Spaces:
Running
on
Zero
Running
on
Zero
File size: 10,372 Bytes
c3c908f |
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 |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import logging
import pickle
import numpy as np
from scipy import stats
import csv
import json
def create_folder(fd):
if not os.path.exists(fd):
os.makedirs(fd, exist_ok=True)
def get_filename(path):
path = os.path.realpath(path)
na_ext = path.split("/")[-1]
na = os.path.splitext(na_ext)[0]
return na
def get_sub_filepaths(folder):
paths = []
for root, dirs, files in os.walk(folder):
for name in files:
path = os.path.join(root, name)
paths.append(path)
return paths
def create_logging(log_dir, filemode):
create_folder(log_dir)
i1 = 0
while os.path.isfile(os.path.join(log_dir, "{:04d}.log".format(i1))):
i1 += 1
log_path = os.path.join(log_dir, "{:04d}.log".format(i1))
logging.basicConfig(
level=logging.DEBUG,
format="%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s",
datefmt="%a, %d %b %Y %H:%M:%S",
filename=log_path,
filemode=filemode,
)
# Print to console
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter("%(name)-12s: %(levelname)-8s %(message)s")
console.setFormatter(formatter)
logging.getLogger("").addHandler(console)
return logging
def read_metadata(csv_path, audio_dir, classes_num, id_to_ix):
"""Read metadata of AudioSet from a csv file.
Args:
csv_path: str
Returns:
meta_dict: {'audio_name': (audios_num,), 'target': (audios_num, classes_num)}
"""
with open(csv_path, "r") as fr:
lines = fr.readlines()
lines = lines[3:] # Remove heads
# first, count the audio names only of existing files on disk only
audios_num = 0
for n, line in enumerate(lines):
items = line.split(", ")
"""items: ['--4gqARaEJE', '0.000', '10.000', '"/m/068hy,/m/07q6cd_,/m/0bt9lr,/m/0jbk"\n']"""
# audio_name = 'Y{}.wav'.format(items[0]) # Audios are started with an extra 'Y' when downloading
audio_name = "{}_{}_{}.flac".format(
items[0], items[1].replace(".", ""), items[2].replace(".", "")
)
audio_name = audio_name.replace("_0000_", "_0_")
if os.path.exists(os.path.join(audio_dir, audio_name)):
audios_num += 1
print("CSV audio files: %d" % (len(lines)))
print("Existing audio files: %d" % audios_num)
# audios_num = len(lines)
targets = np.zeros((audios_num, classes_num), dtype=bool)
audio_names = []
n = 0
for line in lines:
items = line.split(", ")
"""items: ['--4gqARaEJE', '0.000', '10.000', '"/m/068hy,/m/07q6cd_,/m/0bt9lr,/m/0jbk"\n']"""
# audio_name = 'Y{}.wav'.format(items[0]) # Audios are started with an extra 'Y' when downloading
audio_name = "{}_{}_{}.flac".format(
items[0], items[1].replace(".", ""), items[2].replace(".", "")
)
audio_name = audio_name.replace("_0000_", "_0_")
if not os.path.exists(os.path.join(audio_dir, audio_name)):
continue
label_ids = items[3].split('"')[1].split(",")
audio_names.append(audio_name)
# Target
for id in label_ids:
ix = id_to_ix[id]
targets[n, ix] = 1
n += 1
meta_dict = {"audio_name": np.array(audio_names), "target": targets}
return meta_dict
def read_audioset_ontology(id_to_ix):
with open('../metadata/audioset_ontology.json', 'r') as f:
data = json.load(f)
# Output: {'name': 'Bob', 'languages': ['English', 'French']}
sentences = []
for el in data:
print(el.keys())
id = el['id']
if id in id_to_ix:
name = el['name']
desc = el['description']
# if '(' in desc:
# print(name, '---', desc)
# print(id_to_ix[id], name, '---', )
# sent = name
# sent = name + ', ' + desc.replace('(', '').replace(')', '').lower()
# sent = desc.replace('(', '').replace(')', '').lower()
# sentences.append(sent)
sentences.append(desc)
# print(sent)
# break
return sentences
def original_read_metadata(csv_path, classes_num, id_to_ix):
"""Read metadata of AudioSet from a csv file.
