They clip what you said. We clip what you felt.
MoodCutter is an open-source proof of concept for an AI system that automatically detects and extracts emotionally resonant moments from video — funny, sad, and high-energy — to help content creators find their best clips in seconds, not hours.
The proof of concept has achieved its goal: validating that audio-based emotional moment detection in video is technically feasible.
Active development has moved to two separate repositories:
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MoodCutter-Community — Free and open-source. A three-signal emotion detection pipeline (speech sentiment, laughter detection, voice intensity) that content creators and developers can run on their own machines, Kaggle, or Google Colab. No subscription. No hosted backend. Bring your own compute.
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MoodCutter-Pro — Closed-source. The full product. Adds facial emotion recognition, crowd reaction detection, and the fusion layer that combines all signals into a Virality Score with plain-language reasoning. Built for content creators who want a hosted, no-setup experience.
This repository is archived as a reference implementation. No further features will be added here.
Content creators spend hours scrubbing through footage to find the 3 seconds that made someone laugh or cry. Existing AI clippers — OpusClip, TuBoost, and others — analyze transcripts and content patterns. They detect what you said, not how it made people feel.
A transcript can find your most quotable line. It cannot find the moment your voice spiked, the audience laughed, and your face shifted from neutral to pure joy — all in the same second. That is the moment that goes viral. That is what MoodCutter is built to find.
The three-signal pipeline in MoodCutter-Community is open source because it demonstrates the approach and gives the developer community something real to build on.
The advanced features — facial emotion recognition, crowd reaction detection, and the fusion layer — represent the core technical moat of the product. Making them open source would allow well-funded competitors to absorb years of research and development instantly.
MoodCutter-Community is genuinely useful and genuinely free. MoodCutter-Pro is for creators who want the full system without running anything locally.
The v0.1 implementation uses audio-based loudness detection as the foundational signal for emotional moment detection.
- ✅ Audio loudness analysis using RMS energy
- ✅ Dynamic threshold-based loud moment detection
- ✅ Timestamped moment extraction
- ✅ Energy visualization graph
- ✅ Waveform analysis with highlighted segments
- ✅ Command-line interface
Audio analysis results showing detected loud moments with timestamps
Bar chart showing audio energy levels over time with detection threshold
Audio waveform with highlighted loud segments
git clone https://github.com/axtheon/MoodCutter.git
cd MoodCutter
pip install -r requirements.txtpython src/CLI/main.pyThe script will:
- Load the test audio file (
examples/audio/test_audio.wav) - Analyze audio in 0.5-second chunks
- Detect moments exceeding 1.5× average energy
- Display timestamps in terminal
- Show an interactive energy visualization graph
CHUNK_DURATION = 0.5 # Analysis window size (seconds)
THRESHOLD_MULTIPLIER = 1.5 # Sensitivity (lower = more sensitive)- Audio Loading — Loads audio using
librosa - Chunking — Divides audio into fixed-duration segments
- Energy Calculation — Computes RMS energy (loudness) per chunk
- Threshold Detection — Flags chunks exceeding the dynamic threshold
- Visualization — Displays energy levels and detected moments
- Python
- librosa — audio analysis
- matplotlib — visualization
This POC demonstrates one signal. The full MoodCutter system is built on six:
| Signal | What It Detects | Status |
|---|---|---|
| Voice Intensity | Energy spikes, loudness peaks | ✅ This repo |
| Speech Sentiment | Emotional polarity in spoken words | MoodCutter-Community |
| Laughter Detection | Genuine laughter in audio stream | MoodCutter-Community |
| Facial Emotion | Joy, surprise, sadness on subjects' faces | MoodCutter-Pro |
| Crowd Reaction | Audience laughter, applause, gasps | MoodCutter-Pro |
| Virality Score | All signals fused into one score with reasoning | MoodCutter-Pro |
MIT License — see LICENSE for details.
This project is being built in public.
Built by a 14-year-old from Lahore, Pakistan. They clip what you said. We clip what you felt.