"The two transcripts overlapped, the timestamps for part 2 started from 0, and speaker identification was not consistent, creating significant challenges in merging them."
"Instead of asking ChatGPT to merge the transcripts, I could ask it to analyze them, find the solutions to the three problems, and then write code that would merge the transcripts."
"ChatGPT was able to find the solutions for the three problems above and write a script that fixed them automatically, achieving the goal of a final merged transcript."
Merging two overlapping transcripts presents challenges such as timestamp discrepancies and inconsistent speaker identification. Instead of directly merging the texts, a more effective approach involves using an LLM to analyze the transcripts and generate a Python script. This script can identify overlaps, compute necessary timestamp adjustments, and correct speaker identification. The result is a more accurate and automated merging process, yielding a final transcript that resolves the initial issues without altering the original text.
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