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Wjs Syncing Multicam

Community
jianshuo
wjs-syncing-multicam

Use when the user has 2+ video / audio recordings of the same event captured by different devices (cameras, phones, separate audio recorders) and wants them aligned to a single common timeline. Outputs only a lightweight `.sync.json` sidecar per input — original files are never re-encoded. Triggers — "多机位同步", "对齐这几个机位", "match camera timelines", "sync these angles", "audio drift between cameras", "separate audio recorder", "Riverside / Zoom recording that needs to line up".

Overview

Publisherjianshuo
Repositoryclaude-skills
Skill namewjs-syncing-multicam
Stars
137
Forks
22
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by jianshuo on GitHub. Read the source before you install it.

Installation

Install the Wjs Syncing Multicam AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/jianshuo/claude-skills/tree/main/wjs-syncing-multicam
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jianshuo/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/wjs-syncing-multicam .claude/skills/wjs-syncing-multicam
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wjs Syncing Multicam in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wjs Syncing Multicam on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wjs Syncing Multicam is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

wjs-syncing-multicam

Compute a single time offset for each multi-source recording of the same event using audio cross-correlation, and emit a .sync.json sidecar next to each original. Originals are never modified, copied, or re-encoded. Downstream tools use -itsoffset to apply the offset at consume time.

Setup & commands

The implementation lives in the open-source polysync pip package (https://pypi.org/project/polysync/ · https://github.com/jianshuo/polysync) — this skill no longer ships its own scripts. Ensure it's installed, then drive it via its CLI:

bash
python3 -m pip install -U polysync      # needs ffmpeg/ffprobe on PATH

polysync sync   REFERENCE SOURCE        # align SOURCE to REFERENCE, write sidecars
polysync sync   REFERENCE SOURCE --partial   # source covers only part of the session
polysync verify REFERENCE SOURCE SOURCE.sync.json   # independent residual check

Run one polysync sync per non-reference angle (reference first, same reference each time). The sections below document the algorithm, the sidecar schema, and the gotchas baked into the package — read them to interpret output and choose flags.

Design principle — sidecar over re-encode

Earlier versions of this skill produced *_synced.MOV files by trimming + re-encoding to bake the offset into the file. We removed that:

  • Disk — a 75-min 4K shoot from 3 cameras is 60+ GB. Re-encoded synced copies double that for no information gain.
  • Quality — every re-encode is lossy. The originals are the source of truth; sidecars are reversible metadata.
  • Speed — _synced.MOV generation took 10+ min per file on Apple Silicon; sidecar emission takes seconds.
  • Composability — any downstream tool (polysync edit, NLE import, ffmpeg one-liners) reads the sidecar and applies the offset itself. No tool-specific file format lock-in.

When NOT to use

  • Single-camera footage — nothing to sync to. For splitting one source into clips, use video-segmentation.
  • Sources already aligned in an NLE timeline — don't fight the editor.
  • For the auto-edit / cut / PiP rendering step that comes AFTER sync, use wjs-editing-multicam (consumes these sidecars).

Why envelope-based, not raw waveform

Raw PCM cross-correlation gives weak peaks and false matches when the two mics have different gain / room response — i.e., almost always with a secondary cam. The log-energy envelope captures dialogue and music dynamics, which both mics hear regardless of frequency response. Don't skip the envelope step — it's the entire reason this skill is robust at low SNR.

Algorithm

  1. Extract mono PCM at 8 kHz, 16-bit from each input. The audio stream is auto-selected by loudness (loudest_audio_stream): probe each 0:a:N over a 60 s mid-file window and pick the highest mean volume. Multi-track pro cameras break a naive 0:a:0 — Sony FX6 MXF clips carry 4 mono PCM tracks and routinely leave a:0 / a:1 dead (~-90 dB) with the room mic on a:2 / a:3; correlating the silent track fails to sync. Single-stream inputs (most MP4 cams) short-circuit to a:0.
  2. Log-energy envelope at 100 Hz (10 ms hop, 50 ms window). High-pass with a 2nd-order Butterworth, 0.05 Hz cutoff, filtfilt — removes slow drift and gain offsets.
  3. FFT cross-correlate envelopes end-to-end → coarse offset (~10 ms).
  4. Refine at sample level with a 60 s probe from B near the coarse-aligned position in A, ±2 s search window, parabolic peak interpolation.
  5. Multi-probe drift check — repeat step 4 every ~3 min. Linear fit delta(t) = slope·t + intercept reveals real clock drift (5–50 ppm typical). Use the midpoint-canonical offset (slope · midpoint + intercept) so residual error is symmetric around zero.
  6. Compute overlap window in the reference timeline: overlap = [max(0, delta), min(ref_dur, delta + src_dur)].
  7. Emit .sync.json sidecar next to each non-reference input. No file is copied, trimmed, or re-encoded. The reference input gets a sidecar too (with delta_seconds: 0) so downstream code can treat all inputs uniformly.

