ViralClip

Extract the most engaging clips from any video — zero API keys, runs 100% locally.

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Install

pip install viralclip

Optional extras:

pip install "viralclip[captions]"   # burn-in subtitles via local Whisper
pip install "viralclip[smartcrop]"  # face-detect crop via opencv
pip install "viralclip[all]"        # everything

System dependency: ffmpeg must be on your PATH.

brew install ffmpeg        # macOS
sudo apt install ffmpeg    # Ubuntu/Debian

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Usage

# Most engaging 60s from a local file
viralclip clip video.mp4

# Download from YouTube and clip
viralclip clip https://youtube.com/watch?v=xxxxx

# Burn in captions (local Whisper, no API)
viralclip clip video.mp4 --captions

# Export 3 non-overlapping clips
viralclip clip video.mp4 --count 3

# Custom duration
viralclip clip video.mp4 --duration 45

# Output format
viralclip clip video.mp4 --format horizontal   # 16:9 YouTube
viralclip clip video.mp4 --format square        # 1:1 Instagram feed
viralclip clip video.mp4 --format portrait      # 4:5 Instagram portrait
viralclip clip video.mp4 --format vertical      # 9:16 TikTok/Reels (default)

# Smart crop — detect face, crop around subject
viralclip clip video.mp4 --smart-crop

# Preview each clip in system player after export
viralclip clip video.mp4 --preview

# See timestamps without exporting anything
viralclip clip video.mp4 --dry-run

# Custom output location and filename
viralclip clip video.mp4 --output-dir ./clips --output-name my-clip

# Suppress all output (CI/scripting)
viralclip clip video.mp4 --quiet

# Nudge window ±N seconds after dry-run preview
viralclip clip video.mp4 --dry-run          # see timestamps
viralclip clip video.mp4 --offset 8         # shift forward 8s
viralclip clip video.mp4 --offset -5        # shift back 5s

Config file

Persist defaults in ~/.config/viralclip/config.toml so you don't repeat flags every run:

[defaults]
duration = 45
format = "vertical"
output_dir = "~/clips"
smart_crop = true
count = 3

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Output formats

Flag	Ratio	Resolution	Platform
vertical (default)	9:16	1080×1920	TikTok, Reels, Shorts
horizontal	16:9	1920×1080	YouTube, Twitter/X
square	1:1	1080×1080	Instagram feed
portrait	4:5	1080×1350	Instagram portrait

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How the algorithm works

1. YouTube heatmap (when available)

For YouTube URLs, yt-dlp fetches the Most Replayed heatmap — real viewer replay density from millions of views. Each timestamp gets a score 0–1 representing how often that moment was rewatched. When present this carries 65% of the final score.

2. Audio analysis (always runs — 35% with heatmap, 100% without)

Five features computed per second via librosa. Weights adapt automatically based on content type:

Feature	Speech weight	Music weight	What it captures
RMS energy	35%	20%	Loudness / presence
Spectral flux	15%	35%	Rate of change — beat drops, cuts
Onset strength	20%	30%	Word density, musical onsets
Zero Crossing Rate	30%	15%	Consonants, high-freq activity
Crowd reactions	+15% bonus	+15% bonus	Applause / laughter bursts

Content type is detected via spectral flatness (low = tonal/music, high = noisy/speech). Weights interpolate smoothly between speech and music profiles. Silent seconds (< 5% peak RMS) are penalised 10×.

3. Quality filtering

Before window selection, two passes over the video:

• Scene cut detection — ffmpeg finds hard cuts (scene score > 0.3); window starts snap to the nearest cut within ±3s so clips don't open mid-cut
• Black frame detection — windows with > 10% black frames are skipped entirely

4. Smoothing + peak selection

Scores are Gaussian-smoothed (σ=2.5s). Then:

1. Find local score peaks (scipy.signal.find_peaks, min distance = window/2)
2. Place window 40% before / 60% after the peak — buildup + payoff
3. Score each window as 0.7 × mean + 0.3 × peak
4. Prefer window starts that follow a natural dip (breath before the moment)
5. For --count N, greedily pick non-overlapping peaks

5. Crop

Center-crop to target ratio by default. With --smart-crop, OpenCV Haar cascade samples 3 frames (at 25%, 50%, 75% through the clip), detects faces in each, and uses the median face center — more robust than single-frame sampling.

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Feature comparison

Feature	ViralClip	OpusClip	ViralCutter
No API key needed	✅	❌	❌
Works fully offline	✅	❌	❌
YouTube heatmap signal	✅	✅ (cloud)	❌
Local captions (Whisper)	✅	Cloud	Cloud
Face-aware crop	✅	✅	❌
Multiple output formats	✅	✅	❌
YouTube download	✅	✅	✅
Multi-clip export	✅	✅	✅
Dry run / preview	✅	❌	❌
Free	✅	Freemium	Freemium

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License

MIT