conf.py

from data_io import ReadList,read_conf,str_to_bool
import numpy as np
# Reading cfg file
options=read_conf()

#[data]
tr_lst=options.tr_lst
te_lst=options.te_lst
pt_file=options.pt_file
class_dict_file=options.lab_dict
data_folder=options.data_folder+'/'
output_folder=options.output_folder

#[windowing]
fs=int(options.fs)
cw_len=int(options.cw_len)
cw_shift=int(options.cw_shift)

#[cnn]
cnn_N_filt=list(map(int, options.cnn_N_filt.split(',')))
cnn_len_filt=list(map(int, options.cnn_len_filt.split(',')))
cnn_max_pool_len=list(map(int, options.cnn_max_pool_len.split(',')))
cnn_use_laynorm_inp=str_to_bool(options.cnn_use_laynorm_inp)
cnn_use_batchnorm_inp=str_to_bool(options.cnn_use_batchnorm_inp)
cnn_use_laynorm=list(map(str_to_bool, options.cnn_use_laynorm.split(',')))
cnn_use_batchnorm=list(map(str_to_bool, options.cnn_use_batchnorm.split(',')))
cnn_act=list(map(str, options.cnn_act.split(',')))
cnn_drop=list(map(float, options.cnn_drop.split(',')))


#[dnn]
fc_lay=list(map(int, options.fc_lay.split(',')))
fc_drop=list(map(float, options.fc_drop.split(',')))
fc_use_laynorm_inp=str_to_bool(options.fc_use_laynorm_inp)
fc_use_batchnorm_inp=str_to_bool(options.fc_use_batchnorm_inp)
fc_use_batchnorm=list(map(str_to_bool, options.fc_use_batchnorm.split(',')))
fc_use_laynorm=list(map(str_to_bool, options.fc_use_laynorm.split(',')))
fc_act=list(map(str, options.fc_act.split(',')))

#[class]
class_lay=list(map(int, options.class_lay.split(',')))
class_drop=list(map(float, options.class_drop.split(',')))
class_use_laynorm_inp=str_to_bool(options.class_use_laynorm_inp)
class_use_batchnorm_inp=str_to_bool(options.class_use_batchnorm_inp)
class_use_batchnorm=list(map(str_to_bool, options.class_use_batchnorm.split(',')))
class_use_laynorm=list(map(str_to_bool, options.class_use_laynorm.split(',')))
class_act=list(map(str, options.class_act.split(',')))


#[optimization]
lr=float(options.lr)
batch_size=int(options.batch_size)
N_epochs=int(options.N_epochs)
N_batches=int(options.N_batches)
N_eval_epoch=int(options.N_eval_epoch)
seed=int(options.seed)


# training list
wav_lst_tr=ReadList(tr_lst) #Elenco file audio per il train
snt_tr=len(wav_lst_tr)

# test list
wav_lst_te=ReadList(te_lst)
snt_te=len(wav_lst_te)

# Converting context and shift in samples
wlen=int(fs*cw_len/1000.00)
wshift=int(fs*cw_shift/1000.00)

# Batch_dev
Batch_dev=128

# Loading label dictionary
lab_dict=np.load(class_dict_file).item()


# 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)
out_dim = class_lay[0]