95 lines
3.8 KiB
Markdown
95 lines
3.8 KiB
Markdown
# ADR 0011: Use Meeting Lab as the MVP Processing Backend
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## Status
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Accepted
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Supersedes ADR 0002 and amends ADRs 0001 and 0003.
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## Context
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Meeting Lab experiments have validated a practical local pipeline for turning
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a recorded meeting into an editable protocol. Earlier Meeting Assistant plans
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selected `faster-whisper`, treated diarization as required and described a
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separate knowledge-extraction stage as part of the primary pipeline. Work on an
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old feature branch also proposed mandatory fixed five-minute audio chunks and
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chunk-oriented storage.
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The validated backend now has a reusable Python API, optional diarization and a
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direct full-transcript protocol path. The product boundary must keep backend
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processing in Meeting Lab and user interaction in Meeting Assistant.
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## Decision
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Meeting Lab is the reusable processing backend and experimental engine.
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Meeting Assistant is the user-facing application and calls Meeting Lab through
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its Python API rather than launching its CLI as a subprocess.
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The MVP processing flow is:
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```text
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Source audio
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-> FFmpeg preparation (mono, 16 kHz PCM WAV)
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-> whisper.cpp transcription (large-v3-turbo)
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-> optional pyannote.audio Community-1 diarization
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-> direct full-transcript protocol generation
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-> human review and editing
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```
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The productive path does not require fixed five-minute audio chunks. Internal
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streaming or segmentation remains an implementation detail of Meeting Lab and
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must not define Meeting Assistant's domain or storage model.
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Meeting Assistant integrates with these public Meeting Lab interfaces:
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- `MvpMeetingConfig`
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- `MvpRunResult`
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- `run_mvp_meeting(...)`
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- stage-based progress events: `preparing`, `transcription`, `diarization`,
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`protocol_generation`, `completed` and `failed`
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Progress percentages are displayed only when the backend reports real
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measurable progress.
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`MeetingContext` is the structured input containing meeting metadata and
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participants. It may contain explicitly confirmed `SPEAKER_XX -> participant_id`
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mappings. Such mappings are authoritative only when confirmed by the user.
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The system must not infer speaker names automatically.
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The preferred local engines are:
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- `whisper.cpp` with `large-v3-turbo` for transcription
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- `pyannote.audio` Community-1 for optional diarization
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GPU acceleration is optional. Vulkan transcription and PyTorch/ROCm
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diarization are validated acceleration paths on North's AMD RX 9070. CPU
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execution remains the compatibility fallback.
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The direct full-transcript protocol path is the practical MVP direction.
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`qwen3.8:27B` has produced strong readability and contextual synthesis, while
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`qwen3.6:35B-A3B` has sometimes behaved more conservatively. Dual-model and
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diarization-assisted hard-fact experiments have not established sufficiently
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reliable attribution gains to justify mandatory MVP complexity. Model choice
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remains backend configuration, and human review is required.
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Meeting Assistant owns the GUI, structured context editor, participant and
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speaker-mapping controls, progress presentation, protocol editor and export.
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Meeting Lab owns audio preparation, transcription, diarization, orchestration
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and protocol-generation logic.
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The product should ultimately provide a detailed contextual protocol and a
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shorter participant/distribution version. The shorter version may follow the
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first GUI milestone.
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## Consequences
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- ADR 0002's `faster-whisper` selection is no longer current.
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- Fixed-duration chunking and chunk-centric storage are not MVP requirements.
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- Diarization and GPU acceleration remain optional.
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- The Meeting Lab CLI remains useful for command-line operation but is not the
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application integration boundary.
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- Meeting Context is created through the future GUI; users are not expected to
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hand-write YAML.
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- Backend experiments can evolve without moving processing logic into the GUI.
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- Protocols remain AI-assisted outputs that require human review.
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