makaveli
519 lines
21 KiB
Python
519 lines
21 KiB
Python
import websockets
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import time
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import threading
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import json
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import textwrap
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import logging
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# logging.basicConfig(level = logging.INFO)
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from websockets.sync.server import serve
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import torch
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import numpy as np
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import time
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from whisper_live.transcriber import WhisperModel
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class TranscriptionServer:
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"""
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Represents a transcription server that handles incoming audio from clients.
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Attributes:
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RATE (int): The audio sampling rate (constant) set to 16000.
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vad_model (torch.Module): The voice activity detection model.
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vad_threshold (float): The voice activity detection threshold.
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clients (dict): A dictionary to store connected clients.
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websockets (dict): A dictionary to store WebSocket connections.
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clients_start_time (dict): A dictionary to track client start times.
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max_clients (int): Maximum allowed connected clients.
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max_connection_time (int): Maximum allowed connection time in seconds.
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"""
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RATE = 16000
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def __init__(self):
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# voice activity detection model
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self.clients = {}
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self.websockets = {}
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self.clients_start_time = {}
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self.max_clients = 4
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self.max_connection_time = 600
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def get_wait_time(self):
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"""
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Calculate and return the estimated wait time for clients.
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Returns:
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float: The estimated wait time in minutes.
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"""
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wait_time = None
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for k, v in self.clients_start_time.items():
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current_client_time_remaining = self.max_connection_time - (time.time() - v)
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if wait_time is None or current_client_time_remaining < wait_time:
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wait_time = current_client_time_remaining
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return wait_time / 60
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def recv_audio(self, websocket):
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"""
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Receive audio chunks from a client in an infinite loop.
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Continuously receives audio frames from a connected client
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over a WebSocket connection. It processes the audio frames using a
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voice activity detection (VAD) model to determine if they contain speech
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or not. If the audio frame contains speech, it is added to the client's
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audio data for ASR.
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If the maximum number of clients is reached, the method sends a
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"WAIT" status to the client, indicating that they should wait
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until a slot is available.
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If a client's connection exceeds the maximum allowed time, it will
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be disconnected, and the client's resources will be cleaned up.
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Args:
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websocket (WebSocket): The WebSocket connection for the client.
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Raises:
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Exception: If there is an error during the audio frame processing.
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"""
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logging.info("New client connected")
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options = websocket.recv()
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options = json.loads(options)
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if len(self.clients) >= self.max_clients:
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logging.warning("Client Queue Full. Asking client to wait ...")
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wait_time = self.get_wait_time()
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response = {
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"uid": options["uid"],
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"status": "WAIT",
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"message": wait_time,
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}
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websocket.send(json.dumps(response))
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websocket.close()
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del websocket
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return
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client = ServeClient(
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websocket,
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multilingual=options["multilingual"],
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language=options["language"],
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task=options["task"],
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client_uid=options["uid"],
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model_size=options["model_size"],
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initial_prompt=options.get("initial_prompt"),
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vad_parameters=options.get("vad_parameters")
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)
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self.clients[websocket] = client
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self.clients_start_time[websocket] = time.time()
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while True:
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try:
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frame_data = websocket.recv()
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frame_np = np.frombuffer(frame_data, dtype=np.float32)
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self.clients[websocket].add_frames(frame_np)
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elapsed_time = time.time() - self.clients_start_time[websocket]
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if elapsed_time >= self.max_connection_time:
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self.clients[websocket].disconnect()
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logging.warning(f"Client with uid '{self.clients[websocket].client_uid}' disconnected due to overtime.")
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self.clients[websocket].cleanup()
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self.clients.pop(websocket)
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self.clients_start_time.pop(websocket)
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websocket.close()
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del websocket
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break
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except Exception as e:
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logging.info(f"[ERROR]: Client with uid '{self.clients[websocket].client_uid}' Disconnected.")
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if self.clients[websocket].model_size is not None:
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self.clients[websocket].cleanup()
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self.clients.pop(websocket)
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self.clients_start_time.pop(websocket)
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del websocket
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break
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def run(self, host, port=9090):
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"""
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Run the transcription server.
