data_io.py

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