import base64 import faster_whisper import tempfile import numpy as np import torch import time import requests import logging from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect import websockets from pydantic import BaseModel from typing import Optional import sys import asyncio # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s', handlers=[logging.StreamHandler(sys.stdout)], force=True) logger = logging.getLogger(__name__) #logging.getLogger("asyncio").setLevel(logging.DEBUG) device = 'cuda' if torch.cuda.is_available() else 'cpu' logging.info(f'Device selected: {device}') model_name = 'ivrit-ai/faster-whisper-v2-d4' logging.info(f'Loading model: {model_name}') model = faster_whisper.WhisperModel(model_name, device=device) logging.info('Model loaded successfully') # Maximum data size: 200MB MAX_PAYLOAD_SIZE = 200 * 1024 * 1024 logging.info(f'Max payload size set to: {MAX_PAYLOAD_SIZE} bytes') app = FastAPI() # class InputData(BaseModel): # type: str # data: Optional[str] = None # Used for blob input # url: Optional[str] = None # Used for url input # # # def download_file(url, max_size_bytes, output_filename, api_key=None): # """ # Download a file from a given URL with size limit and optional API key. # """ # logging.debug(f'Starting file download from URL: {url}') # try: # headers = {} # if api_key: # headers['Authorization'] = f'Bearer {api_key}' # logging.debug('API key provided, added to headers') # # response = requests.get(url, stream=True, headers=headers) # response.raise_for_status() # # file_size = int(response.headers.get('Content-Length', 0)) # logging.info(f'File size: {file_size} bytes') # # if file_size > max_size_bytes: # logging.error(f'File size exceeds limit: {file_size} > {max_size_bytes}') # return False # # downloaded_size = 0 # with open(output_filename, 'wb') as file: # for chunk in response.iter_content(chunk_size=8192): # downloaded_size += len(chunk) # logging.debug(f'Downloaded {downloaded_size} bytes') # if downloaded_size > max_size_bytes: # logging.error('Downloaded size exceeds maximum allowed payload size') # return False # file.write(chunk) # # logging.info(f'File downloaded successfully: {output_filename}') # return True # # except requests.RequestException as e: # logging.error(f"Error downloading file: {e}") # return False @app.get("/") async def read_root(): return {"message": "This is the Ivrit AI Streaming service."} # async def transcribe_core_ws(audio_file): # ret = {'segments': []} # # try: # # logging.debug(f"Initiating model transcription for file: {audio_file}") # # segs, _ = await asyncio.to_thread(model.transcribe, audio_file, language='he', word_timestamps=True) # logging.info('Transcription completed successfully.') # except Exception as e: # logging.error(f"Error during transcription: {e}") # raise e # # # Track the new segments and update the last transcribed time # for s in segs: # logging.info(f"Processing segment with start time: {s.start} and end time: {s.end}") # # # Only process segments that start after the last transcribed time # logging.info(f"New segment found starting at {s.start} seconds.") # words = [{'start': w.start, 'end': w.end, 'word': w.word, 'probability': w.probability} for w in s.words] # # seg = { # 'id': s.id, 'seek': s.seek, 'start': s.start, 'end': s.end, 'text': s.text, # 'avg_logprob': s.avg_logprob, 'compression_ratio': s.compression_ratio, # 'no_speech_prob': s.no_speech_prob, 'words': words # } # logging.info(f'Adding new transcription segment: {seg}') # ret['segements'].append(seg) # # # Update the last transcribed time to the end of the current segment # # # #logging.info(f"Returning {len(ret['new_segments'])} new segments and updated last transcribed time.") # return ret import tempfile @app.websocket("/ws") async def websocket_endpoint(websocket: WebSocket): """WebSocket endpoint to handle client connections.""" await websocket.accept() client_ip = websocket.client.host logger.info(f"Client connected: {client_ip}") try: await process_audio_stream(websocket) except WebSocketDisconnect: logger.info(f"Client disconnected: {client_ip}") except Exception as e: logger.error(f"Unexpected error: {e}") await websocket.close() async def process_audio_stream(websocket: WebSocket): """Continuously receive audio chunks and initiate transcription tasks.""" sampling_rate = 16000 min_chunk_size = 1 # in seconds audio_buffer = np.array([], dtype=np.float32) transcription_task = None