuracy |
| Whisper (medium) | whisper | 82.6% | Faster, lower accuracy |
| Video OCR (Tesseract) | video-ocr | 10.1% | On-screen text only |
| Manual | manual | — | Hand-transcribed |