Desktop version of Shazam via Python

more user friendly approach to fixing broken mp3 filenames.

I don’t know about you, but I had a bunch of CDs ripped as mp3s with files like track1.mp3, track2.mp3, and all sorts of assorted file names in my mp3 collection. That’s why I created a Python script that interfaces with Shazam in the first place.

Python script that Identifies tracks by AUDIO using Shazam

I started that project as a practical Python script: point it at a folder of MP3s, let Shazam identify tracks, then rename files, write tags, and add covers. It worked, but it behaved as most scripts do: fast, unforgiving, and a little opaque. You ran it, watched lines fly by, and hoped the right things happened to the right files. It worked for me, but I figured a lot more people would find it useful if it was more user friendly.

The Python GUI version was built so that instead of a stream of text, you get a living list of your files with cover, name, embedded metadata, and status - all visible at once. You can scan first, inspect what already exists, see what Shazam proposes, and choose how changes are applied. The app now supports two mindsets: instant write (Auto apply) or review-first (Pending apply then commit). You can scan per file by selecting it and right click.

Under the hood, they share the same core logic. But the experience is fundamentally different.

The GUI lets you:

• Choose the target music folder

• Immediately list MP3 files after folder selection

• View embedded title/artist/album and whether cover art is already embedded

• Preview embedded cover art for selected files

• Run/stop processing with live per-file status updates

• Toggle Auto apply changes:

  • ON: writes rename/tags during processing

  • OFF: detects and fills pending info without writing; use Apply Pending to commit later

• Preview Shazam cover art in pending mode before writing

• Right-click a row for Open File, Open Folder, and Rescan Item

• Starts in dark mode with a toggle for light mode

Hope you will find it useful as I did.

As always, the entire code and release executable can be found on BarnLab’s GitHub repository.