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@@ -85,6 +85,99 @@ if platform.system() != 'Darwin':
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INIT_HANDLE_BUFFER_OVERFLOW = True
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+class TranscriptionWorker:
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+ def __init__(self, conn, stdout_pipe, model_path, compute_type, gpu_device_index, device,
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+ ready_event, shutdown_event, interrupt_stop_event, beam_size, initial_prompt, suppress_tokens):
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+ self.conn = conn
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+ self.stdout_pipe = stdout_pipe
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+ self.model_path = model_path
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+ self.compute_type = compute_type
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+ self.gpu_device_index = gpu_device_index
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+ self.device = device
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+ self.ready_event = ready_event
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+ self.shutdown_event = shutdown_event
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+ self.interrupt_stop_event = interrupt_stop_event
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+ self.beam_size = beam_size
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+ self.initial_prompt = initial_prompt
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+ self.suppress_tokens = suppress_tokens
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+ self.queue = queue.Queue()
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+
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+ def custom_print(self, *args, **kwargs):
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+ message = ' '.join(map(str, args))
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+ try:
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+ self.stdout_pipe.send(message)
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+ except (BrokenPipeError, EOFError, OSError):
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+ pass
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+
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+ def poll_connection(self):
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+ while not self.shutdown_event.is_set():
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+ if self.conn.poll(0.01):
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+ try:
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+ data = self.conn.recv()
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+ self.queue.put(data)
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+ except Exception as e:
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+ logging.error(f"Error receiving data from connection: {e}")
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+ else:
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+ time.sleep(TIME_SLEEP)
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+
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+ def run(self):
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+ system_signal.signal(system_signal.SIGINT, system_signal.SIG_IGN)
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+ __builtins__['print'] = self.custom_print
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+
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+ logging.info(f"Initializing faster_whisper main transcription model {self.model_path}")
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+
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+ try:
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+ model = faster_whisper.WhisperModel(
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+ model_size_or_path=self.model_path,
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+ device=self.device,
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+ compute_type=self.compute_type,
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+ device_index=self.gpu_device_index,
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+ )
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+ except Exception as e:
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+ logging.exception(f"Error initializing main faster_whisper transcription model: {e}")
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+ raise
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+
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+ self.ready_event.set()
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+ logging.debug("Faster_whisper main speech to text transcription model initialized successfully")
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+
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+ # Start the polling thread
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+ polling_thread = threading.Thread(target=self.poll_connection)
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+ polling_thread.start()
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+
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+ try:
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+ while not self.shutdown_event.is_set():
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+ try:
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+ audio, language = self.queue.get(timeout=0.1)
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+ try:
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+ segments, info = model.transcribe(
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+ audio,
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+ language=language if language else None,
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+ beam_size=self.beam_size,
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+ initial_prompt=self.initial_prompt,
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+ suppress_tokens=self.suppress_tokens
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+ )
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+ transcription = " ".join(seg.text for seg in segments).strip()
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+ logging.debug(f"Final text detected with main model: {transcription}")
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+ self.conn.send(('success', (transcription, info)))
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+ except Exception as e:
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+ logging.error(f"General error in transcription: {e}")
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+ self.conn.send(('error', str(e)))
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+ except queue.Empty:
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+ continue
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+ except KeyboardInterrupt:
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+ self.interrupt_stop_event.set()
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+ logging.debug("Transcription worker process finished due to KeyboardInterrupt")
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+ break
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+ except Exception as e:
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+ logging.error(f"General error in processing queue item: {e}")
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+ finally:
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+ __builtins__['print'] = print # Restore the original print function
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+ self.conn.close()
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+ self.stdout_pipe.close()
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+ self.shutdown_event.set() # Ensure the polling thread will stop
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+ polling_thread.join() # Wait for the polling thread to finish
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+
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+
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class AudioToTextRecorder:
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"""
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A class responsible for capturing audio from the microphone, detecting
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@@ -758,136 +851,139 @@ class AudioToTextRecorder:
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break
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time.sleep(0.1)
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- @staticmethod
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- def _transcription_worker(conn,
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- stdout_pipe,
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- model_path,
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- compute_type,
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- gpu_device_index,
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- device,
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- ready_event,
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- shutdown_event,
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- interrupt_stop_event,
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- beam_size,
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- initial_prompt,
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- suppress_tokens
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- ):
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- """
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- Worker method that handles the continuous
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- process of transcribing audio data.
