paces

Turn instructional media into structured, interactive learning material. Put it through its paces.

Take a video of someone teaching something — a dance routine, a kata, a recipe — plus, optionally, notes and a steering prompt. paces segments it into named steps, builds a structured step document (an AST for step-by-step instruction), and renders that into learning material: a practice page with counts and deep links today, other guides later.

pip install paces

Quick example

from paces import segment, to_document, render_html

seg = segment(
    "https://youtu.be/q_TUyxUhoEw",
    steps=[
        ("Mise en place", 2),
        ("Pas pieds pointe et ronde", 6),
        ("Soleil avec les bras", 4),
        ("Déhanchés", 8),
    ],
    grid={"unit": "eight", "subdivisions": 8, "tempoBpm": "129.2", "origin": "51.2"},
)
doc = to_document(
    seg,
    doc_id="que-calor",
    title="Chorégraphie Que Calor",
    source="https://youtu.be/q_TUyxUhoEw",
)
open("page.html", "w").write(render_html(doc))

The page lists every step with its counts, links each one back into the video (both the at-tempo run-through and the slow breakdown, when both are known), and — because the document carries a metric grid — includes a count-along transport that paces you through the routine at the measured tempo.

Same thing from the shell:

paces segment VIDEO_URL --steps steps.json --grid grid.json --output seg.json
paces to-document seg.json --source VIDEO_URL --title "My routine" --output document.json
paces suggest-excerpts document.json --output document.json   # mark each block's loop window
paces derive document.json --media routine.mp4   # real loop clips + gifs + posters
paces render document.json --output page.html

derive (pip install paces[media]) cuts a loopable mp4, a palette-quality gif and a poster for every excerpt-bearing span, writes them to media/ next to the document, and the practice page embeds them as looping clips. Crop recipes persist in a hand-overridable document.recipes.json sidecar; the subject_locator= seam (default: no crop) is where pose-based auto-crop plugs in. Design record: docs/adr/0005-media-derivation.md.

How it thinks

Analysis and rendering are separate phases with a serialisable document between them — like a parser emitting an AST and a backend interpreting it. Renderers depend on the document, never on the analyser.

Segmentation is a seam, not a stage. segment(media, segmenter=...) — segmenters are registered capabilities, the default follows from what is present, and "the user typed the boundaries" is a first-class segmenter, not a fallback. A segmenter that cannot name steps returns honest unnamed boundaries (flags: ['naming-abstained']) rather than inventing names.

The document keeps what the learner actually counts. A dance step lasts "4 eights", not "14.86 seconds" — seconds are derived from the metric grid (tempo + origin), never stored. A step can have several source spans (the run-through and the breakdown are the same step seen twice). Uncertainty is content (OpenQuestion), and human edits are protected from regeneration (Lock).

The pieces

Status

Young and moving. The document schema is validated by round-tripping a real proof of concept (an interactive dance-practice page) through it — see tests/test_roundtrip_poc.py. Media derivation (auto-cropped looping clips), intrinsic segmenters (scene/beat/speech detection), and the evidence layer are designed (see docs/) and arrive next.