Grant Gilliam
b2ae3a62b9
Native SwiftUI menu-bar app (LSUIElement, macOS 13+), generated from project.yml via XcodeGen. Includes: - PermissionsManager (Microphone / Screen Recording / Accessibility) + UI - SparkControlHealth: GET /api/status over self-signed TLS (InsecureTrustDelegate) - AppSettings persistence (host, TLS-skip, output folder, adapter toggles) - Menu-bar panel + Settings, app sandbox & hardened runtime off (LAN tool)
152 lines
8.8 KiB
Markdown
152 lines
8.8 KiB
Markdown
# Project Brief — Ten31 Transcripts
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> Local macOS app that auto-detects conference calls, records local audio, and
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> produces a **visual-derived, timestamped speaker timeline** — then hands the
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> mixed audio + that timeline to the operator's **SparkControl** backend, which
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> diarizes, names the speakers (majority-overlap vote against the timeline), and
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> returns named transcript segments. A growing **voiceprint library** recovers
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> speakers even when the visual cue is missing.
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Master context document. Read this first, then `02_ARCHITECTURE.md`,
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`03_DATA_CONTRACTS.md`, `04_BUILD_PLAN.md`. The SparkControl API is now fully
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specified — see `03_DATA_CONTRACTS.md` (and the source `AUDIO_API.md`).
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---
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## 1. What we are building
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A lightweight, always-running **menu-bar app on macOS** that:
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1. **Detects** when the user joins a call in Google Meet, Zoom, Microsoft Teams,
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or Signal.
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2. **Records two local audio tracks** — system audio (everyone else) and the
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user's microphone (the user) — and **mixes them to one 16 kHz mono WAV** for
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the backend.
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3. **Watches the call window** at ~2–4 fps and, per app, reads participant
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**names** and the **active-speaker cue**, producing a
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`(start, end, name, confidence)` **visual timeline** — its best guess at who
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was talking when.
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4. **Discards every video frame after extraction.** No video is ever written to
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disk. Only audio + the derived timeline persist locally.
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5. On call end, **POSTs the mixed audio + the visual timeline (+ the known
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voiceprint library) to `POST /api/audio/label-merge`** on SparkControl, which
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returns **named, speaker-attributed transcript segments** and a **voiceprint
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per speaker**.
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6. **Persists the returned voiceprints** keyed by name, so the next call can pass
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them as `known_voiceprints` and recover a speaker by voice when the visual cue
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is absent (camera off, a bad OCR frame).
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The app's job ends at receiving and storing the named segments from SparkControl.
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**All transcription, diarization, and the name-merge happen on the backend.** Do
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not build transcription, diarization, or the merge vote in this app.
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## 2. Why the visual timeline still matters (the core idea)
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Audio diarization (NVIDIA Sortformer on the backend) is excellent at
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**segmentation** — precise speaker boundaries — but its clusters are **anonymous**
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(`Speaker_0`, `Speaker_1`…). It cannot name anyone.
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The screen *already knows the names*. Each app visually marks the active speaker
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(colored tile border, animated audio bars, a ring around an avatar) next to that
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person's name/initials.
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So responsibilities split cleanly:
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- **Audio (backend)** owns *segmentation* — the exact *when*.
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- **Visual capture (this app)** owns *identity* — the *who*.
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- **`label-merge` (backend)** fuses them: it diarizes, then assigns each cluster
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the timeline name with the most temporal overlap. The visual track needn't be
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perfect — it only needs to win the per-cluster vote.
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**New compounding layer — the voiceprint library.** Every named cluster comes
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back from the backend with a 192-dim TitaNet voiceprint. The app persists these
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keyed by name and replays them as `known_voiceprints` next time. Resolution order
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per cluster becomes: **visual overlap → voiceprint match → `Unknown_N`** (never
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mislabeled). So the screen capture *enrolls a voice library for free*, and over a
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few calls the system can name regulars even with cameras off.
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## 3. Hard scope boundaries
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**In scope (this app):**
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- Call detection for Meet / Zoom / Teams / Signal.
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- Dual-track local audio capture + mix-to-mono for the backend.
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- Low-fps window capture → OCR (names) + active-speaker cue detection.
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- Per-app "adapter" modules encapsulating each app's UI quirks.
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- Building the visual timeline; **mic-VAD self-labeling** (the mic track is the
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user, so hot-mic spans pre-seed the user's name into the timeline).
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- Chunking long calls (~2–3 min) and calling `label-merge` **sequentially**.
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- A local **voiceprint store** (persist + replay named voiceprints).
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- Storing the backend's named transcript segments locally.
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- A minimal menu-bar UI: status, manual start/stop, recent sessions, adapter
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toggles, backend host/health, output folder.
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**Out of scope (owned by the backend):**
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- Transcription, diarization, the name-merge vote, summarization/analysis.
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**Explicitly not doing:** saving video; cloud anything. Everything stays on the
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operator's LAN.
