import os import time import threading import json import textwrap import functools import logging import torch import numpy as np from websockets.sync.server import serve from whisper_live.vad import VoiceActivityDetection from whisper_live.transcriber import WhisperModel try: from whisper_live.transcriber_tensorrt import WhisperTRTLLM except Exception: pass logging.basicConfig(level=logging.INFO) class VoiceActivityDetector: def __init__(self, threshold=0.5): self.model = VoiceActivityDetection() self.threshold = threshold def __call__(self, audio_frame): speech_prob = self.model(torch.from_numpy(audio_frame), TranscriptionServer.RATE).item() return speech_prob > self.threshold class ClientManager: def __init__(self, max_clients=4, max_connection_time=600): self.clients = {} self.start_times = {} self.max_clients = max_clients self.max_connection_time = max_connection_time def add_client(self, websocket, client): self.clients[websocket] = client self.start_times[websocket] = time.time() def get_client(self, websocket): if websocket in self.clients: return self.clients[websocket] return False def remove_client(self, websocket): client = self.clients.pop(websocket, None) if client: client.cleanup() self.start_times.pop(websocket, None) def get_wait_time(self): """Calculate and return the estimated wait time for clients.""" wait_time = None for start_time in self.start_times.values(): current_client_time_remaining = self.max_connection_time - (time.time() - start_time) if wait_time is None or current_client_time_remaining < wait_time: wait_time = current_client_time_remaining return wait_time / 60 if wait_time is not None else 0 def is_server_full(self, websocket, options): """Check if the server is full and send wait message if necessary.""" if len(self.clients) >= self.max_clients: wait_time = self.get_wait_time() response = {"uid": options["uid"], "status": "WAIT", "message": wait_time} websocket.send(json.dumps(response)) return True return False def is_client_timeout(self, websocket): elapsed_time = time.time() - self.start_times[websocket] if elapsed_time >= self.max_connection_time: self.clients[websocket].disconnect() logging.warning(f"Client with uid '{self.clients[websocket].client_uid}' disconnected due to overtime.") return True return False 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.client_manager = ClientManager() self.no_voice_activity_chunks = 0 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 _, 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 is_server_full(self, websocket, options): if len(self.clients) >= self.max_clients: wait_time = self.get_wait_time() response = {"uid": options["uid"], "status": "WAIT", "message": wait_time} websocket.send(json.dumps(response)) websocket.close() return True return False def initialize_client( self, websocket, options, faster_whisper_custom_model_path, whisper_tensorrt_path, trt_multilingual ): if self.backend == "tensorrt": try: client = ServeClientTensorRT( websocket, multilingual=trt_multilingual, language=options["language"], task=options["task"], client_uid=options["uid"], model=whisper_tensorrt_path ) logging.info("Running TensorRT backend.") except Exception as e: logging.error(f"TensorRT-LLM not supported: {e}") self.client_uid = options["uid"] websocket.send(json.dumps({ "uid": self.client_uid, "status": "WARNING", "message": "TensorRT-LLM not supported on Server yet. " "Reverting to available backend: 'faster_whisper'" })) self.backend = "faster_whisper" if self.backend == "faster_whisper": if faster_whisper_custom_model_path is not None and os.path.exists(faster_whisper_custom_model_path): logging.info(f"Using custom model {faster_whisper_custom_model_path}") options["model"] = faster_whisper_custom_model_path client = ServeClientFasterWhisper( websocket, language=options["language"], task=options["task"], client_uid=options["uid"], model=options["model"], initial_prompt=options.get("initial_prompt"), vad_parameters=options.get("vad_parameters") ) logging.info("Running faster_whisper backend.") # self.clients[websocket] = client # self.clients_start_time[websocket] = time.time() self.client_manager.add_client(websocket, client) def get_audio_from_websocket(self, websocket): frame_data = websocket.recv() return np.frombuffer(frame_data, dtype=np.float32) def recv_audio(self, websocket, backend="faster_whisper", faster_whisper_custom_model_path=None, whisper_tensorrt_path=None, trt_multilingual=False): """ 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. backend (str): The backend to run the server with. faster_whisper_custom_model_path (str): path to custom faster whisper model. whisper_tensorrt_path (str): Required for tensorrt backend. trt_multilingual(bool): Only used for tensorrt, True if multilingual model. Raises: Exception: If there is an error during the audio frame processing. """ logging.info("New client connected") options = websocket.recv() options = json.loads(options) if self.client_manager.is_server_full(websocket, options): websocket.close() return self.backend = backend if self.backend == "tensorrt": self.vad_detector = VoiceActivityDetector() self.initialize_client( websocket, options, faster_whisper_custom_model_path, whisper_tensorrt_path, trt_multilingual) while not self.client_manager.is_client_timeout(websocket): try: frame_np = self.get_audio_from_websocket(websocket) client = self.client_manager.get_client(websocket) # VAD, for faster_whisper VAD model is already integrated if self.backend == "tensorrt": if not self.voice_activity(websocket, frame_np): continue self.no_voice_activity_chunks = 0 client.set_eos(False) client.add_frames(frame_np) except Exception as e: logging.error(e) self.cleanup(websocket) websocket.close() break if