Week 9: Building the Interface + Facing a Setback

As the project nears completion, this week brought a mix of major progress and unexpected difficulty. On one hand, I built the first full-stack implementation of my tabla AI system—bringing together months of modeling work into a usable web interface. On the other, I encountered a serious roadblock in my vocal music dataset that forced a last-minute pivot.

Building the Full-Stack Interface

The major technical milestone this week was creating a web application that allows users to interact with the system in a clean, intuitive way. The app accepts an uploaded vocal recording, runs it through the model, and returns a visualization of predicted sam and khali points overlaid on the waveform. Under the hood, it uses a Flask backend to handle inference and audio processing, and a React-based frontend for interactivity.

This user-facing layer not only makes the project more accessible—it also helps demonstrate how tabla AI could be used in educational or compositional tools. It’s a shift from research to application, and it finally feels like the system has a real interface with the outside world.

Dataset Setback

Unfortunately, the progress on the web app was matched by a serious issue in the vocal music dataset I’d been working with. After closer inspection, I realized that some of the files had mislabeled taals, inconsistent tempos, or unclear phrasing that made sam and khali annotation unreliable. Since the classifier’s success depends on accurate training data, I had to halt further modeling on this dataset.

To recover, I reached out to researchers who had previously published work on Hindustani classical music and requested access to a curated dataset they had compiled. Their dataset is known for having high-quality annotations and cleaner audio, and I’m hopeful that it can support the next iteration of the model—even if it comes slightly after the formal end of this project phase.

Looking Ahead

While the setback was frustrating, the week still marked an important turning point. I now have:

• A working, deployed model interface

• A clear path toward improved data quality

• A concrete understanding of what the system needs next

In the coming days, I’ll finalize the interface, clean up the backend logic, and evaluate how the model performs on the new dataset once I gain access. Despite the bump in the road, the system is alive, and it’s built to listen.