Add all scaffolding, design, and decision documentation AKA first commit

docs/adr/0005-diarization-sherpa-onnx.md

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+# ADR-0005 — Speaker diarization: sherpa-onnx
+
+- **Status:** Accepted
+- **Date:** 2026-06-30
+- **Context source:** Design doc §"Speaker Diarization and Naming"
+
+## Context
+
+WA must label transcript segments by speaker (Speaker 1/2…), allow naming during and after a
+meeting, support merging over-split speakers, and feed calendar participant names into the
+naming UI — all offline.
+
+## Decision
+
+Use **`sherpa-onnx`** offline speaker diarization: a pyannote **segmentation** model + a
+speaker-**embedding** extractor (e.g. 3D-Speaker ERes2Net) + **clustering**, all ONNX and fully
+local. Wrap it behind a `diarization::Diarizer` trait. Diarization runs as **post-processing**
+over the recorded audio (audio is the source of truth, ADR-0006), producing speaker-ID-tagged
+spans that are aligned to whisper segments by timestamp overlap.
+
+During live recording, show provisional speaker turns from segmentation (cheap), then refine
+labels in the post-meeting pass; this keeps latency low while improving final accuracy.
+
+## Consequences
+
+- **Positive:** purpose-built, offline, ONNX (shares the runtime with the NPU transcription path);
+  supports the segmentation+embedding+clustering pipeline the design assumes; models are
+  downloadable and swappable.
+- **Negative:** separate models to download/manage (model-management UI, FR-MODEL-1); C/C++ FFI
+  to integrate; clustering may over/under-split → the **merge** workflow (FR-SPK-3) is required,
+  not optional.
+- Speaker IDs (`S1`, `S2`, …) are internal and stable per meeting; name mappings live in the DB
+  and are applied at render/export time, never destructively rewritten onto segments.
+
+## Revisit if
+A single model gives joint ASR + diarization with better accuracy, or whisper.cpp gains
+production diarization.