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document server

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This commit is contained in:
makaveli10
2023-09-15 11:34:18 +05:30
parent d319aa2fc9
commit c574f5d6a6
1 changed files with 186 additions and 26 deletions
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whisper_live/server.py
+186 -26
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@@ -24,39 +24,76 @@ 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.vad_model, _ = torch.hub.load(repo_or_dir='snakers4/silero-vad',
model='silero_vad',
force_reload=True,
onnx=True
)
"""
Initialize the TranscriptionServer.
"""
# Load the voice activity detection model
self.vad_model, _ = torch.hub.load(
repo_or_dir='snakers4/silero-vad',
model='silero_vad',
force_reload=True,
onnx=True
)
# Voice activity detection threshold
self.vad_threshold = 0.4
self.clients = {}
self.websockets = {}
self.clients_start_time = {}
self.max_clients = 4
self.max_connection_time = 600 # in seconds
self.max_connection_time = 600
def get_wait_time(self):
"""
Calculate and return the estimated wait time for clients.
Returns:
float: The estimated wait time in minutes.
"""
wait_time = None
for k,v in self.clients_start_time.items():
for k, v in self.clients_start_time.items():
current_client_time_remaining = self.max_connection_time - (time.time() - v)
if wait_time is None:
if wait_time is None or current_client_time_remaining < wait_time:
wait_time = current_client_time_remaining
elif current_client_time_remaining < wait_time:
wait_time = current_client_time_remaining
return wait_time/60
return wait_time / 60
def recv_audio(self, websocket):
"""
Receive audio chunks from a client in an infinite loop.
Continuously receives audio frames from a connected client
over a WebSocket connection. It processes the audio frames using a
voice activity detection (VAD) model to determine if they contain speech
or not. If the audio frame contains speech, it is added to the client's
audio data for ASR.
If the maximum number of clients is reached, the method sends a
"WAIT" status to the client, indicating that they should wait
until a slot is available.
If a client's connection exceeds the maximum allowed time, it will
be disconnected, and the client's resources will be cleaned up.
Args:
websocket (WebSocket): The WebSocket connection for the client.
Raises:
Exception: If there is an error during the audio frame processing.
"""
logging.info("New client connected")
options = websocket.recv()
@@ -66,7 +103,7 @@ class TranscriptionServer:
logging.warning("Client Queue Full. Asking client to wait ...")
wait_time = self.get_wait_time()
response = {
"uid" : options["uid"],
"uid": options["uid"],
"status": "WAIT",
"message": wait_time,
}
@@ -74,7 +111,7 @@ class TranscriptionServer:
websocket.close()
del websocket
return
client = ServeClient(
websocket,
multilingual=options["multilingual"],
@@ -82,9 +119,9 @@ class TranscriptionServer:
task=options["task"],
client_uid=options["uid"]
)
self.clients[websocket] = client
self.clients_start_time[websocket] = time.time()
self.clients_start_time[websocket] = time.time()
while True:
try:
@@ -95,10 +132,11 @@ class TranscriptionServer:
speech_prob = self.vad_model(torch.from_numpy(frame_np.copy()), self.RATE).item()
if speech_prob < self.vad_threshold:
continue
except Exception as e:
logging.error(e)
return
self.clients[websocket].add_frames(frame_np)
elapsed_time = time.time() - self.clients_start_time[websocket]
@@ -112,7 +150,6 @@ class TranscriptionServer:
del websocket
break
except Exception as e:
logging.error(e)
self.clients[websocket].cleanup()
@@ -120,7 +157,6 @@ class TranscriptionServer:
self.clients_start_time.pop(websocket)
logging.info("Connection Closed.")
logging.info(self.clients)
del websocket
break
@@ -137,11 +173,54 @@ class TranscriptionServer:
class ServeClient:
"""
Attributes:
RATE (int): The audio sampling rate (constant) set to 16000.
SERVER_READY (str): A constant message indicating that the server is ready.
