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>
This commit is contained in:
egg
45
sidecar/build.py
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sidecar/build.py
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#!/usr/bin/env python3
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"""
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Build script for creating standalone transcriber executable using PyInstaller.
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"""
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import subprocess
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import sys
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import os
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def build():
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"""Build the transcriber executable."""
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# PyInstaller command
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cmd = [
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sys.executable, "-m", "PyInstaller",
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"--onefile",
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"--name", "transcriber",
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"--distpath", "dist",
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"--workpath", "build",
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"--specpath", "build",
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"--hidden-import", "faster_whisper",
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"--hidden-import", "opencc",
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"--hidden-import", "numpy",
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"--hidden-import", "ctranslate2",
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"--hidden-import", "huggingface_hub",
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"--hidden-import", "tokenizers",
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"--collect-data", "faster_whisper",
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"--collect-data", "opencc",
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"transcriber.py"
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]
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print("Building transcriber executable...")
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print(f"Command: {' '.join(cmd)}")
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result = subprocess.run(cmd, cwd=os.path.dirname(os.path.abspath(__file__)))
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if result.returncode == 0:
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print("\nBuild successful! Executable created at: dist/transcriber")
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else:
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print("\nBuild failed!")
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sys.exit(1)
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if __name__ == "__main__":
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build()
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3
sidecar/requirements-dev.txt
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3
sidecar/requirements-dev.txt
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# Development/Build dependencies
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-r requirements.txt
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pyinstaller>=6.0.0
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5
sidecar/requirements.txt
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5
sidecar/requirements.txt
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# Runtime dependencies
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faster-whisper>=1.0.0
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opencc-python-reimplemented>=0.1.7
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numpy>=1.26.0
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onnxruntime>=1.16.0
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510
sidecar/transcriber.py
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510
sidecar/transcriber.py
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#!/usr/bin/env python3
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"""
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Meeting Assistant Transcription Sidecar
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Provides speech-to-text transcription using faster-whisper
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with automatic Traditional Chinese conversion via OpenCC.
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Modes:
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1. File mode: transcriber.py <audio_file>
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2. Server mode: transcriber.py (default, listens on stdin for JSON commands)
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3. Streaming mode: Continuous audio processing with VAD segmentation
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Uses ONNX Runtime for VAD (lightweight, ~20MB vs PyTorch ~2GB)
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"""
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import sys
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import os
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import json
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import tempfile
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import base64
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import uuid
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import re
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import urllib.request
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from pathlib import Path
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from typing import Optional, List
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os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
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try:
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from faster_whisper import WhisperModel
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import opencc
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import numpy as np
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except ImportError as e:
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print(json.dumps({"error": f"Missing dependency: {e}"}), file=sys.stderr)
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sys.exit(1)
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# Try to import ONNX Runtime for VAD
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try:
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import onnxruntime as ort
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ONNX_AVAILABLE = True
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except ImportError:
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ONNX_AVAILABLE = False
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class ChinesePunctuator:
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"""Rule-based Chinese punctuation processor."""
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QUESTION_PATTERNS = [
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r'嗎$', r'呢$', r'什麼$', r'怎麼$', r'為什麼$', r'哪裡$', r'哪個$',
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r'誰$', r'幾$', r'多少$', r'是否$', r'能否$', r'可否$', r'有沒有$',
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r'是不是$', r'會不會$', r'能不能$', r'可不可以$', r'好不好$', r'對不對$'
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]
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def __init__(self):
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self.question_regex = re.compile('|'.join(self.QUESTION_PATTERNS))
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def add_punctuation(self, text: str, word_timestamps: Optional[List] = None) -> str:
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"""Add punctuation to transcribed text."""
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if not text:
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return text
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text = text.strip()
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# Already has ending punctuation
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if text and text[-1] in '。？！，；：':
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return text
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# Check for question patterns
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if self.question_regex.search(text):
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return text + '？'
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# Default to period for statements
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return text + '。'
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def process_segments(self, segments: List[dict]) -> str:
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"""Process multiple segments with timestamps to add punctuation."""
