A powerful media transcoding tool with CUDA hardware acceleration support.
- Transcode video files to web-optimized formats
- NVIDIA CUDA hardware acceleration support for faster transcoding
- Automatic detection of hardware capabilities
- Batch processing of media files
- Database tracking of transcoded files
- Web-safe filename generation
# Using uv (recommended)
uv pip install nirabo-media-transcoder
# Using pip
pip install nirabo-media-transcoder# Using uv (recommended)
sudo uv pip install --system nirabo-media-transcoder
# Using pip
sudo pip install nirabo-media-transcoder- Python 3.8+
- FFmpeg with hardware acceleration support (optional)
- NVIDIA GPU with CUDA support (optional, for hardware acceleration)
# Basic usage
media-transcoder -t /path/to/videos/
# Use hardware acceleration (if available)
media-transcoder -t /path/to/videos/ --keep-originals
# Force software encoding
media-transcoder -t /path/to/videos/ --software-only
# Generate web-safe filenames
media-transcoder -t /path/to/videos/ --web-safe-names
# Verbose output
media-transcoder -t /path/to/videos/ -v
# Force processing of all files
media-transcoder -t /path/to/videos/ -f# Clone the repository
git clone https://github.com/lpetrov/media-transcoder.git
cd media-transcoder
# Install development dependencies using uv
uv pip install -e ".[dev]"# Using uv
uv run pytest
# Or with the script runner
uv run --script tests/test_transcoder.py# Using uv
uv run pytest --cov=media_transcoderTo build the package for distribution:
# Build both wheel and source distribution
uv build
# The built packages will be in the dist/ directory
ls dist/To publish the package to PyPI:
# Test publishing to TestPyPI first
uv publish --index testpypi
# Publish to the official PyPI
uv publishThis project uses UV for dependency management. To update dependencies:
# Update all dependencies
uv pip sync
# Add a new dependency
uv pip install new-packageMIT