Lecture to Notes

Turn a YouTube, Bilibili, or X/Twitter lecture video into a complete, compilable .tex note set and a rendered PDF.

Dependencies

Check before starting (use which). Prompt the user to install any missing tools.

Tool	Required	Purpose
`yt-dlp`	Always	Video/subtitle/metadata download (supports YouTube + Bilibili + X/Twitter). Check `yt-dlp --version`: a build older than ~90 days fails on current Bilibili and YouTube pages
`ffmpeg`	Always	Frame extraction, audio extraction
`xelatex`	Always	LaTeX compilation (TeX Live + CTeX for Chinese)
`magick`	Always	Frame montage and contact sheets
`ffprobe`	Always	Duration and frame height (ships with ffmpeg)
`pdftotext`	Always	Rendered-page checks in `verify_notes.py` (poppler)
`python3`	Always	Installed helper scripts and local ASR support
`sherpa-onnx` + X ASR model	Optional zh/en ASR	Fast local Chinese/English transcription with token timestamps
Whisper backend	ASR fallback / non-zh-en / no-CC	`transcribe_whisper.py` picks `mlx-whisper` (macOS arm64) → `faster-whisper` → `openai-whisper`
`rapidocr-onnxruntime`	Bilibili / any burned-in subtitles	`ocr_hardsubs.py` reads the subtitle band and overlay geometry
`Pillow` + `numpy`	Always	`frame_filter.py` overlay and composite-panel crops, talking-head scores

Install the Python side with pip install rapidocr-onnxruntime Pillow numpy plus one transcription backend: pip install mlx-whisper on Apple silicon, otherwise pip install faster-whisper (or openai-whisper as the slowest fallback). /ABSOLUTE/PATH/TO/lecture-to-notes/assets/smart_crop.py remains an optional experiment.

LaTeX package check (do NOT skip — `which xelatex` alone is insufficient)

which xelatex passing does not mean the required LaTeX packages are installed. On minimal TeX installs (e.g. MacTeX Basic, TeX Live scheme-basic), the binary exists but ctex, tcolorbox, and other packages are missing. This causes silent failures: long Chinese lines overflow \textwidth (no CJK line breaking) or compilation aborts with File '...' not found.

Check required packages before starting:

MISSING=0
for pkg in ctex tcolorbox environ trimspaces listings hyperref booktabs float subcaption etoolbox; do
  if ! kpsewhich "$pkg.sty" >/dev/null 2>&1; then
    echo "❌ Missing LaTeX package: $pkg"
    MISSING=1
  fi
done
if [ "$MISSING" -ne 0 ]; then
  echo "Install missing packages:"
  echo "  tlmgr install ctex tcolorbox environ trimspaces etoolbox"
  echo "Or install full TeX distribution:"
  echo "  macOS:  https://www.tug.org/mactex/  (~4 GB, includes everything)"
  echo "  Linux:  sudo apt install texlive-full"
fi

If ctex cannot be installed (e.g. due to l3kernel version conflicts on minimal installs), the notes-template.tex includes a fallback that uses XeTeX's built-in CJK line breaking (\XeTeXlinebreaklocale "zh") with system fonts — no ctex or xeCJK needed.

Cookies and yt-dlp freshness

Every supported platform can reject an unauthenticated download, and the two failures look different:

Symptom	Platform	Meaning
`Sign in to confirm you're not a bot`	YouTube	bot detection
`HTTP Error 412` on probe or download	Bilibili	anti-scraping check
`WARNING: Your yt-dlp version (...) is older than 90 days`	any	extractors predate the site's current markup

Fix the version first, because a stale extractor produces the same errors that cookies fix: brew upgrade yt-dlp, pipx upgrade yt-dlp, or pip install -U yt-dlp, then confirm with yt-dlp --version. Homebrew disables the self-updater, so yt-dlp -U reports success without changing anything.

Then supply cookies. video_source.py probe takes them directly:

python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/video_source.py" probe \
  --cookies-from-browser chrome "<URL>" > metadata.json

Use --cookies FILE instead when the browser profile is unavailable, for example on a headless host. A probe that fails for an authentication reason now names the flag to retry with, so treat that message as the next action rather than a dead end.

The rest of the workflow calls yt-dlp directly. Rather than editing every command, write a yt-dlp config file once and point XDG_CONFIG_HOME at it, so downloads, subtitle fetches, and thumbnail fetches all pick the cookies up:

mkdir -p "$WORKDIR/.xdg/yt-dlp"
printf -- '--cookies-from-browser chrome\n' > "$WORKDIR/.xdg/yt-dlp/config"
# then prefix every later yt-dlp call in this lecture's workdir:
XDG_CONFIG_HOME="$WORKDIR/.xdg" yt-dlp --no-playlist ... "<URL>"

Browser cookies also unlock Bilibili 1080P+ for logged-in accounts, so the same setup improves frame quality.

Goal

Produce a professional Chinese lecture note from a YouTube, Bilibili, or X/Twitter video URL. The output must:

use the video's actual teaching content, not just subtitle transcription
place the video's original cover image on the front page
include selected full-frame teaching figures chosen by contact-sheet review; when the video places a camera feed and slides side by side, each figure keeps one measured panel so the slide text stays readable
let a capable first-time reader understand the question, mechanism, evidence, consequence, and boundary without decoding the transcript
achieve source-fit information density — every figure, box, formula, and paragraph earns its space
be structurally organized with \section{} / \subsection{}
end with a synthesis section combining speaker's conclusions and your own distillation
be a complete .tex from \documentclass to \end{document}
compile successfully to PDF

Non-negotiable quality bar (Codex / production mode)

STOP and do not claim completion unless every item below is true. Skeleton outlines, image-heavy PDFs with thin prose, or missing intermediate artifacts are automatic failures.

