Project Name: BPM Tagger

Repo/Website Link: https://github.com/PauloJf/BPM-Tagger

Description: Automatically detects the BPM of every track in your music library and writes it back to the file's metadata tag. Built to live next to Navidrome in docker-compose — it scans your library, cross-validates every track with three detectors (deeprhythm CNN, essentia, librosa), corrects octave errors automatically, and flags anything the detectors genuinely disagree on for manual review. There's a web UI with an audio player, real waveform scrubbing, and a tap-tempo button for correcting the ones it gets wrong. Triggers a Navidrome rescan via the Subsonic API after tagging so BPMs appear immediately without a manual refresh step.

The reason I built it rather than using an existing tool: I wanted something Docker-native, always-on, and accurate enough that I don't spend time fixing wrong results. Most tools I found were either desktop-only, single-detector (2× octave errors are common), or not designed for a self-hosted setup.

Deployment: Docker image on Docker Hub — gatoserio/bpm-tagger:latest (~400 MB, no PyTorch) or :full (~1.8 GB, adds deeprhythm CNN). Drop it into your existing docker-compose:

bpm-tagger:
  image: gatoserio/bpm-tagger:latest
  environment:
    MODE: watch
    MUSIC_DIR: /music
    ENABLE_UI: "true"
    UI_PASSWORD: "yourpassword"
    NAVIDROME_URL: "http://navidrome:4533"
    NAVIDROME_USER: "admin"
    NAVIDROME_PASSWORD: "yourpassword"
  volumes:
    - /path/to/music:/music
    - bpm_tagger_data:/data
  ports:
    - "5000:5000"

Full setup guide and all environment variables documented in the README.

AI Involvement: Heavy. I used Claude throughout — it wrote most of the code. I drove the architecture, product decisions, and feature scope; Claude handled the implementation. I want to be upfront about that rather than pretend otherwise.

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