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result_parts = []
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for i, seg in enumerate(segments):
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text = seg.get('text', '').strip()
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if not text:
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continue
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# Check for long pause before next segment (comma opportunity)
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if i < len(segments) - 1:
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next_seg = segments[i + 1]
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gap = next_seg.get('start', 0) - seg.get('end', 0)
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if gap > 0.5 and not text[-1] in '。？！，；：':
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# Long pause, add comma if not end of sentence
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if not self.question_regex.search(text):
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text = text + '，'
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result_parts.append(text)
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# Join and add final punctuation
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result = ''.join(result_parts)
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return self.add_punctuation(result)
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class SileroVAD:
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"""Silero VAD using ONNX Runtime (lightweight alternative to PyTorch)."""
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MODEL_URL = "https://github.com/snakers4/silero-vad/raw/master/src/silero_vad/data/silero_vad.onnx"
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def __init__(self, model_path: Optional[str] = None, threshold: float = 0.5):
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self.threshold = threshold
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self.session = None
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self._h = np.zeros((2, 1, 64), dtype=np.float32)
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self._c = np.zeros((2, 1, 64), dtype=np.float32)
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self.sample_rate = 16000
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if not ONNX_AVAILABLE:
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print(json.dumps({"warning": "onnxruntime not available, VAD disabled"}), file=sys.stderr)
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return
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# Determine model path
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if model_path is None:
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cache_dir = Path.home() / ".cache" / "silero-vad"
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cache_dir.mkdir(parents=True, exist_ok=True)
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model_path = cache_dir / "silero_vad.onnx"
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# Download if not exists
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if not Path(model_path).exists():
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print(json.dumps({"status": "downloading_vad_model"}), file=sys.stderr)
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try:
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urllib.request.urlretrieve(self.MODEL_URL, model_path)
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print(json.dumps({"status": "vad_model_downloaded"}), file=sys.stderr)
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except Exception as e:
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print(json.dumps({"warning": f"VAD model download failed: {e}"}), file=sys.stderr)
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return
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# Load ONNX model
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try:
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self.session = ort.InferenceSession(
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str(model_path),
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providers=['CPUExecutionProvider']
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)
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print(json.dumps({"status": "vad_loaded"}), file=sys.stderr)
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except Exception as e:
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print(json.dumps({"warning": f"VAD load failed: {e}"}), file=sys.stderr)
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def reset_states(self):
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"""Reset hidden states."""
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self._h = np.zeros((2, 1, 64), dtype=np.float32)
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self._c = np.zeros((2, 1, 64), dtype=np.float32)
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def __call__(self, audio: np.ndarray) -> float:
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"""Run VAD on audio chunk, return speech probability."""
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if self.session is None:
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return 0.5 # Neutral if VAD not available
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# Ensure correct shape (batch, samples)
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if audio.ndim == 1:
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audio = audio[np.newaxis, :]
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# Run inference
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ort_inputs = {
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'input': audio.astype(np.float32),
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'sr': np.array([self.sample_rate], dtype=np.int64),
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'h': self._h,
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'c': self._c
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}
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output, self._h, self._c = self.session.run(None, ort_inputs)
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return float(output[0][0])
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class VADProcessor:
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"""Voice Activity Detection processor."""
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def __init__(self, sample_rate: int = 16000, threshold: float = 0.5, vad_model: Optional[SileroVAD] = None):
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self.sample_rate = sample_rate
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self.threshold = threshold
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# Reuse pre-loaded VAD model if provided
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self.vad = vad_model if vad_model else (SileroVAD(threshold=threshold) if ONNX_AVAILABLE else None)
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self.reset()
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def reset(self):
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"""Reset VAD state."""
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self.audio_buffer = np.array([], dtype=np.float32)
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self.speech_buffer = np.array([], dtype=np.float32)
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self.speech_started = False
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self.silence_samples = 0
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self.speech_samples = 0
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if self.vad:
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self.vad.reset_states()
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def process_chunk(self, audio_chunk: np.ndarray) -> Optional[np.ndarray]:
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"""
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Process audio chunk and return speech segment if speech end detected.
