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>
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## Context
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The Meeting Assistant currently uses batch transcription: audio is recorded, saved to file, then sent to Whisper for processing. This creates a poor UX where users must wait until recording stops to see any text. Users also cannot correct transcription errors.
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**Stakeholders**: End users recording meetings, admin reviewing transcripts
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**Constraints**: i5/8GB hardware target, offline capability required
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## Goals / Non-Goals
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### Goals
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- Real-time text display during recording (< 3 second latency)
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- Segment-based editing without disrupting ongoing transcription
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- Punctuation in output (Chinese: 。，？！；：)
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- Maintain offline capability (all processing local)
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### Non-Goals
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- Speaker diarization (who said what) - future enhancement
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- Multi-language mixing - Chinese only for MVP
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- Cloud-based transcription fallback
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## Architecture
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```
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┌─────────────────────────────────────────────────────────────┐
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│ Renderer Process (meeting-detail.html) │
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│ ┌──────────────┐ ┌─────────────────────────────────┐ │
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│ │ MediaRecorder│───▶│ Editable Transcript Component │ │
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│ │ (audio chunks) │ [Segment 1] [Segment 2] [...] │ │
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│ └──────┬───────┘ └─────────────────────────────────┘ │
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│ │ IPC: stream-audio-chunk │
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└─────────┼──────────────────────────────────────────────────┘
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▼
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┌─────────────────────────────────────────────────────────────┐
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│ Main Process (main.js) │
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│ ┌──────────────────┐ ┌─────────────────────────────┐ │
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│ │ Audio Buffer │────▶│ Sidecar (stdin pipe) │ │
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│ │ (accumulate PCM) │ │ │ │
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│ └──────────────────┘ └──────────┬──────────────────┘ │
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│ │ IPC: transcription-segment
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│ ▼ │
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│ Forward to renderer │
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└─────────────────────────────────────────────────────────────┘
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│
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▼ stdin (WAV chunks)
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┌─────────────────────────────────────────────────────────────┐
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│ Sidecar Process (transcriber.py) │
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│ ┌──────────────┐ ┌──────────────┐ ┌────────────────┐ │
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│ │ VAD Buffer │──▶│ Whisper │──▶│ Punctuator │ │
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│ │ (silero-vad) │ │ (transcribe) │ │ (rule-based) │ │
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│ └──────────────┘ └──────────────┘ └────────────────┘ │
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│ │ │ │
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│ │ Detect speech end │ │
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│ ▼ ▼ │
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│ stdout: {"segment_id": 1, "text": "今天開會討論。", ...} │
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└─────────────────────────────────────────────────────────────┘
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```
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## Decisions
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### Decision 1: VAD-triggered Segmentation
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**What**: Use Silero VAD to detect speech boundaries, transcribe complete utterances
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**Why**:
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- More accurate than fixed-interval chunking
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- Natural sentence boundaries
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- Reduces partial/incomplete transcriptions
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**Alternatives**:
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- Fixed 5-second chunks (simpler but cuts mid-sentence)
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- Word-level streaming (too fragmented, higher latency)
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### Decision 2: Segment-based Editing
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**What**: Each VAD segment becomes an editable text block with unique ID
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**Why**:
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- Users can edit specific segments without affecting others
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- New segments append without disrupting editing
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- Simple merge on save (concatenate all segments)
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**Alternatives**:
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- Single textarea (editing conflicts with appending text)
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- Contenteditable div (complex cursor management)
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### Decision 3: Audio Format Pipeline
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**What**: WebM (MediaRecorder) → WAV conversion in main.js → raw PCM to sidecar
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**Why**:
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- MediaRecorder only outputs WebM/Opus in browsers
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- Whisper works best with WAV/PCM
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- Conversion in main.js keeps sidecar simple
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**Alternatives**:
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- ffmpeg in sidecar (adds large dependency)
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- Raw PCM from AudioWorklet (complex, browser compatibility issues)
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### Decision 4: Punctuation via Whisper + Rules
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**What**: Enable Whisper word_timestamps, apply rule-based punctuation after
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**Why**:
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- Whisper alone outputs minimal punctuation for Chinese
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- Rule-based post-processing adds 。，？ based on pauses and patterns
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- No additional model needed
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**Alternatives**:
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- Separate punctuation model (adds latency and complexity)
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- No punctuation (user requirement)
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## Risks / Trade-offs
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| Risk | Mitigation |
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|------|------------|
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| Latency > 3s on slow hardware | Use "tiny" model option, skip VAD if needed |
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| WebM→WAV conversion quality loss | Use lossless conversion, test on various inputs |
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| Memory usage with long meetings | Limit audio buffer to 30s, process and discard |
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| Segment boundary splits words | Use VAD with 500ms silence threshold |
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## Implementation Phases
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1. **Phase 1**: Sidecar streaming mode with VAD
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2. **Phase 2**: IPC audio streaming pipeline
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3. **Phase 3**: Frontend editable segment component
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4. **Phase 4**: Punctuation post-processing
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## Open Questions
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- Should segments be auto-merged after N seconds of no editing?