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Args:
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host (str): The host address to bind the server.
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port (int): The port number to bind the server.
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"""
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with serve(self.recv_audio, host, port) as server:
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server.serve_forever()
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class ServeClient:
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"""
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Attributes:
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RATE (int): The audio sampling rate (constant) set to 16000.
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SERVER_READY (str): A constant message indicating that the server is ready.
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DISCONNECT (str): A constant message indicating that the client should disconnect.
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client_uid (str): A unique identifier for the client.
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data (bytes): Accumulated audio data.
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frames (bytes): Accumulated audio frames.
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language (str): The language for transcription.
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task (str): The task type, e.g., "transcribe."
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transcriber (WhisperModel): The Whisper model for speech-to-text.
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timestamp_offset (float): The offset in audio timestamps.
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frames_np (numpy.ndarray): NumPy array to store audio frames.
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frames_offset (float): The offset in audio frames.
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text (list): List of transcribed text segments.
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current_out (str): The current incomplete transcription.
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prev_out (str): The previous incomplete transcription.
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t_start (float): Timestamp for the start of transcription.
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exit (bool): A flag to exit the transcription thread.
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same_output_threshold (int): Threshold for consecutive same output segments.
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show_prev_out_thresh (int): Threshold for showing previous output segments.
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add_pause_thresh (int): Threshold for adding a pause (blank) segment.
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transcript (list): List of transcribed segments.
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send_last_n_segments (int): Number of last segments to send to the client.
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wrapper (textwrap.TextWrapper): Text wrapper for formatting text.
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pick_previous_segments (int): Number of previous segments to include in the output.
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websocket: The WebSocket connection for the client.
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"""
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RATE = 16000
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SERVER_READY = "SERVER_READY"
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DISCONNECT = "DISCONNECT"
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def __init__(
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self,
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websocket,
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task="transcribe",
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device=None,
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multilingual=False,
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language=None,
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client_uid=None,
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model_size="small",
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initial_prompt=None,
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vad_parameters=None
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):
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"""
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Initialize a ServeClient instance.
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The Whisper model is initialized based on the client's language and device availability.
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The transcription thread is started upon initialization. A "SERVER_READY" message is sent
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to the client to indicate that the server is ready.
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Args:
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websocket (WebSocket): The WebSocket connection for the client.
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task (str, optional): The task type, e.g., "transcribe." Defaults to "transcribe".
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device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None.
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multilingual (bool, optional): Whether the client supports multilingual transcription. Defaults to False.
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language (str, optional): The language for transcription. Defaults to None.
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client_uid (str, optional): A unique identifier for the client. Defaults to None.
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"""
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self.client_uid = client_uid
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self.data = b""
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self.frames = b""
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self.model_sizes = [
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"tiny", "base", "small", "medium", "large-v2", "large-v3"
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]
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self.multilingual = multilingual
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self.model_size = self.get_model_size(model_size)
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self.language = language if self.multilingual else "en"
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self.task = task
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self.websocket = websocket
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self.initial_prompt = initial_prompt
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self.vad_parameters = vad_parameters or {"threshold": 0.5}
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device = "cuda" if torch.cuda.is_available() else "cpu"
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if self.model_size == None:
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return
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self.transcriber = WhisperModel(
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self.model_size,
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device=device,
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compute_type="int8" if device=="cpu" else "float16",
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local_files_only=False,
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)
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self.timestamp_offset = 0.0
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self.frames_np = None
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self.frames_offset = 0.0
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self.text = []
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self.current_out = ''
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self.prev_out = ''
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self.t_start=None
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self.exit = False
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self.same_output_threshold = 0
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self.show_prev_out_thresh = 5 # if pause(no output from whisper) show previous output for 5 seconds
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self.add_pause_thresh = 3 # add a blank to segment list as a pause(no speech) for 3 seconds
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self.transcript = []
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self.send_last_n_segments = 10
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# text formatting
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self.wrapper = textwrap.TextWrapper(width=50)
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self.pick_previous_segments = 2
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# threading
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self.trans_thread = threading.Thread(target=self.speech_to_text)
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self.trans_thread.start()
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self.websocket.send(
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json.dumps(
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{
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"uid": self.client_uid,
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"message": self.SERVER_READY
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}
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)
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)
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def get_model_size(self, model_size):
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"""
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Returns the whisper model size based on multilingual.
