forked from mravanelli/SincNet
-
Notifications
You must be signed in to change notification settings - Fork 0
/
data_io.py
181 lines (141 loc) · 6.2 KB
/
data_io.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
import configparser as ConfigParser
from optparse import OptionParser
import numpy as np
#import scipy.io.wavfile
import torch
def ReadList(list_file):
f=open(list_file,"r")
lines=f.readlines()
list_sig=[]
for x in lines:
list_sig.append(x.rstrip())
f.close()
return list_sig
def read_conf():
parser=OptionParser()
parser.add_option("--cfg") # Mandatory
(options,args)=parser.parse_args()
cfg_file=options.cfg
Config = ConfigParser.ConfigParser()
Config.read(cfg_file)
#[data]
options.tr_lst=Config.get('data', 'tr_lst')
options.te_lst=Config.get('data', 'te_lst')
options.lab_dict=Config.get('data', 'lab_dict')
options.data_folder=Config.get('data', 'data_folder')
options.output_folder=Config.get('data', 'output_folder')
options.pt_file=Config.get('data', 'pt_file')
#[windowing]
options.fs=Config.get('windowing', 'fs')
options.cw_len=Config.get('windowing', 'cw_len')
options.cw_shift=Config.get('windowing', 'cw_shift')
#[cnn]
options.cnn_N_filt=Config.get('cnn', 'cnn_N_filt')
options.cnn_len_filt=Config.get('cnn', 'cnn_len_filt')
options.cnn_max_pool_len=Config.get('cnn', 'cnn_max_pool_len')
options.cnn_use_laynorm_inp=Config.get('cnn', 'cnn_use_laynorm_inp')
options.cnn_use_batchnorm_inp=Config.get('cnn', 'cnn_use_batchnorm_inp')
options.cnn_use_laynorm=Config.get('cnn', 'cnn_use_laynorm')
options.cnn_use_batchnorm=Config.get('cnn', 'cnn_use_batchnorm')
options.cnn_act=Config.get('cnn', 'cnn_act')
options.cnn_drop=Config.get('cnn', 'cnn_drop')
#[dnn]
options.fc_lay=Config.get('dnn', 'fc_lay')
options.fc_drop=Config.get('dnn', 'fc_drop')
options.fc_use_laynorm_inp=Config.get('dnn', 'fc_use_laynorm_inp')
options.fc_use_batchnorm_inp=Config.get('dnn', 'fc_use_batchnorm_inp')
options.fc_use_batchnorm=Config.get('dnn', 'fc_use_batchnorm')
options.fc_use_laynorm=Config.get('dnn', 'fc_use_laynorm')
options.fc_act=Config.get('dnn', 'fc_act')
#[class]
options.class_lay=Config.get('class', 'class_lay')
options.class_drop=Config.get('class', 'class_drop')
options.class_use_laynorm_inp=Config.get('class', 'class_use_laynorm_inp')
options.class_use_batchnorm_inp=Config.get('class', 'class_use_batchnorm_inp')
options.class_use_batchnorm=Config.get('class', 'class_use_batchnorm')
options.class_use_laynorm=Config.get('class', 'class_use_laynorm')
options.class_act=Config.get('class', 'class_act')
#[optimization]
options.lr=Config.get('optimization', 'lr')
options.batch_size=Config.get('optimization', 'batch_size')
options.N_epochs=Config.get('optimization', 'N_epochs')
options.N_batches=Config.get('optimization', 'N_batches')
options.N_eval_epoch=Config.get('optimization', 'N_eval_epoch')
options.seed=Config.get('optimization', 'seed')
return options
def str_to_bool(s):
if s == 'True':
return True
elif s == 'False':
return False
else:
raise ValueError
def create_batches_rnd(batch_size,data_folder,wav_lst,N_snt,wlen,lab_dict,fact_amp):
# Initialization of the minibatch (batch_size,[0=>x_t,1=>x_t+N,1=>random_samp])
sig_batch=np.zeros([batch_size,wlen])
