Chaotics Slice ✂

AI-powered silence remover for video editors
Detects speech with Silero VAD and cuts silences. Runs locally, free forever, always open source.

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✨ What It Does

Upload a video → Pick an aggression level → Slice removes all the silence.

Give it a video file, select how aggressive you want the silence removal (Chill → Savage), and Chaotics Slice automatically detects every moment of speech and cuts everything else. Preview your cuts on an interactive timeline, then either:

• Export the sliced video as a new file, or
• Export a cut list (EDL / FCPXML / Premiere XML) to edit in your NLE

All processing happens locally on your machine. Download once, work offline forever.

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🎯 Features

• Local processing — Everything runs on your machine. No uploads, no cloud, no tracking.
• AI-powered detection — Uses Silero VAD (Voice Activity Detection) to find speech, not just audio levels.
• Flexible aggression levels — Chill, Normal, Tight, or Savage presets, plus full manual control over thresholds.
• NLE-ready exports — Cut lists compatible with Final Cut Pro, Premiere Pro, and DaVinci Resolve.
• Optional GPU acceleration — Auto-detects CUDA (NVIDIA) and MPS (Apple Silicon); falls back to CPU seamlessly.
• Works offline — After the model downloads once, the app is fully offline-capable.
• Free and open source — GPL licensed. No paywalls, no ads, no feature gates.

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📋 Requirements (All Platforms)

Dependency	Version	Notes
Python	3.10 – 3.12	3.13 not yet tested
FFmpeg + FFprobe	6+	Must be on PATH
PyTorch	2.x	CPU works; GPU optional
torchaudio	any	Auto-detected at startup

GPU acceleration is optional. The app auto-detects CUDA and MPS at startup and falls back to CPU silently.

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🚀 Quick Start (Pick Your Platform)

| Python | 3.10 – 3.12 | 3.13 not yet tested | | FFmpeg + FFprobe | 6+ | Must be on PATH | | PyTorch | 2.x | CPU works; GPU optional | | torchaudio | any | Auto-detected at startup |

GPU acceleration is optional. The app auto-detects CUDA and MPS at startup and falls back to CPU silently.

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🚀 Quick Start (Pick Your Platform)

macOS

1. Install system dependencies

# Install Homebrew if you don't have it
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

brew install python@3.11 ffmpeg

2. Clone, create virtual environment, and install

git clone https://github.com/yourname/chaotics-slice.git
cd chaotics-slice

python3.11 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install torch torchvision torchaudio flask waitress

3. Run

python app.py

Browser opens automatically at http://127.0.0.1:5000. Done!

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Windows

1. Install Python

Download Python 3.11 from python.org.
✅ Check "Add Python to PATH" during install.

2. Install FFmpeg

1. Download a build from ffmpeg.org/download.html (e.g. gyan.dev full build)
2. Extract to C:\ffmpeg
3. Add C:\ffmpeg\bin to your System PATH:
   Control Panel → System → Advanced → Environment Variables → Path → Edit → New
4. Verify: open a fresh terminal and run ffmpeg -version

3. Clone, create virtual environment, and install

git clone https://github.com/yourname/chaotics-slice.git
cd chaotics-slice

python -m venv .venv
.venv\Scripts\activate
pip install --upgrade pip

Choose one based on your GPU:

# NVIDIA (CUDA 12.1):
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# CPU only:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

# Then all:
pip install flask waitress

4. Run

python app.py

Browser opens automatically at http://127.0.0.1:5000. Done!

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Linux (Ubuntu / Debian)

1. Install system dependencies

sudo apt update
sudo apt install -y python3.11 python3.11-venv python3-pip ffmpeg git

On Ubuntu 22.04, Python 3.11 may need the deadsnakes PPA:

sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt update
sudo apt install -y python3.11 python3.11-venv

2. Clone, create virtual environment, and install

git clone https://github.com/yourname/chaotics-slice.git
cd chaotics-slice

python3.11 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip

Choose one based on your GPU:

# NVIDIA (CUDA 12.1):
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

# CPU only:
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu

# Then all:
pip install flask waitress

3. Run

python app.py

⚠️ The app won't auto-open a browser on Linux. Visit http://127.0.0.1:5000 manually or Ctrl+Click the URL in the terminal.

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Verify GPU Detection

After the app starts, check the terminal for a line like:

[Chaotics Slice] torch=2.10.0  device=cuda
[Chaotics Slice] torch=2.10.0  device=mps
[Chaotics Slice] torch=2.10.0  device=cpu

If you expected GPU but see cpu:

• CUDA: Verify nvidia-smi shows your driver's max CUDA version, and you used the matching --index-url
• Apple Silicon: Ensure you're running native Python: python3 -c "import platform; print(platform.machine())" should output arm64

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📹 Using Chaotics Slice

Workflow

1. Upload — Choose a video file (.mp4, .mkv, .mov, .avi, .webm, .m4v, .flv; up to 8 GB)
2. Configure — Pick an aggression level or adjust thresholds manually:
   • Chill — Keeps more breathing room; fewer cuts
   • Normal — Balanced; good for most content
   • Tight — Aggressive; minimal silence
   • Savage — Maximum cuts; speech-only edit
3. Preview — See cuts on the interactive timeline before rendering
4. Export — Either:
   • Video — Download the sliced video file
   • Cut List — Export EDL, FCPXML, or Premiere XML to edit in your NLE

Supported Formats

Video: .mp4, .mkv, .mov, .avi, .webm, .m4v, .flv
Maximum upload: 8 GB
Export: Native sliced video or NLE-compatible cut lists

First Run

On your first upload, Silero VAD downloads its model weights (~2 MB) from PyTorch Hub. This requires an internet connection once. After that, the model is cached locally and the app works fully offline.

