Worked on the NVIDIA/NeMo repository to enhance the stability of the BNR 2.0 audio processing pipeline. Addressed a critical inference issue by implementing input signal padding, ensuring that input lengths align with the model’s required sample multiples. This solution, developed in Python and leveraging deep learning and machine learning techniques, prevents input size mismatch errors during inference. The fix improved the robustness of production deployments by reducing runtime failures and minimizing outages. No new features were released during this period, with the primary focus on resolving this high-impact bug to support smoother and more reliable inference in real-world scenarios.