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Returns:
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Speech audio if end detected, None otherwise
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"""
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self.audio_buffer = np.concatenate([self.audio_buffer, audio_chunk])
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# Fallback: time-based segmentation if no VAD
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if self.vad is None or self.vad.session is None:
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# Every 5 seconds, return the buffer
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if len(self.audio_buffer) >= self.sample_rate * 5:
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result = self.audio_buffer.copy()
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self.audio_buffer = np.array([], dtype=np.float32)
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return result
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return None
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# Process in 512-sample windows (32ms at 16kHz)
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window_size = 512
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silence_threshold_samples = int(0.5 * self.sample_rate) # 500ms
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max_speech_samples = int(15 * self.sample_rate) # 15s max
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while len(self.audio_buffer) >= window_size:
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window = self.audio_buffer[:window_size]
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self.audio_buffer = self.audio_buffer[window_size:]
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# Run VAD
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speech_prob = self.vad(window)
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if speech_prob >= self.threshold:
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if not self.speech_started:
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self.speech_started = True
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self.speech_buffer = np.array([], dtype=np.float32)
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self.speech_buffer = np.concatenate([self.speech_buffer, window])
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self.silence_samples = 0
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self.speech_samples += window_size
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else:
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if self.speech_started:
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self.speech_buffer = np.concatenate([self.speech_buffer, window])
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self.silence_samples += window_size
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# Force segment if speech too long
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if self.speech_samples >= max_speech_samples:
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result = self.speech_buffer.copy()
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self.speech_started = False
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self.speech_buffer = np.array([], dtype=np.float32)
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self.silence_samples = 0
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self.speech_samples = 0
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return result
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# Detect end of speech (500ms silence)
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if self.speech_started and self.silence_samples >= silence_threshold_samples:
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if len(self.speech_buffer) > self.sample_rate * 0.3: # At least 300ms
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result = self.speech_buffer.copy()
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self.speech_started = False
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self.speech_buffer = np.array([], dtype=np.float32)
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self.silence_samples = 0
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self.speech_samples = 0
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return result
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return None
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def flush(self) -> Optional[np.ndarray]:
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"""Flush remaining audio."""
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# Combine any remaining audio
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remaining = np.concatenate([self.speech_buffer, self.audio_buffer])
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if len(remaining) > self.sample_rate * 0.5: # At least 500ms
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self.reset()
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return remaining
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self.reset()
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return None
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class StreamingSession:
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"""Manages a streaming transcription session."""
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def __init__(self, transcriber: 'Transcriber', vad_model: Optional[SileroVAD] = None):
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self.session_id = str(uuid.uuid4())
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self.transcriber = transcriber
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self.vad = VADProcessor(vad_model=vad_model)
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self.segment_id = 0
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self.active = True
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def process_chunk(self, audio_data: str) -> Optional[dict]:
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"""Process base64-encoded audio chunk."""
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try:
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# Decode base64 to raw PCM (16-bit, 16kHz, mono)
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pcm_data = base64.b64decode(audio_data)
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audio = np.frombuffer(pcm_data, dtype=np.int16).astype(np.float32) / 32768.0
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# Run VAD
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speech_segment = self.vad.process_chunk(audio)
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if speech_segment is not None and len(speech_segment) > 0:
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return self._transcribe_segment(speech_segment)
|
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|
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return None
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except Exception as e:
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return {"error": f"Chunk processing error: {e}"}
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def _transcribe_segment(self, audio: np.ndarray) -> dict:
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"""Transcribe a speech segment."""