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"""
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if model_size not in self.model_sizes:
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self.websocket.send(
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json.dumps(
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{
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"uid": self.client_uid,
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"status": "ERROR",
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"message": f"Invalid model size {model_size}. Available choices: {self.model_sizes}"
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}
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)
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)
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return None
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if model_size in ["large-v2", "large-v3"]:
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self.multilingual = True
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return model_size
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if not self.multilingual:
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model_size = model_size + ".en"
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return model_size
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def add_frames(self, frame_np):
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"""
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Add audio frames to the ongoing audio stream buffer.
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This method is responsible for maintaining the audio stream buffer, allowing the continuous addition
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of audio frames as they are received. It also ensures that the buffer does not exceed a specified size
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to prevent excessive memory usage.
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If the buffer size exceeds a threshold (45 seconds of audio data), it discards the oldest 30 seconds
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of audio data to maintain a reasonable buffer size. If the buffer is empty, it initializes it with the provided
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audio frame. The audio stream buffer is used for real-time processing of audio data for transcription.
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Args:
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frame_np (numpy.ndarray): The audio frame data as a NumPy array.
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"""
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if self.frames_np is not None and self.frames_np.shape[0] > 45*self.RATE:
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self.frames_offset += 30.0
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self.frames_np = self.frames_np[int(30*self.RATE):]
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if self.frames_np is None:
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self.frames_np = frame_np.copy()
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else:
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self.frames_np = np.concatenate((self.frames_np, frame_np), axis=0)
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def speech_to_text(self):
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"""
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Process an audio stream in an infinite loop, continuously transcribing the speech.
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This method continuously receives audio frames, performs real-time transcription, and sends
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transcribed segments to the client via a WebSocket connection.
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If the client's language is not detected, it waits for 30 seconds of audio input to make a language prediction.
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It utilizes the Whisper ASR model to transcribe the audio, continuously processing and streaming results. Segments
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are sent to the client in real-time, and a history of segments is maintained to provide context.Pauses in speech
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(no output from Whisper) are handled by showing the previous output for a set duration. A blank segment is added if
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there is no speech for a specified duration to indicate a pause.
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Raises:
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Exception: If there is an issue with audio processing or WebSocket communication.
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"""
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while True:
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if self.exit:
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logging.info("Exiting speech to text thread")
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break
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if self.frames_np is None:
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continue
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# clip audio if the current chunk exceeds 30 seconds, this basically implies that
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# no valid segment for the last 30 seconds from whisper
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if self.frames_np[int((self.timestamp_offset - self.frames_offset)*self.RATE):].shape[0] > 25 * self.RATE:
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duration = self.frames_np.shape[0] / self.RATE
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self.timestamp_offset = self.frames_offset + duration - 5
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samples_take = max(0, (self.timestamp_offset - self.frames_offset)*self.RATE)
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input_bytes = self.frames_np[int(samples_take):].copy()
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duration = input_bytes.shape[0] / self.RATE
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if duration<1.0:
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continue
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try:
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input_sample = input_bytes.copy()
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# whisper transcribe with prompt
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result, info = self.transcriber.transcribe(
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input_sample,
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initial_prompt=self.initial_prompt,
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language=self.language,
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task=self.task,
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vad_filter=True,
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vad_parameters=self.vad_parameters
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)
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if self.language is None:
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if info.language_probability > 0.5:
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self.language = info.language
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logging.info(f"Detected language {self.language} with probability {info.language_probability}")
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self.websocket.send(json.dumps(
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{"uid": self.client_uid, "language": self.language, "language_prob": info.language_probability}))
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else:
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# detect language again
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continue
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if len(result):
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self.t_start = None
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last_segment = self.update_segments(result, duration)
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if len(self.transcript) < self.send_last_n_segments:
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segments = self.transcript
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else:
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segments = self.transcript[-self.send_last_n_segments:]
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if last_segment is not None:
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segments = segments + [last_segment]
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else:
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# show previous output if there is pause i.e. no output from whisper
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segments = []
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if self.t_start is None: self.t_start = time.time()
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if time.time() - self.t_start < self.show_prev_out_thresh:
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if len(self.transcript) < self.send_last_n_segments:
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segments = self.transcript
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else:
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segments = self.transcript[-self.send_last_n_segments:]
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# add a blank if there is no speech for 3 seconds
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if len(self.text) and self.text[-1] != '':
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if time.time() - self.t_start > self.add_pause_thresh:
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self.text.append('')
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try:
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self.websocket.send(
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json.dumps({
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"uid": self.client_uid,
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"segments": segments
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})
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)
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except Exception as e:
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logging.error(f"[ERROR]: Failed to send message to client: {e}")
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except Exception as e:
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logging.error(f"[ERROR]: Failed to transcribe audio chunk: {e}")
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time.sleep(0.01)
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def format_segment(self, start, end, text):
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"""Helper function to format a segment with string timestamps."""
