Quick Start

1. Choose your input

Select a single video file, or switch to Batch Folder mode and select a directory. All matching video files inside will be queued.

2. Set your output folder

A subfolder is created per input file — you'll never lose track of which output came from which tape. Results are never overwritten; re-processing appends _1, _2, etc.

3. Pick a preset

Open the Presets menu and choose the one closest to your source material. See Preset System for descriptions.

4. Dry run first

Enable Dry Run before committing to a full export. This runs the full detection pipeline and writes all logs, but cuts no media. Review detect.json and check dataset.jsonl for cuts where only sig_black_boundary fired and confidence is below 0.90 — those are the weakest detections and worth inspecting first.

5. Final export

Disable Dry Run. Enable Re-encode for frame-accurate archival cuts (H.264/AAC). Run.

Recommended workflow:

  1. Enable Dry Run
  2. Review activity log and detect.json
  3. Adjust parameters if needed, re-run dry
  4. Disable Dry Run, enable Re-encode
  5. Run final export

Output Structure

Embedding chapter markers

Keep segments become Content N chapters; commercial blocks become Advertisement N. Embed into the show file:

ffmpeg -i show.mp4 -i logs/chapters.ffmeta \
  -map_metadata 1 -c copy show_with_chapters.mp4

Recognized by VLC, mpv, Kodi, and any player that reads ffmpeg metadata.

How It Works

Each commercial candidate passes through four independent detection passes in sequence. Every signal that fires is recorded against the interval and reflected in its confidence score.

Detection Passes

Scoring Guards

Confidence Score

Every CutInterval carries a confidence float (0.0–1.0) and a signals list. The activity log renders confidence as a star rating:

All Parameters

Input / Output

Black Frame Detection

Cut Behaviour

Advanced Detection (Comskip-derived)

Verbosity

Preset System

AdSlicerProXP ships with three built-in presets and a full save/load system.

Built-in Presets

Preset Menu

Presets
  ── BUILT-IN ──────────────
  Broadcast strict
  Default
  VHS noisy
  ── MY PRESETS ────────────
  my_custom_settings
  ──────────────────────────
  Save Current as Preset…
  ──────────────────────────
  Open User Presets Folder…
  Reload Presets

User Preset Locations

Use Presets → Open User Presets Folder… to open this location. Drop any .json file there and use Reload Presets to make it appear in the menu.

Preset File Format

Plain JSON. _preset sets the menu label; _description sets the tooltip. Unrecognized keys are silently ignored.

{
  "_preset": "My custom VHS settings",
  "_description": "Tuned for my specific deck and capture card.",
  "blackMinDur": 0.10,
  "pixTh": 0.08,
  "picTh": 0.98,
  "mergeGap": 1.5,
  "minCommercial": 5,
  "maxCommercial": 240,
  "silenceNoiseDb": -40,
  "requireDiv5": false
}

Adding a Built-in Preset to the Build

Drop a .json file into src-tauri/presets/ and rebuild. The tauri.conf.json resources glob picks it up — no code changes needed.

Tuning Guide

Thresholds may require tuning for darker or noisier analog captures. Always Dry Run first and review dataset.jsonl before committing to export.

Too many false positives (content being cut)

• Raise blackMinDur (0.15–0.25) — require longer slugs
• Raise picTh (0.99) — require nearly pure black frames
• Increase minShowSegment (60–120 s) — prevent short content being consumed
• Enable requireDiv5 for clean broadcast — non-multiples of 30 s are not real ad breaks
• Raise minCommercial — filter breaks too short to be real commercials
• Check dataset.jsonl for cuts where only sig_black_boundary fired — weakest detections

Missed commercials (breaks not detected)

• Lower blackMinDur (0.06–0.08) — accept shorter slugs
• Raise pixTh (0.10–0.14) — more permissive black definition
• Lower picTh (0.90–0.95) — allow noisier black frames
• Increase mergeGap for flickering VHS slug patterns
• Lower sceneThreshold (0.25–0.35) — catch more cuts within blocks

Noisy or degraded VHS

• Raise pixTh + lower picTh — the standard analog adjustment
• Raise uniformMaxStddev (12–18) — VHS black slugs are never truly uniform
• Set removeBefore 0.1 — recovers content clipped by ambiguous slug entry points
• Disable requireDiv5 — VHS timing is irregular
• Lower silenceNoiseDb to -35 dB — VHS audio floor is noisier