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🏗️ Architecture (For Developers)

Project Structure

chaotics-slice/
├── app.py                    # Flask app + HTTP routes
├── config.py                 # Constants & VAD mode presets
├── ffmpeg.py                 # FFmpeg wrappers (encode, extract audio)
├── vad.py                    # Silero VAD inference + speech detection
├── jobs.py                   # Job queue, cut computation, EDL/XML export
├── test.py                   # Unit tests
├── requirements.txt          # Python dependencies
├── build.bat / build.sh      # PyInstaller bundling scripts
├── chaotics_slice.spec       # PyInstaller spec file
├── static/
│   ├── index.html            # Single-page UI
│   ├── css/ style.css        # Styling
│   ├── js/                   # Frontend logic (app.js, player.js, etc.)
│   └── res/                  # Logo, icons, assets
├── uploads/                  # Temporary upload directory (auto-cleared)
├── outputs/                  # Temporary output directory (auto-deleted)
└── silero_vad/               # Silero VAD submodule & tuning tools
    ├── src/                  # VAD model loading & inference
    └── tuning/               # Threshold optimization utilities

How It Works

1. Audio Extraction — FFmpeg extracts PCM audio (16 kHz, mono) from the video
2. VAD Inference — Silero model identifies speech chunks with configurable thresholds
3. Cut Computation — Combines speech chunks with padding & silence minimums to generate cuts
4. Rendering — FFmpeg re-encodes the video with only the cut segments
5. Export — Generate EDL/FCPXML/Premiere XML for NLE import, or output the final video

Key Parameters (in config.py)

MODE_PRESETS = {
    "chill":  {"padding": 350, "min_silence": 600},    # More breathing room
    "normal": {"padding": 200, "min_silence": 300},    # Balanced
    "tight":  {"padding": 80,  "min_silence": 150},    # Aggressive
    "savage": {"padding": 30,  "min_silence": 80},     # Minimal silence
}

• Padding — Milliseconds of audio to keep around each speech chunk
• Min Silence — Minimum silence duration (ms) before cutting

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🔧 Development & Contributing

Setting up for development

# Clone and navigate
git clone https://github.com/yourname/chaotics-slice.git
cd chaotics-slice

# Create virtual environment
python3.11 -m venv .venv
source .venv/bin/activate

# Install dev dependencies
pip install -r requirements.txt

# Run tests
python test.py

Building a standalone executable

Uses PyInstaller to bundle the app:

# macOS / Linux:
bash build.sh

# Windows:
build.bat

Output: dist/Chaotics-Slice.app (macOS) or dist/Chaotics-Slice.exe (Windows)

Contributing

We welcome bug reports, feature requests, and pull requests! If you're interested in contributing:

1. Fork the repository
2. Create a feature branch — git checkout -b feature/your-feature
3. Make your changes and write/update tests
4. Submit a pull request with a clear description

Areas we're looking for help with:

• Performance optimizations (VAD inference, FFmpeg encoding)
• Additional NLE export formats (Avid AAF, Media Composer, etc.)
• Batch processing mode
• GUI improvements and accessibility
• Language/localization support
• Platform-specific installers (DMG, MSI, deb/rpm packages)

For major features or architectural changes, please open an issue for discussion first.

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🐛 Troubleshooting

Error	Solution
ffmpeg: command not found	FFmpeg is not on your PATH. Re-check the install step for your platform and open a fresh terminal.
No speech detected	Try Chill mode or lower the VAD Threshold slider. Noisy audio or non-speech content (music, B-roll) may cause misdetection.
FFmpeg render failed	Check the Activity log for details. Common causes: corrupted file, unsupported codec, or disk full.
torchaudio requires torchcodec	You have torchaudio ≥ 2.9. The app uses stdlib wave for audio; this is handled automatically. If you still see it, update to the latest app.py.
Port 5000 already in use	Edit the last line of app.py to use a different port: serve(app, host="127.0.0.1", port=5001)
GPU not detected when expected	Verify CUDA version matches your PyTorch --index-url. On Apple Silicon, confirm native Python: python -c "import platform; print(platform.machine())" → arm64

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📄 Notes & Caveats

• Supported audio codecs — AAC, MP3, FLAC, PCM, Opus, Vorbis. Unusual codecs may cause FFmpeg errors.
• Silero VAD language — Trained on multilingual data; works best with clear speech (English, Ukrainian, Russian, and other languages with similar phonetics).
• GPU memory — VAD inference is memory-intensive; GPU acceleration is most beneficial on audio files with long, continuous segments.
• Output file size — Sliced output is typically 60–80% of the original on podcasts; less on videos with substantial B-roll.

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📜 License

GPL License. See LICENSE for details.

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🙏 Credits & Attribution

• Silero VAD — VAD model and framework (github.com/snakers4/silero-vad)
• Flask — Web framework
• FFmpeg — Audio/video processing
• PyTorch — ML inference backend

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💬 Questions or Feedback?

• 💡 Feature request? Open a GitHub issue.
• 🐛 Bug? Describe steps to reproduce; include terminal output and OS/GPU details.
• 💭 General question? Start a discussion or check existing issues.