Emotion & Genre Classifier

A teammate and I built an ML pipeline that takes a single audio file and spits out the song’s mood, dominant emotion, and genre. We wanted to use both the sound and the lyrics—so the system fetches lyrics (via Genius), runs emotion models on the audio and the text, and does zero-shot genre prediction. One function, classify_song(), ties it all together: you drop in a file and get back mood tags, valence–arousal scores, and a genre label.

Figuring out how to wire the pieces together was the interesting part. We had to decide where each model lived in the flow, how to normalize the outputs, and how to handle missing or messy lyrics. I focused a lot on making the pipeline clear and easy to extend so we could swap or add models without breaking everything.

It was a small team and we both had a hand in the architecture and the code. Getting the first end-to-end run to work—from raw audio to a coherent set of labels—felt like we’d actually built something you could hand to someone and say, “here, try it.”