Spaces:
Build error
Build error
File size: 14,594 Bytes
15ac91d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 |
import numpy as np
import h5py
import csv
import time
import logging
from utilities import int16_to_float32
def read_black_list(black_list_csv):
"""Read audio names from black list.
"""
with open(black_list_csv, 'r') as fr:
reader = csv.reader(fr)
lines = list(reader)
black_list_names = ['Y{}.wav'.format(line[0]) for line in lines]
return black_list_names
class AudioSetDataset(object):
def __init__(self, sample_rate=32000):
"""This class takes the meta of an audio clip as input, and return
the waveform and target of the audio clip. This class is used by DataLoader.
"""
self.sample_rate = sample_rate
def __getitem__(self, meta):
"""Load waveform and target of an audio clip.
Args:
meta: {
'hdf5_path': str,
'index_in_hdf5': int}
Returns:
data_dict: {
'audio_name': str,
'waveform': (clip_samples,),
'target': (classes_num,)}
"""
hdf5_path = meta['hdf5_path']
index_in_hdf5 = meta['index_in_hdf5']
with h5py.File(hdf5_path, 'r') as hf:
audio_name = hf['audio_name'][index_in_hdf5].decode()
waveform = int16_to_float32(hf['waveform'][index_in_hdf5])
waveform = self.resample(waveform)
target = hf['target'][index_in_hdf5].astype(np.float32)
data_dict = {
'audio_name': audio_name, 'waveform': waveform, 'target': target}
return data_dict
def resample(self, waveform):
"""Resample.
Args:
waveform: (clip_samples,)
Returns:
(resampled_clip_samples,)
"""
if self.sample_rate == 32000:
return waveform
elif self.sample_rate == 16000:
return waveform[0 :: 2]
elif self.sample_rate == 8000:
return waveform[0 :: 4]
else:
raise Exception('Incorrect sample rate!')
class Base(object):
def __init__(self, indexes_hdf5_path, batch_size, black_list_csv, random_seed):
"""Base class of train sampler.
Args:
indexes_hdf5_path: string
batch_size: int
black_list_csv: string
random_seed: int
"""
self.batch_size = batch_size
self.random_state = np.random.RandomState(random_seed)
# Black list
if black_list_csv:
self.black_list_names = read_black_list(black_list_csv)
else:
self.black_list_names = []
logging.info('Black list samples: {}'.format(len(self.black_list_names)))
# Load target
load_time = time.time()
with h5py.File(indexes_hdf5_path, 'r') as hf:
self.audio_names = [audio_name.decode() for audio_name in hf['audio_name'][:]]
self.hdf5_paths = [hdf5_path.decode() for hdf5_path in hf['hdf5_path'][:]]
self.indexes_in_hdf5 = hf['index_in_hdf5'][:]
self.targets = hf['target'][:].astype(np.float32)
(self.audios_num, self.classes_num) = self.targets.shape
logging.info('Training number: {}'.format(self.audios_num))
logging.info('Load target time: {:.3f} s'.format(time.time() - load_time))
class TrainSampler(Base):
def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None,
random_seed=1234):
"""Balanced sampler. Generate batch meta for training.
Args:
indexes_hdf5_path: string
batch_size: int
black_list_csv: string
random_seed: int
"""
super(TrainSampler, self).__init__(indexes_hdf5_path, batch_size,
black_list_csv, random_seed)
self.indexes = np.arange(self.audios_num)
# Shuffle indexes
self.random_state.shuffle(self.indexes)
self.pointer = 0
def __iter__(self):
"""Generate batch meta for training.
Returns:
batch_meta: e.g.: [
{'hdf5_path': string, 'index_in_hdf5': int},
...]
