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data_io_new.py
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data_io_new.py
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import configparser as ConfigParser
from optparse import OptionParser
import numpy as np
import scipy
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):
print(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]
# randint(0, snt_len-2*wlen-1)
snt_beg = np.random.randint(snt_len-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