Mandatory workdir artifacts (must exist on disk before saying "done")

File	Required content
`metadata.json`	From `video_source.py probe` (or equivalent full dump)
`audio.srt`	Final subtitle track used for writing (manual CC, cleaned auto, hard-sub OCR, X ASR, or Whisper)
`audio_corrected.srt`	Copy of final track, or glossary/LLM-corrected track when local ASR was used
`hardsub_ocr.srt`	`ocr_hardsubs.py extract` output when `ocr_hardsubs.py detect` reported burned-in subtitles; absent otherwise
`bands.json`	Overlay geometry (navigation strip, subtitle band) from `ocr_hardsubs.py detect --geometry` or `frame_filter.py bands`
`layout.json`	Panel layout from `frame_filter.py layout` over the dense sample; always measured, `composite: false` records a single-picture video
`cover.jpg`	Front-page cover
`video.mp4`	Source for frames (may omit only if user forbids download and provides frames)
`frames/`	Dense sample, default 1 frame / 15s
`frame_scores.json`	`frame_filter.py score` over the dense sample; mandatory when the host cannot show you images
`figures/`	Selected figure assets with semantic names (`fig_01_topic.jpg`, …), overlays cropped via `bands.json`, composites cropped to one panel via `layout.json`
`figure_manifest.tsv`	Header `figure\tframe\tstart\tend\ttopic\tpanel` — one row per figure image; `panel` names the `layout.json` panel it keeps (`main`, `left`, …) or `full`
`figure_verification.txt`	Full stdout of `verify_figures.py` over all manifest timestamps
`lecture_profile.json`	Reader and source-fit profile; required fields are described below
`teaching_atoms.tsv`	Header `atom\tstatus\tevidence` — every teaching atom mapped to the notes
`numerical_claims.tsv`	Generated by `extract_claims.py extract`, filled by `extract_claims.py check --write`; header-only is valid when the lecture contains no numerical claims
`notes.tex`	Complete Chinese lecture notes
`notes.pdf`	Two-pass `xelatex` output
`verify_notes.txt`	Full stdout of `verify_notes.py`, ending in `OVERALL PASS`

Source-fit profile and reader contract (hard)

After subtitle correction and contact-sheet review, create lecture_profile.json before outlining the notes:

{
  "mode": "technical-slide",
  "audience": "capable first-time reader",
  "central_question": "What should the reader be able to explain after reading?",
  "reader_outcome": "A concrete capability, decision, or mental model",
  "visual_teaching_atoms": 24,
  "formula_teaching_atoms": 18
}

Choose exactly one mode:

technical-slide: slides, board work, formulas, code, or diagrams carry most teaching content;
conceptual-talk: a talk, interview, or discussion carries most content through claims, examples, and reasoning rather than visual mechanisms;
mixed: both modes contribute substantial teaching content.

Count visual_teaching_atoms and formula_teaching_atoms from the source, not from the draft. A visual atom is a distinct source visual that helps teach a point. A formula atom is a source equation or derivation worth preserving. Do not inflate these counts to justify more assets, and do not classify a conceptual talk as technical merely to trigger quotas.

For every mode, the hard gates are source fidelity, complete teaching-atom coverage, traceable numerical claims, clear source attribution, and a coherent reader path. The numeric density gates below are source-fit backstops. They never authorize invented equations, low-value talking-head screenshots, repetitive boxes, or synonym padding.

Density and structure gates (source-adaptive)

For a lecture of duration $T$ minutes (from metadata):

Gate vs target (do not confuse): the numbers below are hard gates (fail = no delivery). A high-quality Codex-class note often lands ~25–40% above the CJK gate; that is a target, not a second hard floor. Raising the hard floor to the measured gold value encourages padding, not more teaching atoms. (Validated on CS336 L3: $T\approx 89$ → gate CJK 6246 / figs 25; gold ~8035 CJK / 27 figs / 31 pages.)

