Spaces:
Runtime error
Runtime error
File size: 12,579 Bytes
07b71bb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 |
"""
Parallel Processing Module for DittoTalkingHead
Implements concurrent audio and image preprocessing
"""
import asyncio
import concurrent.futures
from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
import time
from typing import Tuple, Dict, Any, Optional, Callable
import numpy as np
from pathlib import Path
import threading
import queue
import torch
from functools import partial
class ParallelProcessor:
"""
Parallel processing for audio and image preprocessing
"""
def __init__(
self,
num_threads: int = 4,
num_processes: int = 2,
use_cuda_streams: bool = True
):
"""
Initialize parallel processor
Args:
num_threads: Number of threads for I/O operations
num_processes: Number of processes for CPU-intensive tasks
use_cuda_streams: Use CUDA streams for GPU operations
"""
self.num_threads = num_threads
self.num_processes = num_processes
self.use_cuda_streams = use_cuda_streams and torch.cuda.is_available()
# Thread pool for I/O operations
self.thread_executor = ThreadPoolExecutor(max_workers=num_threads)
# Process pool for CPU-intensive operations
self.process_executor = ProcessPoolExecutor(max_workers=num_processes)
# CUDA streams for GPU operations
if self.use_cuda_streams:
self.cuda_streams = [torch.cuda.Stream() for _ in range(2)]
else:
self.cuda_streams = None
print(f"✅ ParallelProcessor initialized: {num_threads} threads, {num_processes} processes")
if self.use_cuda_streams:
print("✅ CUDA streams enabled for GPU parallelism")
def preprocess_audio_parallel(self, audio_path: str) -> Dict[str, Any]:
"""
Preprocess audio file in parallel
Args:
audio_path: Path to audio file
Returns:
Preprocessed audio data
"""
import librosa
# Define subtasks
def load_audio():
return librosa.load(audio_path, sr=16000)
def extract_features(audio, sr):
# Extract various audio features in parallel
features = {}
# MFCC features
features['mfcc'] = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=13)
# Spectral features
features['spectral_centroid'] = librosa.feature.spectral_centroid(y=audio, sr=sr)
features['spectral_rolloff'] = librosa.feature.spectral_rolloff(y=audio, sr=sr)
return features
# Load audio
audio, sr = load_audio()
# Extract features in parallel (if needed)
features = extract_features(audio, sr)
return {
'audio': audio,
'sample_rate': sr,
'features': features,
'duration': len(audio) / sr
}
def preprocess_image_parallel(self, image_path: str, target_size: int = 320) -> Dict[str, Any]:
"""
Preprocess image file in parallel
Args:
image_path: Path to image file
target_size: Target resolution
Returns:
Preprocessed image data
"""
from PIL import Image
import cv2
# Define subtasks
def load_and_resize():
# Load image
img = Image.open(image_path).convert('RGB')
# Resize
img = img.resize((target_size, target_size), Image.Resampling.LANCZOS)
return np.array(img)
def extract_face_landmarks(img_array):
# Face detection and landmark extraction
# Simplified version - in production, use MediaPipe or similar
return {
'has_face': True,
'landmarks': None # Placeholder
}
# Execute in parallel
future_img = self.thread_executor.submit(load_and_resize)
# Get results
img_array = future_img.result()
# Extract landmarks
landmarks = extract_face_landmarks(img_array)
return {
'image': img_array,
'shape': img_array.shape,
'landmarks': landmarks
}
async def preprocess_parallel_async(
self,
audio_path: str,
image_path: str,
target_size: int = 320
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
"""
Asynchronously preprocess audio and image in parallel
Args:
audio_path: Path to audio file
image_path: Path to image file
target_size: Target image resolution
Returns:
Tuple of (audio_data, image_data)
"""
loop = asyncio.get_event_loop()
# Create tasks for parallel execution
audio_task = loop.run_in_executor(
self.thread_executor,
self.preprocess_audio_parallel,
audio_path
)
image_task = loop.run_in_executor(
self.thread_executor,
partial(self.preprocess_image_parallel, target_size=target_size),
image_path
)
# Wait for both tasks to complete
audio_data, image_data = await asyncio.gather(audio_task, image_task)
return audio_data, image_data
def preprocess_parallel_sync(
self,
audio_path: str,
image_path: str,
target_size: int = 320
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
"""
Synchronously preprocess audio and image in parallel
Args:
audio_path: Path to audio file
image_path: Path to image file
target_size: Target image resolution
Returns:
Tuple of (audio_data, image_data)
"""
# Submit tasks to thread pool
audio_future = self.thread_executor.submit(
