import asyncio import logging import traceback from time import time from typing import Any, AsyncGenerator, List, Optional, Union import numpy as np from whisperlivekit.core import (TranscriptionEngine, online_diarization_factory, online_factory, online_translation_factory) from whisperlivekit.ffmpeg_manager import FFmpegManager, FFmpegState from whisperlivekit.silero_vad_iterator import FixedVADIterator from whisperlivekit.timed_objects import (ASRToken, ChangeSpeaker, FrontData, Segment, Silence, State, Transcript) from whisperlivekit.tokens_alignment import TokensAlignment logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s") logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) SENTINEL = object() # unique sentinel object for end of stream marker MIN_DURATION_REAL_SILENCE = 5 async def get_all_from_queue(queue: asyncio.Queue) -> Union[object, Silence, np.ndarray, List[Any]]: items: List[Any] = [] first_item = await queue.get() queue.task_done() if first_item is SENTINEL: return first_item if isinstance(first_item, Silence): return first_item items.append(first_item) while True: if not queue._queue: break next_item = queue._queue[0] if next_item is SENTINEL: break if isinstance(next_item, Silence): break items.append(await queue.get()) queue.task_done() if isinstance(items[0], np.ndarray): return np.concatenate(items) else: #translation return items class AudioProcessor: """ Processes audio streams for transcription and diarization. Handles audio processing, state management, and result formatting. """ def __init__(self, **kwargs: Any) -> None: """Initialize the audio processor with configuration, models, and state.""" if 'transcription_engine' in kwargs and isinstance(kwargs['transcription_engine'], TranscriptionEngine): models = kwargs['transcription_engine'] else: models = TranscriptionEngine(**kwargs) # Audio processing settings self.args = models.args self.sample_rate = 16000 self.channels = 1 self.samples_per_sec = int(self.sample_rate * self.args.min_chunk_size) self.bytes_per_sample = 2 self.bytes_per_sec = self.samples_per_sec * self.bytes_per_sample self.max_bytes_per_sec = 32000 * 5 # 5 seconds of audio at 32 kHz self.is_pcm_input = self.args.pcm_input # State management self.is_stopping: bool = False self.current_silence: Optional[Silence] = None self.state: State = State() self.lock: asyncio.Lock = asyncio.Lock() self.sep: str = " " # Default separator self.last_response_content: FrontData = FrontData() self.tokens_alignment: TokensAlignment = TokensAlignment(self.state, self.args, self.sep) self.beg_loop: Optional[float] = None # Models and processing self.asr: Any = models.asr self.vac_model: Any = models.vac_model if self.args.vac: self.vac: Optional[FixedVADIterator] = FixedVADIterator(models.vac_model) else: self.vac: Optional[FixedVADIterator] = None self.ffmpeg_manager: Optional[FFmpegManager] = None self.ffmpeg_reader_task: Optional[asyncio.Task] = None self._ffmpeg_error: Optional[str] = None if not self.is_pcm_input: self.ffmpeg_manager = FFmpegManager( sample_rate=self.sample_rate, channels=self.channels ) async def handle_ffmpeg_error(error_type: str): logger.error(f"FFmpeg error: {error_type}") self._ffmpeg_error = error_type self.ffmpeg_manager.on_error_callback = handle_ffmpeg_error self.transcription_queue: Optional[asyncio.Queue] = asyncio.Queue() if self.args.transcription else None self.diarization_queue: Optional[asyncio.Queue] = asyncio.Queue() if self.args.diarization else None self.translation_queue: Optional[asyncio.Queue] = asyncio.Queue() if self.args.target_language else None self.pcm_buffer: bytearray = bytearray() self.total_pcm_samples: int = 0 self.transcription_task: Optional[asyncio.Task] = None self.diarization_task: Optional[asyncio.Task] = None self.translation_task: Optional[asyncio.Task] = None self.watchdog_task: Optional[asyncio.Task] = None self.all_tasks_for_cleanup: List[asyncio.Task] = [] self.transcription: Optional[Any] = None self.translation: Optional[Any] = None self.diarization: Optional[Any] = None if self.args.transcription: self.transcription = online_factory(self.args, models.asr) self.sep = self.transcription.asr.sep if self.args.diarization: self.diarization = online_diarization_factory(self.args, models.diarization_model) if