DISCONNECT (str): A constant message indicating that the client should disconnect.
client_uid (str): A unique identifier for the client.
data (bytes): Accumulated audio data.
frames (bytes): Accumulated audio frames.
language (str): The language for transcription.
task (str): The task type, e.g., "transcribe."
transcriber (WhisperModel): The Whisper model for speech-to-text.
timestamp_offset (float): The offset in audio timestamps.
frames_np (numpy.ndarray): NumPy array to store audio frames.
frames_offset (float): The offset in audio frames.
text (list): List of transcribed text segments.
current_out (str): The current incomplete transcription.
prev_out (str): The previous incomplete transcription.
t_start (float): Timestamp for the start of transcription.
exit (bool): A flag to exit the transcription thread.
same_output_threshold (int): Threshold for consecutive same output segments.
show_prev_out_thresh (int): Threshold for showing previous output segments.
add_pause_thresh (int): Threshold for adding a pause (blank) segment.
transcript (list): List of transcribed segments.
send_last_n_segments (int): Number of last segments to send to the client.
wrapper (textwrap.TextWrapper): Text wrapper for formatting text.
pick_previous_segments (int): Number of previous segments to include in the output.
websocket: The WebSocket connection for the client.
"""
RATE = 16000
SERVER_READY = "SERVER_READY"
DISCONNECT = "DISCONNECT"
def __init__(self, websocket, task="transcribe", device=None, multilingual=False, language=None, client_uid=None):
"""
Initialize a ServeClient instance.
The Whisper model is initialized based on the client's language and device availability.
The transcription thread is started upon initialization. A "SERVER_READY" message is sent
to the client to indicate that the server is ready.
Args:
websocket (WebSocket): The WebSocket connection for the client.
task (str, optional): The task type, e.g., "transcribe." Defaults to "transcribe".
device (str, optional): The device type for Whisper, "cuda" or "cpu". Defaults to None.
multilingual (bool, optional): Whether the client supports multilingual transcription. Defaults to False.
language (str, optional): The language for transcription. Defaults to None.
client_uid (str, optional): A unique identifier for the client. Defaults to None.
"""
self.client_uid = client_uid
self.data = b""
self.frames = b""
@@ -188,14 +267,24 @@ class ServeClient:
def fill_output(self, output):
"""
Format output with current and previous complete segments
into two lines of 50 characters.
Format the current incomplete transcription output by combining it with previous complete segments.
The resulting transcription is wrapped into two lines, each containing a maximum of 50 characters.
Details:
- This method is responsible for combining the current incomplete segment with a history of
previous complete segments to provide a coherent and visually organized transcription.
- It ensures that the combined transcription fits within two lines, with a maximum of 50 characters per line.
- Segments are concatenated in the order they exist in the list of previous segments, with the most
recent complete segment first and older segments appended as needed to maintain the character limit.
- If a 3-second pause is detected in the previous segments, any text preceding it is discarded to ensure
the transcription starts with the most recent complete content. The resulting transcription is returned
as a single string.
Args:
output(str): current incomplete segment
output(str): The current incomplete transcription segment.
Returns:
transcription wrapped in two lines
str: A formatted transcription wrapped in two lines.
"""
text = ''
pick_prev = min(len(self.text), self.pick_previous_segments)
@@ -209,6 +298,26 @@ class ServeClient:
return wrapped
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.
Details:
- The method appends incoming audio frames to the ongoing audio stream buffer.
- 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.
Returns:
None
"""
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):]
@@ -219,7 +328,24 @@ class ServeClient:
def speech_to_text(self):
"""
Process audio stream in an infinite loop.
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.
Details:
- 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.
Returns:
None
Raises:
Exception: If there is an issue with audio processing or WebSocket communication.
"""
# detect language
if self.language is None:
@@ -327,12 +453,27 @@ class ServeClient:
Processes the segments from whisper. Appends all the segments to the list
except for the last segment assuming that it is incomplete.
This method takes segments obtained from the Whisper, processes them, and updates the
ongoing transcript with the transcribed text. It handles complete segments, incomplete
segments, and repeated segments while maintaining chronological order.
Details:
- The method 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:
transcription for the current chunk
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 = ''
@@ -391,6 +532,15 @@ class ServeClient:
return last_segment
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.
Returns:
None
"""
self.websocket.send(
json.dumps(
{
@@ -401,6 +551,16 @@ class ServeClient:
)
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.
Returns:
None
"""
logging.info("Cleaning up.")
self.exit = True
self.transcriber.destroy()