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Run ffmpeg silencedetect (n=-35dB, d=0.4s) on the original audio to find silence midpoints. Build chunk boundaries every 180s, snapping to the nearest silence midpoint within ±30s (fallback: hard cut). Each chunk is transcribed independently with its own CUDA context; timestamps are shifted by chunk_start before merging. Progress is scaled per-chunk across the overall 0-100% job range. Result on 101-min YouTube audio (34 chunks, 1714 silence points): - Previous: x1025 'Yeah.' + x1008 sentence-length loops (hallucinations) - After: x4 max consecutive run, all repetitions verified genuine Also refactored TranscribeRequest to carry on_progress: Box<dyn Fn(u8)> instead of a raw ProgressTx so each chunk can independently scale its contribution to the job's broadcast channel. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>