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self.segment_id += 1
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|
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# Save to temp file for Whisper
|
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temp_file = tempfile.NamedTemporaryFile(suffix='.wav', delete=False)
|
||||
try:
|
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import wave
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with wave.open(temp_file.name, 'wb') as wf:
|
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wf.setnchannels(1)
|
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wf.setsampwidth(2)
|
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wf.setframerate(16000)
|
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wf.writeframes((audio * 32768).astype(np.int16).tobytes())
|
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|
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# Transcribe
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text = self.transcriber.transcribe_file(temp_file.name, add_punctuation=True)
|
||||
|
||||
return {
|
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"segment_id": self.segment_id,
|
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"text": text,
|
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"is_final": True,
|
||||
"duration": len(audio) / 16000
|
||||
}
|
||||
finally:
|
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os.unlink(temp_file.name)
|
||||
|
||||
def stop(self) -> dict:
|
||||
"""Stop the session and flush remaining audio."""
|
||||
self.active = False
|
||||
results = []
|
||||
|
||||
# Flush VAD buffer
|
||||
remaining = self.vad.flush()
|
||||
if remaining is not None and len(remaining) > 0:
|
||||
result = self._transcribe_segment(remaining)
|
||||
if result and not result.get('error'):
|
||||
results.append(result)
|
||||
|
||||
return {
|
||||
"status": "stream_stopped",
|
||||
"session_id": self.session_id,
|
||||
"total_segments": self.segment_id,
|
||||
"final_segments": results
|
||||
}
|
||||
|
||||
|
||||
class Transcriber:
|
||||
"""Main transcription engine."""
|
||||
|
||||
def __init__(self, model_size: str = "medium", device: str = "cpu", compute_type: str = "int8"):
|
||||
self.model = None
|
||||
self.converter = None
|
||||
self.punctuator = ChinesePunctuator()
|
||||
self.streaming_session: Optional[StreamingSession] = None
|
||||
self.vad_model: Optional[SileroVAD] = None
|
||||
|
||||
try:
|
||||
print(json.dumps({"status": "loading_model", "model": model_size}), file=sys.stderr)
|
||||
self.model = WhisperModel(model_size, device=device, compute_type=compute_type)
|
||||
self.converter = opencc.OpenCC("s2twp")
|
||||
print(json.dumps({"status": "model_loaded"}), file=sys.stderr)
|
||||
|
||||
# Pre-load VAD model at startup (not when streaming starts)
|
||||
if ONNX_AVAILABLE:
|
||||
self.vad_model = SileroVAD()
|
||||
|
||||
except Exception as e:
|
||||
print(json.dumps({"error": f"Failed to load model: {e}"}), file=sys.stderr)
|
||||
raise
|
||||
|
||||
def transcribe_file(self, audio_path: str, add_punctuation: bool = False) -> str:
|
||||
"""Transcribe an audio file to text."""
|
||||
if not self.model:
|
||||
return ""
|
||||
|
||||
if not os.path.exists(audio_path):
|
||||
print(json.dumps({"error": f"File not found: {audio_path}"}), file=sys.stderr)
|
||||
return ""
|
||||
|
||||
try:
|
||||
segments, info = self.model.transcribe(
|
||||
audio_path,
|
||||
language="zh", # Use "nan" for Taiwanese/Hokkien, "zh" for Mandarin
|
||||
beam_size=5,
|
||||
vad_filter=True,
|
||||
word_timestamps=add_punctuation,
|
||||
# Anti-hallucination settings
|
||||
condition_on_previous_text=False, # Prevents hallucination propagation
|
||||
no_speech_threshold=0.6, # Higher = stricter silence detection
|
||||
compression_ratio_threshold=2.4, # Filter repetitive/hallucinated text
|
||||
log_prob_threshold=-1.0, # Filter low-confidence output
|
||||
temperature=0.0, # Deterministic output (no sampling)
|
||||
)
|
||||
|
||||
if add_punctuation:
|
||||
# Collect segments with timestamps for punctuation
|
||||
seg_list = []
|
||||
for segment in segments:
|
||||
seg_list.append({
|
||||
'text': segment.text,
|
||||
'start': segment.start,
|
||||
'end': segment.end
|
||||
})
|
||||
text = self.punctuator.process_segments(seg_list)
|
||||
else:
|
||||
text = ""
|
||||
for segment in segments:
|
||||
text += segment.text
|
||||
|
||||
# Convert to Traditional Chinese
|
||||
if text and self.converter:
|
||||
text = self.converter.convert(text)
|
||||
|
||||
return text.strip()
|
||||
|
||||
except Exception as e:
|
||||
print(json.dumps({"error": f"Transcription error: {e}"}), file=sys.stderr)
|
||||
return ""
|
||||
|
||||
def handle_command(self, cmd: dict) -> Optional[dict]:
|
||||
"""Handle a JSON command."""
