forked from soerenab/AudioMNIST
/
preprocess_data.py
228 lines (172 loc) · 9.26 KB
/
preprocess_data.py
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
# -*- coding: utf-8 -*-
import numpy as np
import glob
import os
import sys
import scipy.io.wavfile as wavf
import scipy.signal
import h5py
import json
import librosa
import multiprocessing
import argparse
def preprocess_data(src, dst, src_meta, n_processes=15):
"""
Calls for distibuted preprocessing of the data.
Parameters:
-----------
src: string
Path to data directory.
dst: string
Path to directory where preprocessed data shall be stored.
stc_meta: string
Path to meta_information file.
n_processes: int
number of simultaneous processes to use for data preprocessing.
"""
folders = []
for folder in os.listdir(src):
# only process folders
if not os.path.isdir(os.path.join(src, folder)):
continue
folders.append(folder)
pool=multiprocessing.Pool(processes=n_processes)
_=pool.map(_preprocess_data, [(os.path.join(src, folder),
os.path.join(dst, folder),
src_meta) for folder in sorted(folders)])
def _preprocess_data(src_dst_meta):
"""
Preprocessing for all data files in given directory.
Preprocessing includes:
AlexNet: resampling to 8000 Hz,
embedding in zero vector,
transformation to amplitute spectrogram representation in dB.
AudioNet: resampling to 8000 Hz,
embedding in zero vector,
normalization at 95th percentile.
Preprocessed data will be stored in hdf5 files with one datum per file.
In terms of I/O, this is not very efficient but it allows to easily change
training, validation, and test sets without re-preprocessing or redundant
storage of preprocessed files.
Parameters:
-----------
src_dst_meta: tuple of 3 strings
Tuple (path to data directory, path to destination directory, path
to meta file)
"""
src, dst, src_meta = src_dst_meta
print("processing {}".format(src))
metaData = json.load(open(src_meta))
# create folder for hdf5 files
if not os.path.exists(dst):
os.makedirs(dst)
# loop over recordings
for filepath in sorted(glob.glob(os.path.join(src, "*.wav"))):
# infer sample info from name
dig, vp, rep = filepath.rstrip(".wav").split("/")[-1].split("_")
# read data
fs, data = wavf.read(filepath)
# resample
data = librosa.core.resample(y=data.astype(np.float32), orig_sr=fs, target_sr=8000, res_type="scipy")
# zero padding
if len(data) > 8000:
raise ValueError("data length cannot exceed padding length.")
elif len(data) < 8000:
embedded_data = np.zeros(8000)
offset = np.random.randint(low = 0, high = 8000 - len(data))
embedded_data[offset:offset+len(data)] = data
elif len(data) == 8000:
# nothing to do here
embedded_data = data
pass
##### AlexNet #####
# stft, with seleced parameters, spectrogram will have shape (228,230)
f, t, Zxx = scipy.signal.stft(embedded_data, 8000, nperseg = 455, noverlap = 420, window='hann')
# get amplitude
Zxx = np.abs(Zxx[0:227, 2:-1])
Zxx = np.atleast_3d(Zxx).transpose(2,0,1)
# convert to decibel
Zxx = librosa.amplitude_to_db(Zxx, ref = np.max)
# save as hdf5 file
with h5py.File(os.path.join(dst, "AlexNet_{}_{}_{}.hdf5".format(dig, vp, rep)), "w") as f:
tmp_X = np.zeros([1, 1, 227, 227])
tmp_X[0, 0] = Zxx
f['data'] = tmp_X
f['label'] = np.array([[int(dig), 0 if metaData[vp]["gender"] == "male" else 1]])
##### AudioNet #####
embedded_data /= (np.percentile(embedded_data, 95) + 0.001)
with h5py.File(os.path.join(dst, "AudioNet_{}_{}_{}.hdf5".format(dig, vp, rep)), "w") as f:
tmp_X = np.zeros([1, 1, 1, 8000])
tmp_X[0, 0, 0] = embedded_data
f['data'] = tmp_X
f['label'] = np.array([[int(dig), 0 if metaData[vp]["gender"] == "male" else 1]])
return
def create_splits(src, dst):
"""
Creation of text files specifying which files training, validation and test
set consist of for each cross-validation split.
Parameters:
-----------
src: string
Path to directory containing the directories for each subject that
hold the preprocessed data in hdf5 format.
dst: string
Destination where to store the text files specifying training,
validation and test splits.