Args:
csv_path: str
Returns:
meta_dict: {'audio_name': (audios_num,), 'target': (audios_num, classes_num)}
"""
with open(csv_path, "r") as fr:
lines = fr.readlines()
lines = lines[3:] # Remove heads
# Thomas Pellegrini: added 02/12/2022
# check if the audio files indeed exist, otherwise remove from list
audios_num = len(lines)
targets = np.zeros((audios_num, classes_num), dtype=bool)
audio_names = []
for n, line in enumerate(lines):
items = line.split(", ")
"""items: ['--4gqARaEJE', '0.000', '10.000', '"/m/068hy,/m/07q6cd_,/m/0bt9lr,/m/0jbk"\n']"""
audio_name = "{}_{}_{}.flac".format(
items[0], items[1].replace(".", ""), items[2].replace(".", "")
) # Audios are started with an extra 'Y' when downloading
audio_name = audio_name.replace("_0000_", "_0_")
label_ids = items[3].split('"')[1].split(",")
audio_names.append(audio_name)
# Target
for id in label_ids:
ix = id_to_ix[id]
targets[n, ix] = 1
meta_dict = {"audio_name": np.array(audio_names), "target": targets}
return meta_dict
def read_audioset_label_tags(class_labels_indices_csv):
with open(class_labels_indices_csv, 'r') as f:
reader = csv.reader(f, delimiter=',')
lines = list(reader)
labels = []
ids = [] # Each label has a unique id such as "/m/068hy"
for i1 in range(1, len(lines)):
id = lines[i1][1]
label = lines[i1][2]
ids.append(id)
labels.append(label)
classes_num = len(labels)
lb_to_ix = {label : i for i, label in enumerate(labels)}
ix_to_lb = {i : label for i, label in enumerate(labels)}
id_to_ix = {id : i for i, id in enumerate(ids)}
ix_to_id = {i : id for i, id in enumerate(ids)}
return lb_to_ix, ix_to_lb, id_to_ix, ix_to_id
def float32_to_int16(x):
# assert np.max(np.abs(x)) <= 1.5
x = np.clip(x, -1, 1)
return (x * 32767.0).astype(np.int16)
def int16_to_float32(x):
return (x / 32767.0).astype(np.float32)
def pad_or_truncate(x, audio_length):
"""Pad all audio to specific length."""
if len(x) <= audio_length:
return np.concatenate((x, np.zeros(audio_length - len(x))), axis=0)
else:
return x[0:audio_length]
def pad_audio(x, audio_length):
"""Pad all audio to specific length."""
if len(x) <= audio_length:
return np.concatenate((x, np.zeros(audio_length - len(x))), axis=0)
else:
return x
def d_prime(auc):
d_prime = stats.norm().ppf(auc) * np.sqrt(2.0)
return d_prime
class Mixup(object):
def __init__(self, mixup_alpha, random_seed=1234):
"""Mixup coefficient generator."""
self.mixup_alpha = mixup_alpha
self.random_state = np.random.RandomState(random_seed)
def get_lambda(self, batch_size):
"""Get mixup random coefficients.
Args:
batch_size: int
Returns:
mixup_lambdas: (batch_size,)
"""
mixup_lambdas = []
for n in range(0, batch_size, 2):
lam = self.random_state.beta(self.mixup_alpha, self.mixup_alpha, 1)[0]
mixup_lambdas.append(lam)
mixup_lambdas.append(1.0 - lam)
return np.array(mixup_lambdas)
class StatisticsContainer(object):
def __init__(self, statistics_path):
"""Contain statistics of different training iterations."""
self.statistics_path = statistics_path
self.backup_statistics_path = "{}_{}.pkl".format(
os.path.splitext(self.statistics_path)[0],
datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"),
)
self.statistics_dict = {"bal": [], "test": []}
def append(self, iteration, statistics, data_type):
statistics["iteration"] = iteration
self.statistics_dict[data_type].append(statistics)
def dump(self):
pickle.dump(self.statistics_dict, open(self.statistics_path, "wb"))
pickle.dump(self.statistics_dict, open(self.backup_statistics_path, "wb"))
logging.info(" Dump statistics to {}".format(self.statistics_path))
logging.info(" Dump statistics to {}".format(self.backup_statistics_path))
def load_state_dict(self, resume_iteration):
self.statistics_dict = pickle.load(open(self.statistics_path, "rb"))
resume_statistics_dict = {"bal": [], "test": []}
for key in self.statistics_dict.keys():
for statistics in self.statistics_dict[key]:
if statistics["iteration"] <= resume_iteration:
resume_statistics_dict[key].append(statistics)
self.statistics_dict = resume_statistics_dict |