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+ def _transcription_worker(*args, **kwargs):
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+ worker = TranscriptionWorker(*args, **kwargs)
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+ worker.run()
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+ # @staticmethod
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+ # def _transcription_worker(conn,
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+ # stdout_pipe,
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+ # model_path,
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+ # compute_type,
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+ # gpu_device_index,
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+ # device,
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+ # ready_event,
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+ # shutdown_event,
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+ # interrupt_stop_event,
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+ # beam_size,
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+ # initial_prompt,
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+ # suppress_tokens
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+ # ):
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+ # """
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+ # Worker method that handles the continuous
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+ # process of transcribing audio data.
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- This method runs in a separate process and is responsible for:
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- - Initializing the `faster_whisper` model used for transcription.
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- - Receiving audio data sent through a pipe and using the model
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- to transcribe it.
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- - Sending transcription results back through the pipe.
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- - Continuously checking for a shutdown event to gracefully
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- terminate the transcription process.
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+ # This method runs in a separate process and is responsible for:
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+ # - Initializing the `faster_whisper` model used for transcription.
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+ # - Receiving audio data sent through a pipe and using the model
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+ # to transcribe it.
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+ # - Sending transcription results back through the pipe.
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+ # - Continuously checking for a shutdown event to gracefully
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+ # terminate the transcription process.
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- Args:
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- conn (multiprocessing.Connection): The connection endpoint used
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- for receiving audio data and sending transcription results.
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- model_path (str): The path to the pre-trained faster_whisper model
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- for transcription.
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- compute_type (str): Specifies the type of computation to be used
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- for transcription.
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- gpu_device_index (int): Device ID to use.
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- device (str): Device for model to use.
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- ready_event (threading.Event): An event that is set when the
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- transcription model is successfully initialized and ready.
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- shutdown_event (threading.Event): An event that, when set,
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- signals this worker method to terminate.
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- interrupt_stop_event (threading.Event): An event that, when set,
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- signals this worker method to stop processing audio data.
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- beam_size (int): The beam size to use for beam search decoding.
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- initial_prompt (str or iterable of int): Initial prompt to be fed
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- to the transcription model.
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- suppress_tokens (list of int): Tokens to be suppressed from the
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- transcription output.
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- Raises:
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- Exception: If there is an error while initializing the
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- transcription model.
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- """
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+ # Args:
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+ # conn (multiprocessing.Connection): The connection endpoint used
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+ # for receiving audio data and sending transcription results.
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+ # model_path (str): The path to the pre-trained faster_whisper model
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+ # for transcription.
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+ # compute_type (str): Specifies the type of computation to be used
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+ # for transcription.
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+ # gpu_device_index (int): Device ID to use.
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+ # device (str): Device for model to use.
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+ # ready_event (threading.Event): An event that is set when the
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+ # transcription model is successfully initialized and ready.
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+ # shutdown_event (threading.Event): An event that, when set,
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+ # signals this worker method to terminate.
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+ # interrupt_stop_event (threading.Event): An event that, when set,
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+ # signals this worker method to stop processing audio data.
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+ # beam_size (int): The beam size to use for beam search decoding.
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+ # initial_prompt (str or iterable of int): Initial prompt to be fed
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+ # to the transcription model.
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+ # suppress_tokens (list of int): Tokens to be suppressed from the
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+ # transcription output.
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+ # Raises:
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+ # Exception: If there is an error while initializing the
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+ # transcription model.
