ValueError: operands could not be broadcast together with shapes (400,2) (400,)

[kaleemdhu]

Dear concerns : I am extracting features from wav , using PLP , this ( Pyhton 3.6 -Anaconda Spyder ) after execute i am facing error in this line

Give the name of the folder that you want to read data: samples
Give the number of samples in the specific folder: 5
/samples/1.wav
The sample rate of the audio is: 16000
Sampwidth: 2
Traceback (most recent call last):

File "", line 1, in
runfile('C:/Speech_Processing/2-Speech_Signal_Processing_and_Classification-master/feature_extraction_techniques/plp.py', wdir='C:/Speech_Processing/2-Speech_Signal_Processing_and_Classification-master/feature_extraction_techniques')

File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 704, in runfile
execfile(filename, namespace)

File "C:\ProgramData\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)

File "C:/Speech_Processing/2-Speech_Signal_Processing_and_Classification-master/feature_extraction_techniques/plp.py", line 54, in
main()

File "C:/Speech_Processing/2-Speech_Signal_Processing_and_Classification-master/feature_extraction_techniques/plp.py", line 52, in main
PLP()

File "C:/Speech_Processing/2-Speech_Signal_Processing_and_Classification-master/feature_extraction_techniques/plp.py", line 38, in PLP
plp_features = plp(signal,rasta=True)

File "C:\ProgramData\Anaconda3\lib\site-packages\sidekit\frontend\features.py", line 921, in plp
powspec, log_energy = power_spectrum(input_sig, fs, nwin, shift, prefac)

File "C:\ProgramData\Anaconda3\lib\site-packages\sidekit\frontend\features.py", line 399, in power_spectrum
ahan = framed[start:stop, :] *window

ValueError: operands could not be broadcast together with shapes (400,2) (400,)

#!usr/bin/python
import numpy.matlib
import scipy
from scipy.fftpack.realtransforms import dct
from sidekit.frontend.vad import pre_emphasis
from sidekit.frontend.io import *
from sidekit.frontend.normfeat import *
from sidekit.frontend.features import *
import scipy.io.wavfile as wav
import numpy as np



def readWavFile(wav):
        #given a path from the keyboard to read a .wav file
        #wav = raw_input('Give me the path of the .wav file you want to read: ')
        inputWav = 'C:/Speech_Processing/2-Speech_Signal_Processing_and_Classification-master/feature_extraction_techniques'+wav
        return inputWav
#reading the .wav file (signal file) and extract the information we need
def initialize(inputWav):
        rate , signal  = wav.read(readWavFile(inputWav)) # returns a wave_read object , rate: sampling frequency
        sig = wave.open(readWavFile(inputWav))
        # signal is the numpy 2D array with the date of the .wav file
        # len(signal) number of samples
        sampwidth = sig.getsampwidth()
        print ('The sample rate of the audio is: ',rate)
        print ('Sampwidth: ',sampwidth)
        return signal ,  rate
def PLP():
        folder = input('Give the name of the folder that you want to read data: ')
        amount = input('Give the number of samples in the specific folder: ')
        for x in range(1,int(amount)+1):
                wav = '/'+folder+'/'+str(x)+'.wav'
                print (wav)
                #inputWav = readWavFile(wav)
                signal,rate = initialize(wav)
                #returns PLP coefficients for every frame
                plp_features = plp(signal,rasta=True)
                meanFeatures(plp_features[0])
#compute the mean features for one .wav file (take the features for every frame and make a mean for the sample)
def meanFeatures(plp_features):
        #make a numpy array with length the number of plp features
        mean_features=np.zeros(len(plp_features[0]))
        #for one input take the sum of all frames in a specific feature and divide them with the number of frames
        for x in range(len(plp_features)):
                for y in range(len(plp_features[x])):
                        mean_features[y]+=plp_features[x][y]
        mean_features = (mean_features / len(plp_features))
        print (mean_features)

def main():
        PLP()

main()