Overview
Matchering is a Python library and Docker-deployable web service for automated audio mastering. Given a target track and a reference song, it analyzes the reference's loudness, stereo width, and spectral balance and applies matching processing to the target. It is designed as an open-source alternative to cloud mastering services like LANDR and eMastered. A minimal web UI and REST API are available for integration into music production pipelines.
Where it falls short of Zapier
- Mastering quality depends entirely on reference track choice; no AI-driven style presets like LANDR
- No stem separation, noise reduction, or restoration processing
- Web UI is very minimal — not a polished production tool without custom frontend work
- Processing is CPU-only by default; no GPU acceleration for batch workflows
We list the gaps honestly so you can decide if the trade-off is worth owning your data.
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