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#!/usr/bin/env python3
"""
Unified WebSocket/HTTP Whisper Transcription Server
Handles real-time audio streaming, transcription using Whisper, and HTTP serving
"""
import asyncio
import websockets
import json
import numpy as np
import torch
import logging
import traceback
import os
from typing import Dict, Any
from aiohttp import web, WSMsgType
from aiohttp.web_ws import WebSocketResponse
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
try:
from whisper_stream import load_streaming_model_correct
from whisper_stream.streaming_decoding import DecodingOptions
except ImportError:
logger.error("whisper_stream not found. Please install it or use regular whisper")
# Fallback to regular whisper if whisper_stream is not available
import whisper
class UnifiedTranscriptionServer:
def __init__(self, host: str = "0.0.0.0", port: int = 8000):
self.host = host
self.port = port
self.clients: Dict[str, Dict[str, Any]] = {}
self.app = web.Application()
self.setup_routes()
def setup_routes(self):
"""Setup HTTP routes and WebSocket endpoint"""
# HTTP routes
self.app.router.add_get('/', self.serve_index)
self.app.router.add_get('/health', self.health_check)
# WebSocket endpoint
self.app.router.add_get('/ws', self.websocket_handler)
# Static file serving (if needed)
if os.path.exists('static'):
self.app.router.add_static('/static/', 'static')
async def serve_index(self, request):
"""Serve the main HTML page"""
try:
with open("./static/client.html", "r", encoding='utf-8') as f:
html_content = f.read()
return web.Response(text=html_content, content_type='text/html')
except FileNotFoundError:
return web.Response(text="client.html not found!", status=404)
except Exception as e:
logger.error(f"Error serving client.html! {e}")
return web.Response(text="Error loading page...", status=500)
async def health_check(self, request):
"""Health check endpoint"""
return web.json_response({"status": "healthy", "cuda": torch.cuda.is_available()})
async def websocket_handler(self, request):
"""Handle WebSocket connections"""
ws = WebSocketResponse()
await ws.prepare(request)
# Generate client ID
client_id = f"{request.remote}:{id(ws)}"
logger.info(f"New WebSocket client connected: {client_id}")
# Initialize client state
self.clients[client_id] = {
'websocket': ws,
'model': None,
'config': None,
'buffer': bytearray(),
'total_samples': 0,
'is_first_chunk': True
}
try:
await self.process_websocket_messages(client_id)
except Exception as e:
logger.error(f"Error handling WebSocket client {client_id}: {e}")
logger.error(traceback.format_exc())
finally:
# Cleanup
if client_id in self.clients:
del self.clients[client_id]
if not ws.closed:
await ws.close()
return ws
async def process_websocket_messages(self, client_id: str):
"""Process messages from a WebSocket client"""
client = self.clients[client_id]
ws = client['websocket']
async for msg in ws:
if msg.type == WSMsgType.TEXT:
# Handle configuration message
await self.handle_config_message(client_id, msg.data)
elif msg.type == WSMsgType.BINARY:
# Handle audio data
await self.handle_audio_data(client_id, msg.data)
elif msg.type == WSMsgType.ERROR:
logger.error(f'WebSocket error for client {client_id}: {ws.exception()}')
break
async def handle_config_message(self, client_id: str, message: str):
"""Handle configuration message from client"""
client = self.clients[client_id]
ws = client['websocket']
try:
config = json.loads(message)
logger.info(f"Received config from {client_id}: {config}")
# Validate config
required_fields = ['model_size', 'chunk_size', 'beam_size', 'language']
for field in required_fields:
if field not in config:
await ws.send_str(json.dumps({"error": f"Missing required field: {field}"}))
return
# Load model
model_size = config['model_size']
chunk_size = config['chunk_size']
logger.info(f"Loading model {model_size} for client {client_id}")