|
||||
action = cmd.get("action")
|
||||
|
||||
if action == "transcribe":
|
||||
# File-based transcription (legacy)
|
||||
audio_path = cmd.get("file")
|
||||
if audio_path:
|
||||
text = self.transcribe_file(audio_path, add_punctuation=True)
|
||||
return {"result": text, "file": audio_path}
|
||||
return {"error": "No file specified"}
|
||||
|
||||
elif action == "start_stream":
|
||||
# Start streaming session
|
||||
if self.streaming_session and self.streaming_session.active:
|
||||
return {"error": "Stream already active"}
|
||||
# Pass pre-loaded VAD model to avoid download delay
|
||||
self.streaming_session = StreamingSession(self, vad_model=self.vad_model)
|
||||
return {
|
||||
"status": "streaming",
|
||||
"session_id": self.streaming_session.session_id
|
||||
}
|
||||
|
||||
elif action == "audio_chunk":
|
||||
# Process audio chunk
|
||||
if not self.streaming_session or not self.streaming_session.active:
|
||||
return {"error": "No active stream"}
|
||||
data = cmd.get("data")
|
||||
if not data:
|
||||
return {"error": "No audio data"}
|
||||
result = self.streaming_session.process_chunk(data)
|
||||
return result # May be None if no segment ready
|
||||
|
||||
elif action == "stop_stream":
|
||||
# Stop streaming session
|
||||
if not self.streaming_session:
|
||||
return {"error": "No active stream"}
|
||||
result = self.streaming_session.stop()
|
||||
self.streaming_session = None
|
||||
return result
|
||||
|
||||
elif action == "ping":
|
||||
return {"status": "pong"}
|
||||
|
||||
elif action == "quit":
|
||||
return {"status": "exiting"}
|
||||
|
||||
else:
|
||||
return {"error": f"Unknown action: {action}"}
|
||||
|
||||
def run_server(self):
|
||||
"""Run in server mode, reading JSON commands from stdin."""
|
||||
print(json.dumps({"status": "ready"}))
|
||||
sys.stdout.flush()
|
||||
|
||||
for line in sys.stdin:
|
||||
line = line.strip()
|
||||
if not line:
|
||||
continue
|
||||
|
||||
try:
|
||||
cmd = json.loads(line)
|
||||
result = self.handle_command(cmd)
|
||||
|
||||
if result:
|
||||
print(json.dumps(result))
|
||||
sys.stdout.flush()
|
||||
|
||||
if cmd.get("action") == "quit":
|
||||
break
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
print(json.dumps({"error": f"Invalid JSON: {e}"}))
|
||||
sys.stdout.flush()
|
||||
|
||||
|
||||
def main():
|
||||
model_size = os.environ.get("WHISPER_MODEL", "small")
|
||||
device = os.environ.get("WHISPER_DEVICE", "cpu")
|
||||
compute_type = os.environ.get("WHISPER_COMPUTE", "int8")
|
||||
|
||||
try:
|
||||
transcriber = Transcriber(model_size, device, compute_type)
|
||||
|
||||
if len(sys.argv) > 1:
|
||||
if sys.argv[1] == "--server":
|
||||
transcriber.run_server()
|
||||
else:
|
||||
# File mode
|
||||
text = transcriber.transcribe_file(sys.argv[1], add_punctuation=True)
|
||||
print(text)
|
||||
else:
|
||||
# Default to server mode
|
||||
transcriber.run_server()
|
||||
|
||||
except Exception as e:
|
||||
print(json.dumps({"error": str(e)}), file=sys.stderr)
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user