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- Maximum segment count before auto-archiving old segments?
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# Change: Add Real-time Streaming Transcription
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## Why
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Current transcription workflow requires users to stop recording before seeing results. Users cannot edit transcription errors, and output lacks punctuation. For meeting scenarios, real-time feedback with editable text is essential for immediate correction and context awareness.
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## What Changes
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- **Sidecar**: Implement streaming VAD-based transcription with sentence segmentation
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- **IPC**: Add continuous audio streaming from renderer to main process to sidecar
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- **Frontend**: Make transcript editable with real-time segment updates
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- **Punctuation**: Enable Whisper's word timestamps and add sentence boundary detection
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## Impact
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- Affected specs: `transcription` (new), `frontend-transcript` (new)
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- Affected code:
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- `sidecar/transcriber.py` - Add streaming mode with VAD
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- `client/src/main.js` - Add audio streaming IPC handlers
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- `client/src/preload.js` - Expose streaming APIs
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- `client/src/pages/meeting-detail.html` - Editable transcript component
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## Success Criteria
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1. User sees text appearing within 2-3 seconds of speaking
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2. Each segment is individually editable
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3. Output includes punctuation (。，？！)
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4. Recording can continue while user edits previous segments
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## ADDED Requirements
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### Requirement: Editable Transcript Segments
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The frontend SHALL display transcribed text as individually editable segments that can be modified without disrupting ongoing transcription.
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#### Scenario: Display new segment
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- **WHEN** a new transcription segment is received from sidecar
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- **THEN** a new editable text block SHALL appear in the transcript area
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- **AND** the block SHALL be visually distinct (e.g., border, background)
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- **AND** the block SHALL be immediately editable
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#### Scenario: Edit existing segment
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- **WHEN** user modifies text in a segment
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- **THEN** only that segment's local data SHALL be updated
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- **AND** new incoming segments SHALL continue to append below
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- **AND** the edited segment SHALL show an "edited" indicator
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#### Scenario: Save merged transcript
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- **WHEN** user clicks Save button
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- **THEN** all segments (edited and unedited) SHALL be concatenated in order
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- **AND** the merged text SHALL be saved as transcript_blob
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### Requirement: Real-time Streaming UI
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The frontend SHALL provide clear visual feedback during streaming transcription.
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#### Scenario: Recording active indicator
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- **WHEN** streaming recording is active
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- **THEN** a pulsing recording indicator SHALL be visible
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- **AND** the current/active segment SHALL have distinct styling (e.g., highlighted border)
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- **AND** the Start Recording button SHALL change to Stop Recording
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#### Scenario: Processing indicator
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- **WHEN** audio is being processed but no text has appeared yet
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- **THEN** a "Processing..." indicator SHALL appear in the active segment area
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- **AND** the indicator SHALL disappear when text arrives
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#### Scenario: Streaming status display
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- **WHEN** streaming session is active
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- **THEN** the UI SHALL display segment count (e.g., "Segment 5/5")
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- **AND** total recording duration
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### Requirement: Audio Streaming IPC
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The Electron main process SHALL provide IPC handlers for continuous audio streaming between renderer and sidecar.
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#### Scenario: Start streaming
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- **WHEN** renderer calls `startRecordingStream()`
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- **THEN** main process SHALL send start_stream command to sidecar
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- **AND** return session confirmation to renderer
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#### Scenario: Stream audio data
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- **WHEN** renderer sends audio chunk via `streamAudioChunk(arrayBuffer)`
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- **THEN** main process SHALL convert WebM to PCM if needed
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- **AND** forward to sidecar stdin as base64-encoded audio_chunk command
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#### Scenario: Receive transcription
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- **WHEN** sidecar emits a segment result on stdout
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- **THEN** main process SHALL parse the JSON
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- **AND** forward to renderer via `transcription-segment` IPC event
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## ADDED Requirements
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### Requirement: Streaming Transcription Mode
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The sidecar SHALL support a streaming mode where audio chunks are continuously received and transcribed in real-time with VAD-triggered segmentation.
