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| import multiprocessing | |
| import threading | |
| import time | |
| from src.vad import AbstractTranscription, TranscriptionConfig | |
| from src.whisperContainer import WhisperCallback | |
| from multiprocessing import Pool | |
| from typing import List | |
| import os | |
| class ParallelContext: | |
| def __init__(self, num_processes: int = None, auto_cleanup_timeout_seconds: float = None): | |
| self.num_processes = num_processes | |
| self.auto_cleanup_timeout_seconds = auto_cleanup_timeout_seconds | |
| self.lock = threading.Lock() | |
| self.ref_count = 0 | |
| self.pool = None | |
| self.cleanup_timer = None | |
| def get_pool(self): | |
| # Initialize pool lazily | |
| if (self.pool is None): | |
| context = multiprocessing.get_context('spawn') | |
| self.pool = context.Pool(self.num_processes) | |
| self.ref_count = self.ref_count + 1 | |
| if (self.auto_cleanup_timeout_seconds is not None): | |
| self._stop_auto_cleanup() | |
| return self.pool | |
| def return_pool(self, pool): | |
| if (self.pool == pool and self.ref_count > 0): | |
| self.ref_count = self.ref_count - 1 | |
| if (self.ref_count == 0): | |
| if (self.auto_cleanup_timeout_seconds is not None): | |
| self._start_auto_cleanup() | |
| def _start_auto_cleanup(self): | |
| if (self.cleanup_timer is not None): | |
| self.cleanup_timer.cancel() | |
| self.cleanup_timer = threading.Timer(self.auto_cleanup_timeout_seconds, self._execute_cleanup) | |
| self.cleanup_timer.start() | |
| print("Started auto cleanup of pool in " + str(self.auto_cleanup_timeout_seconds) + " seconds") | |
| def _stop_auto_cleanup(self): | |
| if (self.cleanup_timer is not None): | |
| self.cleanup_timer.cancel() | |
| self.cleanup_timer = None | |
| print("Stopped auto cleanup of pool") | |
| def _execute_cleanup(self): | |
| print("Executing cleanup of pool") | |
| if (self.ref_count == 0): | |
| self.close() | |
| def close(self): | |
| self._stop_auto_cleanup() | |
| if (self.pool is not None): | |
| print("Closing pool of " + str(self.num_processes) + " processes") | |
| self.pool.close() | |
| self.pool.join() | |
| self.pool = None | |
| class ParallelTranscriptionConfig(TranscriptionConfig): | |
| def __init__(self, device_id: str, override_timestamps, initial_segment_index, copy: TranscriptionConfig = None): | |
| super().__init__(copy.non_speech_strategy, copy.segment_padding_left, copy.segment_padding_right, copy.max_silent_period, copy.max_merge_size, copy.max_prompt_window, initial_segment_index) | |
| self.device_id = device_id | |
| self.override_timestamps = override_timestamps | |
| class ParallelTranscription(AbstractTranscription): | |
| def __init__(self, sampling_rate: int = 16000): | |
| super().__init__(sampling_rate=sampling_rate) | |
| def transcribe_parallel(self, transcription: AbstractTranscription, audio: str, whisperCallable: WhisperCallback, config: TranscriptionConfig, devices: List[str], parallel_context: ParallelContext = None): | |
| # First, get the timestamps for the original audio | |
| merged = transcription.get_merged_timestamps(audio, config) | |
| # Split into a list for each device | |
| # TODO: Split by time instead of by number of chunks | |
| merged_split = list(self._split(merged, len(devices))) | |
| # Parameters that will be passed to the transcribe function | |
| parameters = [] | |
| segment_index = config.initial_segment_index | |
| for i in range(len(merged_split)): | |
| device_segment_list = list(merged_split[i]) | |
| device_id = devices[i] | |
| if (len(device_segment_list) <= 0): | |
| continue | |
| print("Device " + device_id + " (index " + str(i) + ") has " + str(len(device_segment_list)) + " segments") | |
| # Create a new config with the given device ID | |
| device_config = ParallelTranscriptionConfig(devices[i], device_segment_list, segment_index, config) | |
| segment_index += len(device_segment_list) | |
| parameters.append([audio, whisperCallable, device_config]); | |
| merged = { | |
| 'text': '', | |
| 'segments': [], | |
| 'language': None | |
| } | |
| created_context = False | |
| # Spawn a separate process for each device | |
| try: | |
| if (parallel_context is None): | |
| parallel_context = ParallelContext(len(devices)) | |
| created_context = True | |
| # Get a pool of processes | |
| pool = parallel_context.get_pool() | |
| # Run the transcription in parallel | |
| results = pool.starmap(self.transcribe, parameters) | |
| for result in results: | |
| # Merge the results | |
| if (result['text'] is not None): | |
| merged['text'] += result['text'] | |
| if (result['segments'] is not None): | |
| merged['segments'].extend(result['segments']) | |
| if (result['language'] is not None): | |
| merged['language'] = result['language'] | |
| finally: | |
| # Return the pool to the context | |
| if (parallel_context is not None): | |
| parallel_context.return_pool(pool) | |
| # Always close the context if we created it | |
| if (created_context): | |
| parallel_context.close() | |
| return merged | |
| def get_transcribe_timestamps(self, audio: str, config: ParallelTranscriptionConfig): | |
| return [] | |
| def get_merged_timestamps(self, audio: str, config: ParallelTranscriptionConfig): | |
| # Override timestamps that will be processed | |
| if (config.override_timestamps is not None): | |
| print("Using override timestamps of size " + str(len(config.override_timestamps))) | |
| return config.override_timestamps | |
| return super().get_merged_timestamps(audio, config) | |
| def transcribe(self, audio: str, whisperCallable: WhisperCallback, config: ParallelTranscriptionConfig): | |
| # Override device ID | |
| if (config.device_id is not None): | |
| print("Using device " + config.device_id) | |
| os.environ["CUDA_VISIBLE_DEVICES"] = config.device_id | |
| return super().transcribe(audio, whisperCallable, config) | |
| def _split(self, a, n): | |
| """Split a list into n approximately equal parts.""" | |
| k, m = divmod(len(a), n) | |
| return (a[i*k+min(i, m):(i+1)*k+min(i+1, m)] for i in range(n)) | |