DubGuard: Evidence Backed Narration Review

In industrial training and maintenance videos, one wrong word can change an entire instruction. “Switch the power off” becomes “switch the power on.” A 15-minute wait becomes 50 minutes. Comparing transcripts can reveal these differences, but knowing whether a change is an acceptable paraphrase or alters a critical requirement takes closer review. DubGuard helps reviewers make that distinction, with evidence they can check. Users can explore predefined scenarios or add their own reference scripts and mark requirements that need particular attention, such as numbers, warnings, on/off instructions and the order of steps. They then record or upload English narration. DubGuard keeps the original audio and uses Whisper, running locally on the CPU, to produce a timestamped draft transcript. Rule-based checks flag possible deviations and areas of uncertainty for review. For an additional perspective, users can choose to enable semantic analysis through AMD Token Factory using DeepSeek-V4-Flash. This sends only the script, review policy and transcript text—not the original audio. DubGuard checks that the returned citations match exact passages in the source text, and treats the model’s findings as advisory. Reviewers can listen to the audio, inspect flagged passages, correct transcription mistakes, document their decisions and export the results. Organizational sign-off remains with an authorized human reviewer, and critical findings or unresolved evidence cannot be automatically approved. Built with React, TypeScript, FastAPI, SQLite and Whisper, DubGuard helps people review industrial narration more consistently and keep a clear record of their decisions. It supports human judgment; it does not certify that an instruction is safe.