import websockets import time import threading import json import textwrap import logging logging.basicConfig(level = logging.INFO) from websockets.sync.server import serve import torch import numpy as np import queue from whisper_live.vad import VoiceActivityDetection from whisper_live.trt_transcriber import WhisperTRTLLM from scipy.io.wavfile import write import functools save_counter = 0 def save_wav(normalized_float32): global save_counter scaled_int16 = (normalized_float32 * 32768).astype(np.int16) write(f"outputs/output{save_counter}.wav", 16000, scaled_int16) save_counter += 1 class TranscriptionServer: """ Represents a transcription server that handles incoming audio from clients. Attributes: RATE (int): The audio sampling rate (constant) set to 16000. vad_model (torch.Module): The voice activity detection model. vad_threshold (float): The voice activity detection threshold. clients (dict): A dictionary to store connected clients. websockets (dict): A dictionary to store WebSocket connections. clients_start_time (dict): A dictionary to track client start times. max_clients (int): Maximum allowed connected clients. max_connection_time (int): Maximum allowed connection time in seconds. """ RATE = 16000 def __init__(self): # voice activity detection model self.clients = {} self.websockets = {} self.clients_start_time = {} self.max_clients = 4 self.max_connection_time = 600 print("done loading") def get_wait_time(self): """ Calculate and return the estimated wait time for clients. Returns: float: The estimated wait time in minutes. """ wait_time = None for k, v in self.clients_start_time.items(): current_client_time_remaining = self.max_connection_time - (time.time() - v) if wait_time is None or current_client_time_remaining < wait_time: wait_time = current_client_time_remaining return wait_time / 60 def recv_audio(self, websocket, transcription_queue=None, llm_queue=None, whisper_tensorrt_path=None): """ Receive audio chunks from a client in an infinite loop. Continuously receives audio frames from a connected client over a WebSocket connection. It processes the audio frames using a voice activity detection (VAD) model to determine if they contain speech or not. If the audio frame contains speech, it is added to the client's audio data for ASR. If the maximum number of clients is reached, the method sends a "WAIT" status to the client, indicating that they should wait until a slot is available. If a client's connection exceeds the maximum allowed time, it will be disconnected, and the client's resources will be cleaned up. Args: websocket (WebSocket): The WebSocket connection for the client. Raises: Exception: If there is an error during the audio frame processing. """ self.vad_model = VoiceActivityDetection() self.vad_threshold = 0.5 logging.info("New client connected") options = websocket.recv() options = json.loads(options) if len(self.clients) >= self.max_clients: logging.warning("Client Queue Full. Asking client to wait ...") wait_time = self.get_wait_time() response = { "uid": options["uid"], "status": "WAIT", "message": wait_time, } websocket.send(json.dumps(response)) websocket.close() del websocket return client = ServeClient( websocket, multilingual=options["multilingual"], language=options["language"], task=options["task"], client_uid=options["uid"], transcription_queue=transcription_queue, llm_queue=llm_queue, model_path=whisper_tensorrt_path ) self.clients[websocket] = client self.clients_start_time[websocket] = time.time() no_voice_activity_chunks = 0 print() while True: try: frame_data = websocket.recv() frame_np = np.frombuffer(frame_data, dtype=np.float32) # VAD try: speech_prob = self.vad_model(torch.from_numpy(frame_np.copy()), self.RATE).item() if speech_prob < self.vad_threshold: no_voice_activity_chunks += 1 if no_voice_activity_chunks > 3: if not self.clients[websocket].eos: self.clients[websocket].set_eos(True) time.sleep(0.1) # EOS stop receiving frames for a 100ms(to send output to LLM.) continue no_voice_activity_chunks = 0 self.clients[websocket].set_eos(False) except Exception as e: logging.error(e) return self.clients[websocket].add_frames(frame_np) elapsed_time = time.time() - self.clients_start_time[websocket] if elapsed_time >= self.max_connection_time: self.clients[websocket].disconnect() logging.warning(f"{self.clients[websocket]} Client disconnected due to overtime.") self.clients[websocket].cleanup() self.clients.pop(websocket) self.clients_start_time.pop(websocket) websocket.close() del websocket break except Exception as e: logging.error(e) self.clients[websocket].cleanup() self.clients.pop(websocket) self.clients_start_time.pop(websocket) logging.info("Connection Closed.") logging.info(self.clients) del websocket break def run(self, host, port=9090, transcription_queue=None, llm_queue=None, whisper_tensorrt_path=None): """ Run the transcription server. Args: host (str): The host address to bind the server. port (int): The port number to bind the server. """ with serve( functools.partial( self.recv_audio, transcription_queue=transcription_queue, llm_queue=llm_queue, whisper_tensorrt_path=whisper_tensorrt_path ), host, port ) as server: server.serve_forever() class ServeClient: """ Attributes: RATE (int): The audio sampling rate (constant) set to 16000. SERVER_READY (str): A constant message indicating that the server is ready. DISCONNECT (str): A constant message indicating that the client should disconnect. client_uid (str): A unique identifier for the client. data (bytes): Accumulated audio data. frames (bytes): Accumulated audio frames. language (str): The language for transcription. task (str): The task type, e.g., "transcribe." transcriber (WhisperModel): The Whisper model for speech-to-text. timestamp_offset (float): The offset in audio timestamps. frames_np (numpy.ndarray): NumPy array to store audio frames. frames_offset (float): The offset in audio frames. text (list): List of transcribed text segments. current_out (str): The current incomplete transcription. prev_out (str): The previous incomplete transcription. t_start (float): Timestamp for the start of transcription. exit (bool): A flag to exit the transcription thread. same_output_threshold (int): Threshold for consecutive same output segments. show_prev_out_thresh (int): Threshold for showing previous output segments. add_pause_thresh (int): Threshold for adding a pause (blank) segment. transcript (list): List of transcribed segments. send_last_n_segments (int): Number of last segments to send to the client. wrapper (textwrap.TextWrapper): Text wrapper for formatting text. pick_previous_segments (int): Number of previous segments to include in the output. websocket: The WebSocket connection for the client. """ RATE = 16000 SERVER_READY = "SERVER_READY" DISCONNECT = "DISCONNECT" def __init__( self, websocket, task="transcribe", device=None, multilingual=False, language=None, client_uid=None, transcription_queue=None, llm_queue=None, model_path=None ): """ Initialize a ServeClient instance. The Whisper model is initialized based on the client's language and device availability. The transcription thread is started upon initialization. A "SERVER_READY" message is sent to the client to indicate that the server is ready. Args: websocket (WebSocket): The WebSocket connection for the client. task (str, optional): The task type, e.g., "transcribe." Defaults to "transcribe". device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None. multilingual (bool, optional): Whether the client supports multilingual transcription. Defaults to False. language (str, optional): The language for transcription. Defaults to None. client_uid (str, optional): A unique identifier for the client. Defaults to None. """ self.client_uid = client_uid self.transcription_queue = transcription_queue self.llm_queue = llm_queue self.data = b"" self.frames = b"" self.language = language if multilingual else "en" self.task = task self.transcriber = WhisperTRTLLM(model_path, False, "assets", device="cuda") self.last_prompt = None self.timestamp_offset = 0.0 self.frames_np = None self.frames_offset = 0.0 self.text = [] self.current_out = '' self.prev_out = '' self.t_start=None self.exit = False self.same_output_threshold = 0 self.show_prev_out_thresh = 5 # if pause(no output from whisper) show previous output for 5 seconds self.add_pause_thresh = 3 # add a blank to segment list as a pause(no speech) for 3 seconds self.transcript = [] self.prompt = None self.send_last_n_segments = 10 # text formatting self.wrapper = textwrap.TextWrapper(width=50) self.pick_previous_segments = 2 # threading self.websocket = websocket self.lock = threading.Lock() self.eos = False self.trans_thread = threading.Thread(target=self.speech_to_text) self.trans_thread.start() self.websocket.send( json.dumps( { "uid": self.client_uid, "message": self.SERVER_READY } ) ) def set_eos(self, eos): self.lock.acquire() self.eos = eos self.lock.release() def add_frames(self, frame_np): """ Add audio frames to the ongoing audio stream buffer. This method is responsible for maintaining the audio stream