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#### Scenario: Start streaming session
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- **WHEN** sidecar receives `{"action": "start_stream"}` command
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- **THEN** it SHALL initialize audio buffer and VAD processor
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- **AND** respond with `{"status": "streaming", "session_id": "<uuid>"}`
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#### Scenario: Process audio chunk
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- **WHEN** sidecar receives `{"action": "audio_chunk", "data": "<base64_pcm>"}` during active stream
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- **THEN** it SHALL append audio to buffer and run VAD detection
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- **AND** if speech boundary detected, transcribe accumulated audio
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- **AND** emit `{"segment_id": <int>, "text": "<transcription>", "is_final": true}`
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#### Scenario: Stop streaming session
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- **WHEN** sidecar receives `{"action": "stop_stream"}` command
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- **THEN** it SHALL transcribe any remaining buffered audio
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- **AND** respond with `{"status": "stream_stopped", "total_segments": <int>}`
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### Requirement: VAD-based Speech Segmentation
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The sidecar SHALL use Voice Activity Detection to identify natural speech boundaries for segmentation.
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#### Scenario: Detect speech end
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- **WHEN** VAD detects silence exceeding 500ms after speech
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- **THEN** the accumulated speech audio SHALL be sent for transcription
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- **AND** a new segment SHALL begin for subsequent speech
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#### Scenario: Handle continuous speech
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- **WHEN** speech continues for more than 15 seconds without pause
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- **THEN** the sidecar SHALL force a segment boundary
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- **AND** transcribe the 15-second chunk to prevent excessive latency
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### Requirement: Punctuation in Transcription Output
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The sidecar SHALL output transcribed text with appropriate Chinese punctuation marks.
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#### Scenario: Add sentence-ending punctuation
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- **WHEN** transcription completes for a segment
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- **THEN** the output SHALL include period (。) at natural sentence boundaries
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- **AND** question marks (？) for interrogative sentences
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- **AND** commas (，) for clause breaks within sentences
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#### Scenario: Detect question patterns
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- **WHEN** transcribed text ends with question particles (嗎、呢、什麼、怎麼、為什麼)
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- **THEN** the punctuation processor SHALL append question mark (？)
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## 1. Sidecar Streaming Infrastructure
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- [x] 1.1 Add silero-vad dependency to requirements.txt
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- [x] 1.2 Implement VADProcessor class with speech boundary detection
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- [x] 1.3 Add streaming mode to Transcriber (action: "start_stream", "audio_chunk", "stop_stream")
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- [x] 1.4 Implement audio buffer with VAD-triggered transcription
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- [x] 1.5 Add segment_id tracking for each utterance
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- [x] 1.6 Test VAD with sample Chinese speech audio
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## 2. Punctuation Processing
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- [x] 2.1 Enable word_timestamps in Whisper transcribe()
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- [x] 2.2 Implement ChinesePunctuator class with rule-based punctuation
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- [x] 2.3 Add pause-based sentence boundary detection (>500ms → period)
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- [x] 2.4 Add question detection (嗎、呢、什麼 patterns → ？)
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- [x] 2.5 Test punctuation output quality with sample transcripts
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## 3. IPC Audio Streaming
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- [x] 3.1 Add "start-recording-stream" IPC handler in main.js
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- [x] 3.2 Add "stream-audio-chunk" IPC handler to forward audio to sidecar
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- [x] 3.3 Add "stop-recording-stream" IPC handler
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- [x] 3.4 Implement WebM to PCM conversion using web-audio-api or ffmpeg.wasm
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- [x] 3.5 Forward sidecar segment events to renderer via "transcription-segment" IPC
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- [x] 3.6 Update preload.js with streaming API exposure
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## 4. Frontend Editable Transcript
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- [x] 4.1 Create TranscriptSegment component (editable text block with segment_id)
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- [x] 4.2 Implement segment container with append-only behavior during recording
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- [x] 4.3 Add edit handler that updates local segment data
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- [x] 4.4 Style active segment (currently receiving text) differently
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- [x] 4.5 Update Save button to merge all segments into transcript_blob
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- [x] 4.6 Add visual indicator for streaming status
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## 5. Integration & Testing
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- [x] 5.1 End-to-end test: start recording → speak → see text appear
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- [x] 5.2 Test editing segment while new segments arrive
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- [x] 5.3 Test save with mixed edited/unedited segments
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- [x] 5.4 Performance test on i5/8GB target hardware
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- [x] 5.5 Test with 30+ minute continuous recording
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- [x] 5.6 Update meeting-detail.html recording flow documentation
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## Dependencies
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- Task 3 depends on Task 1 (sidecar must support streaming first)
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- Task 4 depends on Task 3 (frontend needs IPC to receive segments)
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- Task 2 can run in parallel with Task 3
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## Parallelizable Work
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- Tasks 1 and 4 can start simultaneously (sidecar and frontend scaffolding)
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- Task 2 can run in parallel with Task 3
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## Implementation Notes
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- VAD uses Silero VAD with fallback to 5-second time-based segmentation if torch unavailable
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- Audio captured at 16kHz mono, converted to int16 PCM, sent as base64
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- ChinesePunctuator uses regex patterns for question detection
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- Segments are editable immediately, edited segments marked with orange border
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