I've seen a lot of discussion here about managing Suno outputs, especially for producers who need to integrate tracks into a larger project. The lack of BPM and key metadata in the exported files is a real workflow killer. Manually analyzing and tagging dozens of files is not a scalable or cost-effective use of time.
I built a lightweight local tool to solve exactly that. It's a Python script that processes a directory of your Suno exports (MP3 or WAV). It uses `librosa` for analysis to detect the BPM and estimate the musical key, then writes that data directly into the file's ID3 tags. This means your DAW or music library can read the metadata immediately.
Key features and considerations:
* **Local Processing:** No uploading your files to a third-party service. This keeps your work private and avoids any potential SaaS fees or data handling issues.
* **Total Cost of Ownership:** Zero. It runs on your machine. The libraries used are open-source.
* **Batch Processing:** Point it at a folder and walk away. Efficiency is the goal.
* **Accuracy Note:** BPM detection is solid on consistent rhythms. Key estimation is a best-effort from an algorithm; always trust your ear for final confirmation, but it gives you a 90% accurate starting point.
The primary value is in volume. If you generate 20-30 ideas in a session, having them all pre-tagged for sorting and filtering changes how you approach organization and licensing. You can instantly filter for all "120 BPM, C minor" tracks, for example.
If there's interest, I can clean up the code and post the core logic. It's about 50 lines, plus dependencies. Would this be useful for others, or are you all using a different method to handle this metadata gap?
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