Chinese character count in the notes.tex body (CJK unified ideographs only):
- technical-slide: at least $\max(5000,\ \mathrm{round}(70\times T))$;
- mixed: at least $\max(3500,\ \mathrm{round}(55\times T))$;
- conceptual-talk: at least $\max(2500,\ \mathrm{round}(45\times T))$. Do not count English jargon, LaTeX commands, or captions alone. These are completeness floors, not invitations to repeat the same idea.
Figures: include every distinct visual teaching atom that materially improves understanding, up to the technical target $\max(20,\ \mathrm{round}(T/3.5))$. A conceptual-talk with four real visual atoms should contain four verified figures, not twenty talking-head frames. A slide-led lecture with thirty distinct mechanisms must not stop at twenty.
Sections for $T\ge 60$: use at least 8 for technical-slide, 6 for mixed, and 5 for conceptual-talk, unless fewer reader questions produce a demonstrably clearer structure. Each major section answers one reader question and ends with \subsection{本章小结} that states the answer and prepares the next question.
Judgment boxes: use boxes only when they separate a definition, background dependency, decision rule, or failure boundary from the main flow. The technical default is 12 for $T\ge60$; mixed and conceptual-talk have no box quota. Never split continuous reasoning into boxes merely to raise a count.
Teaching atom shape (adapt, do not stamp out a template):
- state what the reader needs to understand and why it matters;
- explain the mechanism or reasoning in the order needed to follow it;
- attach source evidence: a number, example, quotation-level paraphrase, formula, code, table, or verified figure as appropriate;
- state the consequence or boundary when it changes interpretation. Use only the components that the source and the reader need. After every display formula with at least two symbols, add an immediate symbol explanation list.
No outline-only sections. If a 5-minute span of the lecture introduces a distinct mechanism, number, or design choice, it must appear as its own subsection or a clearly labeled paragraph with evidence — not a bullet in a summary list.
Coverage audit before compile (timeline): walk the timeline in 3–5 minute steps using the SRT; list any gap without corresponding prose or figure; fill gaps before xelatex.
Teaching-atom checklist (topic coverage, hard for technical lectures): Before delivery, extract a lecture-specific atom list from SRT + slides (15–40 atoms). Each atom must map to a subsection, a labeled paragraph, or a figure+caption in notes.tex. Missing atoms → expand prose; do not “pass” on CJK alone. Example atoms for an LLM-architecture lecture: Pre/Post-Norm, LN vs RMSNorm, FLOPs≠runtime, SwiGLU, serial vs parallel block, RoPE, FFN ratio, head dim, width/depth, vocab size, dropout vs weight decay, z-loss, QK-Norm, Prefill vs Decode, MQA/GQA, sliding window / interleaved / hybrid attention. Persist the checklist as teaching_atoms.tsv (atom\tstatus\tevidence).
Mechanism density (hard — L2 gold comparison 2026-07-12): CJK/fig/box gates alone are not enough. CS336 L2 first Grok pass: 5463 CJK / 8 formulas / 0 code (gate PASS, gold incomplete); after claim-driven补全: ~6450 CJK / 31 formulas / 3 code / 33 pages (matches Codex). Also require:
- Display math: for source formula atoms, preserve at least $\min(N_{\mathrm{formula\ atoms}},\ \max(10,\ \mathrm{round}(T/4)))$ blocks (\[ ... \] or equation), each with an immediate symbol itemize when ≥2 symbols appear. If the source has no formula atoms, the correct count is zero; never invent equations to satisfy a density gate.
- Numerical claims file numerical_claims.tsv (header claim\tvalue\tsource_time\tin_notes): before writing, generate it from the subtitle track plus every OCR track — the script, not the writer, decides which numbers exist in the source: python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/extract_claims.py" extract audio.srt --ocr hardsub_ocr.srt --out numerical_claims.tsv. Add rows by hand only for numbers the regex cannot see (e.g. 6PT, 12 bytes/param, I_*=295, 53.3B, peak TFLOP/s, bandwidth). After writing, run python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/extract_claims.py" check numerical_claims.tsv notes.tex --write; every row must be in_notes=yes. Topic-only atoms without numbers still FAIL if the lecture stated a number.
- Code: if slides/SRT show ≥1 non-trivial code fragment (einops, timing, AMP, …), notes must include ≥1 lstlisting (or equivalent verbatim) with the same mechanism, not a prose paraphrase only.
- Comparison tables: if the lecture contrasts ≥3 formats/ops/modes (e.g. FP32/FP16/BF16/FP8), include a compact tabular — do not leave it only as prose.
- Per-section floor (for technical-slide with $T\ge 60$): each major \section except title/appendix must have ≥ $\max(300,\ \mathrm{round}(0.7\times \mathrm{CJK}/N_{\mathrm{sec}}))$ Chinese chars and at least one of: display formula, table, code block, or ≥2 judgment boxes. A section that is only “术语澄清 + 小结” is FAIL.
- Fine-grained subsections: for each 5–8 min teaching span that introduces a distinct mechanism, prefer a dedicated \subsection (Codex L2: 42 subs vs thin notes ~35). Merging is OK only if the merged subsection still contains all numbers and derivations from both spans.
Write from evidence and a reader map, not from transcript order: Order of work after frames/manifest: (a) build numerical_claims.tsv + teaching_atoms.tsv + lecture_profile.json; (b) write one-line answers for the central question and each planned section question; (c) draft a section outline keyed to those answers and the source evidence; (d) write prose that discharges every claim in a reader-comprehensible order; (e) run the reader-first revision passes in the Phase 3 reference; (f) run source-fit density, formula, claim, compilation, and rendered-page gates.

Forbidden shortcuts

Stopping after a “skeleton PDF” that only titles topics and pastes slides.
Writing captions from section titles without reading the full-resolution frame.
Claiming high density without measuring CJK character count against the gate above.
Omitting figure_manifest.tsv / figure_verification.txt “because figures look fine”.
Merging unrelated teaching points to reduce page count.
Padding CJK with repeated slogans or synonym paraphrases that add no mechanism, formula, or decision rule.
Treating the target (~Codex measured density) as a second hard gate and stuffing filler to hit it.
Inventing formulas, examples, causal links, or certainty that the source does not support.
Adding low-information screenshots, boxes, or micro-sections only to satisfy a numeric quota.
Preserving oral repetition, self-correction, filler transitions, or Q&A order when they obstruct the teaching argument.
Giving every paragraph the same claim-list-summary rhythm; density without sentence and paragraph flow still fails.
Summary-only mechanism sections: describing Roofline / MFU / $6BP$ / checkpointing in words while omitting the lecture’s actual formulas, critical constants, and worked numerical examples.
Marking a teaching atom ok because a topic word appears, when the lecture’s number or derivation is absent.

Platform Detection

Detect the platform from the URL:

Pattern	Platform
`youtube.com`, `youtu.be`	YouTube
`bilibili.com/video/BV`, `b23.tv`	Bilibili
`x.com/<user>/status/<id>[/video/<n>]`	X/Twitter
`twitter.com/<user>/status/<id>[/video/<n>]`	X/Twitter

Adapt the acquisition workflow accordingly (see below).

For X/Twitter, preserve the exact input URL throughout acquisition. Do not shorten, canonicalize, or remove an optional /video/<n> suffix.

Workflow

Step 0 — Installation and host capability check (STOP on failure)

Resolve the absolute assets directory from the loaded SKILL.md before running helpers. Then substitute that literal directory for /ABSOLUTE/PATH/TO/lecture-to-notes/assets everywhere below before executing a command. Each fenced command may run in a fresh shell, so never rely on a path variable defined by an earlier command.

