feat: Meeting Assistant MVP - Complete implementation

Enterprise Meeting Knowledge Management System with:

Backend (FastAPI):
- Authentication proxy with JWT (pj-auth-api integration)
- MySQL database with 4 tables (users, meetings, conclusions, actions)
- Meeting CRUD with system code generation (C-YYYYMMDD-XX, A-YYYYMMDD-XX)
- Dify LLM integration for AI summarization
- Excel export with openpyxl
- 20 unit tests (all passing)

Client (Electron):
- Login page with company auth
- Meeting list with create/delete
- Meeting detail with real-time transcription
- Editable transcript textarea (single block, easy editing)
- AI summarization with conclusions/action items
- 5-second segment recording (efficient for long meetings)

Sidecar (Python):
- faster-whisper medium model with int8 quantization
- ONNX Runtime VAD (lightweight, ~20MB vs PyTorch ~2GB)
- Chinese punctuation processing
- OpenCC for Traditional Chinese conversion
- Anti-hallucination parameters
- Auto-cleanup of temp audio files

OpenSpec:
- add-meeting-assistant-mvp (47 tasks, archived)
- add-realtime-transcription (29 tasks, archived)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

openspec/specs/transcription/spec.md

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+# transcription Specification
+
+## Purpose
+TBD - created by archiving change add-meeting-assistant-mvp. Update Purpose after archive.
+## Requirements
+### Requirement: Edge Speech-to-Text
+The Electron client SHALL perform speech-to-text conversion locally using faster-whisper int8 model.
+
+#### Scenario: Successful transcription
+- **WHEN** user records audio during a meeting
+- **THEN** the audio SHALL be transcribed locally without network dependency
+
+#### Scenario: Transcription on target hardware
+- **WHEN** running on i5 processor with 8GB RAM
+- **THEN** transcription SHALL complete within acceptable latency for real-time display
+
+### Requirement: Traditional Chinese Output
+The transcription engine SHALL output Traditional Chinese (繁體中文) text.
+
+#### Scenario: Simplified to Traditional conversion
+- **WHEN** whisper outputs Simplified Chinese characters
+- **THEN** OpenCC SHALL convert output to Traditional Chinese
+
+#### Scenario: Native Traditional Chinese
+- **WHEN** whisper outputs Traditional Chinese directly
+- **THEN** the text SHALL pass through unchanged
+
+### Requirement: Real-time Display
+The Electron client SHALL display transcription results in real-time.
+
+#### Scenario: Streaming transcription
+- **WHEN** user is recording
+- **THEN** transcribed text SHALL appear in the left panel within seconds of speech
+
+### Requirement: Python Sidecar
+The transcription engine SHALL be packaged as a Python sidecar using PyInstaller.
+
+#### Scenario: Sidecar startup
+- **WHEN** Electron app launches
+- **THEN** the Python sidecar containing faster-whisper and OpenCC SHALL be available
+
+#### Scenario: Sidecar communication
+- **WHEN** Electron sends audio data to sidecar
+- **THEN** transcribed text SHALL be returned via IPC
+
+### Requirement: Streaming Transcription Mode
+The sidecar SHALL support a streaming mode where audio chunks are continuously received and transcribed in real-time with VAD-triggered segmentation.
+
+#### Scenario: Start streaming session
+- **WHEN** sidecar receives `{"action": "start_stream"}` command
+- **THEN** it SHALL initialize audio buffer and VAD processor
+- **AND** respond with `{"status": "streaming", "session_id": "<uuid>"}`
+
+#### Scenario: Process audio chunk
+- **WHEN** sidecar receives `{"action": "audio_chunk", "data": "<base64_pcm>"}` during active stream
+- **THEN** it SHALL append audio to buffer and run VAD detection
+- **AND** if speech boundary detected, transcribe accumulated audio
+- **AND** emit `{"segment_id": <int>, "text": "<transcription>", "is_final": true}`
+
+#### Scenario: Stop streaming session
+- **WHEN** sidecar receives `{"action": "stop_stream"}` command
+- **THEN** it SHALL transcribe any remaining buffered audio
+- **AND** respond with `{"status": "stream_stopped", "total_segments": <int>}`
+
+### Requirement: VAD-based Speech Segmentation
+The sidecar SHALL use Voice Activity Detection to identify natural speech boundaries for segmentation.
+
+#### Scenario: Detect speech end
+- **WHEN** VAD detects silence exceeding 500ms after speech
+- **THEN** the accumulated speech audio SHALL be sent for transcription
+- **AND** a new segment SHALL begin for subsequent speech
+
+#### Scenario: Handle continuous speech
+- **WHEN** speech continues for more than 15 seconds without pause
+- **THEN** the sidecar SHALL force a segment boundary
+- **AND** transcribe the 15-second chunk to prevent excessive latency
+
+### Requirement: Punctuation in Transcription Output
+The sidecar SHALL output transcribed text with appropriate Chinese punctuation marks.
+
+#### Scenario: Add sentence-ending punctuation
+- **WHEN** transcription completes for a segment
+- **THEN** the output SHALL include period (。) at natural sentence boundaries
+- **AND** question marks (？) for interrogative sentences
+- **AND** commas (，) for clause breaks within sentences
+
+#### Scenario: Detect question patterns
+- **WHEN** transcribed text ends with question particles (嗎、呢、什麼、怎麼、為什麼)
+- **THEN** the punctuation processor SHALL append question mark (？)
+