"""
batch_size = self.batch_size
while True:
batch_meta = []
i = 0
while i < batch_size:
index = self.indexes[self.pointer]
self.pointer += 1
# Shuffle indexes and reset pointer
if self.pointer >= self.audios_num:
self.pointer = 0
self.random_state.shuffle(self.indexes)
# If audio in black list then continue
if self.audio_names[index] in self.black_list_names:
continue
else:
batch_meta.append({
'hdf5_path': self.hdf5_paths[index],
'index_in_hdf5': self.indexes_in_hdf5[index]})
i += 1
yield batch_meta
def state_dict(self):
state = {
'indexes': self.indexes,
'pointer': self.pointer}
return state
def load_state_dict(self, state):
self.indexes = state['indexes']
self.pointer = state['pointer']
class BalancedTrainSampler(Base):
def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None,
random_seed=1234):
"""Balanced sampler. Generate batch meta for training. Data are equally
sampled from different sound classes.
Args:
indexes_hdf5_path: string
batch_size: int
black_list_csv: string
random_seed: int
"""
super(BalancedTrainSampler, self).__init__(indexes_hdf5_path,
batch_size, black_list_csv, random_seed)
self.samples_num_per_class = np.sum(self.targets, axis=0)
logging.info('samples_num_per_class: {}'.format(
self.samples_num_per_class.astype(np.int32)))
# Training indexes of all sound classes. E.g.:
# [[0, 11, 12, ...], [3, 4, 15, 16, ...], [7, 8, ...], ...]
self.indexes_per_class = []
for k in range(self.classes_num):
self.indexes_per_class.append(
np.where(self.targets[:, k] == 1)[0])
# Shuffle indexes
for k in range(self.classes_num):
self.random_state.shuffle(self.indexes_per_class[k])
self.queue = []
self.pointers_of_classes = [0] * self.classes_num
def expand_queue(self, queue):
classes_set = np.arange(self.classes_num).tolist()
self.random_state.shuffle(classes_set)
queue += classes_set
return queue
def __iter__(self):
"""Generate batch meta for training.
Returns:
batch_meta: e.g.: [
{'hdf5_path': string, 'index_in_hdf5': int},
...]
"""
batch_size = self.batch_size
while True:
batch_meta = []
i = 0
while i < batch_size:
if len(self.queue) == 0:
self.queue = self.expand_queue(self.queue)
class_id = self.queue.pop(0)
pointer = self.pointers_of_classes[class_id]
self.pointers_of_classes[class_id] += 1
index = self.indexes_per_class[class_id][pointer]
# When finish one epoch of a sound class, then shuffle its indexes and reset pointer
if self.pointers_of_classes[class_id] >= self.samples_num_per_class[class_id]:
self.pointers_of_classes[class_id] = 0
self.random_state.shuffle(self.indexes_per_class[class_id])
# If audio in black list then continue
if self.audio_names[index] in self.black_list_names:
continue
else:
batch_meta.append({
'hdf5_path': self.hdf5_paths[index],
'index_in_hdf5': self.indexes_in_hdf5[index]})
i += 1
yield batch_meta
def state_dict(self):
state = {
'indexes_per_class': self.indexes_per_class,
'queue': self.queue,
'pointers_of_classes': self.pointers_of_classes}
return state
def load_state_dict(self, state):
self.indexes_per_class = state['indexes_per_class']
self.queue = state['queue']
self.pointers_of_classes = state['pointers_of_classes']
class AlternateTrainSampler(Base):
def __init__(self, indexes_hdf5_path, batch_size, black_list_csv=None,
random_seed=1234):
"""AlternateSampler is a combination of Sampler and Balanced Sampler.
AlternateSampler alternately sample data from Sampler and Blanced Sampler.
Args:
indexes_hdf5_path: string
batch_size: int
black_list_csv: string
random_seed: int
"""
self.sampler1 = TrainSampler(indexes_hdf5_path, batch_size,
black_list_csv, random_seed)
self.sampler2 = BalancedTrainSampler(indexes_hdf5_path, batch_size,
black_list_csv, random_seed)
self.batch_size = batch_size
self.count = 0
def __iter__(self):
"""Generate batch meta for training.
Returns:
batch_meta: e.g.: [
{'hdf5_path': string, 'index_in_hdf5': int},
...]