Run this loop before any download. If it prints Missing installed helper, STOP and tell the user to reinstall the skill with install_skill.sh from the repository; do not improvise replacements for the missing helper, and do not fall back to another skill.

for helper in \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/video_source.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/check_srt_health.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/clean_subs.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/transcribe_x_asr.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/correct_srt.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/llm_correct_srt.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/verify_figures.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/transcribe_whisper.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/ocr_hardsubs.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/frame_filter.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/extract_claims.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/verify_notes.py" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/prepare_cover.sh" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/notes-template.tex" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/nju_os.txt" \
  "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/glossary_nju_os.json"; do
  test -e "$helper" || { echo "Missing installed helper: $helper" >&2; exit 1; }
done

Host image input. Try to read one image (the cover, once downloaded, or any PNG) with the host's image-reading tool. If the host or model refuses (does not declare image input, "cannot read as an image"), record "vision": "no" in lecture_profile.json and follow every "host without image input" rule below; otherwise record "vision": "yes". Never write a caption as if you had seen a frame you could not open.

CRITICAL: Always use absolute paths for background commands (local ASR, video download). Claude Code's shell resets the working directory between commands. Background tasks that use relative paths will write output to the wrong location.

Recommended naming: <course_id>_<lecture_number>_<short_title>/

Example: nju_os_01_intro/, nju_os_02_app_view/, tamu_biegler_nlp/

Context hygiene (keep the transcript out of the prompt)

A 60-minute lecture is ~35k characters of subtitles; reading it whole into the conversation, then re-reading it on every later request, was the largest avoidable token cost measured on 2026-09-03 (transcript and OCR dumps of ~60k characters stayed resident for 70 requests). Rules:

Write transcript_indexed.txt (one [HH:MM:SS] text line per entry) once, then read only the window for the section being written (sed -n by line range or a timestamp filter).
Keep OCR output in files (hardsub_ocr.srt, frame_scores.json) and query them with grep/python3 for the timestamps you need; never print a whole OCR digest.
Never glob the whole workspace (**/*lecture* and similar); operate inside the working directory created for this lecture.
Background jobs: poll with a bounded wait and read only the tail of their output; when a job has produced no output for the budget in transcribe_whisper.py, treat it as failed and switch backends instead of waiting again.

Phase 1: Source Acquisition

1a. Offline Platform Detection and Bilibili Part Selection

python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/video_source.py" detect "<URL>"

This detection is offline. If it prints bilibili, enumerate the parts before any metadata, subtitle, audio, thumbnail, or video acquisition. This discovery command must not use --no-playlist, because the playlist is the information being inspected:

yt-dlp --flat-playlist --print "%(playlist_index)s\t%(title)s" "<URL>"

STOP and ask the user which part(s) to process. Do not continue until the selection is known. For each selected part, use a part-specific Bilibili URL such as https://www.bilibili.com/video/<BV_ID>?p=<n>, create a separate working directory and run the entire workflow there, and replace <URL> below with that part-specific URL before the metadata probe. Process multiple selected parts as separate runs.

1b. Metadata Inspection

python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/video_source.py" probe "<URL>" > metadata.json
# Bilibili answers an unauthenticated probe with HTTP 412; add
#   --cookies-from-browser chrome
# before the URL. See "Cookies and yt-dlp freshness" above.

Extract: platform, title, uploader, duration, thumbnail availability, and subtitle languages. For X/Twitter, pass the full original <URL> including optional /video/<n>; /ABSOLUTE/PATH/TO/lecture-to-notes/assets/video_source.py retains that URL and probes it with --no-playlist.

1c. Subtitle Acquisition (Five-Stage Fallback: Manual CC → Automatic Captions → Burned-in Subtitle OCR → Local ASR → Visual-Only)

Stage 1 — Manual CC subtitles:

# YouTube
yt-dlp --no-playlist --write-subs --sub-langs "zh.*,en.*" --convert-subs srt --skip-download "<URL>"

# Bilibili
yt-dlp --no-playlist --write-subs --sub-langs "zh-Hans,zh-CN,zh,ai-zh" --convert-subs srt --skip-download "<URL>"

# X/Twitter manual caption tracks (keep the full input URL)
yt-dlp --no-playlist --write-subs --sub-langs "all,-live_chat" --convert-subs srt --skip-download -o "x_caption.%(id)s.%(ext)s" "<URL>"

Stage 2 — Automatic captions (when no manual CC):

# YouTube
yt-dlp --no-playlist --write-auto-subs --sub-langs "en" --convert-subs srt --skip-download "<URL>"
# IMPORTANT: Clean duplicates — YouTube auto-subs repeat every line 2-3x
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/clean_subs.py" subs.en.srt --stats

# X/Twitter automatic caption tracks (keep the full input URL)
yt-dlp --no-playlist --write-auto-subs --sub-langs "all,-live_chat" --convert-subs srt --skip-download -o "x_caption.%(id)s.%(ext)s" "<URL>"

X/Twitter caption acceptance gate (mandatory):

After conversion, the deterministic template produces language-tagged candidates such as x_caption.<id>.<lang>.srt. Enumerate every candidate and run the structural health check with the duration extracted from metadata.json:

# X_CAPTION_HEALTH_BLOCK
DURATION="$(python3 -c 'import json,sys; print(json.load(sys.stdin)["duration"])' < metadata.json)"
find . -maxdepth 1 -type f -name 'x_caption.*.srt' -print > x_caption_candidates.txt
if [ ! -s x_caption_candidates.txt ]; then
  echo "No X caption candidates; continue with local ASR fallback."
else
  while IFS= read -r srt; do
    if python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/check_srt_health.py" "$srt" --duration "$DURATION"; then
      echo "Structurally healthy candidate: $srt"
    else
      echo "Rejected structurally unhealthy candidate: $srt"
    fi
  done < x_caption_candidates.txt
fi

For every candidate reported as structurally healthy, sample that specific track against both the audio and visible teaching content at 10%, 50%, and 90% of the runtime. Record the three results per candidate. Select exactly one track only after all three checks align, and persist the explicit choice:

printf '%s\n' "<one explicitly accepted x_caption path>" > selected_x_caption.txt

Do not select a merely structurally healthy track. If there are no candidates, or no candidate passes all three semantic samples, use X audio → local ASR → the existing dictionary and LLM SRT correction passes below.

