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Clippper
Feature

AI highlight detection for gaming streams

Don\u2019t scrub through hours of footage. Clippper\u2019s multi-signal pipeline analyzes audio, motion, transcript, and silence patterns to find your best moments automatically.

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Four signals, one decision

Most tools use a single metric (like audio volume) to find highlights. Clippper combines four independent signals for better accuracy.

Audio energy

Per-second RMS and VAD detect moments when you or your chat get loud — clutch plays, team wipes, rage moments. Rolling z-scores flag spikes against the baseline.

Visual motion

Frame-by-frame difference analysis at 1 fps detects action sequences. Fast-moving gameplay, camera shakes, or sudden scene changes register as motion peaks.

Transcript analysis

Word-level Deepgram output flags reaction words ("let’s go", "no way", "oh my god"), sentence boundaries for natural clip start/end, and pauses that indicate moment breaks.

Silence detection

FFmpeg silencedetect identifies quiet stretches. These serve as natural clip boundaries and help avoid cutting mid-sentence or mid-action.

The highlight detection pipeline

From raw video to scored clips in five steps.

  1. 1

    Signal extraction

    FFmpeg extracts 16 kHz mono audio. Per-second analysis computes RMS energy, VAD (voice activity), motion from frame differences, silence detection, and rolling z-scores. Output: a timeline of raw signals.

  2. 2

    Annotation

    Signals merge with word-level transcript data from Deepgram. Each second gets tags: excitement peaks, audio spikes, action peaks, reaction words, sentence boundaries, pauses, and scene labels (active gameplay, talking, idle).

  3. 3

    Candidate generation

    Weighted trigger tags are clustered. Nearby triggers merge into candidate windows. Sentence and pause boundaries refine start/end points. Windows target 30–90 seconds with natural openings and closings.

  4. 4

    LLM scoring

    Each candidate window is scored by an LLM for hook quality, energy, narrative arc, and viral potential. Sub-scores are weighted and combined. Candidates below the threshold are filtered out.

  5. 5

    Extraction

    Overlapping candidates are deduplicated. Clip count scales with video length. FFmpeg extracts each clip as a separate MP4, with padding applied for natural starts and endings.

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AI highlight detection FAQ

How Clippper finds the best moments in your gaming VODs.

How does AI highlight detection work for gaming?
Clippper analyzes five signals in parallel: audio energy (RMS/VAD), visual motion (frame diffs), transcript reaction words, silence patterns, and scene classification. These merge into per-second annotations, which are clustered into candidate windows and scored by an LLM.
How accurate is the highlight detection?
It works best for gaming streams with clear audio, face cam reactions, and active gameplay. The multi-signal approach catches moments that single-signal systems miss — a quiet clutch play with high motion, or a loud reaction during slow gameplay.
How many highlights does it find in a long VOD?
Clip count scales with the square root of video duration. A 1-hour stream typically yields 4–8 clips. A 4-hour stream might yield 8–15. Premium users get more clips per stream than free users.
Can I choose which highlights to keep?
After processing, you see all detected highlights with scores. You can download any of them. There’s no manual selection before processing — the AI scores all candidates and gives you the best ones.
What’s the ideal clip length?
The candidate generator targets 30–90 second windows by default, which is the sweet spot for TikTok and YouTube Shorts. Sentence boundaries and pauses are used to find natural start and end points.
Does it work for non-gaming content?
The highlight system is tuned for gaming streams with face cam. It can work for other content, but the signal weights and reaction word lists are optimized for gaming. For general-purpose clipping, other tools may be more appropriate.