self.client_manager.get_client(websocket): self.cleanup(websocket) websocket.close() del websocket def run(self, host, port=9090, backend="tensorrt", faster_whisper_custom_model_path=None, whisper_tensorrt_path=None, trt_multilingual=False): """ 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, backend=backend, faster_whisper_custom_model_path=faster_whisper_custom_model_path, whisper_tensorrt_path=whisper_tensorrt_path, trt_multilingual=trt_multilingual ), host, port ) as server: server.serve_forever() def voice_activity(self, websocket, frame_np): if not self.vad_detector(frame_np): self.no_voice_activity_chunks += 1 if self.no_voice_activity_chunks > 3: client = self.client_manager.get_client(websocket) if not client.eos: client.set_eos(True) time.sleep(0.1) # Sleep 100m; wait some voice activity. return False return True def cleanup(self, websocket): if self.client_manager.get_client(websocket): self.client_manager.remove_client(websocket) class ServeClientBase(object): RATE = 16000 SERVER_READY = "SERVER_READY" DISCONNECT = "DISCONNECT" def __init__(self, client_uid, websocket): self.client_uid = client_uid self.websocket = websocket self.data = b"" self.frames = b"" 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.send_last_n_segments = 10 # text formatting self.wrapper = textwrap.TextWrapper(width=50) self.pick_previous_segments = 2 # threading self.lock = threading.Lock() def speech_to_text(self): raise NotImplementedError def transcribe_audio(self): raise NotImplementedError def handle_transcription_output(self): raise NotImplementedError 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 clip_audio_if_no_valid_segment(self): """ Update the timestamp offset based on audio buffer status. 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 def get_audio_chunk_for_processing(self): """Retrieve the next chunk of audio data for processing.""" 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 return input_bytes, duration def prepare_segments(self, last_segment=None): """Prepare the segments to be sent to the client.""" segments = [] if len(self.transcript) >= self.send_last_n_segments: segments = self.transcript[-self.send_last_n_segments:].copy() else: segments = self.transcript.copy() if last_segment is not None: segments = segments + [last_segment] return segments def get_audio_chunk_duration(self, input_bytes): """Calculate the duration of the current audio chunk.""" return input_bytes.shape[0] / self.RATE def send_transcription_to_client(self, segments): """Send the transcription segments to the client.""" try: self.websocket.send( json.dumps({ "uid": self.client_uid, "segments": segments, }) ) except Exception as e: logging.error(f"[ERROR]: Sending data to client: {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 class ServeClientTensorRT(ServeClientBase): """ 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. """ def __init__(self, websocket, task="transcribe", multilingual=False, language=None, client_uid=None, model=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. """ super().__init__(client_uid, websocket) self.language = language if multilingual else "en" self.task = task self.eos = False self.transcriber = WhisperTRTLLM( model, assets_dir="assets", device="cuda", is_multilingual=multilingual, language=self.language, task=self.task ) self.warmup() # threading 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, "backend": "tensorrt" })) def warmup(self, warmup_steps=10): logging.info("[INFO:] Warming up TensorRT engine..") mel, _ = self.transcriber.log_mel_spectrogram("tests/jfk.flac") for i in range(warmup_steps): self.transcriber.transcribe(mel) def set_eos(self, eos): self.lock.acquire() self.eos = eos self.lock.release() def handle_transcription_output(self, last_segment, duration): """Handle the transcription output, updating the transcript and sending data to the client.""" segments = self.prepare_segments({"text": last_segment}) self.send_transcription_to_client(segments) if self.eos: self.update_timestamp_offset(last_segment, duration) def transcribe_audio(self, input_bytes): """Transcribe the audio chunk and send the results to the client.""" logging.info(f"[WhisperTensorRT:] Processing audio with duration: {input_bytes.shape[0] / self.RATE}") mel, duration = self.transcriber.log_mel_spectrogram(input_bytes) last_segment = self.transcriber.transcribe(mel) if last_segment: self.handle_transcription_output(last_segment, duration) def update_timestamp_offset(self, last_segment, duration): if not len(self.transcript): self.transcript.append({"text": last_segment + " "}) elif self.transcript[-1]["text"].strip() != last_segment: self.transcript.append({"text": last_segment + " "}) self.timestamp_offset += duration 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: 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 self.clip_audio_if_no_valid_segment() input_bytes, duration = self.get_audio_chunk_for_processing() if duration < 0.4: continue try: input_sample = input_bytes.copy() logging.info(f"[WhisperTensorRT:] Processing audio with duration: {duration}") self.transcribe_audio(input_sample) except Exception as e: logging.error(f"[ERROR]: {e}") class ServeClientFasterWhisper(ServeClientBase): """ 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. """ def __init__(self, websocket, task="transcribe", device=None, language=None, client_uid=None, model="small.en", initial_prompt=None, vad_parameters=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. language (str, optional): The language for transcription. Defaults to None. client_uid (str, optional): A unique identifier for the client. Defaults to None. """ super().