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update method docstrings

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makaveli10
2024-02-09 16:08:18 +05:30
parent ac00e28b86
commit 170ba22e5b
1 changed files with 231 additions and 122 deletions
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whisper_live/server.py
+231 -122
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@@ -2,14 +2,13 @@ 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.vad import VoiceActivityDetector
from whisper_live.transcriber import WhisperModel
try:
from whisper_live.transcriber_tensorrt import WhisperTRTLLM
@@ -19,40 +18,66 @@ except Exception:
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):
"""
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):
"""Calculate and return the estimated wait time for clients."""
"""
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)
@@ -61,7 +86,16 @@ class ClientManager:
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."""
"""
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}
@@ -70,6 +104,15 @@ class ClientManager:
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()
@@ -79,53 +122,12 @@ class ClientManager:
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
@@ -172,6 +174,15 @@ class TranscriptionServer:
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()
return np.frombuffer(frame_data, dtype=np.float32)
@@ -215,7 +226,7 @@ class TranscriptionServer:
self.backend = backend
if self.backend == "tensorrt":
self.vad_detector = VoiceActivityDetector()
self.vad_detector = VoiceActivityDetector(frame_rate=self.RATE)
self.initialize_client(
websocket, options, faster_whisper_custom_model_path, whisper_tensorrt_path, trt_multilingual)
@@ -273,6 +284,25 @@ class TranscriptionServer:
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:
@@ -284,6 +314,12 @@ class TranscriptionServer:
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)
@@ -296,7 +332,6 @@ class ServeClientBase(object):
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
@@ -313,7 +348,6 @@ class ServeClientBase(object):
self.send_last_n_segments = 10
# text formatting
self.wrapper = textwrap.TextWrapper(width=50)
self.pick_previous_segments = 2
# threading
@@ -365,14 +399,40 @@ class ServeClientBase(object):
self.timestamp_offset = self.frames_offset + duration - 5
def get_audio_chunk_for_processing(self):
"""Retrieve the next chunk of audio data for processing."""
"""
Retrieves the next chunk of audio data for processing based on the current offsets.
Calculates which part of the audio data should be processed next, based on
the difference between the current timestamp offset and the frame's offset, scaled by
the audio sample rate (RATE). It then returns this chunk of audio data along with its
duration in seconds.
Returns:
tuple: A tuple containing:
- input_bytes (np.ndarray): The next chunk of audio data to be processed.
- duration (float): The duration of the audio chunk in seconds.
"""
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."""
"""
Prepares the segments of transcribed text to be sent to the client.
This method compiles the recent segments of transcribed text, ensuring that only the
specified number of the most recent segments are included. It also appends the most
recent segment of text if provided (which is considered incomplete because of the possibility
of the last word being truncated in the audio chunk).
Args:
last_segment (str, optional): The most recent segment of transcribed text to be added
to the list of segments. Defaults to None.
Returns:
list: A list of transcribed text 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()
@@ -383,11 +443,27 @@ class ServeClientBase(object):
return segments
def get_audio_chunk_duration(self, input_bytes):
"""Calculate the duration of the current audio chunk."""
"""
Calculates the duration of the provided audio chunk.
Args:
input_bytes (numpy.ndarray): The audio chunk for which to calculate the duration.
Returns:
float: The duration of the audio chunk in seconds.
"""
return input_bytes.shape[0] / self.RATE
def send_transcription_to_client(self, segments):
"""Send the transcription segments to the client."""
"""
Sends the specified transcription segments to the client over the websocket connection.
This method formats the transcription segments into a JSON object and attempts to send
this object to the client. If an error occurs during the send operation, it logs the error.
Returns:
segments (list): A list of transcription segments to be sent to the client.
"""
try:
self.websocket.send(
json.dumps({
@@ -425,34 +501,6 @@ class ServeClientBase(object):
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.
@@ -494,25 +542,49 @@ class ServeClientTensorRT(ServeClientBase):
}))
def warmup(self, warmup_steps=10):
"""
Warmup TensorRT since first few inferences are slow.
Args:
warmup_steps (int): Number of steps to warm up the model for.
"""
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):
"""
Sets the End of Speech (EOS) flag.
Args:
eos (bool): The value to set for the EOS flag.
"""
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."""
