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import os
import time
import threading
import json
import functools
import logging
from enum import Enum
from typing import List, Optional
import numpy as np
from websockets.sync.server import serve
from websockets.exceptions import ConnectionClosed
from whisper_live.vad import VoiceActivityDetector
from whisper_live.backend.base import ServeClientBase
logging.basicConfig(level=logging.INFO)
class ClientManager:
def __init__(self, max_clients=4, max_connection_time=600):
"""
Initializes the ClientManager with specified limits on client connections and connection durations.
Args:
max_clients (int, optional): The maximum number of simultaneous client connections allowed. Defaults to 4.
max_connection_time (int, optional): The maximum duration (in seconds) a client can stay connected. Defaults
to 600 seconds (10 minutes).
"""
self.clients = {}
self.start_times = {}
self.max_clients = max_clients
self.max_connection_time = max_connection_time
def add_client(self, websocket, client):
"""
Adds a client and their connection start time to the tracking dictionaries.
Args:
websocket: The websocket associated with the client to add.
client: The client object to be added and tracked.
"""
self.clients[websocket] = client
self.start_times[websocket] = time.time()
def get_client(self, websocket):
"""
Retrieves a client associated with the given websocket.
Args:
websocket: The websocket associated with the client to retrieve.
Returns:
The client object if found, False otherwise.
"""
if websocket in self.clients:
return self.clients[websocket]
return False
def remove_client(self, websocket):
"""
Removes a client and their connection start time from the tracking dictionaries. Performs cleanup on the
client if necessary.
Args:
websocket: The websocket associated with the client to be removed.
"""
client = self.clients.pop(websocket, None)
if client:
client.cleanup()
self.start_times.pop(websocket, None)
def get_wait_time(self):
"""
Calculates the estimated wait time for new clients based on the remaining connection times of current clients.
Returns:
The estimated wait time in minutes for new clients to connect. Returns 0 if there are available slots.
"""
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):
"""
Checks if the server is at its maximum client capacity and sends a wait message to the client if necessary.
Args:
websocket: The websocket of the client attempting to connect.
options: A dictionary of options that may include the client's unique identifier.
Returns:
True if the server is full, False otherwise.
"""
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):
"""
Checks if a client has exceeded the maximum allowed connection time and disconnects them if so, issuing a warning.
Args:
websocket: The websocket associated with the client to check.
Returns:
True if the client's connection time has exceeded the maximum limit, False otherwise.
"""
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 BackendType(Enum):
FASTER_WHISPER = "faster_whisper"
TENSORRT = "tensorrt"
OPENVINO = "openvino"
@staticmethod
def valid_types() -> List[str]:
return [backend_type.value for backend_type in BackendType]
@staticmethod
def is_valid(backend: str) -> bool:
return backend in BackendType.valid_types()
def is_faster_whisper(self) -> bool:
return self == BackendType.FASTER_WHISPER
def is_tensorrt(self) -> bool:
return self == BackendType.TENSORRT
def is_openvino(self) -> bool:
return self == BackendType.OPENVINO
class TranscriptionServer:
RATE = 16000
def __init__(self):
self.client_manager = None
self.no_voice_activity_chunks = 0
self.use_vad = True
self.single_model = False
def initialize_client(
self, websocket, options, faster_whisper_custom_model_path,
whisper_tensorrt_path, trt_multilingual, trt_py_session=False,
):
client: Optional[ServeClientBase] = None
if self.backend.is_tensorrt():
try:
from whisper_live.backend.trt_backend import ServeClientTensorRT
client = ServeClientTensorRT(
websocket,
multilingual=trt_multilingual,
language=options["language"],
task=options["task"],
client_uid=options["uid"],
model=whisper_tensorrt_path,
single_model=self.single_model,
use_py_session=trt_py_session,
send_last_n_segments=options.get("send_last_n_segments", 10),
no_speech_thresh=options.get("no_speech_thresh", 0.45),
clip_audio=options.get("clip_audio", False),
same_output_threshold=options.get("same_output_threshold", 10),
)
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 = BackendType.FASTER_WHISPER
if self.backend.is_openvino():
try:
from whisper_live.backend.openvino_backend import ServeClientOpenVINO
client = ServeClientOpenVINO(
websocket,
language=options["language"],
task=options["task"],
client_uid=options["uid"],
model=options["model"],
single_model=self.single_model,
send_last_n_segments=options.get("send_last_n_segments", 10),
no_speech_thresh=options.get("no_speech_thresh", 0.45),
clip_audio=options.get("clip_audio", False),
same_output_threshold=options.get("same_output_threshold", 10),
)
logging.info("Running OpenVINO backend.")
except Exception as e:
logging.error(f"OpenVINO not supported: {e}")
self.backend = BackendType.FASTER_WHISPER
self.client_uid = options["uid"]
websocket.send(json.dumps({
"uid": self.client_uid,
"status": "WARNING",
"message": "OpenVINO not supported on Server yet. "
"Reverting to available backend: 'faster_whisper'"
}))
try:
if self.backend.is_faster_whisper():
from whisper_live.backend.faster_whisper_backend import ServeClientFasterWhisper
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"),
use_vad=self.use_vad,
single_model=self.single_model,
send_last_n_segments=options.get("send_last_n_segments", 10),
no_speech_thresh=options.get("no_speech_thresh", 0.45),
clip_audio=options.get("clip_audio", False),
same_output_threshold=options.get("same_output_threshold", 10),
)
logging.info("Running faster_whisper backend.")
except Exception as e:
logging.error(e)
return
if client is None:
raise ValueError(f"Backend type {self.backend.value} not recognised or not handled.")
self.client_manager.add_client(websocket, client)
def get_audio_from_websocket(self, websocket):
"""
Receives audio buffer from websocket and creates a numpy array out of it.