polysync sync is the implementation. It emits only the .sync.json sidecar — no _synced.MOV, no re-encode.

Sidecar schema (<input>.sync.json)

One sidecar per original input, written next to it. Pure JSON, no comments in-file — the field reference below is canonical.

json
{
  "_about": "Sync metadata for cam_b.MOV. Apply via ffmpeg -itsoffset. See wjs-syncing-multicam SKILL.md for full schema.",
  "schema_version": 1,
  "source": "cam_b.MOV",
  "reference": "cam_a.MOV",
  "delta_seconds": 12.345,
  "drift_slope": 1.8e-5,
  "overlap_in_reference": [12.345, 4512.180],
  "overlap_in_source":    [0.000,   4499.835],
  "verification": {
    "median_residual_ms": 4.2,
    "residual_spread_ms": 11.8,
    "probe_count": 24
  }
}

Field reference

FieldTypeMeaning
_aboutstringHuman-readable one-liner. Includes pointer back to this SKILL.md. Always present.
schema_versionintBumps on any breaking change to this schema. Current: 1.
sourcestringFilename of the original this sidecar describes. Relative to the sidecar's directory. Never points to a re-encoded file.
referencestringThe input whose timeline we're aligned to. Reference's own sidecar lists itself here.
delta_secondsfloatThe source's t=0 expressed in the reference's timeline. If positive, source starts after reference; pass to ffmpeg as -itsoffset <delta>. Can be negative (source starts before reference, e.g. early-rolling camera).
drift_slopefloatLinear clock-drift slope (dimensionless, ~10⁻⁵). 0.0 means no measurable drift. Downstream applies atempo = 1 + drift_slope to the source ONLY for sync-sound / long-form lip-sync — for camera-cut editing, ignore.
overlap_in_reference[start, end] (seconds)The window during which both source and reference have coverage, expressed in the reference's timeline. Use this to trim outputs to mutually-valid time ranges.
overlap_in_source[start, end] (seconds)Same window expressed in the source's local timeline. overlap_in_reference[0] - delta_seconds = overlap_in_source[0].
verificationobjectOutput of running polysync verify — drives a "did sync converge?" gate. median_residual_ms should be a few ms; residual_spread_ms > 1 frame at delivery fps means drift correction was needed but skipped.

How downstream consumes the sidecar

-itsoffset is per-input in ffmpeg and applies BEFORE -i. Always read the source's delta_seconds from the sidecar:

bash
# Play cam_b aligned to cam_a's timeline
ffmpeg -itsoffset $(jq -r .delta_seconds cam_b.MOV.sync.json) -i cam_b.MOV \
       -i cam_a.MOV \
       -filter_complex "[0:v][1:v]hstack" out.mp4

# Trim to mutual overlap window (read from cam_b.MOV.sync.json)
ffmpeg -ss <overlap_in_source[0]> -i cam_b.MOV -t <overlap_dur> ...

For wjs-editing-multicam, polysync edit ingests every <input>.sync.json automatically; you don't compose these flags by hand.

Partial-coverage clips — polysync sync --partial

Common case — main cams cover 75 min, a Riverside / phone / lavalier recorder only covers the middle 30 min. Run polysync sync REF.MOV NEW.mp4 --partial:

  1. Cross-correlates the new input against the reference (same envelope algorithm as full-overlap mode).
  2. Finds where the new clip's t=0 sits in the reference timeline (delta_seconds may be large, e.g. 1842.5).
  3. Writes ONLY the source sidecar — no black padding, no audio padding, no re-encode. overlap_in_reference tells consumers exactly when this input has coverage; outside that window, fall back to the main cams.