chunk_counter = 0 total_bytes_received = 0 while True: try: # Receive audio data from client data = await websocket.receive_bytes() if not data: logger.info("No data received, closing connection") break chunk_counter += 1 chunk_size = len(data) total_bytes_received += chunk_size logger.debug(f"Received chunk {chunk_counter}: {chunk_size} bytes") audio_chunk = process_received_audio(data) logger.debug(f"Processed audio chunk {chunk_counter}: {len(audio_chunk)} samples") audio_buffer = np.concatenate((audio_buffer, audio_chunk)) logger.debug(f"Audio buffer size: {len(audio_buffer)} samples") except Exception as e: logger.error(f"Error receiving data: {e}") break # Check if enough audio has been buffered if len(audio_buffer) >= min_chunk_size * sampling_rate: if transcription_task is None or transcription_task.done(): # Start a new transcription task logger.info(f"Starting transcription task for {len(audio_buffer)} samples") transcription_task = asyncio.create_task( transcribe_and_send(websocket, audio_buffer.copy()) ) audio_buffer = np.array([], dtype=np.float32) logger.debug("Audio buffer reset after starting transcription task") async def transcribe_and_send(websocket: WebSocket, audio_data): """Run transcription in a separate thread and send the result to the client.""" logger.debug(f"Transcription task started for {len(audio_data)} samples") transcription_result = await asyncio.to_thread(sync_transcribe_audio, audio_data) if transcription_result: try: # Send the result as JSON await websocket.send_json(transcription_result) logger.info("Transcription JSON sent to client") except Exception as e: logger.error(f"Error sending transcription: {e}") else: logger.warning("No transcription result to send") def sync_transcribe_audio(audio_data): """Synchronously transcribe audio data using the ASR model and format the result.""" try: logger.info('Starting transcription...') segments, info = model.transcribe( audio_data, language="he", beam_size=5, word_timestamps=True ) logger.info('Transcription completed') # Build the transcription result as per your requirement ret = {'segments': []} for s in segments: logger.debug(f"Processing segment {s.id} with start time: {s.start} and end time: {s.end}") # Process words in the segment words = [{ 'start': float(w.start), 'end': float(w.end), 'word': w.word, 'probability': float(w.probability) } for w in s.words] seg = { 'id': int(s.id), 'seek': int(s.seek), 'start': float(s.start), 'end': float(s.end), 'text': s.text, 'avg_logprob': float(s.avg_logprob), 'compression_ratio': float(s.compression_ratio), 'no_speech_prob': float(s.no_speech_prob), 'words': words } logger.debug(f'Adding new transcription segment: {seg}') ret['segments'].append(seg) logger.debug(f"Total segments in transcription result: {len(ret['segments'])}") return ret except Exception as e: logger.error(f"Transcription error: {e}") return {} def process_received_audio(data): """Convert received bytes into normalized float32 NumPy array.""" logger.debug(f"Processing received audio data of size {len(data)} bytes") audio_int16 = np.frombuffer(data, dtype=np.int16) logger.debug(f"Converted to int16 NumPy array with {len(audio_int16)} samples") audio_float32 = audio_int16.astype(np.float32) / 32768.0 # Normalize to [-1, 1] logger.debug(f"Normalized audio data to float32 with {len(audio_float32)} samples") return audio_float32 # @app.websocket("/wtranscribe") # async def websocket_transcribe(websocket: WebSocket): # logging.info("New WebSocket connection request received.") # await websocket.accept() # logging.info("WebSocket connection established successfully.") # # try: # while True: # try: # audio_chunk = await websocket.receive_bytes() # if not audio_chunk: # logging.warning("Received empty audio chunk, skipping processing.") # continue # with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio_file: ##new temp file for every chunk # logging.info(f"Temporary audio file created at {temp_audio_file.name}") # # Receive the next chunk of audio data # # # # partial_result = await transcribe_core_ws(temp_audio_file.name) # await websocket.send_json(partial_result) # # except WebSocketDisconnect: # logging.info("WebSocket connection closed by the client.") # break # # except Exception as e: # logging.error(f"Unexpected error during WebSocket transcription: {e}") # await websocket.send_json({"error": str(e)}) # # finally: # logging.info("Cleaning up and closing WebSocket connection.")