Args:
websocket: The websocket to receive audio from.
Returns:
A numpy array containing the audio.
"""
frame_data = websocket.recv()
if frame_data == b"END_OF_AUDIO":
return False
return np.frombuffer(frame_data, dtype=np.float32)
def handle_new_connection(self, websocket, faster_whisper_custom_model_path,
whisper_tensorrt_path, trt_multilingual, trt_py_session=False):
try:
logging.info("New client connected")
options = websocket.recv()
options = json.loads(options)
if self.client_manager is None:
max_clients = options.get('max_clients', 4)
max_connection_time = options.get('max_connection_time', 600)
self.client_manager = ClientManager(max_clients, max_connection_time)
self.use_vad = options.get('use_vad')
if self.client_manager.is_server_full(websocket, options):
websocket.close()
return False # Indicates that the connection should not continue
if self.backend.is_tensorrt():
self.vad_detector = VoiceActivityDetector(frame_rate=self.RATE)
self.initialize_client(websocket, options, faster_whisper_custom_model_path,
whisper_tensorrt_path, trt_multilingual, trt_py_session=trt_py_session)
return True
except json.JSONDecodeError:
logging.error("Failed to decode JSON from client")
return False
except ConnectionClosed:
logging.info("Connection closed by client")
return False
except Exception as e:
logging.error(f"Error during new connection initialization: {str(e)}")
return False
def process_audio_frames(self, websocket):
frame_np = self.get_audio_from_websocket(websocket)
client = self.client_manager.get_client(websocket)
if frame_np is False:
if self.backend.is_tensorrt():
client.set_eos(True)
return False
if self.backend.is_tensorrt():
voice_active = self.voice_activity(websocket, frame_np)
if voice_active:
self.no_voice_activity_chunks = 0
client.set_eos(False)
if self.use_vad and not voice_active:
return True
client.add_frames(frame_np)
return True
def recv_audio(self,
websocket,
backend: BackendType = BackendType.FASTER_WHISPER,
faster_whisper_custom_model_path=None,
whisper_tensorrt_path=None,
trt_multilingual=False,
trt_py_session=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.
"""
self.backend = backend
if not self.handle_new_connection(websocket, faster_whisper_custom_model_path,
whisper_tensorrt_path, trt_multilingual, trt_py_session=trt_py_session):
return
try:
while not self.client_manager.is_client_timeout(websocket):
if not self.process_audio_frames(websocket):
break
except ConnectionClosed:
logging.info("Connection closed by client")
except Exception as e:
logging.error(f"Unexpected error: {str(e)}")
finally:
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,
trt_py_session=False,
single_model=False):
"""
Run the transcription server.
Args:
host (str): The host address to bind the server.
port (int): The port number to bind the server.
"""
if faster_whisper_custom_model_path is not None and not os.path.exists(faster_whisper_custom_model_path):
raise ValueError(f"Custom faster_whisper model '{faster_whisper_custom_model_path}' is not a valid path.")
if whisper_tensorrt_path is not None and not os.path.exists(whisper_tensorrt_path):
raise ValueError(f"TensorRT model '{whisper_tensorrt_path}' is not a valid path.")
if single_model:
if faster_whisper_custom_model_path or whisper_tensorrt_path:
logging.info("Custom model option was provided. Switching to single model mode.")
self.single_model = True
# TODO: load model initially
else:
logging.info("Single model mode currently only works with custom models.")
if not BackendType.is_valid(backend):
raise ValueError(f"{backend} is not a valid backend type. Choose backend from {BackendType.valid_types()}")
with serve(
functools.partial(
self.recv_audio,
backend=BackendType(backend),
faster_whisper_custom_model_path=faster_whisper_custom_model_path,
whisper_tensorrt_path=whisper_tensorrt_path,
trt_multilingual=trt_multilingual,
trt_py_session=trt_py_session,
),
host,
port
) as server:
server.serve_forever()
def voice_activity(self, websocket, frame_np):
"""
Evaluates the voice activity in a given audio frame and manages the state of voice activity detection.
This method uses the configured voice activity detection (VAD) model to assess whether the given audio frame
contains speech. If the VAD model detects no voice activity for more than three consecutive frames,
it sets an end-of-speech (EOS) flag for the associated client. This method aims to efficiently manage
speech detection to improve subsequent processing steps.
Args:
websocket: The websocket associated with the current client. Used to retrieve the client object
from the client manager for state management.
frame_np (numpy.ndarray): The audio frame to be analyzed. This should be a NumPy array containing
the audio data for the current frame.
Returns:
bool: True if voice activity is detected in the current frame, False otherwise. When returning False
after detecting no voice activity for more than three consecutive frames, it also triggers the
end-of-speech (EOS) flag for the client.
"""
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):
"""
Cleans up resources associated with a given client's websocket.
Args:
websocket: The websocket associated with the client to be cleaned up.
"""
if self.client_manager.get_client(websocket):
self.client_manager.remove_client(websocket)
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