lab_batch=np.zeros(batch_size)
snt_id_arr=np.random.randint(N_snt, size=batch_size)
rand_amp_arr = np.random.uniform(1.0-fact_amp,1+fact_amp,batch_size)
for i in range(batch_size):
# select a random sentence from the list (joint distribution)
[fs,signal]=scipy.io.wavfile.read(data_folder+wav_lst[snt_id_arr[i]])
signal=signal.astype(float)/32768
# accesing to a random chunk
snt_len=signal.shape[0]
snt_beg=np.random.randint(snt_len-wlen-1) #randint(0, snt_len-2*wlen-1)
snt_end=snt_beg+wlen
sig_batch[i,:]=signal[snt_beg:snt_end]*rand_amp_arr[i]
lab_batch[i]=lab_dict[wav_lst[snt_id_arr[i]]]
inp=torch.from_numpy(sig_batch).float().cuda().contiguous() # Current Frame
lab=torch.from_numpy(lab_batch).float().cuda().contiguous()
return inp,lab
def read_conf_inp(cfg_file):
parser=OptionParser()
(options,args)=parser.parse_args()
Config = ConfigParser.ConfigParser()
Config.read(cfg_file)
#[data]
options.tr_lst=Config.get('data', 'tr_lst')
options.te_lst=Config.get('data', 'te_lst')
options.lab_dict=Config.get('data', 'lab_dict')
options.data_folder=Config.get('data', 'data_folder')
options.output_folder=Config.get('data', 'output_folder')
options.pt_file=Config.get('data', 'pt_file')
#[windowing]
options.fs=Config.get('windowing', 'fs')
options.cw_len=Config.get('windowing', 'cw_len')
options.cw_shift=Config.get('windowing', 'cw_shift')
#[cnn]
options.cnn_N_filt=Config.get('cnn', 'cnn_N_filt')
options.cnn_len_filt=Config.get('cnn', 'cnn_len_filt')
options.cnn_max_pool_len=Config.get('cnn', 'cnn_max_pool_len')
options.cnn_use_laynorm_inp=Config.get('cnn', 'cnn_use_laynorm_inp')
options.cnn_use_batchnorm_inp=Config.get('cnn', 'cnn_use_batchnorm_inp')
options.cnn_use_laynorm=Config.get('cnn', 'cnn_use_laynorm')
options.cnn_use_batchnorm=Config.get('cnn', 'cnn_use_batchnorm')
options.cnn_act=Config.get('cnn', 'cnn_act')
options.cnn_drop=Config.get('cnn', 'cnn_drop')
#[dnn]
options.fc_lay=Config.get('dnn', 'fc_lay')
options.fc_drop=Config.get('dnn', 'fc_drop')
options.fc_use_laynorm_inp=Config.get('dnn', 'fc_use_laynorm_inp')
options.fc_use_batchnorm_inp=Config.get('dnn', 'fc_use_batchnorm_inp')
options.fc_use_batchnorm=Config.get('dnn', 'fc_use_batchnorm')
options.fc_use_laynorm=Config.get('dnn', 'fc_use_laynorm')
options.fc_act=Config.get('dnn', 'fc_act')
#[class]
options.class_lay=Config.get('class', 'class_lay')
options.class_drop=Config.get('class', 'class_drop')
options.class_use_laynorm_inp=Config.get('class', 'class_use_laynorm_inp')
options.class_use_batchnorm_inp=Config.get('class', 'class_use_batchnorm_inp')
options.class_use_batchnorm=Config.get('class', 'class_use_batchnorm')
options.class_use_laynorm=Config.get('class', 'class_use_laynorm')
options.class_act=Config.get('class', 'class_act')
#[optimization]
options.lr=Config.get('optimization', 'lr')
options.batch_size=Config.get('optimization', 'batch_size')
options.N_epochs=Config.get('optimization', 'N_epochs')
options.N_batches=Config.get('optimization', 'N_batches')
options.N_eval_epoch=Config.get('optimization', 'N_eval_epoch')
options.seed=Config.get('optimization', 'seed')
return options