Using an LLM to automate a task that used to take hours by hand

I want to share a concrete example of using an LLM to automate a manual process in my workflow. Not chatbot stuff. An actual pipeline step that used to require a human sitting with two audio tracks for hours. I build live speech-to-speech translation. To measure latency, I need to know which phrase in the source audio corresponds to which phrase in the translated audio, so I can measure the time gap between them. This alignment used to be done by hand. A person listens to both tracks, matches up the phrases, and logs timestamps. For a 6-minute session that's easily an afternoon of work. The hard part isn't the math. It's the alignment. Languages reorder things. German puts verbs at the end. Arabic restructures sentences. A Spanish phrase at position 3 might map to an English phrase at position 7. This is exactly the kind of thing LLMs are good at. They understand semantic equivalence across languages and handle reordering naturally. So I replaced the manual step with an LLM call: What used to take hours now takes a couple of minutes. No human in the loop. The reason I'm sharing this is that the pattern generalizes. If you have a workflow step where a human reads two things and figures out how they correspond, an LLM can probably do it. The key is that I'm not asking it for a judgment call or creative output. I'm asking it to do structured alignment, a well-constrained task where it's reliable. The LLM only handles the one step that actually needs language understanding. Everything else (force alignment, timestamp extraction, aggregation) is regular code. Full methodology: Automating ear-voice span Code: VoiceFrom/live-s2st-eval