Prime's ask (via the coordinator): investigate the "Parakeet skips
stretches of speech" finding, including other Parakeet weights.
Investigation only; nothing deployed.
Against ground truth (official SCOTUS transcript, Gutenberg #38916) the
drops are real: production v3 loses 140 / 66 clean words per transcript on
the two public files and ~50 on each private one (Whisper-referenced,
Canary-confirmed; adjudicator 129/129 correct on the calibration). Cause:
the v2/v3 0.6B weights collapse deep inside long full-attention windows;
the encoder output is degraded, the audio alone transcribes fine, and
1.1B TDT/RNNT/CTC and CTC-0.6B never do it. Decoding (CUDA graphs, greedy
variants, max_symbols, beam), slice length, local attention, loudness,
resampling and a noise floor do not fix it. Controls: A-vs-A, silence
positive control (>=15 words 36/36), null control, bootstrap floor.
Proposed patch 0002 re-transcribes >=3 s stretches where the audio holds
speech but no word came out (-80 to -90 % lost words on all four
recordings, lower WER, no invented text, +10 MiB) and adds an explicit
PARAKEET_MODEL_PATH with the loaded model recorded in JSON and ModelUsed.
Reviewed at high effort, all findings fixed; built and tested as
scriberr:local-blackwell-a353078-dropout2, not deployed.
scriberr-rebuild: --patches takes DIR[:DIR...]; embeds and seam-checks
both Parakeet scripts (seam-check --standard for the short-audio one).
Scriberr moved to fv-ml1 GPU 3 on 2026-09-30, so the card is no longer idle when the rebuild runs. The memory stage now requires >= 20 GB free instead of an idle card (GPUs 0-2 still fail that) and attributes the peak only to the host PIDs of its own container, captured with docker top alongside the 0.2 s nvidia-smi samples. Verified with five processes on the card: peak 5,496 MiB, identical to the exclusive-card figure.
Carry patches/0001 on our Scriberr build (upstream a353078): adjacent
buffered chunks overlap by 4 s inside --chunk-len and hand over at a word
both chunks transcribed alike, instead of cutting at fixed marks with no
overlap. Pause-aware cutting is included as an opt-in (--pause-search);
it measured neutral once the stitch was right. The Go<->Python CLI and
JSON seam is unchanged.
Bench (4 recordings, 118 min, 3 cut placements each, against a no-cut
whole-file reference; metrics only, private audio stays on fv-ml1):
cuts with an error within +-3 s fall from 52% (93/179) to 22% (41/184)
against a 19% background; floor +-0.08. Positive control: upstream's
cutter +0.33 over background. A-vs-A byte-identical in-process and
across CLI processes. Peak GPU memory unchanged at 5,496 MiB (n=3).
Also found: Parakeet skips runs of >=10 words mid-chunk with any
slicer, upstream's included; not addressed here.
scripts/scriberr-rebuild clones a pinned upstream sha into a new
/opt/docker/src dir, git-apply-checks the patches, builds a distinct
tag, and checks embed, unit tests, the JSON seam (scriberr-seam-check.py)
and the memory budget on idle GPU 3. Deploy stays manual. The upstream
PR is prepared under patches/upstream-pr/ and not opened.
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