Sampled Gemini voice matching could leave one person under separate IDs
across transcription chunks. Use hosted Precision-2 on the complete
recording and align its exclusive speaker timeline with MAI word timing.
Preserve transcription text, order, and timestamps; leave ambiguous word
assignments unknown instead of merging identities to satisfy a count.
Upload lossless FLAC, bound polling, retry transient status reads against
the same job, and validate results before emitting stdout. Keep optional
speaker-count and hosted model overrides. Disable MAI diarization and use
30-minute transcription uploads in both modes. Respect environment keys,
keep authentication out of process arguments, and save optional diagnostics
without audio, credentials, or signed upload URLs.
Test Plan:
- python3 -B -m unittest -v: all 101 regression tests passed, including
mocked provider upload, polling, alignment, failure, and cleanup paths.
- bash -n transcribe and shellcheck transcribe: passed.
- git diff --cached --check: passed.
- Prepared the 74-minute input locally and verified identical decoded PCM
hashes for the normalized WAV and full-recording lossless FLAC.
- Live API testing was explicitly deferred by the user; mocked results do
not establish speaker accuracy on the recording.
BREAKING CHANGE: Default diarization requires PYANNOTE_API_KEY or its
1Password reference. --speaker-model accepts hosted precision-2 or
community-1 instead of OpenRouter audio-chat model IDs.
Short chunk overlaps often contain only one participant, so returning
speakers receive new transcript IDs. MAI can also split one voice into
several IDs within a single request, which overlap stitching cannot fix.
Add an opt-in audio comparison pass: --speakers N selects Gemini 3.8 Flash
with a known count, and --speaker-model allows another compatible model.
Keep request-local identities separate, select up to three clean excerpts
per ID, and compare all excerpts in one OpenRouter request. Gemini uses
Google AI Studio because the Vertex backend limits prompts to one audio
file. No local model or extra Python dependency is required.
Only reconcile identities whose sample assignments agree. Preserve
uncertain identities separately, reject incomplete or malformed replies,
and retain the original words and timestamps. Bound sample count and
request size, clip sampling to the real audio duration, and extend worker
cleanup and buffered output to the comparison step. Document the sampled
method's limitations and additional provider request.
Test Plan:
- python3 -B -m unittest -v: all 82 tests passed, including real MP3
encoding with fake transcription/comparison responses and cancellation.
- bash -n transcribe: passed.
- git diff --cached --check: passed.
- Live API and voice accuracy unverified: this workspace could not access
the configured 1Password credential. Mocked responses prove pipeline
behavior, not model accuracy on the supplied recording.
ddidderr