"""
print("creating splits")
splits={"digit":{ "train":[ set([28, 56, 7, 19, 35, 1, 6, 16, 23, 34, 46, 53, 36, 57, 9, 24, 37, 2, \
8, 17, 29, 39, 48, 54, 43, 58, 14, 25, 38, 3, 10, 20, 30, 40, 49, 55]),
set([36, 57, 9, 24, 37, 2, 8, 17, 29, 39, 48, 54, 43, 58, 14, 25, 38, 3, \
10, 20, 30, 40, 49, 55, 12, 47, 59, 15, 27, 41, 4, 11, 21, 31, 44, 50]),
set([43, 58, 14, 25, 38, 3, 10, 20, 30, 40, 49, 55, 12, 47, 59, 15, 27, 41, \
4, 11, 21, 31, 44, 50, 26, 52, 60, 18, 32, 42, 5, 13, 22, 33, 45, 51]),
set([12, 47, 59, 15, 27, 41, 4, 11, 21, 31, 44, 50, 26, 52, 60, 18, 32, 42, \
5, 13, 22, 33, 45, 51, 28, 56, 7, 19, 35, 1, 6, 16, 23, 34, 46, 53]),
set([26, 52, 60, 18, 32, 42, 5, 13, 22, 33, 45, 51, 28, 56, 7, 19, 35, 1, \
6, 16, 23, 34, 46, 53, 36, 57, 9, 24, 37, 2, 8, 17, 29, 39, 48, 54])],
"validate":[set([12, 47, 59, 15, 27, 41, 4, 11, 21, 31, 44, 50]),
set([26, 52, 60, 18, 32, 42, 5, 13, 22, 33, 45, 51]),
set([28, 56, 7, 19, 35, 1, 6, 16, 23, 34, 46, 53]),
set([36, 57, 9, 24, 37, 2, 8, 17, 29, 39, 48, 54]),
set([43, 58, 14, 25, 38, 3, 10, 20, 30, 40, 49, 55])],
"test":[ set([26, 52, 60, 18, 32, 42, 5, 13, 22, 33, 45, 51]),
set([28, 56, 7, 19, 35, 1, 6, 16, 23, 34, 46, 53]),
set([36, 57, 9, 24, 37, 2, 8, 17, 29, 39, 48, 54]),
set([43, 58, 14, 25, 38, 3, 10, 20, 30, 40, 49, 55]),
set([12, 47, 59, 15, 27, 41, 4, 11, 21, 31, 44, 50])]},
"gender":{ "train":[ set([36, 47, 56, 26, 12, 57, 2, 44, 50, 25, 37, 45]),
set([26, 12, 57, 43, 28, 52, 25, 37, 45, 48, 53, 41]),
set([43, 28, 52, 58, 59, 60, 48, 53, 41, 7, 23, 38]),
set([58, 59, 60, 36, 47, 56, 7, 23, 38, 2, 44, 50])],
"validate":[set([43, 28, 52, 48, 53, 41]),
set([58, 59, 60, 7, 23, 38]),
set([36, 47, 56, 2, 44, 50]),
set([26, 12, 57, 25, 37, 45])],
"test":[ set([58, 59, 60, 7, 23, 38]),
set([36, 47, 56, 2, 44, 50]),
set([26, 12, 57, 25, 37, 45]),
set([43, 28, 52, 48, 53, 41])]}}
for split in range(5):
for modus in ["train", "validate", "test"]:
for task in ["digit", "gender"]:
if task == "gender" and split > 3:
continue
with open(os.path.join(dst, "AlexNet_{}_{}_{}.txt".format(task, split, modus)), mode = "w") as txt_file:
for vp in splits[task][modus][split]:
for filepath in glob.glob(os.path.join(src, "{:02d}".format(vp), "AlexNet*.hdf5")):
txt_file.write(filepath+"\n")
with open(os.path.join(dst, "AudioNet_{}_{}_{}.txt".format(task, split, modus)), mode = "w") as txt_file:
for vp in splits[task][modus][split]:
for filepath in glob.glob(os.path.join(src, "{:02d}".format(vp), "AudioNet*.hdf5")):
txt_file.write(filepath+"\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--source', '-src', default=os.path.join(os.getcwd(), "data"), help="Path to folder containing each participant's data directory.")
parser.add_argument('--destination', '-dst', default=os.path.join(os.getcwd(), "preprocessed_data"), help="Destination where preprocessed data shall be stored.")
parser.add_argument('--meta', '-m', default=os.path.join(os.getcwd(), "data", "audioMNIST_meta.txt"), help="Path to meta_information json file.")
args = parser.parse_args()
# preprocessing
preprocess_data(src=args.source, dst=args.destination, src_meta=args.meta)
# create training, validation and test sets
create_splits(src=args.destination, dst=args.destination)