models.translation_model: self.translation = online_translation_factory(self.args, models.translation_model) async def _push_silence_event(self) -> None: if self.transcription_queue: await self.transcription_queue.put(self.current_silence) if self.args.diarization and self.diarization_queue: await self.diarization_queue.put(self.current_silence) if self.translation_queue: await self.translation_queue.put(self.current_silence) async def _begin_silence(self) -> None: if self.current_silence: return now = time() - self.beg_loop self.current_silence = Silence( is_starting=True, start=now ) await self._push_silence_event() async def _end_silence(self) -> None: if not self.current_silence: return now = time() - self.beg_loop self.current_silence.end = now self.current_silence.is_starting=False self.current_silence.has_ended=True self.current_silence.compute_duration() if self.current_silence.duration > MIN_DURATION_REAL_SILENCE: self.state.new_tokens.append(self.current_silence) await self._push_silence_event() self.current_silence = None async def _enqueue_active_audio(self, pcm_chunk: np.ndarray) -> None: if pcm_chunk is None or pcm_chunk.size == 0: return if self.transcription_queue: await self.transcription_queue.put(pcm_chunk.copy()) if self.args.diarization and self.diarization_queue: await self.diarization_queue.put(pcm_chunk.copy()) def _slice_before_silence(self, pcm_array: np.ndarray, chunk_sample_start: int, silence_sample: Optional[int]) -> Optional[np.ndarray]: if silence_sample is None: return None relative_index = int(silence_sample - chunk_sample_start) if relative_index <= 0: return None split_index = min(relative_index, len(pcm_array)) if split_index <= 0: return None return pcm_array[:split_index] def convert_pcm_to_float(self, pcm_buffer: Union[bytes, bytearray]) -> np.ndarray: """Convert PCM buffer in s16le format to normalized NumPy array.""" return np.frombuffer(pcm_buffer, dtype=np.int16).astype(np.float32) / 32768.0 async def get_current_state(self) -> State: """Get current state.""" async with self.lock: current_time = time() remaining_transcription = 0 if self.state.end_buffer > 0: remaining_transcription = max(0, round(current_time - self.beg_loop - self.state.end_buffer, 1)) remaining_diarization = 0 if self.state.tokens: latest_end = max(self.state.end_buffer, self.state.tokens[-1].end if self.state.tokens else 0) remaining_diarization = max(0, round(latest_end - self.state.end_attributed_speaker, 1)) self.state.remaining_time_transcription = remaining_transcription self.state.remaining_time_diarization = remaining_diarization return self.state async def ffmpeg_stdout_reader(self) -> None: """Read audio data from FFmpeg stdout and process it into the PCM pipeline.""" beg = time() while True: try: if self.is_stopping: logger.info("Stopping ffmpeg_stdout_reader due to stopping flag.") break state = await self.ffmpeg_manager.get_state() if self.ffmpeg_manager else FFmpegState.STOPPED if state == FFmpegState.FAILED: logger.error("FFmpeg is in FAILED state, cannot read data") break elif state == FFmpegState.STOPPED: logger.info("FFmpeg is stopped") break elif state != FFmpegState.RUNNING: await asyncio.sleep(0.1) continue current_time = time() elapsed_time = max(0.0, current_time - beg) buffer_size = max(int(32000 * elapsed_time), 4096) # dynamic read beg = current_time chunk = await self.ffmpeg_manager.read_data(buffer_size) if not chunk: # No data currently available await asyncio.sleep(0.05) continue self.pcm_buffer.extend(chunk) await self.handle_pcm_data() except asyncio.CancelledError: logger.info("ffmpeg_stdout_reader cancelled.") break except Exception as e: logger.warning(f"Exception in ffmpeg_stdout_reader: {e}") logger.debug(f"Traceback: {traceback.format_exc()}") await asyncio.sleep(0.2) logger.info("FFmpeg stdout processing finished. Signaling downstream processors if needed.") if self.transcription_queue: await self.transcription_queue.put(SENTINEL) if self.diarization: await self.diarization_queue.put(SENTINEL) if self.translation: await self.translation_queue.put(SENTINEL) async def transcription_processor(self) -> None: """Process audio chunks for transcription.""" cumulative_pcm_duration_stream_time = 0.0 while True: try: # item = await