Clean off-air broadcast

• Enable requireDiv5 — US TV commercials are exact 15/30/60/90 s units
• Set alwaysKeepFirst 15 and alwaysKeepLast 15 — protect cold opens and credits
• Lower uniformMaxStddev to 5–6 — broadcast slates are near-perfect
• Raise sceneThreshold to 0.45 — hard cuts only; avoid dissolve false positives

Recommended workflow

1. Run with dryRun enabled — review detect.json and the activity log
2. Check dataset.jsonl — confidence below 0.90 or only sig_black_boundary firing is worth inspecting
3. Adjust parameters and re-run dry until the plan is correct
4. Remove dryRun and enable reencode for final archival export

ML Dataset Output

Every run writes logs/dataset.jsonl — one JSON object per line, one line per segment, 74 columns. Load it directly:

import pandas as pd
df = pd.read_json("logs/dataset.jsonl", lines=True)

Column Groups

Identity (4 cols)

Timing (9 cols)

Signal Indicators — all 0.0 or 1.0 (11 cols)

Classification (3 cols)

Usage Examples

import pandas as pd
from sklearn.ensemble import RandomForestClassifier

df = pd.read_json("logs/dataset.jsonl", lines=True)

# Feature matrix
X = df[[
    "dur_s", "dur_norm", "start_norm",
    "black_left_dur_s", "black_right_dur_s",
    "silence_coverage", "has_silence_overlap",
    "scene_change_rate", "scene_change_rate_vs_avg",
    "sig_black_boundary", "sig_silence_overlap",
]]
y = df["label_int"]

# Compare across parameter tuning runs
runs = pd.concat([
    pd.read_json("run1/logs/dataset.jsonl", lines=True),
    pd.read_json("run2/logs/dataset.jsonl", lines=True),
])
runs.groupby("param_scene_threshold")["run_commercial_ratio"].mean()

# Inspect low-confidence cuts
df[(df["label"] == "commercial") & (df["confidence"] < 0.9)]

Use Cases

Archiving Full Broadcasts With and Without Commercials

A collector digitizes a 1992 NBC Sunday Night Movie from VHS. They want to preserve the entire broadcast — including vintage promos and ads — but also need a clean version for watching. AdSlicerProXP outputs both automatically: show/ (clean movie) and commercials/ (all ad blocks), plus detect.json for reproducible archive metadata.

Digitizing VHS Tapes With Automatic Cleanup

A preservation group receives 300 home-recorded VHS tapes spanning 1986–2004. Batch-mode processing handles entire shelves at once. Threshold tuning ensures detection works across varied analog sources. Hundreds of hours are segmented, cleaned, exported into uniform directory structures, and logged for verification — eliminating months of manual editing.

Preparing Footage for YouTube or Streaming

A creator uploading 1990s cartoons needs commercial breaks removed to avoid Content ID strikes. AdSlicerProXP with reencode produces frame-accurate clean masters with no leftover partial-commercial frames.

Building ML Training Sets

A research lab training a commercial-boundary detection model needs ground-truth timestamps for black slugs, silence regions, ad gaps, and keep segments. AdSlicerProXP's dataset.jsonl provides a complete labeled dataset — 74 feature columns per segment — without manual annotation.

Creating Commercial Compilations

An editor wants all McDonald's commercials from 1997 ABC broadcasts. Commercials are already cleanly extracted into individual files in commercials/ — drop them into a timeline or sort by brand via captioning or logo detection.

High-Volume TV Archive Processing

A university lab processes 1,200 Betacam SP and VHS tapes from 1980–2002. AdSlicerProXP's structured run_manifest.json and detect.json provide the audit trail. Every tape gets a uniform directory layout with consistent metadata ready for digital asset management ingestion.

Building from Source

First-time setup

Download static ffmpeg/ffprobe builds into src-tauri/binaries/. Run once before first build.

./build.sh setup-bins

Dev mode

cd src-tauri
cargo tauri dev

Release builds

ffmpeg and ffprobe are bundled automatically. Users need no external dependencies.

Adding a built-in preset

Drop a .json file into src-tauri/presets/ and run ./build.sh. The tauri.conf.json resources glob picks it up — no code changes needed.