External official captions may be used only when all of the following are documented:

identity proof that the caption source and X post contain the same lecture and video variant
constant-offset alignment using one fixed time shift, never independent per-segment shifts
three-point audio/visual validation at 10%, 50%, and 90% after applying that offset
provenance disclosure naming the external caption URL/provider and the applied offset

Put an external official track under the deterministic x_caption.<id>.<lang>.srt naming scheme and apply the same structural and semantic gates; provenance never bypasses validation.

Stage 3a — Burned-in subtitles (when captions are absent or rejected; needs video.mp4 from Phase 1d, so run that download first):

Bilibili uploads very often carry subtitles burned into the picture. Reading that band with OCR is faster than any speech-to-text and spells every term the way the speaker's editor did, so check before transcribing:

python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/ocr_hardsubs.py" detect video.mp4 --geometry bands.json
# has_hardsubs=true → OCR the whole band (measured ≈ 2 s per video minute on Apple silicon)
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/ocr_hardsubs.py" extract video.mp4 --out hardsub_ocr.srt --fps 1
RUNTIME="$(python3 -c 'import json,sys; print(json.load(sys.stdin)["duration"])' < metadata.json)"
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/check_srt_health.py" hardsub_ocr.srt --duration "$RUNTIME"

bands.json also records the navigation strip and subtitle band for Phase 2 cropping. When hardsub_ocr.srt passes the health check and a 10% / 50% / 90% spot-check against the audio, copy it to audio.srt and skip local ASR. When it is partial (subtitles only in some chapters), still keep it: Stage A below turns it into a correction glossary for the ASR track.

Stage 3b — Local speech-to-text (no captions and no usable hard subs):

For Chinese, English, or mixed zh/en lectures, prefer the local X ASR INT8 model when it is already available or can be cached once. It is substantially faster on CPU and returns token timestamps, but it is not trusted merely because it completed. Preserve the raw SRT, run check_srt_health.py, and compare audio/visible content at 10%, 50%, and 90%. If X ASR is unavailable or any gate fails, use Whisper. Use Whisper directly for languages outside the selected X ASR model's documented scope.

The tested model is the official sherpa-onnx release asset sherpa-onnx-x-asr-zipformer-transducer-zh-en-punct-int8-2026-06-03.tar.bz2 (SHA-256 5d02c36d7b44e886b7c8f0d8e051f8713acab96c264bb6ef9e718be39a6a2224). Keep downloaded models outside Git. Install the optional runtime with python3 -m pip install "numpy>=1.24" "sherpa-onnx>=1.13.6", extract the model, then run:

yt-dlp --no-playlist -x --audio-format wav -o "audio.%(ext)s" "<URL>"
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/transcribe_x_asr.py" audio.wav \
  --model-dir "/ABSOLUTE/PATH/TO/CACHED/X-ASR-MODEL" \
  --output audio.srt --report x_asr_report.json
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/check_srt_health.py" \
  audio.srt --duration "<VIDEO_DURATION_SECONDS>"

The offline X ASR encoder requires bounded chunks. The helper normalizes audio with ffmpeg, selects low-energy boundaries, keeps every chunk at or below 30 seconds, and turns token timestamps into non-overlapping SRT cues. Do not pass a whole long lecture directly to the model.

Whisper fallback (X ASR unavailable, a gate failed, or the language is out of scope):

yt-dlp --no-playlist -x --audio-format wav -o "audio.%(ext)s" "<URL>"
# IMPORTANT: Use absolute paths — the shell may reset cwd between commands.
# IMPORTANT: ffmpeg must be on PATH — every Whisper backend reads audio through it.
WORKDIR="$(pwd)"
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/transcribe_whisper.py" "$WORKDIR/audio.wav" \
  --workdir "$WORKDIR" --language zh --budget-minutes 10 \
  --initial-prompt "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/nju_os.txt"  # Optional: domain glossary

transcribe_whisper.py selects the backend (mlx-whisper on Apple silicon, faster-whisper elsewhere, openai-whisper last), moves model caches under the workdir when ~/.cache is not writable (sandboxed hosts), streams progress, and exits 3 when no segment has appeared within --budget-minutes. Exit 3 means switch --backend or use a smaller --model; never wait past the budget for a silent process, and never run the bare whisper CLI in the background without this budget. Measured 2026-09-03 on the same 61-minute audio: CPU whisper --model medium produced nothing in 85 minutes; mlx-whisper large-v3-turbo finished in 6 minutes.

Whisper initial_prompt (strongly recommended for technical lectures): Point --initial_prompt at a plain-text file enumerating domain terms (syscalls, APIs, speaker names, course-specific jargon). See /ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/nju_os.txt for a working example. This dramatically reduces same-sound errors like "PASSNAME" instead of "pathname" or "SAM" instead of "sum".

Post-ASR SRT correction passes:

# Stage A0 — derive the glossary from the video itself when hard subs exist (even partial)
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/ocr_hardsubs.py" glossary hardsub_ocr.srt audio.srt \
    --out glossary_auto.json --min-count 2
# Review glossary_auto.json: keep term corrections (刻石→刻蚀, 光眼膜→光掩膜), delete
# pairs that only reflect OCR noise, then apply it with Stage A.

# Stage A — fast dictionary-level fix (wrong → right pairs)
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/correct_srt.py" audio.srt \
    -g glossary_auto.json -o audio_corrected.srt --stats
# Course glossaries still apply, e.g.
# -g "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/glossary_nju_os.json"

# Stage B — slow LLM + multimodal fix (uses Claude Code CLI, no API key needed)
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/llm_correct_srt.py" \
    --srt audio.srt --frames frames/ --out corrected.srt \
    --context "南京大学操作系统原理，讲师 jyy"

Stage A is essentially free and catches common wrong characters. Stage B is expensive (one Claude call per ~90s of audio) and only worth running for notes you plan to publish.