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## 4. Key decisions (now resolved against the real contract)
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| Decision | Choice | Why |
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| Language / framework | Native Swift + SwiftUI menu-bar app (`LSUIElement`) | System audio, window capture, Vision all native; one codebase. |
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| Audio capture | ScreenCaptureKit (system audio) + AVFoundation (mic) | No virtual audio device; works with headphones; macOS 13+. |
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| Backend audio format | **Mixed-mono 16 kHz WAV** | Diarizer separates speakers from one mixed stream; 16 kHz is ideal. |
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| Call detection | CoreAudio "mic running somewhere" + known-app / Meet-tab heuristic | Clean live-mic signal + app disambiguation. |
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| Speaker naming | **Backend, via `POST /api/audio/label-merge`** | One call does diarize + overlap-vote naming + transcription. No client merge. |
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| Identity recovery | **Local voiceprint library** replayed as `known_voiceprints` | Recovers camera-off / OCR-missed speakers by voice; compounds over calls. |
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| Self-identity | mic-VAD → pre-seed user's name in timeline | The mic track is the user; gives the backend a strong prior + enrolls the user's voiceprint immediately. |
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| Requests | **Sequential, one audio request in flight** | Parallel audio requests trip a backend GPU race (`503 + Retry-After`). |
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| Long calls | Chunk ~2–3 min, sequential, stitch via names+voiceprints | Diarizer caps at **4 speakers/chunk**; voiceprints + names unify across chunks. |
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| Transport / TLS | `multipart/form-data`, file field `file`; self-signed Start9 cert (skip verify or trust the Root CA); **no auth on LAN** | Matches every other SparkControl endpoint. |
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| Timing | Batch after call (sync endpoints, no polling) | Endpoints are synchronous; no job/poll machinery needed. |
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### On forking Hyprnote
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Unchanged recommendation: the audio capture is the trivial part (~200 lines of
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native Swift) and the rest (Vision screen-reading) is native too. Build native;
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use Hyprnote's capture/detection only as reference. Fork remains an override.
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## 5. Target apps & identifiers
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| App | Join via | Bundle ID(s) | Speaking cue / names |
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|---|---|---|---|
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| Zoom | Native | `us.zoom.xos` | Colored tile border; name label in tile. |
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| Microsoft Teams | Native (new) | `com.microsoft.teams2` (new), `com.microsoft.teams` (classic) | Colored ring/border; labeled. |
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| Signal | Native (Electron) | `org.whispersystems.signal-desktop` | Ring around avatar/initials; try Accessibility names first. |
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| Google Meet | Browser tab | `com.google.Chrome`, `com.apple.Safari`, `company.thebrowser.Browser` (Arc)… | Canvas video → Vision for the cue; DOM names → Accessibility/AppleScript; confirm via active-tab URL `meet.google.com`. |
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Four required adapters; adding a 5th must be one new file conforming to the
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`AppAdapter` protocol.
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## 6. The backend (do not rebuild) — now concrete
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SparkControl, on the operator's Start9 LAN, fronting two DGX Sparks:
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- **STT:** NVIDIA Parakeet TDT 0.6B — `POST /v1/audio/transcriptions` (OpenAI-compatible).
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- **Diarization:** NVIDIA Sortformer 4spk-v1 — `POST /api/audio/diarize-chunk`
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(anonymous clusters + voiceprints) and `POST /api/audio/transcribe-with-speakers`.
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- **Embeddings:** NVIDIA TitaNet (192-dim voiceprints).
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- **★ Primary endpoint for this app:** `POST /api/audio/label-merge` — diarize +
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name from the visual timeline (+ voiceprint fallback), optionally transcribe,
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in one synchronous call.
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- Health/discovery: `GET /api/status`, `GET /api/endpoints`, `GET /v1/models`.
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Full request/response shapes, curl examples, limits, and error formats are in
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`03_DATA_CONTRACTS.md`.
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## 7. Remaining open items (small)
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1. **Base URL — RESOLVED.** `https://your-spark-backend.local:62419`, also
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`https://your-spark-backend.local:62419` (prefer the `.local` form; it survives IP
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changes). Ship the `.local` host as the default; keep it editable in settings.
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Service-discovery at `GET /api/endpoints`.
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2. **Send trigger** — assume auto-POST on call end; expose a "hold for review"
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toggle if the user wants to eyeball the timeline first.
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3. **Retention** — keep the session folder after a successful hand-off, or prune
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audio and keep only `speakers.json` + voiceprints? Default: keep everything,
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user-configurable.
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4. **Voiceprint update policy** — overwrite vs running-average a person's stored
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voiceprint across calls (see `02_ARCHITECTURE.md §2.9`). Start simple
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(store/refresh latest high-confidence), refine later.
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5. **Signing** — stable identity so macOS doesn't re-prompt for permissions on
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each rebuild.
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