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emodata1d.py
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# use raw time-domain speech signal as input to cnn for SER
import numpy as np
import os
import librosa
datapath = '/data/home/vandana/emodb/wav'
classes = ['W','L','E','A','F','T','N'] # 7 classes
seg_len = 16000 # signal split length (in samples) in time domain
seg_ov = int(seg_len*0.5) # 50% overlap
def normalize(s):
# RMS normalization
new_s = s/np.sqrt(np.sum(np.square((np.abs(s))))/len(s))
return new_s
def countclasses(fnames):
dict = {classes[0]:0,classes[1]:0,classes[2]:0,classes[3]:0,classes[4]:0,classes[5]:0,classes[6]:0}
for name in fnames:
if name[5] in classes:
dict[name[5]]+=1
return dict
def data1d(path):
fnames = os.listdir(datapath)
dict = countclasses(fnames)
print('Total Data',dict)
num_cl = len(classes)
train_dict = {classes[0]:0,classes[1]:0,classes[2]:0,classes[3]:0,classes[4]:0,classes[5]:0,classes[6]:0}
test_dict = {classes[0]:0,classes[1]:0,classes[2]:0,classes[3]:0,classes[4]:0,classes[5]:0,classes[6]:0}
val_dict = {classes[0]:0,classes[1]:0,classes[2]:0,classes[3]:0,classes[4]:0,classes[5]:0,classes[6]:0}
for i in range(num_cl):
cname = dict.keys()[i]
cnum = dict[cname]
t = round(0.8*cnum)
test_dict[cname] = int(cnum - t)
val_dict[cname] = int(round(0.2*t))
train_dict[cname] = int(t - val_dict[cname])
print('Class:',cname,'train:',train_dict[cname],'val:',val_dict[cname],'test:',test_dict[cname])
x_train = []
y_train = []
x_test = []
y_test = []
x_val = []
y_val = []
count = {classes[0]:0,classes[1]:0,classes[2]:0,classes[3]:0,classes[4]:0,classes[5]:0,classes[6]:0}
for name in fnames:
if name[5] in classes:
sig,fs = librosa.load(datapath+'/'+name, sr=16000)
# normalize signal
data = normalize(sig)
if(len(data) < seg_len):
pad_len = int(seg_len - len(data))
pad_rem = int(pad_len % 2)
pad_len /= 2
signal = np.pad(data,(int(pad_len), int(pad_len+pad_rem)),'constant',constant_values=0)
elif(len(data) > seg_len):
signal = []
end = seg_len
st = 0
while(end < len(data)):
signal.append(data[st:end])
st = st + seg_ov
end = st + seg_len
signal = np.array(signal)
if(end >= len(data)):
num_zeros = int(end-len(data))
if(num_zeros > 0):
n1 = np.array(data[st:end])
n2 = np.zeros([num_zeros])
s = np.concatenate([n1,n2],0)
else:
s = np.array(data[int(st):int(end)])
signal = np.vstack([signal,s])
else:
signal = data
if(count[name[5]] < train_dict[name[5]]):
if(signal.ndim>1):
for i in range(signal.shape[0]):
x_train.append(signal[i])
y_train.append(name[5])
else:
x_train.append(signal)
y_train.append(name[5])
else:
if((count[name[5]]-train_dict[name[5]]) < val_dict[name[5]]):
if(signal.ndim>1):
for i in range(signal.shape[0]):
x_val.append(signal[i])
y_val.append(name[5])
else:
x_val.append(signal)
y_val.append(name[5])
else:
if(signal.ndim>1):
for i in range(signal.shape[0]):
x_test.append(signal[i])
y_test.append(name[5])
else:
x_test.append(signal)
y_test.append(name[5])
count[name[5]]+=1
return np.float32(x_train),y_train,np.float32(x_test),y_test,np.float32(x_val),y_val
def string2num(y):
y1 = []
for i in y:
if(i == classes[0]):
y1.append(0)
elif(i == classes[1]):
y1.append(1)
elif(i == classes[2]):
y1.append(2)
elif(i == classes[3]):
y1.append(3)
elif(i == classes[4]):
y1.append(4)
elif(i == classes[5]):
y1.append(5)
else:
y1.append(6)
y1 = np.float32(np.array(y1))
return y1
def load_data():
x_tr,y_tr,x_t,y_t,x_v,y_v = data1d(datapath)
y_tr = string2num(y_tr)
y_t = string2num(y_t)
y_v = string2num(y_v)
return x_tr, y_tr, x_t, y_t, x_v, y_v