"""
Handle the transcription output, updating the transcript and sending data to the client.
Args:
last_segment (str): The last segment from the whisper output which is considered to be incomplete because
of the possibility of word being truncated.
duration (float): Duration of the transcribed audio chunk.
"""
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."""
"""
Transcribe the audio chunk and send the results to the client.
Args:
input_bytes (np.array): The audio chunk to transcribe.
"""
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)
@@ -520,6 +592,13 @@ class ServeClientTensorRT(ServeClientBase):
self.handle_transcription_output(last_segment, duration)
def update_timestamp_offset(self, last_segment, duration):
"""
Update timestamp offset and transcript.
Args:
last_segment (str): Last transcribed audio from the whisper model.
duration (float): Duration of the last audio chunk.
"""
if not len(self.transcript):
self.transcript.append({"text": last_segment + " "})
elif self.transcript[-1]["text"].strip() != last_segment:
@@ -568,34 +647,6 @@ class ServeClientTensorRT(ServeClientBase):
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):
"""
@@ -610,7 +661,8 @@ class ServeClientFasterWhisper(ServeClientBase):
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.
model (str, optional): The whisper model size. Defaults to 'small.en'
initial_prompt (str, optional): Prompt for whisper inference. Defaults to None.
"""
super().__init__(client_uid, websocket)
self.model_sizes = [
@@ -676,6 +728,15 @@ class ServeClientFasterWhisper(ServeClientBase):
return model_size
def set_language(self, info):
"""
Updates the language attribute based on the detected language information.
Args:
info (object): An object containing the detected language and its probability. This object
must have at least two attributes: `language`, a string indicating the detected
language, and `language_probability`, a float representing the confidence level
of the language detection.
"""
if info.language_probability > 0.5:
self.language = info.language
logging.info(f"Detected language {self.language} with probability {info.language_probability}")
@@ -683,6 +744,21 @@ class ServeClientFasterWhisper(ServeClientBase):
{"uid": self.client_uid, "language": self.language, "language_prob": info.language_probability}))
def transcribe_audio(self, input_sample):
"""
Transcribes the provided audio sample using the configured transcriber instance.
If the language has not been set, it updates the session's language based on the transcription
information.
Args:
input_sample (np.array): The audio chunk to be transcribed. This should be a NumPy
array representing the audio data.
Returns:
The transcription result from the transcriber. The exact format of this result
depends on the implementation of the `transcriber.transcribe` method but typically
includes the transcribed text.
"""
result, info = self.transcriber.transcribe(
input_sample,
initial_prompt=self.initial_prompt,
@@ -695,6 +771,20 @@ class ServeClientFasterWhisper(ServeClientBase):
return result
def get_previous_output(self):
"""
Retrieves previously generated transcription outputs if no new transcription is available
from the current audio chunks.
Checks the time since the last transcription output and, if it is within a specified
threshold, returns the most recent segments of transcribed text. It also manages
adding a pause (blank segment) to indicate a significant gap in speech based on a defined
threshold.
Returns:
segments (list): A list of transcription segments. This may include the most recent
transcribed text segments or a blank segment to indicate a pause
in speech.
"""
segments = []
if self.t_start is None:
self.t_start = time.time()
@@ -708,6 +798,13 @@ class ServeClientFasterWhisper(ServeClientBase):
return segments
def handle_transcription_output(self, result, duration):
"""
Handle the transcription output, updating the transcript and sending data to the client.
Args:
result (str): The result from whisper inference i.e. the list of segments.
duration (float): Duration of the transcribed audio chunk.
"""
segments = []
if len(result):
self.t_start = None
@@ -763,7 +860,19 @@ class ServeClientFasterWhisper(ServeClientBase):
time.sleep(0.01)
def format_segment(self, start, end, text):
"""Helper function to format a segment with string timestamps."""
"""
Formats a transcription segment with precise start and end times alongside the transcribed text.
Args:
start (float): The start time of the transcription segment in seconds.
end (float): The end time of the transcription segment in seconds.
text (str): The transcribed text corresponding to the segment.
Returns:
dict: A dictionary representing the formatted transcription segment, including
'start' and 'end' times as strings with three decimal places and the 'text'
of the transcription.
"""
return {
'start': "{:.3f}".format(start),
'end': "{:.3f}".format(end),