# Check - if language is other than english, throw an error.
# Only large-v2 300msec is available.
if multilingual := config['language'] != "en":
if model_size != "large-v2" or chunk_size != 300:
await ws.send_str(json.dumps({"error": f"Running multilingual transcription is available for now only on large-v2 model using chunk size of 300ms."}))
return
# Try to use whisper_stream, fallback to regular whisper
try:
model = load_streaming_model_correct(model_size, chunk_size, multilingual)
client['first_chunk'] = True
if torch.cuda.is_available():
model = model.to("cuda")
logger.info(f"Model loaded on GPU for client {client_id}")
else:
logger.info(f"Model loaded on CPU for client {client_id}")
model.reset(use_stream=True)
model.eval()
client['model'] = model
client['config'] = config
await ws.send_str(json.dumps({"status": "CONFIG_RECEIVED", "gpu": torch.cuda.is_available()}))
except Exception as e:
logger.error(f"Error loading streaming model: {e}")
# Fallback to regular whisper
try:
model = whisper.load_model(model_size)
if torch.cuda.is_available():
model = model.to("cuda")
client['model'] = model
client['config'] = config
client['use_streaming'] = False
await ws.send_str(json.dumps({"status": "CONFIG_RECEIVED", "gpu": torch.cuda.is_available(), "fallback": True}))
except Exception as e2:
logger.error(f"Error loading fallback model: {e2}")
await ws.send_str(json.dumps({"error": f"Failed to load model: {e2}"}))
except json.JSONDecodeError as e:
await ws.send_str(json.dumps({"error": f"Invalid JSON: {e}"}))
except Exception as e:
logger.error(f"Error handling config for client {client_id}: {e}")
await ws.send_str(json.dumps({"error": str(e)}))
async def handle_audio_data(self, client_id: str, audio_data: bytes):
"""Handle audio data from client"""
client = self.clients[client_id]
ws = client['websocket']
if client['config'] is None:
await ws.send_str(json.dumps({"error": "Config not set"}))
return
if client['model'] is None:
await ws.send_str(json.dumps({"error": "Model not loaded"}))
return
# Add audio data to buffer
client['buffer'].extend(audio_data)
# Calculate chunk size in bytes
chunk_size_ms = client['config']['chunk_size']
sample_rate = 16000
chunk_samples = int(sample_rate * (chunk_size_ms / 1000))
chunk_bytes = chunk_samples * 2 # 16-bit audio = 2 bytes per sample
if client.get('first_chunk', True):
chunk_bytes += 720
# Process complete chunks
while len(client['buffer']) >= chunk_bytes:
chunk = client['buffer'][:chunk_bytes]
client['buffer'] = client['buffer'][chunk_bytes:]
try:
if client.get('first_chunk', True):
client['first_chunk'] = False
await self.transcribe_chunk(client_id, chunk)
except Exception as e:
logger.error(f"Error transcribing chunk for client {client_id}: {e}")
await ws.send_str(json.dumps({"error": f"Transcription error: {str(e)}"}))
async def transcribe_chunk(self, client_id: str, chunk: bytes):
"""Transcribe audio chunk"""
client = self.clients[client_id]
ws = client['websocket']
model = client['model']
config = client['config']
try:
# Convert bytes to numpy array
pcm = np.frombuffer(chunk, dtype=np.int16).astype(np.float32) / 32768.0
# Convert to torch tensor
audio = torch.tensor(pcm)
if torch.cuda.is_available() and next(model.parameters()).is_cuda:
audio = audio.to("cuda")
# Transcribe based on model type
if hasattr(model, 'decode') and 'use_streaming' not in client:
# Using whisper_stream
decoding_options = DecodingOptions(
language=config['language'],
gran=(config['chunk_size'] // 20),
single_frame_mel=True,
without_timestamps=True,
beam_size=config['beam_size'],
stream_decode=True,
use_ca_kv_cache=True,
look_ahead_blocks=model.extra_gran_blocks
)
result = model.decode(audio, decoding_options, use_frames=True)
text = result.text
else:
# Using regular whisper
# Pad audio to minimum length if needed
min_length = 16000 # 1 second at 16kHz
if len(audio) < min_length:
audio = torch.nn.functional.pad(audio, (0, min_length - len(audio)))
result = model.transcribe(audio.cpu().numpy(),
language="en",
beam_size=config['beam_size'],
temperature=config['temperature'])
text = result['text']
# Send transcription result
if text.strip():
client['total_samples'] += len(pcm)
duration = client['total_samples'] / 16000 # seconds
await ws.send_str(json.dumps({
"text": text.strip(),
"timestamp": duration,
"chunk_duration": len(pcm) / 16000
}))
except Exception as e:
logger.error(f"Error in transcription for client {client_id}: {e}")
logger.exception("Exception occurred")
raise
async def start_server(self):
"""Start the unified server"""
logger.info(f"Starting unified server on {self.host}:{self.port}")
logger.info(f"CUDA available: {torch.cuda.is_available()}")
runner = web.AppRunner(self.app)
await runner.setup()
site = web.TCPSite(runner, self.host, self.port)
await site.start()
logger.info(f"Server running on http://{self.host}:{self.port}")
logger.info(f"WebSocket endpoint: ws://{self.host}:{self.port}/ws")
# Keep the server running
try:
await asyncio.Future() # Run forever
except KeyboardInterrupt:
logger.info("Server stopped by user")
finally:
await runner.cleanup()
def main():
import argparse
parser = argparse.ArgumentParser(description='Unified WebSocket/HTTP Whisper Transcription Server')
parser.add_argument('--host', default='0.0.0.0', help='Host to bind to')
parser.add_argument('--port', type=int, default=8000, help='Port to bind to')
parser.add_argument('--log-level', default='INFO', help='Log level')
args = parser.parse_args()
# Set log level
logging.getLogger().setLevel(getattr(logging, args.log_level.upper()))
server = UnifiedTranscriptionServer(args.host, args.port)
try:
asyncio.run(server.start_server())
except KeyboardInterrupt:
logger.info("Server stopped by user")
except Exception as e:
logger.error(f"Server error: {e}")
logger.error(traceback.format_exc())
if __name__ == '__main__':
main() |