self.transcription_queue.get() item = await get_all_from_queue(self.transcription_queue) if item is SENTINEL: logger.debug("Transcription processor received sentinel. Finishing.") break asr_internal_buffer_duration_s = len(getattr(self.transcription, 'audio_buffer', [])) / self.transcription.SAMPLING_RATE transcription_lag_s = max(0.0, time() - self.beg_loop - self.state.end_buffer) asr_processing_logs = f"internal_buffer={asr_internal_buffer_duration_s:.2f}s | lag={transcription_lag_s:.2f}s |" stream_time_end_of_current_pcm = cumulative_pcm_duration_stream_time new_tokens = [] current_audio_processed_upto = self.state.end_buffer if isinstance(item, Silence): if item.is_starting: new_tokens, current_audio_processed_upto = await asyncio.to_thread( self.transcription.start_silence ) asr_processing_logs += f" + Silence starting" if item.has_ended: asr_processing_logs += f" + Silence of = {item.duration:.2f}s" cumulative_pcm_duration_stream_time += item.duration current_audio_processed_upto = cumulative_pcm_duration_stream_time self.transcription.end_silence(item.duration, self.state.tokens[-1].end if self.state.tokens else 0) if self.state.tokens: asr_processing_logs += f" | last_end = {self.state.tokens[-1].end} |" logger.info(asr_processing_logs) new_tokens = new_tokens or [] current_audio_processed_upto = max(current_audio_processed_upto, stream_time_end_of_current_pcm) elif isinstance(item, ChangeSpeaker): self.transcription.new_speaker(item) continue elif isinstance(item, np.ndarray): pcm_array = item logger.info(asr_processing_logs) cumulative_pcm_duration_stream_time += len(pcm_array) / self.sample_rate stream_time_end_of_current_pcm = cumulative_pcm_duration_stream_time self.transcription.insert_audio_chunk(pcm_array, stream_time_end_of_current_pcm) new_tokens, current_audio_processed_upto = await asyncio.to_thread(self.transcription.process_iter) new_tokens = new_tokens or [] _buffer_transcript = self.transcription.get_buffer() buffer_text = _buffer_transcript.text if new_tokens: validated_text = self.sep.join([t.text for t in new_tokens]) if buffer_text.startswith(validated_text): _buffer_transcript.text = buffer_text[len(validated_text):].lstrip() candidate_end_times = [self.state.end_buffer] if new_tokens: candidate_end_times.append(new_tokens[-1].end) if _buffer_transcript.end is not None: candidate_end_times.append(_buffer_transcript.end) candidate_end_times.append(current_audio_processed_upto) async with self.lock: self.state.tokens.extend(new_tokens) self.state.buffer_transcription = _buffer_transcript self.state.end_buffer = max(candidate_end_times) self.state.new_tokens.extend(new_tokens) self.state.new_tokens_buffer = _buffer_transcript if self.translation_queue: for token in new_tokens: await self.translation_queue.put(token) except Exception as e: logger.warning(f"Exception in transcription_processor: {e}") logger.warning(f"Traceback: {traceback.format_exc()}") if 'pcm_array' in locals() and pcm_array is not SENTINEL : # Check if pcm_array was assigned from queue self.transcription_queue.task_done() if self.is_stopping: logger.info("Transcription processor finishing due to stopping flag.") if self.diarization_queue: await self.diarization_queue.put(SENTINEL) if self.translation_queue: await self.translation_queue.put(SENTINEL) logger.info("Transcription processor task finished.") async def diarization_processor(self) -> None: while True: try: item = await get_all_from_queue(self.diarization_queue) if item is SENTINEL: break elif type(item) is Silence: if item.has_ended: self.diarization.insert_silence(item.duration) continue self.diarization.insert_audio_chunk(item) diarization_segments = await self.diarization.diarize() self.state.new_diarization = diarization_segments except Exception as e: logger.warning(f"Exception in diarization_processor: {e}") logger.warning(f"Traceback: {traceback.format_exc()}") logger.info("Diarization processor task finished.") async def translation_processor(self) -> None: # the idea is to ignore diarization for the moment. We use only transcription tokens. # And the speaker is attributed given the segments used for the translation # in the future we want to have different languages for each speaker etc, so it will be more complex. while True: try: item = await