buffer, allowing the continuous addition of audio frames as they are received. It also ensures that the buffer does not exceed a specified size to prevent excessive memory usage. If the buffer size exceeds a threshold (45 seconds of audio data), it discards the oldest 30 seconds of audio data to maintain a reasonable buffer size. If the buffer is empty, it initializes it with the provided audio frame. The audio stream buffer is used for real-time processing of audio data for transcription. Args: frame_np (numpy.ndarray): The audio frame data as a NumPy array. """ self.lock.acquire() if self.frames_np is not None and self.frames_np.shape[0] > 45*self.RATE: self.frames_offset += 30.0 self.frames_np = self.frames_np[int(30*self.RATE):] if self.frames_np is None: self.frames_np = frame_np.copy() else: self.frames_np = np.concatenate((self.frames_np, frame_np), axis=0) self.lock.release() def speech_to_text(self): """ Process an audio stream in an infinite loop, continuously transcribing the speech. This method continuously receives audio frames, performs real-time transcription, and sends transcribed segments to the client via a WebSocket connection. If the client's language is not detected, it waits for 30 seconds of audio input to make a language prediction. It utilizes the Whisper ASR model to transcribe the audio, continuously processing and streaming results. Segments are sent to the client in real-time, and a history of segments is maintained to provide context.Pauses in speech (no output from Whisper) are handled by showing the previous output for a set duration. A blank segment is added if there is no speech for a specified duration to indicate a pause. Raises: Exception: If there is an issue with audio processing or WebSocket communication. """ while True: # send the LLM outputs try: llm_response = None if self.llm_queue is not None: while not self.llm_queue.empty(): llm_response = self.llm_queue.get() if llm_response: eos = llm_response["eos"] if eos: logging.info(f"[LLM INFO:] sending response to client : {llm_response}") self.websocket.send(json.dumps(llm_response)) except queue.Empty: pass if self.exit: logging.info("Exiting speech to text thread") break if self.frames_np is None: time.sleep(0.02) # wait for any audio to arrive continue # clip audio if the current chunk exceeds 30 seconds, this basically implies that # no valid segment for the last 30 seconds from whisper if self.frames_np[int((self.timestamp_offset - self.frames_offset)*self.RATE):].shape[0] > 25 * self.RATE: duration = self.frames_np.shape[0] / self.RATE self.timestamp_offset = self.frames_offset + duration - 5 samples_take = max(0, (self.timestamp_offset - self.frames_offset)*self.RATE) input_bytes = self.frames_np[int(samples_take):].copy() duration = input_bytes.shape[0] / self.RATE if duration<0.4: time.sleep(0.01) # 5ms sleep to wait for some voice active audio to arrive continue try: input_sample = input_bytes.copy() mel, duration = self.transcriber.log_mel_spectrogram(input_sample) last_segment = self.transcriber.transcribe(mel) segments = [] if len(last_segment): segments.append({"text": last_segment}) try: self.prompt = ' '.join(segment['text'] for segment in segments) if self.last_prompt != self.prompt: self.websocket.send( json.dumps({ "uid": self.client_uid, "segments": segments, "eos": self.eos }) ) self.transcription_queue.put({"uid": self.client_uid, "prompt": self.prompt, "eos": self.eos}) if self.eos: # self.append_segment(last_segment) self.timestamp_offset += duration logging.info(f"[INFO]: {segments}, eos: {self.eos}") logging.info( f"[INFO:] Processed : {self.timestamp_offset} seconds / {self.frames_np.shape[0] / self.RATE} seconds" ) except Exception as e: logging.error(f"[ERROR]: {e}") except Exception as e: logging.error(f"[ERROR]: {e}") def disconnect(self): """ Notify the client of disconnection and send a disconnect message. This method sends a disconnect message to the client via the WebSocket connection to notify them that the transcription service is disconnecting gracefully. """ self.websocket.send( json.dumps( { "uid": self.client_uid, "message": self.DISCONNECT } ) ) def cleanup(self): """ Perform cleanup tasks before exiting the transcription service. This method performs necessary cleanup tasks, including stopping the transcription thread, marking the exit flag to indicate the transcription thread should exit gracefully, and destroying resources associated with the transcription process. """ logging.info("Cleaning up.") self.exit = True # self.transcriber.destroy()