Add unified per-speaker font support and remote transcription service
Font changes: - Consolidate font settings into single Display Settings section - Support Web-Safe, Google Fonts, and Custom File uploads for both displays - Fix Google Fonts URL encoding (use + instead of %2B for spaces) - Fix per-speaker font inline style quote escaping in Node.js display - Add font debug logging to help diagnose font issues - Update web server to sync all font settings on settings change - Remove deprecated PHP server documentation files New features: - Add remote transcription service for GPU offloading - Add instance lock to prevent multiple app instances - Add version tracking Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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366
server/transcription-service/server.py
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366
server/transcription-service/server.py
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"""
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Remote Transcription Service
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A standalone FastAPI WebSocket server that accepts audio streams and returns transcriptions.
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Designed to run on a GPU-equipped server for offloading transcription processing.
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Usage:
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python server.py [--host HOST] [--port PORT] [--model MODEL]
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Environment variables:
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TRANSCRIPTION_API_KEY: Required API key for authentication
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TRANSCRIPTION_MODEL: Whisper model to use (default: base.en)
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"""
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import asyncio
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import argparse
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import os
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import sys
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import json
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import base64
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import logging
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from datetime import datetime
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from pathlib import Path
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from typing import Optional, Dict, Set
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from threading import Thread, Lock
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import numpy as np
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from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException, Depends
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from fastapi.responses import JSONResponse
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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import uvicorn
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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# API Key authentication
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API_KEYS: Set[str] = set()
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def load_api_keys():
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"""Load API keys from environment variable."""
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global API_KEYS
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keys_env = os.environ.get('TRANSCRIPTION_API_KEYS', '')
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if keys_env:
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API_KEYS = set(key.strip() for key in keys_env.split(',') if key.strip())
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# Also support single key
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single_key = os.environ.get('TRANSCRIPTION_API_KEY', '')
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if single_key:
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API_KEYS.add(single_key)
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if not API_KEYS:
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logger.warning("No API keys configured. Set TRANSCRIPTION_API_KEY or TRANSCRIPTION_API_KEYS environment variable.")
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logger.warning("Service will accept all connections (INSECURE for production).")
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def verify_api_key(api_key: str) -> bool:
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"""Verify if the API key is valid."""
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if not API_KEYS:
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return True # No authentication if no keys configured
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return api_key in API_KEYS
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app = FastAPI(
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title="Remote Transcription Service",
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description="GPU-accelerated speech-to-text transcription service",
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version="1.0.0"
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)
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# Enable CORS for all origins (configure appropriately for production)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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class TranscriptionEngine:
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"""Manages the transcription engine with thread-safe access."""
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def __init__(self, model: str = "base.en", device: str = "auto"):
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self.model_name = model
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self.device = device
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self.recorder = None
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self.lock = Lock()
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self.is_initialized = False
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def initialize(self):
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"""Initialize the transcription engine."""
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if self.is_initialized:
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return True
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try:
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from RealtimeSTT import AudioToTextRecorder
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# Determine device
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if self.device == "auto":
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import torch
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if torch.cuda.is_available():
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self.device = "cuda"
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else:
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self.device = "cpu"
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logger.info(f"Initializing transcription engine with model={self.model_name}, device={self.device}")
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# Create recorder with minimal configuration
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# We'll feed audio directly, not capture from microphone
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self.recorder = AudioToTextRecorder(
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model=self.model_name,
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language="en",
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device=self.device,
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compute_type="default",
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input_device_index=None, # No mic capture
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silero_sensitivity=0.4,
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webrtc_sensitivity=3,
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post_speech_silence_duration=0.3,
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min_length_of_recording=0.5,
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enable_realtime_transcription=True,
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realtime_model_type="tiny.en",
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)
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self.is_initialized = True
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logger.info("Transcription engine initialized successfully")
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return True
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except Exception as e:
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logger.error(f"Failed to initialize transcription engine: {e}")
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return False
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def transcribe(self, audio_data: np.ndarray, sample_rate: int = 16000) -> Optional[str]:
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"""
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Transcribe audio data.
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Args:
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audio_data: Audio data as numpy array
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sample_rate: Sample rate of the audio
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Returns:
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Transcribed text or None if failed
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"""
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with self.lock:
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if not self.is_initialized:
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return None
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try:
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# Use faster-whisper directly for one-shot transcription
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from faster_whisper import WhisperModel
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if not hasattr(self, '_whisper_model'):
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self._whisper_model = WhisperModel(
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self.model_name,
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device=self.device,
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compute_type="default"
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)
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# Transcribe
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segments, info = self._whisper_model.transcribe(
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audio_data,
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beam_size=5,
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language="en"
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)
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# Combine segments
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text = " ".join(segment.text for segment in segments)
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return text.strip()
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except Exception as e:
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logger.error(f"Transcription error: {e}")
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return None
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# Global transcription engine
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engine: Optional[TranscriptionEngine] = None
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class ClientConnection:
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"""Represents an active client connection."""