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+ # """
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- system_signal.signal(system_signal.SIGINT, system_signal.SIG_IGN)
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+ # system_signal.signal(system_signal.SIGINT, system_signal.SIG_IGN)
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- def custom_print(*args, **kwargs):
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- message = ' '.join(map(str, args))
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- try:
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- stdout_pipe.send(message)
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- except (BrokenPipeError, EOFError, OSError):
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- # The pipe probably has been closed, so we ignore the error
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- pass
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+ # def custom_print(*args, **kwargs):
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+ # message = ' '.join(map(str, args))
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+ # try:
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+ # stdout_pipe.send(message)
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+ # except (BrokenPipeError, EOFError, OSError):
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+ # # The pipe probably has been closed, so we ignore the error
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+ # pass
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- # Replace the built-in print function with our custom one
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- __builtins__['print'] = custom_print
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+ # # Replace the built-in print function with our custom one
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+ # __builtins__['print'] = custom_print
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- logging.info("Initializing faster_whisper "
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- f"main transcription model {model_path}"
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- )
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+ # logging.info("Initializing faster_whisper "
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+ # f"main transcription model {model_path}"
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+ # )
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- try:
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- model = faster_whisper.WhisperModel(
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- model_size_or_path=model_path,
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- device=device,
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- compute_type=compute_type,
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- device_index=gpu_device_index,
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- )
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+ # try:
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+ # model = faster_whisper.WhisperModel(
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+ # model_size_or_path=model_path,
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+ # device=device,
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+ # compute_type=compute_type,
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+ # device_index=gpu_device_index,
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+ # )
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- except Exception as e:
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- logging.exception("Error initializing main "
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- f"faster_whisper transcription model: {e}"
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- )
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- raise
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+ # except Exception as e:
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+ # logging.exception("Error initializing main "
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+ # f"faster_whisper transcription model: {e}"
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+ # )
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+ # raise
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- ready_event.set()
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+ # ready_event.set()
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- logging.debug("Faster_whisper main speech to text "
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- "transcription model initialized successfully"
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- )
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+ # logging.debug("Faster_whisper main speech to text "
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+ # "transcription model initialized successfully"
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+ # )
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- try:
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- while not shutdown_event.is_set():
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- try:
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- if conn.poll(0.01):
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- logging.debug("Receive from _transcription_worker pipe")
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- audio, language = conn.recv()
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- try:
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- segments, info = model.transcribe(
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- audio,
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- language=language if language else None,
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- beam_size=beam_size,
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- initial_prompt=initial_prompt,
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- suppress_tokens=suppress_tokens
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- )
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- transcription = " ".join(seg.text for seg in segments)
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- transcription = transcription.strip()
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- logging.debug(f"Final text detected with main model: {transcription}")
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- conn.send(('success', (transcription, info)))
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- except Exception as e:
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- logging.error(f"General error in _transcription_worker in transcription: {e}")
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- conn.send(('error', str(e)))
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- else:
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- time.sleep(TIME_SLEEP)
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+ # try:
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+ # while not shutdown_event.is_set():
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+ # try:
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+ # if conn.poll(0.01):
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+ # logging.debug("Receive from _transcription_worker pipe")
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+ # audio, language = conn.recv()
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+ # try:
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+ # segments, info = model.transcribe(
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+ # audio,
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+ # language=language if language else None,
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+ # beam_size=beam_size,
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+ # initial_prompt=initial_prompt,
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+ # suppress_tokens=suppress_tokens
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+ # )
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+ # transcription = " ".join(seg.text for seg in segments)
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+ # transcription = transcription.strip()
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+ # logging.debug(f"Final text detected with main model: {transcription}")
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+ # conn.send(('success', (transcription, info)))
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+ # except Exception as e:
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+ # logging.error(f"General error in _transcription_worker in transcription: {e}")
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+ # conn.send(('error', str(e)))
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+ # else:
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+ # time.sleep(TIME_SLEEP)
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- except KeyboardInterrupt:
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- interrupt_stop_event.set()
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+ # except KeyboardInterrupt:
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+ # interrupt_stop_event.set()
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- logging.debug("Transcription worker process "
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- "finished due to KeyboardInterrupt"
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- )
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- stdout_pipe.close()
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- break
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+ # logging.debug("Transcription worker process "
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+ # "finished due to KeyboardInterrupt"
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+ # )
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+ # stdout_pipe.close()
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+ # break
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- except Exception as e:
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- logging.error(f"General error in _transcription_worker in accessing pipe: {e}")
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+ # except Exception as e:
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+ # logging.error(f"General error in _transcription_worker in accessing pipe: {e}")
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- finally:
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- __builtins__['print'] = print # Restore the original print function
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- conn.close()
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- stdout_pipe.close()
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+ # finally:
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+ # __builtins__['print'] = print # Restore the original print function
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+ # conn.close()
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+ # stdout_pipe.close()
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@staticmethod
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def _audio_data_worker(audio_queue,
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KoljaB