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>
set_no_context(true) stops whisper from feeding its own output back as
context for the next segment. Without this, at audio end the model
halluccinates a phrase ('All right.', 'So I think we're going to wrap up.')
and repeats it hundreds of times in a tight loop.
Observed: 759x 'All right.' + 750x 'So I think we're going to wrap up.'
in the final 8 seconds of a 101min YouTube conference recording.
After fix: clean termination with no repetition loops.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
- Revert no_speech_thold from 0.0 back to 0.6 (whisper.cpp default)
0.0 means 'suppress if p(no-speech) > 0.0' which fires on every segment,
silently producing 0-segment output for all real-world audio
- Revert SamplingStrategy from Greedy{best_of:5} back to BeamSearch{beam_size:5}
Greedy with temperature=0.0 and best_of>1 is undefined in whisper.cpp
- Restore entropy_thold=2.4 and logprob_thold=-1.0 defaults
- Keep flash_attn disabled (was causing silent failures on conference audio)
- Tested: 59 segments on 5 min YouTube conference audio, 29 on repair audio
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