Stage 4 — Visual-only mode (when audio quality is unusable): Skip subtitles. Use dense frame sampling (fps=1) and rely entirely on visual content.

1d. Video and Cover Download

# Cover image (may be webp/png/jpg depending on platform)
yt-dlp --no-playlist --write-thumbnail --skip-download -o "cover" "<URL>"
# Convert to jpg for xelatex compatibility
bash "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/prepare_cover.sh" .

# Video (for frame extraction)
yt-dlp --no-playlist -f "bestvideo+bestaudio/best" --merge-output-format mp4 -o "video.mp4" "<URL>"

# Bilibili 1080P+ (if user has logged in):
# yt-dlp --no-playlist --cookies-from-browser chrome -f "bestvideo+bestaudio/best" -o "video.mp4" "<URL>"

For every X/Twitter thumbnail, audio, and video command above, <URL> must be the unchanged input URL, including an optional /video/<n>, and --no-playlist must remain present. The same rule applies to X metadata probing as described in Phase 1b.

Phase 2: Frame Extraction and Full-Frame Selection

Use dense extraction, contact-sheet review, and full-frame verification:

Stage 1: Dense frame extraction by chapter

mkdir -p frames
# Extract 1 frame every 15 seconds per chapter
ffmpeg -ss <start> -to <end> -i video.mp4 -vf "fps=1/15" frames/ch<N>_%03d.png

Stage 2: Frame selection (no automatic cropping of slide content)

Use the original full frames directly. Do NOT crop slide or board content by guessing which region matters — heuristic region cropping is unreliable (it misidentifies blackboard content as "low information" regions). Two crops are permitted, because both remove geometry measured from the video itself rather than content judged unimportant:

Overlays — the burned-in subtitle band and the static navigation strip in bands.json, so a subtitle line never sits on top of a diagram in the PDF.
Composite panels — many recordings place a camera feed and a screen capture side by side in one picture (NJU GSE 2026: 1280×410, camera left, slides right; CMU 11-768: 1280×720 slides beside a 640×360 camera). Printed at \textwidth, such a frame gives the slides only 57–67% of the page width and their text becomes unreadable. layout.json records the panels once per video, and each figure keeps the panel that carries its teaching content.

# bands.json comes from Phase 1 Stage 3a; without it, measure from the dense sample:
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/frame_filter.py" bands frames/*.png --json bands.json
# Measure the panel layout once per video, then open the preview before cropping:
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/frame_filter.py" layout frames/*.png \
    --json layout.json --preview layout_preview.png
# Apply to every selected figure (never to contact sheets):
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/frame_filter.py" crop frames/ch2_031.png \
    --out figures/fig_07_cz_diagram.jpg --bands bands.json
# In a composite video, add the panel the figure keeps:
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/frame_filter.py" crop frames/ch2_031.png \
    --out figures/fig_07_cz_diagram.jpg --bands bands.json --layout layout.json --panel main

Read layout.json before selecting figures:

composite: false — the video is one picture (full-screen slides, camera, or screen); crop overlays only. When warnings names a panel that shows in only part of the frames, the recording switches layouts: open the preview (grey candidate boxes) and record the panels with --box. A layout present in fewer than half of the sampled frames is not detected at all; the contact sheet shows it, and --box records it.
composite: true — open layout_preview.png. main is the largest screen-shaped panel (16:9, 16:10, or 4:3) and was the slide panel in all three composites measured on 2026-09-13; left/right/top/bottom hold what remains beside it. When a box misses the slides, read the edges off the preview's pixel ruler and record them by hand: frame_filter.py layout frames/*.png --box main=X0,Y0,X1,Y1 --box left=X0,Y0,X1,Y1 --json layout.json. A consistency below 0.9 lists unmatched_frames whose layout differs (a full-screen demo, a transition); view those frames before cropping them. Each entry in warnings (fewer than 20 sampled frames or distinct pictures, a remaining panel larger than main, frames of another size) means the same: confirm the boxes on the preview before any crop.

Decide per figure after reading the full-resolution frame:

What the frame teaches	Figure files	`panel` in the manifest
Slides, code, or a screen demo	one crop of the screen panel	`main`
Board writing or a physical demo	one crop of the camera panel	`left`, `right`, …
Slides and board writing that adds to them	two crops stacked in one `figure`	one row per crop
A layout the preview does not cover	the full frame, with the reason in `topic`	`full`

Never place a side-by-side composite at \textwidth. When both panels teach, stack them so each prints as large as the page allows:

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{figures/fig_22_attention_slide.jpg}\\[4pt]
\includegraphics[width=0.62\textwidth]{figures/fig_22_attention_board.jpg}
\caption{课件与板书共同说明的内容\vtag}
\end{figure}
\srcnote{00:47:30--00:47:45}

Host without image input (mandatory fallback): when Step 0 recorded "vision": "no", contact-sheet review is impossible, so text signals decide:

Score the dense sample: python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/frame_filter.py" score frames/*.png --json frame_scores.json.
Never select a frame flagged talking_head; the 2026-09-03 run shipped three presenter-only frames as "figures" because nothing rejected them.
Read on-screen text for candidates with OCR (ocr_hardsubs.py extract video.mp4 --band 0:1 --fps 0.2 --out screen_text.srt) and select only frames whose OCR text names a labelled diagram, formula, table, or process, not merely the subtitle line.
Write captions from that OCR text plus the subtitle at the timestamp, and say so in figure_manifest.tsv (topic column ends with [ocr]).
When layout.json reports composite: true, crop every figure to main and check that OCR of the crop still reads the slide text; board writing cannot be judged without vision.

Future direction: Use multimodal LLM (e.g., Claude Vision API) to classify frames and decide cropping per-frame. For now, use full frames (or one measured panel of a composite) and select the best ones manually via contact sheet review.

