From Unknown Artist to Custom Code: Building a Music Recognizer

For over a decade, a digital library grew into a chaotic archive of 12,000 MP3 files. Each track was labeled simply as "Unknown Artist — Track 01," a testament to years of downloading and procrastinating on organization. This massive collection of untitled music represented a daunting digital cleanup project that seemed impossible to tackle manually.

The sheer volume of files made standard sorting methods ineffective. The owner realized that to reclaim this library, a more sophisticated approach was needed. This realization sparked a weekend-long coding marathon to build a custom solution from scratch, aiming to finally give every track its proper name.

The project's core objective was to create an asynchronous music recognizer using Python. By leveraging the Shazam API, the tool could query song identities without the manual effort of searching for each track individually. The developer dedicated a single weekend to writing the code, turning a long-standing problem into a focused, intensive development sprint.

The goal was not just identification, but also efficiency. The solution needed to process thousands of files without overwhelming system resources or hitting API restrictions. This required a carefully designed architecture that could handle a massive queue of audio files in parallel.

• Process 12,000 MP3 files automatically
• Integrate with the Shazam API for identification
• Ensure the script runs within a single weekend
• Make the final code open-source for others

Developing the recognizer presented several significant engineering challenges. The primary obstacle was navigating the API rate limiting imposed by the identification service. To avoid being blocked, the script had to intelligently manage request timing and spacing. Additionally, the collection contained numerous files with corrupted or non-standard encodings, which required a robust pre-processing step to ensure the audio data could be read correctly.

Perhaps the most critical constraint was memory management. Loading a massive queue of files simultaneously could easily exhaust system RAM. The developer engineered the tool to be memory-efficient, processing files in a controlled stream rather than in bulk. This ensured stability and allowed the entire operation to complete successfully.

How to bypass rate limiting, fix broken encodings, and avoid consuming all memory.

The final tool operates as a streamlined pipeline. First, it scans the directory of untitled MP3s, reading each file's audio signature. It then formats these signatures for the Shazam API, sending requests asynchronously to maximize throughput. The script is designed to gracefully handle errors, such as unreadable files or API timeouts, logging them for review without halting the entire process.

As tracks are successfully identified, the tool can update the file metadata, transforming "Unknown Artist — Track 01" into "Actual Artist — Actual Song Title." This automated process converts a chaotic folder into a searchable, organized music library. The developer has made the code publicly available, allowing others with similar digital hoarding problems to benefit from the solution.

This project demonstrates how a targeted coding effort can solve a personal but widespread problem: digital disorganization. By building a custom tool, the developer successfully processed a 15-year collection of music in a single weekend, proving the power of automation. The open-source release of the code provides a valuable resource for the developer community.

The initiative highlights several important principles for software development:

• Directly address personal pain points with custom tools
• Anticipate and engineer solutions for API limitations
• Prioritize memory efficiency in data-heavy applications
• Share successful solutions with the open-source community