The --partial flag changes only the failure philosophy: it degrades gracefully (median delta on few probes, coarse delta if none) instead of failing on <3 good probes, and skips the reference sidecar (the reference is assumed to already belong to an established sync set). Everything else is identical to the default mode.

When to skip drift correction

For camera-cut editing (the common case), ±25 ms residual across an hour is below human perception — pass drift_slope: 0.0 and use only the midpoint delta_seconds.

For sync-sound / lip-sync at long durations (>30 min and verification.residual_spread_ms > 40), downstream applies atempo = 1 + drift_slope to the source. Source files are still not modified — the atempo filter runs at consume time.

Verification (always run)

polysync verify REF.MOV SRC.MOV SRC.sync.json re-extracts audio from BOTH originals natively (loudest stream, no ffmpeg offset) and runs multi-probe correlation again. It applies the sidecar's delta_seconds (and, with --apply-drift, the drift slope) as index arithmetic in numpy — a probe at reference time bs is drawn from the source at local time bs - delta, then sought near reference index bs; the peak offset is the residual. Writes results back into the sidecar's verification field.

Pass criteria — median_residual_ms < 15 and residual_spread_ms < 1 frame at delivery fps. Fail = retry with drift correction enabled.

A spread-only fail with a near-zero median is usually noise, not desync. Far-field mics on a wide / B-roll camera (high reverb, low SNR, ncoef ~0.2) produce a few outlier probes that blow up the spread while the median stays at a few ms. For camera-cut editing that is aligned — the median is the truth; the spread gate is conservative. Only chase it for long-form lip-sync.

Common pitfalls

  • Raw waveform cross-correlation gives false peaks under low SNR. Always envelope first — this is not a tunable, it's the entire premise.
  • -itsoffset semantics differ for audio vs video — for sync-correctness it must be the FIRST flag for that input. ffmpeg -i src -itsoffset X is wrong; ffmpeg -itsoffset X -i src is right.
  • -itsoffset is a NO-OP when muxing to headerless raw PCM (-f s16le). There are no container timestamps to carry the offset, so ffmpeg silently drops it and inserts NO leading silence. Any analysis that extracts to raw PCM (like polysync verify) must apply the offset by index arithmetic in numpy, never via -itsoffset on the extraction. Symptom of getting this wrong: verification residuals scatter by hundreds of ms with ncoef ~0 even though sync itself was perfect.
  • Naive 0:a:0 extraction silently syncs against a dead track. Multi-track cameras (Sony FX6 MXF: a:0/a:1 often -90 dB, mic on a:2) need loudness-based stream selection. polysync sync and polysync verify both pick the SAME loudest track automatically — if you ever reimplement, keep that invariant or residuals are meaningless.
  • Sidecar paths must be relative to the sidecar file's directory, not the working directory of the consuming process. Resolve source / reference against Path(sidecar).parent.
  • Don't bake drift_slope into the sidecar's delta_seconds. They're separate fields for a reason — naive consumers can ignore drift, sync-sound consumers can apply it. Mixing them loses information.

Frequently asked questions

What does the Wjs Syncing Multicam AI skill do?

Use when the user has 2+ video / audio recordings of the same event captured by different devices (cameras, phones, separate audio recorders) and wants them aligned to a single common timeline. Outputs only a lightweight `.sync.json` sidecar per input — original files are never re-encoded. Triggers — "多机位同步", "对齐这几个机位", "match camera timelines", "sync these angles", "audio drift between cameras", "separate audio recorder", "Riverside / Zoom recording that needs to line up".

Why use Wjs Syncing Multicam on TypingMind?

Because you install it once and use it with any model. Wjs Syncing Multicam is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wjs Syncing Multicam in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jianshuo/claude-skills/tree/main/wjs-syncing-multicam. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wjs Syncing Multicam?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wjs Syncing Multicam?

As many as you like. As long as a model supports skills, you can use Wjs Syncing Multicam with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wjs Syncing Multicam AI skill free?

Yes. It is published on GitHub by jianshuo under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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