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import json
import logging
import threading
import time
import numpy as np
class ServeClientBase(object):
RATE = 16000
SERVER_READY = "SERVER_READY"
DISCONNECT = "DISCONNECT"
client_uid: str
"""A unique identifier for the client."""
websocket: object
"""The WebSocket connection for the client."""
send_last_n_segments: int
"""Number of most recent segments to send to the client."""
no_speech_thresh: float
"""Segments with no speech probability above this threshold will be discarded."""
clip_audio: bool
"""Whether to clip audio with no valid segments."""
same_output_threshold: int
"""Number of repeated outputs before considering it as a valid segment."""
def __init__(
self,
client_uid,
websocket,
send_last_n_segments=10,
no_speech_thresh=0.45,
clip_audio=False,
same_output_threshold=10,
):
self.client_uid = client_uid
self.websocket = websocket
self.send_last_n_segments = send_last_n_segments
self.no_speech_thresh = no_speech_thresh
self.clip_audio = clip_audio
self.same_output_threshold = same_output_threshold
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.exit = False
self.same_output_count = 0
self.transcript = []
self.end_time_for_same_output = None
# threading
self.lock = threading.Lock()
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.
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
if self.clip_audio:
self.clip_audio_if_no_valid_segment()
input_bytes, duration = self.get_audio_chunk_for_processing()
if duration < 1.0:
time.sleep(0.1) # wait for audio chunks to arrive
continue
try:
input_sample = input_bytes.copy()
result = self.transcribe_audio(input_sample)
if result is None or self.language is None:
self.timestamp_offset += duration
time.sleep(0.25) # wait for voice activity, result is None when no voice activity
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 transcribe_audio(self):
raise NotImplementedError
def handle_transcription_output(self, result, duration):
raise NotImplementedError
def format_segment(self, start, end, text, completed=False):
"""
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),
'text': text,
'completed': completed
}
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):]
# check timestamp offset(should be >= self.frame_offset)
# this basically means that there is no speech as timestamp offset hasnt updated
# and is less than frame_offset
if self.timestamp_offset < self.frames_offset:
self.timestamp_offset = self.frames_offset
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
"""
with self.lock:
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):
"""
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.
"""
with self.lock:
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):
"""
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()
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):
"""
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):
"""
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({
"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
def get_segment_no_speech_prob(self, segment):
return getattr(segment, "no_speech_prob", 0)
def get_segment_start(self, segment):
return getattr(segment, "start", getattr(segment, "start_ts", 0))
def get_segment_end(self, segment):
return getattr(segment, "end", getattr(segment, "end_ts", 0))
def update_segments(self, segments, duration):
"""
Processes the segments from Whisper and updates the transcript.
Uses helper methods to account for differences between backends.
Args:
segments (list): List of segments returned by the transcriber.
duration (float): Duration of the current audio chunk.
Returns:
dict or None: The last processed segment (if any).
"""
offset = None
self.current_out = ''
last_segment = None
# Process complete segments only if there are more than one
# and if the last segment's no_speech_prob is below the threshold.
if len(segments) > 1 and self.get_segment_no_speech_prob(segments[-1]) <= self.no_speech_thresh:
for s in segments[:-1]:
text_ = s.text
self.text.append(text_)
with self.lock:
start = self.timestamp_offset + self.get_segment_start(s)
end = self.timestamp_offset + min(duration, self.get_segment_end(s))
if start >= end:
continue
if self.get_segment_no_speech_prob(s) > self.no_speech_thresh:
continue
self.transcript.append(self.format_segment(start, end, text_, completed=True))
offset = min(duration, self.get_segment_end(s))
# Process the last segment if its no_speech_prob is acceptable.
if self.get_segment_no_speech_prob(segments[-1]) <= self.no_speech_thresh:
self.current_out += segments[-1].text
with self.lock:
last_segment = self.format_segment(
self.timestamp_offset + self.get_segment_start(segments[-1]),
self.timestamp_offset + min(duration, self.get_segment_end(segments[-1])),
self.current_out,
completed=False
)
# Handle repeated output logic.
if self.current_out.strip() == self.prev_out.strip() and self.current_out != '':
self.same_output_count += 1
# if we remove the audio because of same output on the nth reptition we might remove the
# audio thats not yet transcribed so, capturing the time when it was repeated for the first time
if self.end_time_for_same_output is None:
self.end_time_for_same_output = self.get_segment_end(segments[-1])
time.sleep(0.1) # wait briefly for any new voice activity
else:
self.same_output_count = 0
self.end_time_for_same_output = None
# If the same incomplete segment is repeated too many times,
# append it to the transcript and update the offset.
if self.same_output_count > self.same_output_threshold:
if not self.text or self.text[-1].strip().lower() != self.current_out.strip().lower():
self.text.append(self.current_out)
with self.lock:
self.transcript.append(self.format_segment(
self.timestamp_offset,
self.timestamp_offset + min(duration, self.end_time_for_same_output),
self.current_out,
completed=True
))
self.current_out = ''
offset = min(duration, self.end_time_for_same_output)
self.same_output_count = 0
last_segment = None
self.end_time_for_same_output = None
else:
self.prev_out = self.current_out
if offset is not None:
with self.lock:
self.timestamp_offset += offset
return last_segment
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