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def __init__(self, websocket: WebSocket, client_id: str):
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self.websocket = websocket
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self.client_id = client_id
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self.audio_buffer = []
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self.sample_rate = 16000
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self.connected_at = datetime.now()
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# Active connections
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active_connections: Dict[str, ClientConnection] = {}
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@app.on_event("startup")
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async def startup_event():
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"""Initialize service on startup."""
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load_api_keys()
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global engine
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model = os.environ.get('TRANSCRIPTION_MODEL', 'base.en')
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engine = TranscriptionEngine(model=model)
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# Initialize in background thread to not block startup
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def init_engine():
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engine.initialize()
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Thread(target=init_engine, daemon=True).start()
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logger.info("Remote Transcription Service started")
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@app.get("/")
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async def root():
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"""Health check endpoint."""
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return {
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"service": "Remote Transcription Service",
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"status": "running",
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"model": engine.model_name if engine else "not loaded",
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"device": engine.device if engine else "unknown",
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"active_connections": len(active_connections)
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}
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@app.get("/health")
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async def health():
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"""Detailed health check."""
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return {
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"status": "healthy" if engine and engine.is_initialized else "initializing",
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"model": engine.model_name if engine else None,
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"device": engine.device if engine else None,
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"initialized": engine.is_initialized if engine else False,
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"connections": len(active_connections)
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}
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@app.websocket("/ws/transcribe")
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async def websocket_transcribe(websocket: WebSocket):
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"""
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WebSocket endpoint for audio transcription.
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Protocol:
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1. Client sends: {"type": "auth", "api_key": "your-key"}
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2. Server responds: {"type": "auth_result", "success": true/false}
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3. Client sends audio chunks: {"type": "audio", "data": base64_audio, "sample_rate": 16000}
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4. Server responds with transcription: {"type": "transcription", "text": "...", "is_preview": false}
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5. Client can send: {"type": "end"} to close connection
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"""
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await websocket.accept()
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client_id = f"client_{id(websocket)}_{datetime.now().timestamp()}"
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authenticated = False
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logger.info(f"New WebSocket connection: {client_id}")
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try:
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while True:
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data = await websocket.receive_text()
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message = json.loads(data)
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msg_type = message.get("type", "")
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if msg_type == "auth":
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# Authenticate client
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api_key = message.get("api_key", "")
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if verify_api_key(api_key):
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authenticated = True
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active_connections[client_id] = ClientConnection(websocket, client_id)
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await websocket.send_json({
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"type": "auth_result",
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"success": True,
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"message": "Authentication successful"
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})
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logger.info(f"Client {client_id} authenticated")
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else:
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await websocket.send_json({
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"type": "auth_result",
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"success": False,
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"message": "Invalid API key"
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})
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logger.warning(f"Client {client_id} failed authentication")
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await websocket.close(code=4001, reason="Invalid API key")
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return
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elif msg_type == "audio":
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if not authenticated:
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await websocket.send_json({
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"type": "error",
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"message": "Not authenticated"
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})
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continue
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# Decode audio data
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audio_b64 = message.get("data", "")
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sample_rate = message.get("sample_rate", 16000)
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if audio_b64:
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try:
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audio_bytes = base64.b64decode(audio_b64)
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audio_data = np.frombuffer(audio_bytes, dtype=np.float32)
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# Transcribe
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if engine and engine.is_initialized:
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text = engine.transcribe(audio_data, sample_rate)
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if text:
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await websocket.send_json({
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"type": "transcription",
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"text": text,
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"is_preview": False,
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"timestamp": datetime.now().isoformat()
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})
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else:
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await websocket.send_json({
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"type": "error",
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"message": "Transcription engine not ready"
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})
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except Exception as e:
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logger.error(f"Audio processing error: {e}")
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await websocket.send_json({
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"type": "error",
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"message": f"Audio processing error: {str(e)}"
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})
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elif msg_type == "end":
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logger.info(f"Client {client_id} requested disconnect")
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break
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elif msg_type == "ping":
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await websocket.send_json({"type": "pong"})
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except WebSocketDisconnect:
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logger.info(f"Client {client_id} disconnected")
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except Exception as e:
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logger.error(f"WebSocket error for {client_id}: {e}")
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finally:
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if client_id in active_connections:
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del active_connections[client_id]
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def main():
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"""Main entry point."""
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parser = argparse.ArgumentParser(description="Remote Transcription Service")
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parser.add_argument("--host", default="0.0.0.0", help="Host to bind to")
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parser.add_argument("--port", type=int, default=8765, help="Port to bind to")
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parser.add_argument("--model", default="base.en", help="Whisper model to use")
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args = parser.parse_args()
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# Set model from command line
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os.environ.setdefault('TRANSCRIPTION_MODEL', args.model)
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logger.info(f"Starting Remote Transcription Service on {args.host}:{args.port}")
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logger.info(f"Model: {args.model}")
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uvicorn.run(
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app,
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host=args.host,
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port=args.port,
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log_level="info"
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)
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if __name__ == "__main__":
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main()
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