Stage 3: Contact sheet review

# Generate contact sheets per chapter (keeps montage size manageable)
# For chapters: montage per chapter directory
magick montage frames/ch<N>_*.png -tile 5x -geometry 384x216+2+2 contact_ch<N>.png

# For unchaptered videos with many frames (>100):
# Split into batches of 50 to avoid montage failures
for i in $(seq 1 50 $(ls frames/*.png | wc -l)); do
  ls frames/*.png | tail -n +$i | head -50 | xargs magick montage \
    -tile 5x -geometry 384x216+2+2 contact_batch_${i}.png
done

Review contact sheets to select the best frames. Criteria:

Pick the final fully-populated state of progressive reveals
Prefer the frame with the most complete and readable information
Drop repetitive or low-information frames
Keep every frame that teaches something distinct

Stage 4: Figure Verification (CRITICAL — prevents figure-text mismatch)

Problem: ~40% of figures in early versions had mismatched timestamps, captions, or surrounding text. Root cause: timestamps were estimated from frame numbers, captions were written from "what should be here" rather than "what IS here", and content was inferred from section structure rather than verified against the actual frame.

Before writing ANY figure into LaTeX, perform this three-way verification:

For each candidate figure:

Compute exact timestamp: chapter_start_seconds + (frame_number - 1) × 15

Cross-reference with subtitle: Find the Whisper/CC subtitle entry at that timestamp. Read 2-3 subtitle lines before and after. This tells you what the speaker is ACTUALLY saying at the moment of that frame.

# Quick lookup: what was being said at timestamp T?
import re
target_sec = 285  # example: 4:45
for entry in srt_entries:
    if abs(entry.start - target_sec) < 15:
        print(f"{entry.start}s: {entry.text}")

Read the frame at full resolution: Use the Read tool to view the actual frame image. Identify the ACTUAL text/diagram/code shown on screen — not what you think should be there. With "vision": "no", substitute the OCR text of that frame (Stage 2 fallback) and never describe visual elements the OCR did not report.
Three-way match check: Verify that ALL THREE align:
- ✅ Frame visual content (what the slide/screen actually shows)
- ✅ Subtitle content (what the speaker is saying at that moment)
- ✅ Your caption + surrounding text (what you plan to write)
If any mismatch: either pick a different frame, adjust the timestamp, or rewrite the caption.
Persist audit artifacts (mandatory):
- Append every accepted figure to figure_manifest.tsv with columns figure, frame, start, end, topic, panel (topic in Chinese, concrete; panel as chosen in Stage 2, empty only when layout.json reports composite: false).
- Run verify_figures.py on all start times and save full stdout to figure_verification.txt. Do not delete these files after compile.

Common failure modes to watch for:

Failure	Example	Fix
Frame shows slide A, caption describes slide B	Frame shows "Language" section but caption says "Transformer"	Read frame at full res before writing caption
Timestamp off by 1-2 minutes	@07:00 claimed but actual content is at @09:00	Cross-check with subtitle timestamps
Caption describes the section topic, not the frame	"Scaling Law 幂律关系" but frame shows a chess board	Write caption from frame content, not section title
Frame is transitional (between slides)	Half old slide, half new slide	Pick a frame 15s earlier or later
Too few figures / outline-only notes	15 figures + 3k CJK chars for an 86-min lecture	Enforce density gates in "Non-negotiable quality bar"

Phase 3: Writing

Reader-first prose reference (mandatory)

Read references/reader-first-writing.md after the final subtitle track, teaching atoms, numerical claims, and verified figures are ready. Use it to build the reader argument map, draft the notes, and run the final prose passes. The reference is self-contained; using this skill must not depend on another installed writing skill or a network call.

Teaching Content Rules

Include: title, chapters, on-screen diagrams/formulas/tables/code, subtitle explanations, speaker emphasis.

Exclude: greetings, small talk, sponsorship, channel logistics, 一键三连, 关注投币, closing pleasantries.

Preserve: speaker's closing discussion when it carries teaching value (synthesis, limitations, advice, open questions).

Writing Rules

Chinese by default unless the user requests otherwise. Write authored teaching prose, not line-edited subtitles.
Organize with \section{} / \subsection{} around reader questions and prerequisites. Reconstruct the teaching flow; do not mirror subtitle or Q&A order.
Before outlining, complete lecture_profile.json and write one-line answers for the central question, reader outcome, and each planned section question.
Start from /ABSOLUTE/PATH/TO/lecture-to-notes/assets/notes-template.tex. Fill metadata and replace the body block.
Front page cover: use the video's original cover image, visually distinct from in-body figures.
Source voice: distinguish established background, the speaker's claim or forecast, and the note writer's synthesis. Preserve uncertainty and qualifiers; do not silently strengthen a claim while translating or compressing it.
Paragraph flow: give each paragraph one main job. Lead with the answer or mechanism, place evidence next to the point it supports, and end with the consequence or boundary. Do not force every paragraph into the same template.
Terminology: introduce plain meaning before acronyms, stage labels, variants, or project-specific terms. Keep one stable term for one concept.
Figures: use full frames, or one measured panel of a composite. Use each distinct visual teaching atom that materially improves understanding. Every figure MUST pass the Stage 4 three-way verification before being written into LaTeX. Never write a caption from section context alone.
No figures inside boxes. importantbox, knowledgebox, warningbox must not contain \includegraphics.
Math: use display math only for a source formula or a faithful derivation needed to explain it. Follow each display with an immediate symbol explanation list when at least two symbols appear. Never invent an equation to make conceptual material look technical.
Code: wrap source-grounded code in lstlisting with a descriptive caption.
Box strategy — use boxes only when they improve the teaching signal:

importantbox: core concepts, definitions, key mechanisms, theorem-like statements
knowledgebox: background, history, design tradeoffs, terminology, analogies
warningbox: common mistakes, hidden assumptions, pitfalls, causal confusions

Every major \section ends with \subsection{本章小结}. Answer the section's reader question and create a natural handoff; do not repeat its subsection list. Add \subsection{拓展阅读} only when the source or verified external material supports it.
Final section \section{总结与延伸}:
- Speaker's substantive closing (no sign-off fluff)
- Your structured distillation of core claims and mechanisms
- Cross-section synthesis, conceptual compression
- Concrete takeaways, open questions, next steps
No [cite] placeholders, invented citations, or unattributed external facts.

