faster-whisper-webui / src /vadParallel.py
aadnk's picture
Cache parallel processes
31f7bdb
raw
history blame
6.2 kB
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 = self._chunks(merged, len(merged) // len(devices))
# Parameters that will be passed to the transcribe function
parameters = []
segment_index = config.initial_segment_index
for i in range(len(devices)):
device_segment_list = merged_split[i]
# 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 _chunks(self, lst, n):
"""Yield successive n-sized chunks from lst."""
return [lst[i:i + n] for i in range(0, len(lst), n)]