__init__(client_uid, websocket) self.model_sizes = [ "tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large-v2", "large-v3", ] if not os.path.exists(model): self.model_size_or_path = self.check_valid_model(model) else: self.model_size_or_path = model self.language = "en" if self.model_size_or_path.endswith("en") else language self.task = task self.initial_prompt = initial_prompt self.vad_parameters = vad_parameters or {"threshold": 0.5} self.no_speech_thresh = 0.45 device = "cuda" if torch.cuda.is_available() else "cpu" if self.model_size_or_path is None: return self.transcriber = WhisperModel( self.model_size_or_path, device=device, compute_type="int8" if device == "cpu" else "float16", local_files_only=False, ) # threading 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, "backend": "faster_whisper" } ) ) def check_valid_model(self, model_size): """ Check if it's a valid whisper model size. Args: model_size (str): The name of the model size to check. Returns: str: The model size if valid, None otherwise. """ if model_size not in self.model_sizes: self.websocket.send( json.dumps( { "uid": self.client_uid, "status": "ERROR", "message": f"Invalid model size {model_size}. Available choices: {self.model_sizes}" } ) ) return None return model_size def set_language(self, info): if info.language_probability > 0.5: self.language = info.language logging.info(f"Detected language {self.language} with probability {info.language_probability}") self.websocket.send(json.dumps( {"uid": self.client_uid, "language": self.language, "language_prob": info.language_probability})) def transcribe_audio(self, input_sample): result, info = self.transcriber.transcribe( input_sample, initial_prompt=self.initial_prompt, language=self.language, task=self.task, vad_filter=True, vad_parameters=self.vad_parameters) if self.language is None: self.set_language(info) return result def get_previous_output(self): segments = [] if self.t_start is None: self.t_start = time.time() if time.time() - self.t_start < self.show_prev_out_thresh: segments = self.prepare_segments() # add a blank if there is no speech for 3 seconds if len(self.text) and self.text[-1] != '': if time.time() - self.t_start > self.add_pause_thresh: self.text.append('') return segments def handle_transcription_output(self, result, duration): segments = [] if len(result): self.t_start = None last_segment = self.update_segments(result, duration) segments = self.prepare_segments(last_segment) else: # show previous output if there is pause i.e. no output from whisper segments = self.get_previous_output() if len(segments): self.send_transcription_to_client(segments) 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: if self.exit: logging.info("Exiting speech to text thread") break if self.frames_np is None: continue self.clip_audio_if_no_valid_segment() input_bytes, duration = self.get_audio_chunk_for_processing() if duration < 1.0: continue try: input_sample = input_bytes.copy() result = self.transcribe_audio(input_sample) if self.language is None: continue self.handle_transcription_output(result, duration) except Exception as e: logging.error(f"[ERROR]: Failed to transcribe audio chunk: {e}") time.sleep(0.01) def format_segment(self, start, end, text): """Helper function to format a segment with string timestamps.""" return { 'start': "{:.3f}".format(start), 'end': "{:.3f}".format(end), 'text': text } def update_segments(self, segments, duration): """ Processes the segments from whisper. Appends all the segments to the list except for the last segment assuming that it is incomplete. Updates the ongoing transcript with transcribed segments, including their start and end times. Complete segments are appended to the transcript in chronological order. Incomplete segments (assumed to be the last one) are processed to identify repeated content. If the same incomplete segment is seen multiple times, it updates the offset and appends the segment to the transcript. A threshold is used to detect repeated content and ensure it is only included once in the transcript. The timestamp offset is updated based on the duration of processed segments. The method returns the last processed segment, allowing it to be sent to the client for real-time updates. Args: segments(dict) : dictionary of segments as returned by whisper duration(float): duration of the current chunk Returns: dict or None: The last processed segment with its start time, end time, and transcribed text. Returns None if there are no valid segments to process. """ offset = None self.current_out = '' last_segment = None # process complete segments if len(segments) > 1: for i, s in enumerate(segments[:-1]): text_ = s.text self.text.append(text_) start, end = self.timestamp_offset + s.start, self.timestamp_offset + min(duration, s.end) if start >= end: continue if s.no_speech_prob > self.no_speech_thresh: continue self.transcript.append(self.format_segment(start, end, text_)) offset = min(duration, s.end) self.current_out += segments[-1].text last_segment = self.format_segment( self.timestamp_offset + segments[-1].start, self.timestamp_offset + min(duration, segments[-1].end), self.current_out ) # if same incomplete segment is seen multiple times then update the offset # and append the segment to the list if self.current_out.strip() == self.prev_out.strip() and self.current_out != '': self.same_output_threshold += 1 else: self.same_output_threshold = 0 if self.same_output_threshold > 5: if not len(self.text) or self.text[-1].strip().lower() != self.current_out.strip().lower(): self.text.append(self.current_out) self.transcript.append(self.format_segment( self.timestamp_offset, self.timestamp_offset + duration, self.current_out )) self.current_out = '' offset = duration self.same_output_threshold = 0 last_segment = None else: self.prev_out = self.current_out # update offset if offset is not None: self.timestamp_offset += offset return last_segment