get_all_from_queue(self.translation_queue) if item is SENTINEL: logger.debug("Translation processor received sentinel. Finishing.") break elif type(item) is Silence: if item.is_starting: new_translation, new_translation_buffer = self.translation.validate_buffer_and_reset() if item.has_ended: self.translation.insert_silence(item.duration) continue elif isinstance(item, ChangeSpeaker): new_translation, new_translation_buffer = self.translation.validate_buffer_and_reset() pass else: self.translation.insert_tokens(item) new_translation, new_translation_buffer = await asyncio.to_thread(self.translation.process) async with self.lock: self.state.new_translation.append(new_translation) self.state.new_translation_buffer = new_translation_buffer except Exception as e: logger.warning(f"Exception in translation_processor: {e}") logger.warning(f"Traceback: {traceback.format_exc()}") logger.info("Translation processor task finished.") async def results_formatter(self) -> AsyncGenerator[FrontData, None]: """Format processing results for output.""" while True: try: if self._ffmpeg_error: yield FrontData(status="error", error=f"FFmpeg error: {self._ffmpeg_error}") self._ffmpeg_error = None await asyncio.sleep(1) continue self.tokens_alignment.update() lines, buffer_diarization_text, buffer_translation_text = self.tokens_alignment.get_lines( diarization=self.args.diarization, translation=bool(self.translation), current_silence=self.current_silence ) state = await self.get_current_state() buffer_transcription_text = state.buffer_transcription.text if state.buffer_transcription else '' response_status = "active_transcription" if not lines and not buffer_transcription_text and not buffer_diarization_text: response_status = "no_audio_detected" response = FrontData( status=response_status, lines=lines, buffer_transcription=buffer_transcription_text, buffer_diarization=buffer_diarization_text, buffer_translation=buffer_translation_text, remaining_time_transcription=state.remaining_time_transcription, remaining_time_diarization=state.remaining_time_diarization if self.args.diarization else 0 ) should_push = (response != self.last_response_content) if should_push: yield response self.last_response_content = response if self.is_stopping and self._processing_tasks_done(): logger.info("Results formatter: All upstream processors are done and in stopping state. Terminating.") return await asyncio.sleep(0.05) except Exception as e: logger.warning(f"Exception in results_formatter. Traceback: {traceback.format_exc()}") await asyncio.sleep(0.5) async def create_tasks(self) -> AsyncGenerator[FrontData, None]: """Create and start processing tasks.""" self.all_tasks_for_cleanup = [] processing_tasks_for_watchdog: List[asyncio.Task] = [] # If using FFmpeg (non-PCM input), start it and spawn stdout reader if not self.is_pcm_input: success = await self.ffmpeg_manager.start() if not success: logger.error("Failed to start FFmpeg manager") async def error_generator() -> AsyncGenerator[FrontData, None]: yield FrontData( status="error", error="FFmpeg failed to start. Please check that FFmpeg is installed." ) return error_generator() self.ffmpeg_reader_task = asyncio.create_task(self.ffmpeg_stdout_reader()) self.all_tasks_for_cleanup.append(self.ffmpeg_reader_task) processing_tasks_for_watchdog.append(self.ffmpeg_reader_task) if self.transcription: self.transcription_task = asyncio.create_task(self.transcription_processor()) self.all_tasks_for_cleanup.append(self.transcription_task) processing_tasks_for_watchdog.append(self.transcription_task) if self.diarization: self.diarization_task = asyncio.create_task(self.diarization_processor()) self.all_tasks_for_cleanup.append(self.diarization_task) processing_tasks_for_watchdog.append(self.diarization_task) if self.translation: self.translation_task = asyncio.create_task(self.translation_processor()) self.all_tasks_for_cleanup.append(self.translation_task) processing_tasks_for_watchdog.append(self.translation_task) # Monitor overall system health self.watchdog_task = asyncio.create_task(self.watchdog(processing_tasks_for_watchdog)) self.all_tasks_for_cleanup.append(self.watchdog_task) return self.results_formatter() async def watchdog(self, tasks_to_monitor: List[asyncio.Task]) -> None: """Monitors the