Figure Time Provenance

Every figure from a video frame must have a same-page footnote with the source time interval:

\begin{figure}[H]
\centering
\includegraphics[width=\textwidth]{figures/fig_xxx.png}
\caption{描述\vtag}
\end{figure}
\srcnote{00:12:31--00:12:46}

\vtag and \srcnote are defined in the template (they expand to \protect\footnotemark / \footnotetext{视频画面时间区间：…}); verify_notes.py accepts both spellings.
Time intervals come from subtitle alignment, not chapter-level guesses.
Use [H] or stable placement to keep figure and footnote on the same page.
Units: write 1150\,\degC, 20--50\,\um, 14\,\nm, 5.43\,\angstrom with the template macros. Hand-written $^\circ$\mathrm{C}$ and \mathrm{\AA} produced an error cascade and seven invalid in math mode warnings on 2026-09-03; verify_notes.py fails on those warnings.

Visualization

For concepts that screenshots and prose can't explain clearly, add visualizations:

LaTeX-native: TikZ / PGFPlots
Pre-generated: Python matplotlib scripts

Use for: process flows, architecture layouts, scaling-law plots, comparison charts. No decorative graphics.

Phase 4: Compilation, Reader-First Gate, Density Gate, and Delivery

xelatex -interaction=nonstopmode notes.tex && xelatex -interaction=nonstopmode notes.tex

Pre-delivery gate (one command; run and report its output)

python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/extract_claims.py" check numerical_claims.tsv notes.tex --write
python3 "/ABSOLUTE/PATH/TO/lecture-to-notes/assets/verify_notes.py" --workdir . --overfull-pt 10 | tee verify_notes.txt

verify_notes.py runs every mechanical check in one pass and prints one PASS/FAIL line each, ending with OVERALL PASS or OVERALL FAIL:

density — lecture_profile.json mode and atom counts against metadata.json duration: CJK, figure, section, box, and display-math floors; teaching_atoms.tsv all ok; numerical_claims.tsv all in_notes=yes;
artifacts — figure_manifest.tsv, figure_verification.txt, audio.srt non-empty;
layout — every figure_manifest.tsv image against layout.json, which must exist: in a composite video it names a panel and keeps that panel's pixel size (crop, never resize); when layout.json names partial candidates, no figure is wider than 2:1 without panel=full;
compile log — no ! errors, no Missing character, no undefined references, no invalid in math mode, no Overfull \hbox above the threshold;
figures — every \includegraphics file exists, every video frame has a time footnote, and (via pdftotext) the footnote renders on the same page as its caption.

If it prints OVERALL FAIL, fix the named item and rerun. Do not add formulas, figures, boxes, or prose that the source and reader do not need, and do not re-implement any of these checks by hand: on 2026-09-03 the ad-hoc equivalents took 31 requests and half of the run's input tokens.

Reader-first prose check (manual and mandatory)

After the two-pass compile:

Extract the rendered text with pdftotext notes.pdf rendered_notes.txt when available.
Read the opening, every section opening, every 本章小结, all figure/table captions, and the final synthesis in sequence.
Run the seven revision passes and final checklist in references/reader-first-writing.md.
Verify that each strong claim is traceable, each speaker opinion is attributed where needed, terms appear after plain meanings, and adjacent paragraphs hand off naturally.
Inspect the final diff after prose revision to ensure no number, qualifier, timestamp, label, or claim boundary changed accidentally.

Phrase searches may identify candidates such as repeated “值得注意的是” or “不是……而是……”; they cannot pass or fail the prose by themselves. A clean compile and high density counts do not compensate for transcript-like, repetitive, or inflated prose.

Delivery Checklist

Assets

/ABSOLUTE/PATH/TO/lecture-to-notes/assets/notes-template.tex: LaTeX template
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/video_source.py: YouTube / Bilibili / X/Twitter URL detection and metadata probe
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/check_srt_health.py: Structural health gate for downloaded X/Twitter SRT tracks
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/clean_subs.py: YouTube auto-subtitle deduplication
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/transcribe_x_asr.py: optional local X ASR zh/en transcription with bounded chunks and SRT timestamps
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/correct_srt.py: Whisper SRT dictionary-level fix (fast, data-driven)
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/llm_correct_srt.py: Whisper SRT LLM + multimodal segment-level fix (slow, uses Claude Code CLI — no API key needed)
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/verify_figures.py: Three-way figure verification (timestamp × subtitle × frame)
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/transcribe_whisper.py: Whisper with platform-aware backend selection (mlx / faster / openai), workdir model caches, and a no-progress time budget
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/ocr_hardsubs.py: Burned-in subtitle detection, band OCR to SRT, overlay geometry, and Whisper glossary derivation
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/frame_filter.py: Overlay band and composite panel measurement, overlay and panel crops, plus talking-head scores for hosts without image input
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/extract_claims.py: Builds numerical_claims.tsv from subtitle/OCR tracks and checks every number against notes.tex
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/verify_notes.py: One-shot pre-delivery gate (density, artifacts, compile log, figure files, same-page footnotes)
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/prepare_cover.sh: Cover image format conversion (webp/png → jpg)
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/smart_crop.py: Slide-region detector; optional and experimental, while production uses full frames
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/nju_os.txt: Whisper --initial_prompt glossary example
/ABSOLUTE/PATH/TO/lecture-to-notes/assets/whisper_prompts/glossary_nju_os.json: Dictionary of wrong → right pairs for /ABSOLUTE/PATH/TO/lecture-to-notes/assets/correct_srt.py

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