Traditional debugging often relies heavily on visual attention: verbose logs, profilers, dashboards, and charts. However, the human brain is particularly efficient at detecting auditory patterns such as rhythm, repetition, and interruption. This talk explores how Python execution events can be transformed into sound, making functions, loops, errors, and performance bottlenecks immediately distinguishable. Using decorators, execution timing, and lightweight audio libraries, an otherwise opaque Python pipeline becomes perceptible through hearing alone. Live demonstrations show how sonification reveals hidden behaviors and anomalies without requiring line-by-line code inspection. The approach is designed to be practical and industry-ready: it relies exclusively on standard Python tools, requires no machine learning, and avoids complex infrastructure. The talk presents clear principles and reusable patterns for improving code observability by complementing visual debugging techniques with auditory perception.