health of critical processing tasks.""" tasks_remaining: List[asyncio.Task] = [task for task in tasks_to_monitor if task] while True: try: if not tasks_remaining: logger.info("Watchdog task finishing: all monitored tasks completed.") return await asyncio.sleep(10) for i, task in enumerate(list(tasks_remaining)): if task.done(): exc = task.exception() task_name = task.get_name() if hasattr(task, 'get_name') else f"Monitored Task {i}" if exc: logger.error(f"{task_name} unexpectedly completed with exception: {exc}") else: logger.info(f"{task_name} completed normally.") tasks_remaining.remove(task) except asyncio.CancelledError: logger.info("Watchdog task cancelled.") break except Exception as e: logger.error(f"Error in watchdog task: {e}", exc_info=True) async def cleanup(self) -> None: """Clean up resources when processing is complete.""" logger.info("Starting cleanup of AudioProcessor resources.") self.is_stopping = True for task in self.all_tasks_for_cleanup: if task and not task.done(): task.cancel() created_tasks = [t for t in self.all_tasks_for_cleanup if t] if created_tasks: await asyncio.gather(*created_tasks, return_exceptions=True) logger.info("All processing tasks cancelled or finished.") if not self.is_pcm_input and self.ffmpeg_manager: try: await self.ffmpeg_manager.stop() logger.info("FFmpeg manager stopped.") except Exception as e: logger.warning(f"Error stopping FFmpeg manager: {e}") if self.diarization: self.diarization.close() logger.info("AudioProcessor cleanup complete.") def _processing_tasks_done(self) -> bool: """Return True when all active processing tasks have completed.""" tasks_to_check = [ self.transcription_task, self.diarization_task, self.translation_task, self.ffmpeg_reader_task, ] return all(task.done() for task in tasks_to_check if task) async def process_audio(self, message: Optional[bytes]) -> None: """Process incoming audio data.""" if not self.beg_loop: self.beg_loop = time() self.current_silence = Silence(start=0.0, is_starting=True) self.tokens_alignment.beg_loop = self.beg_loop if not message: logger.info("Empty audio message received, initiating stop sequence.") self.is_stopping = True if self.transcription_queue: await self.transcription_queue.put(SENTINEL) if not self.is_pcm_input and self.ffmpeg_manager: await self.ffmpeg_manager.stop() return if self.is_stopping: logger.warning("AudioProcessor is stopping. Ignoring incoming audio.") return if self.is_pcm_input: self.pcm_buffer.extend(message) await self.handle_pcm_data() else: if not self.ffmpeg_manager: logger.error("FFmpeg manager not initialized for non-PCM input.") return success = await self.ffmpeg_manager.write_data(message) if not success: ffmpeg_state = await self.ffmpeg_manager.get_state() if ffmpeg_state == FFmpegState.FAILED: logger.error("FFmpeg is in FAILED state, cannot process audio") else: logger.warning("Failed to write audio data to FFmpeg") async def handle_pcm_data(self) -> None: # Process when enough data if len(self.pcm_buffer) < self.bytes_per_sec: return if len(self.pcm_buffer) > self.max_bytes_per_sec: logger.warning( f"Audio buffer too large: {len(self.pcm_buffer) / self.bytes_per_sec:.2f}s. " f"Consider using a smaller model." ) chunk_size = min(len(self.pcm_buffer), self.max_bytes_per_sec) aligned_chunk_size = (chunk_size // self.bytes_per_sample) * self.bytes_per_sample if aligned_chunk_size == 0: return pcm_array = self.convert_pcm_to_float(self.pcm_buffer[:aligned_chunk_size]) self.pcm_buffer = self.pcm_buffer[aligned_chunk_size:] num_samples = len(pcm_array) chunk_sample_start = self.total_pcm_samples chunk_sample_end = chunk_sample_start + num_samples res = None if self.args.vac: res = self.vac(pcm_array) if res is not None: if "start" in res and self.current_silence: await self._end_silence() if "end" in res and not self.current_silence: pre_silence_chunk = self._slice_before_silence( pcm_array, chunk_sample_start, res.get("end") ) if pre_silence_chunk is not None and pre_silence_chunk.size > 0: await self._enqueue_active_audio(pre_silence_chunk) await self._begin_silence() if not self.current_silence: await self._enqueue_active_audio(pcm_array) self.total_pcm_samples = chunk_sample_end if not self.args.transcription and not self.args.diarization: await asyncio.sleep(0.1)