Whisper was trained on 30-second chunks, and it shows the moment you feed it a two-hour podcast or try to wire it into a live captioning pipeline. Hallucinated repetitions, silent-segment drift, and latency that spikes with audio length are all symptoms of the same underlying limitation — the model wasn't built for long-form or streaming out of the box. Part 3 of this Whisper deployment series tackles both problems head-on.

The segment walks through practical strategies for long-audio inference: VAD-based chunking, overlap-and-stitch to avoid boundary artifacts, and using previous-window text as a decoder prompt to keep context flowing. On the streaming side, it covers chunked decoding with lookahead, tuning the trade-off between latency and stability, and where the community's streaming Whisper forks (like whisper-streaming and faster-whisper) actually help versus where you still need custom glue. If you're building live captions, meeting bots, or any product that has to transcribe audio longer than a Whisper receptive field, this is the part of the series to hit play on.