Imported from ahkamboh/agent-caption (SKILL.md). Install upstream with npx skills add ahkamboh/agent-caption. Copyright stays with the author.

agent-caption — SKILL

You are an AI captioning agent. This repo adds accurate, perfectly-timed captions/subtitles to any video or audio, in any language (English by default), for both speech and music, with first-class Hinglish / code-switched support. Runs on Windows, macOS, and Linux.

Repo: https://github.com/ahkamboh/agent-caption — clone: git clone https://github.com/ahkamboh/agent-caption This file, raw (for chat agents to fetch): https://raw.githubusercontent.com/ahkamboh/agent-caption/master/SKILL.md

Core principle: words come from speech recognition, but TIMING comes from forced alignment on the waveform — never raw ASR timestamps. So captions never drift early/late, even when the transcript has spelling errors.

One-time setup (Windows / macOS / Linux)

Requires: Python 3.10+, ffmpeg, ~4 GB disk. (torch ships wheels through 3.14; if whisperx lacks a wheel on a brand-new Python, use 3.11–3.12.)

python setup.py        # cross-platform: creates ./.venv-whisperx and installs everything
# macOS/Linux convenience: bash setup.sh      ·      Windows convenience: setup.bat

ffmpeg: macOS brew install ffmpeg · Ubuntu sudo apt install ffmpeg · Windows winget install ffmpeg. Models (Whisper large-v3 — the default — MMS_FA, whisperX) auto-download on first run.

Easiest path — the one-command wrapper (PREFER THIS)

For "caption this video" requests, just run the top-level wrapper. It does transcribe → align → burn with auto-proportioned captions (correct size/spacing/alignment for any resolution), and falls back to a libass-free renderer (Pillow + overlay) if the user's ffmpeg has no subtitle support:

python caption.py <video>                                  # English, clean, proportioned -> <video>.captioned.mp4
python caption.py <video> --hinglish --style tiktok        # Hinglish + TikTok box
python caption.py <video> --lang ur --pos center --size 6  # other language / placement / size
python caption.py <video> --srt                            # just write a .srt (no burn)
python caption.py <video> --from-srt FILE                  # burn an existing .srt, proportioned

15 styles (bundled fonts): clean (default) bold hormozi green beast impact bebas tiktok pill boxed yellow neon gradient minimal subtitle. Drop to the individual scripts below only when you need the raw transcript, custom rendering, or translation.

Model: large-v3 is the DEFAULT (best accuracy) — no flag needed; setup.py pre-fetches it. Want speed on clean English? add --fast (small model). --accurate still exists but is redundant now. The Hinglish path always uses large-v3.

Custom style — build ANY look the user describes. Map the user's words to these flags (they override --style):

• --font <name|path> — a bundled font (Poppins-Bold, Poppins-Black, Anton-Regular, BebasNeue-Regular, ArchivoBlack-Regular) or any .ttf path
• --fill #hex (text colour) · --outline #hex · --box #hex|#hexAA|none (pill behind text)
• --caps / --no-caps · --gradient "#hex,#hex,#hex" · --glow · --ow N (outline thickness)
• --size <pct> (height) · --pos bottom|center|top

Example — user says "hot-pink caps in a dark rounded box, big, Anton font" →

python caption.py v.mp4 --fill "#ff3da6" --box "#10141ae0" --caps --font Anton-Regular.ttf --size 6

To make a brand-new named style permanent, add an entry to the STYLES dict in caption.py.

Accuracy — kill mis-heard words (free, no tokens, no human glance)

Forced alignment makes timing correct by construction. The only thing it can't fix is whether a word was heard right ("their"/"there", names, slang). Three free levers push that toward zero:

• --glossary "..." — feed names/brands/slang (a list or a .txt path). Biases the ASR's initial-prompt so proper nouns stop being mis-heard. Biggest single win on names. One-time list.

  python caption.py talk.mp4 --glossary "Xaibridge, Hinglish, Xotion, Kamboh"
  

• --script FILE — if the correct words already exist (lyrics, a script), pass them. The tool skips ASR entirely and only force-aligns the given words → 100% content accuracy.

  python caption.py song.mp4 --script lyrics.txt
  

• --grammar — offline homophone/grammar pass over the captions ("their"→"there", "your"→"you're"). Runs LanguageTool locally (no upload, no tokens); timing is never touched. Optional dependency — enable once with python setup.py --grammar (needs Java/JRE 8+); skips cleanly if absent.

  python caption.py talk.mp4 --accurate --glossary names.txt --grammar    # stack them
  

Bang-for-buck order: --glossary (names) → --script (when text exists) → --grammar (homophones). The base model is already large-v3 by default. The only residue these can't catch needs a human or an LLM — that's the one cost worth spending tokens on, and only on the flagged words, not the whole transcript.

