data_io.py

import configparser as ConfigParser
from optparse import OptionParser
import numpy as np
#import scipy.io.wavfile


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(cfg_file=None):
 parser=OptionParser()
 parser.add_option("--cfg") # Mandatory
 (options,args)=parser.parse_args()
 if cfg_file is None:
  cfg_file=options.cfg
 if options.cfg is None and cfg_file is None:
  cfg_file='cfg/SincNet_TIMIT.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