In Part 2 , we successfully trained a Transformer model to map sequences of body keypoints to sign language glosses using CTC loss. However, training on pre-segmented videos is one thing; making it work in the real world—where a webcam stream is infinite and boundaries are unknown—is an entirely different beast. In this article, we tear down inference/realtime.py , the beating heart of the asl-to-voice project. We will explore how we handle infinite video streams, decode raw probabilities into words, and use Large Language Models (LLMs) to generate beautiful, spoken English on the fly. Stage 3: The Sliding Window and CTC Decoding When a user turns on their webcam, we don't know when a sentence begins or ends. To solve this, we implemented a Sliding Window architecture. As the camera captures frames, MediaPipe extracts the keypoints and appends them to a collections.deque (a highly efficient queue). We maintain a window of W frames (e.g., 64 frames, representing about 2 seconds of video).…