self.preprocess_audio_parallel,
audio_path
)
image_future = self.thread_executor.submit(
self.preprocess_image_parallel,
image_path,
target_size
)
# Wait for results
audio_data = audio_future.result()
image_data = image_future.result()
return audio_data, image_data
def process_gpu_parallel(
self,
audio_tensor: torch.Tensor,
image_tensor: torch.Tensor,
model_audio: torch.nn.Module,
model_image: torch.nn.Module
) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Process audio and image through models using CUDA streams
Args:
audio_tensor: Audio tensor
image_tensor: Image tensor
model_audio: Audio processing model
model_image: Image processing model
Returns:
Tuple of processed tensors
"""
if not self.use_cuda_streams:
# Fallback to sequential processing
audio_out = model_audio(audio_tensor)
image_out = model_image(image_tensor)
return audio_out, image_out
# Use CUDA streams for parallel GPU processing
with torch.cuda.stream(self.cuda_streams[0]):
audio_out = model_audio(audio_tensor)
with torch.cuda.stream(self.cuda_streams[1]):
image_out = model_image(image_tensor)
# Synchronize streams
torch.cuda.synchronize()
return audio_out, image_out
def shutdown(self):
"""Shutdown executors"""
self.thread_executor.shutdown(wait=True)
self.process_executor.shutdown(wait=True)
print("✅ ParallelProcessor shutdown complete")
class PipelineProcessor:
"""
Pipeline-based processing for continuous operations
"""
def __init__(self, stages: Dict[str, Callable], buffer_size: int = 10):
"""
Initialize pipeline processor
Args:
stages: Dictionary of stage_name -> processing_function
buffer_size: Size of queues between stages
"""
self.stages = stages
self.buffer_size = buffer_size
# Create queues between stages
self.queues = {}
stage_names = list(stages.keys())
for i in range(len(stage_names) - 1):
queue_name = f"{stage_names[i]}_to_{stage_names[i+1]}"
self.queues[queue_name] = queue.Queue(maxsize=buffer_size)
# Input and output queues
self.input_queue = queue.Queue(maxsize=buffer_size)
self.output_queue = queue.Queue(maxsize=buffer_size)
# Worker threads
self.workers = []
self.stop_event = threading.Event()
def _worker(self, stage_name: str, process_func: Callable, input_q: queue.Queue, output_q: queue.Queue):
"""Worker thread for a pipeline stage"""
while not self.stop_event.is_set():
try:
# Get input with timeout
item = input_q.get(timeout=0.1)
if item is None: # Poison pill
output_q.put(None)
break
# Process item
result = process_func(item)
# Put result
output_q.put(result)
except queue.Empty:
continue
except Exception as e:
print(f"Error in stage {stage_name}: {e}")
output_q.put(None)
def start(self):
"""Start pipeline processing"""
stage_names = list(self.stages.keys())
# Create worker threads
for i, (stage_name, process_func) in enumerate(self.stages.items()):
# Determine input and output queues
if i == 0:
input_q = self.input_queue
else:
queue_name = f"{stage_names[i-1]}_to_{stage_names[i]}"
input_q = self.queues[queue_name]
if i == len(stage_names) - 1:
output_q = self.output_queue
else:
queue_name = f"{stage_names[i]}_to_{stage_names[i+1]}"
output_q = self.queues[queue_name]
# Create and start worker
worker = threading.Thread(
target=self._worker,
args=(stage_name, process_func, input_q, output_q)
)
worker.start()
self.workers.append(worker)
print(f"✅ Pipeline started with {len(self.workers)} stages")
def process(self, item: Any) -> Any:
"""Process an item through the pipeline"""
self.input_queue.put(item)
return self.output_queue.get()
def stop(self):
"""Stop pipeline processing"""
self.stop_event.set()
# Send poison pills
self.input_queue.put(None)
# Wait for workers
for worker in self.workers:
worker.join()
print("✅ Pipeline stopped")
def benchmark_parallel_processing():
"""Benchmark parallel vs sequential processing"""
import time
print("\n=== Parallel Processing Benchmark ===")
# Create processor
processor = ParallelProcessor(num_threads=4)
# Test files (using example files)
audio_path = "example/audio.wav"
image_path = "example/image.png"
# Sequential processing
start_seq = time.time()
audio_data_seq = processor.preprocess_audio_parallel(audio_path)
image_data_seq = processor.preprocess_image_parallel(image_path)
time_seq = time.time() - start_seq
# Parallel processing
start_par = time.time()
audio_data_par, image_data_par = processor.preprocess_parallel_sync(audio_path, image_path)
time_par = time.time() - start_par
# Results
print(f"Sequential processing: {time_seq:.3f}s")
print(f"Parallel processing: {time_par:.3f}s")
print(f"Speedup: {time_seq/time_par:.2f}x")
processor.shutdown()
return {
'sequential_time': time_seq,
'parallel_time': time_par,
'speedup': time_seq / time_par
} |