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return {
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'start': "{:.3f}".format(start),
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'end': "{:.3f}".format(end),
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'text': text
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}
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def update_segments(self, segments, duration):
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"""
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Processes the segments from whisper. Appends all the segments to the list
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except for the last segment assuming that it is incomplete.
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Updates the ongoing transcript with transcribed segments, including their start and end times.
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Complete segments are appended to the transcript in chronological order. Incomplete segments
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(assumed to be the last one) are processed to identify repeated content. If the same incomplete
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segment is seen multiple times, it updates the offset and appends the segment to the transcript.
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A threshold is used to detect repeated content and ensure it is only included once in the transcript.
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The timestamp offset is updated based on the duration of processed segments. The method returns the
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last processed segment, allowing it to be sent to the client for real-time updates.
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Args:
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segments(dict) : dictionary of segments as returned by whisper
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duration(float): duration of the current chunk
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Returns:
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dict or None: The last processed segment with its start time, end time, and transcribed text.
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Returns None if there are no valid segments to process.
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"""
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offset = None
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self.current_out = ''
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last_segment = None
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# process complete segments
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if len(segments) > 1:
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for i, s in enumerate(segments[:-1]):
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text_ = s.text
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self.text.append(text_)
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start, end = self.timestamp_offset + s.start, self.timestamp_offset + min(duration, s.end)
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self.transcript.append(self.format_segment(start, end, text_))
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offset = min(duration, s.end)
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self.current_out += segments[-1].text
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last_segment = self.format_segment(
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self.timestamp_offset + segments[-1].start,
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self.timestamp_offset + min(duration, segments[-1].end),
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self.current_out
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)
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# if same incomplete segment is seen multiple times then update the offset
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# and append the segment to the list
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if self.current_out.strip() == self.prev_out.strip() and self.current_out != '':
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self.same_output_threshold += 1
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else:
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self.same_output_threshold = 0
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if self.same_output_threshold > 5:
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if not len(self.text) or self.text[-1].strip().lower()!=self.current_out.strip().lower():
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self.text.append(self.current_out)
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self.transcript.append(self.format_segment(
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self.timestamp_offset,
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self.timestamp_offset + duration,
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self.current_out
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))
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self.current_out = ''
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offset = duration
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self.same_output_threshold = 0
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last_segment = None
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else:
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self.prev_out = self.current_out
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# update offset
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if offset is not None:
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self.timestamp_offset += offset
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return last_segment
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def disconnect(self):
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"""
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Notify the client of disconnection and send a disconnect message.
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This method sends a disconnect message to the client via the WebSocket connection to notify them
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that the transcription service is disconnecting gracefully.
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"""
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self.websocket.send(
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json.dumps(
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{
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"uid": self.client_uid,
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"message": self.DISCONNECT
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}
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)
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)
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def cleanup(self):
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"""
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Perform cleanup tasks before exiting the transcription service.
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This method performs necessary cleanup tasks, including stopping the transcription thread, marking
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the exit flag to indicate the transcription thread should exit gracefully, and destroying resources
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associated with the transcription process.
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|
|
|
"""
|
|
logging.info("Cleaning up.")
|
|
self.exit = True
|