The bench and CLI kept emitting BLM for vLLM. Three layers:
1. rules.py: add default rule BLM -> vLLM (word-boundary, case-insensitive) so the default post_mode=rules path restores it everywhere.
2. benchmark/runner.py: the bench never ran postprocessing - it measured raw engine text, so entity retention (66.7%) never reflected rules/glossary. _transcribe_clip now builds Segments and runs run_postprocess(settings, glossary) before metrics; manifest top-level glossary {pattern: replacement} is supported and recorded in run_config (post_mode/glossary).
3. glossary plumbing: CLI transcribe gains --glossary KEY=VALUE (repeatable, parsed + validated); BatchPipeline.run accepts glossary= and passes it to run_postprocess; the in-proc worker forwards job.options.glossary or post_correction.
Notebook: transcribe cells pass --glossary BLM=vLLM, bench manifest carries glossary, postprocess section documents rules/glossary/hotword.
+ 9 unit tests (BLM rule, boundary/case behavior, bench postprocess + glossary entity retention). 147 tests pass, ruff clean.
Colab run 4 (A100): same namedtuple bug as segments — faster-whisper
returns TranscriptionInfo (namedtuple) but batch.py reads
outcome['info'].get('language') -> AttributeError 'TranscriptionInfo'
object has no attribute 'get' on every real transcription, failing the
CLI, API auto-worker job, worker drain, and bench clip alike.
FasterWhisperEngine now normalizes info to a dict at the boundary
(_to_dict_info: _asdict -> dataclasses.asdict -> known-field fallback).
+ 2 unit tests (namedtuple/dict info); 138 tests pass, ruff clean.
Colab run 3 (A100) surfaced three GPU/API-path bugs mocks couldn't catch:
1. faster-whisper yields namedtuple Segments, but batch/bench consume
them as dicts (.get) -> AttributeError 'Segment' has no attribute
'get' on every real transcription. Engine now normalizes segments
to dicts (_to_dict_segments) at the boundary.
2. API TranscribeOptions carries engine-irrelevant keys (formats,
timestamps, glossary_id, post_correction, diarize); worker's
TranscriptionOptions(**job.options) crashed with TypeError. Worker
now filters job.options to TranscriptionOptions.__slots__.
3. in-proc server never consumed its own queue (jobs stayed queued
forever). Added opt-in Settings.auto_worker (default off): lifespan
starts a daemon Worker thread for inproc backend, stopped on
shutdown. Notebook enables it via LUKESCRIBE_AUTO_WORKER=true so the
API upload -> completed flow works end to end.
Notebook: bench manifest now uses clips schema (audio_path/duration_sec/
entities); cell 22 reads error_message/error_code; upload poll window
raised to 4min (first-run model download).
+ 5 tests (namedtuple/dict segments, API-style options, auto_worker
on/off); 136 tests pass, ruff clean.
Colab A100 run exposed a GPU-only bug: DeviceManager returns
selected_device='cuda:0' and the engine passed it verbatim to
faster-whisper, but CTranslate2 only accepts device='cuda' with a
separate device_index arg -> 'unsupported device cuda:0' on every GPU
transcription. Fix: FasterWhisperEngine._split_device() splits
'cuda:N' -> ('cuda', N) and passes device_index to WhisperModel.
Notebook: server cell now pkills stale servers (old process holds
port 8000 and 401s on new keys since KeyStore loads at startup),
sets LUKESCRIBE_API_KEY_FILE explicitly, and verifies auth with
RAW_KEY before proceeding; worker cell guards read_result() is None.
+ 4 unit tests (device split contract), 131 tests pass, ruff clean.
lukehemmin