"""
batch_size = self.batch_size
while True:
self.count += 1
if self.count % 2 == 0:
batch_meta = []
i = 0
while i < batch_size:
index = self.sampler1.indexes[self.sampler1.pointer]
self.sampler1.pointer += 1
# Shuffle indexes and reset pointer
if self.sampler1.pointer >= self.sampler1.audios_num:
self.sampler1.pointer = 0
self.sampler1.random_state.shuffle(self.sampler1.indexes)
# If audio in black list then continue
if self.sampler1.audio_names[index] in self.sampler1.black_list_names:
continue
else:
batch_meta.append({
'hdf5_path': self.sampler1.hdf5_paths[index],
'index_in_hdf5': self.sampler1.indexes_in_hdf5[index]})
i += 1
elif self.count % 2 == 1:
batch_meta = []
i = 0
while i < batch_size:
if len(self.sampler2.queue) == 0:
self.sampler2.queue = self.sampler2.expand_queue(self.sampler2.queue)
class_id = self.sampler2.queue.pop(0)
pointer = self.sampler2.pointers_of_classes[class_id]
self.sampler2.pointers_of_classes[class_id] += 1
index = self.sampler2.indexes_per_class[class_id][pointer]
# When finish one epoch of a sound class, then shuffle its indexes and reset pointer
if self.sampler2.pointers_of_classes[class_id] >= self.sampler2.samples_num_per_class[class_id]:
self.sampler2.pointers_of_classes[class_id] = 0
self.sampler2.random_state.shuffle(self.sampler2.indexes_per_class[class_id])
# If audio in black list then continue
if self.sampler2.audio_names[index] in self.sampler2.black_list_names:
continue
else:
batch_meta.append({
'hdf5_path': self.sampler2.hdf5_paths[index],
'index_in_hdf5': self.sampler2.indexes_in_hdf5[index]})
i += 1
yield batch_meta
def state_dict(self):
state = {
'sampler1': self.sampler1.state_dict(),
'sampler2': self.sampler2.state_dict()}
return state
def load_state_dict(self, state):
self.sampler1.load_state_dict(state['sampler1'])
self.sampler2.load_state_dict(state['sampler2'])
class EvaluateSampler(object):
def __init__(self, indexes_hdf5_path, batch_size):
"""Evaluate sampler. Generate batch meta for evaluation.
Args:
indexes_hdf5_path: string
batch_size: int
"""
self.batch_size = batch_size
with h5py.File(indexes_hdf5_path, 'r') as hf:
self.audio_names = [audio_name.decode() for audio_name in hf['audio_name'][:]]
self.hdf5_paths = [hdf5_path.decode() for hdf5_path in hf['hdf5_path'][:]]
self.indexes_in_hdf5 = hf['index_in_hdf5'][:]
self.targets = hf['target'][:].astype(np.float32)
self.audios_num = len(self.audio_names)
def __iter__(self):
"""Generate batch meta for training.
Returns:
batch_meta: e.g.: [
{'hdf5_path': string,
'index_in_hdf5': int}
...]
"""
batch_size = self.batch_size
pointer = 0
while pointer < self.audios_num:
batch_indexes = np.arange(pointer,
min(pointer + batch_size, self.audios_num))
batch_meta = []
for index in batch_indexes:
batch_meta.append({
'audio_name': self.audio_names[index],
'hdf5_path': self.hdf5_paths[index],
'index_in_hdf5': self.indexes_in_hdf5[index],
'target': self.targets[index]})
pointer += batch_size
yield batch_meta
def collate_fn(list_data_dict):
"""Collate data.
Args:
list_data_dict, e.g., [{'audio_name': str, 'waveform': (clip_samples,), ...},
{'audio_name': str, 'waveform': (clip_samples,), ...},
...]
Returns:
np_data_dict, dict, e.g.,
{'audio_name': (batch_size,), 'waveform': (batch_size, clip_samples), ...}
"""
np_data_dict = {}
for key in list_data_dict[0].keys():
np_data_dict[key] = np.array([data_dict[key] for data_dict in list_data_dict])
return np_data_dict |