Songs — isolate the vocals first (--content)

For music, separating the vocal stem before ASR makes lyric transcription far cleaner (Whisper stops fighting the backing track). It's conditional — pointless/slow on clean speech — so it's gated:

python caption.py song.mp4 --content music                 # force vocal isolation (demucs)
python caption.py clip.mp4                                  # --content auto (default): detects music, isolates only then
python caption.py podcast.mp4 --content speech              # never isolate (or --no-isolate)
python caption.py song.mp4 --content music --script lyrics.txt   # cleanest songs: known words, timed on the vocal stem

auto runs a quick music/speech probe and isolates ONLY when it hears music — so podcasts/interviews are never slowed. Burning always uses the original video; only what we transcribe/align is the stem. Needs demucs (installed by python setup.py); falls back to the original audio if absent.

Always run scripts with the venv's python — written here as PY:

• macOS / Linux: PY = ./.venv-whisperx/bin/python
• Windows: PY = .venv-whisperx\Scripts\python

The scripts

• transcribe.py — Whisper → word-level transcript (English default; --lang xx for others).
• align.py — forced alignment for frame-accurate timing. --code-switch for Hinglish/mixed.
• cs_transcribe.py — code-switch-robust word recognition (per-segment language ID).
• mms_align.py — universal MMS_FA forced aligner (1100+ languages, any script).
• validate_timing.py — timing sanity-check used by the code-switch path.
• export-subs.py — transcript → .srt + .vtt.
• multilang-subs.py — translate subtitles into many languages, offline.
• grammar_fix.py — offline homophone/grammar fix on an .srt (timing preserved); used by --grammar.
• isolate_vocals.py — Demucs vocal isolation for songs + a music-vs-speech probe; used by --content.
• qa.py — fast deterministic QA gate (cue sanity + ffprobe output check) → work/<base>.qa.json. Runs automatically after every burn; --strict makes a broken output exit non-zero.
• footprint.py — renders <base>.footprint.svg: a one-glance caption-health map (each word colored by forced-align confidence + gap markers + verdict). Auto-runs after each caption; --no-footprint to skip.
• benchmark.py — measure the real accuracy gain: WER of the old default (small) vs the new default (large-v3 [+ Demucs]) against a reference transcript. PY scripts/benchmark.py CLIP --ref truth.txt [--music] [--hinglish].
• caption.py — all-in-one styled captions (karaoke/word/line; Hormozi/TikTok/neon…) → animated captions.js.

Caption a video (the main job) — default language = English

For another language pass --lang <code>. For Hinglish / mixed use --code-switch.

1) Frame-accurate word timings

Speech (talking, podcast, UGC):

PY scripts/align.py INPUT --lang en --out work/transcript.json

Hinglish / mixed Hindi-English (or any code-switched audio):

PY scripts/align.py INPUT --code-switch --dual hi en --out work/transcript.json

Any other language (Urdu, Spanish, Arabic, Chinese, …):

PY scripts/align.py INPUT --lang ur --out work/transcript.json

2a) Subtitles (.srt / .vtt) + burn into the video

PY scripts/export-subs.py work/transcript.json --out work/subs
ffmpeg -i INPUT -vf "subtitles=work/subs.srt" -c:a copy captioned.mp4

The .srt is also perfect to upload to YouTube directly (selectable + SEO).

2b) Styled / animated captions (Hormozi, TikTok, karaoke — short-form)

PY scripts/caption.py INPUT --lang en --content speech --style karaoke --preset hormozi --out out/

Presets: hormozi, beast, pill, neon, gradient, minimal, tiktok. Use --content music for songs/lyrics. See docs/caption-styles.md.

3) Translate captions into any language (optional)

PY scripts/multilang-subs.py work/subs.srt --to "hi,ur,es,fr,ar,de" [--burn INPUT]

Rules (baked in — do not override)

• Timing is ALWAYS forced alignment, never raw ASR timestamps (no lead/lag).
• Default caption language = English; pass --lang for anything else.
• Music/singing → --content music; speech/talking → --content speech.
• Run every script with PY (the venv python) so it has whisperx/torch.
• Before delivering, spot-check a few captions at their word onsets to confirm sync.

Language codes

en (default), hi, ur, pa, es, fr, de, pt, ar, zh, ja, ko, ru, it, tr, id, nl, pl, uk, … — Whisper supports ~99 languages; MMS_FA aligns 1100+.