faster-whisper-webui-v2 / src /vadParallel.py
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Adding support for faster_whisper
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import multiprocessing
from queue import Empty
import threading
import time
from src.hooks.progressListener import ProgressListener
from src.vad import AbstractTranscription, TranscriptionConfig, get_audio_duration
from multiprocessing import Pool, Queue
from typing import Any, Dict, List, Union
import os
from src.whisper.abstractWhisperContainer import AbstractWhisperCallback
class _ProgressListenerToQueue(ProgressListener):
def __init__(self, progress_queue: Queue):
self.progress_queue = progress_queue
self.progress_total = 0
self.prev_progress = 0
def on_progress(self, current: Union[int, float], total: Union[int, float]):
delta = current - self.prev_progress
self.prev_progress = current
self.progress_total = total
self.progress_queue.put(delta)
def on_finished(self):
if self.progress_total > self.prev_progress:
delta = self.progress_total - self.prev_progress
self.progress_queue.put(delta)
self.prev_progress = self.progress_total
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):
# Silero VAD typically takes about 3 seconds per minute, so there's no need to split the chunks
# into smaller segments than 2 minute (min 6 seconds per CPU core)
MIN_CPU_CHUNK_SIZE_SECONDS = 2 * 60
def __init__(self, sampling_rate: int = 16000):
super().__init__(sampling_rate=sampling_rate)
def transcribe_parallel(self, transcription: AbstractTranscription, audio: str, whisperCallable: AbstractWhisperCallback, config: TranscriptionConfig,
cpu_device_count: int, gpu_devices: List[str], cpu_parallel_context: ParallelContext = None, gpu_parallel_context: ParallelContext = None,
progress_listener: ProgressListener = None):
total_duration = get_audio_duration(audio)
# First, get the timestamps for the original audio
if (cpu_device_count > 1 and not transcription.is_transcribe_timestamps_fast()):
merged = self._get_merged_timestamps_parallel(transcription, audio, config, total_duration, cpu_device_count, cpu_parallel_context)
else:
timestamp_segments = transcription.get_transcribe_timestamps(audio, config, 0, total_duration)
merged = transcription.get_merged_timestamps(timestamp_segments, config, total_duration)
# We must make sure the whisper model is downloaded
if (len(gpu_devices) > 1):
whisperCallable.model_container.ensure_downloaded()
# Split into a list for each device
# TODO: Split by time instead of by number of chunks
merged_split = list(self._split(merged, len(gpu_devices)))
# Parameters that will be passed to the transcribe function
parameters = []
segment_index = config.initial_segment_index
processing_manager = multiprocessing.Manager()
progress_queue = processing_manager.Queue()
for i in range(len(gpu_devices)):
# Note that device_segment_list can be empty. But we will still create a process for it,
# as otherwise we run the risk of assigning the same device to multiple processes.
device_segment_list = list(merged_split[i]) if i < len(merged_split) else []
device_id = gpu_devices[i]
print("Device " + str(device_id) + " (index " + str(i) + ") has " + str(len(device_segment_list)) + " segments")
# Create a new config with the given device ID
device_config = ParallelTranscriptionConfig(device_id, device_segment_list, segment_index, config)
segment_index += len(device_segment_list)
progress_listener_to_queue = _ProgressListenerToQueue(progress_queue)
parameters.append([audio, whisperCallable, device_config, progress_listener_to_queue]);
merged = {
'text': '',
'segments': [],
'language': None
}
created_context = False
perf_start_gpu = time.perf_counter()
# Spawn a separate process for each device
try:
if (gpu_parallel_context is None):
gpu_parallel_context = ParallelContext(len(gpu_devices))
created_context = True
# Get a pool of processes
pool = gpu_parallel_context.get_pool()
# Run the transcription in parallel
results_async = pool.starmap_async(self.transcribe, parameters)
total_progress = 0
while not results_async.ready():
try:
delta = progress_queue.get(timeout=5) # Set a timeout of 5 seconds
except Empty:
continue
total_progress += delta
if progress_listener is not None:
progress_listener.on_progress(total_progress, total_duration)
results = results_async.get()
# Call the finished callback
if progress_listener is not None:
progress_listener.on_finished()
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 (gpu_parallel_context is not None):
gpu_parallel_context.return_pool(pool)
# Always close the context if we created it
if (created_context):
gpu_parallel_context.close()
perf_end_gpu = time.perf_counter()
print("Parallel transcription took " + str(perf_end_gpu - perf_start_gpu) + " seconds")
return merged
def _get_merged_timestamps_parallel(self, transcription: AbstractTranscription, audio: str, config: TranscriptionConfig, total_duration: float,
cpu_device_count: int, cpu_parallel_context: ParallelContext = None):
parameters = []
chunk_size = max(total_duration / cpu_device_count, self.MIN_CPU_CHUNK_SIZE_SECONDS)
chunk_start = 0
cpu_device_id = 0
perf_start_time = time.perf_counter()
# Create chunks that will be processed on the CPU
while (chunk_start < total_duration):
chunk_end = min(chunk_start + chunk_size, total_duration)
if (chunk_end - chunk_start < 1):
# No need to process chunks that are less than 1 second
break
print("Parallel VAD: Executing chunk from " + str(chunk_start) + " to " +
str(chunk_end) + " on CPU device " + str(cpu_device_id))
parameters.append([audio, config, chunk_start, chunk_end]);
cpu_device_id += 1
chunk_start = chunk_end
created_context = False
# Spawn a separate process for each device
try:
if (cpu_parallel_context is None):
cpu_parallel_context = ParallelContext(cpu_device_count)
created_context = True
# Get a pool of processes
pool = cpu_parallel_context.get_pool()
# Run the transcription in parallel. Note that transcription must be picklable.
results = pool.starmap(transcription.get_transcribe_timestamps, parameters)
timestamps = []
# Flatten the results
for result in results:
timestamps.extend(result)
merged = transcription.get_merged_timestamps(timestamps, config, total_duration)
perf_end_time = time.perf_counter()
print("Parallel VAD processing took {} seconds".format(perf_end_time - perf_start_time))
return merged
finally:
# Return the pool to the context
if (cpu_parallel_context is not None):
cpu_parallel_context.return_pool(pool)
# Always close the context if we created it
if (created_context):
cpu_parallel_context.close()
def get_transcribe_timestamps(self, audio: str, config: ParallelTranscriptionConfig, start_time: float, duration: float):
return []
def get_merged_timestamps(self, timestamps: List[Dict[str, Any]], config: ParallelTranscriptionConfig, total_duration: float):
# Override timestamps that will be processed
if (config.override_timestamps is not None):
print("(get_merged_timestamps) Using override timestamps of size " + str(len(config.override_timestamps)))
return config.override_timestamps
return super().get_merged_timestamps(timestamps, config, total_duration)
def transcribe(self, audio: str, whisperCallable: AbstractWhisperCallback, config: ParallelTranscriptionConfig,
progressListener: ProgressListener = None):
# Override device ID the first time
if (os.environ.get("INITIALIZED", None) is None):
os.environ["INITIALIZED"] = "1"
# Note that this may be None if the user didn't specify a device. In that case, Whisper will
# just use the default GPU device.
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, progressListener)
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))