unpack requires a byte object of length 17665190

I am trying to read the data from a .wav file for an audio visualization however I get the following error.

Here I leave my code I hope you can help me

import matplotlib
matplotlib.use('Agg')
from matplotlib import pylab
import matplotlib.pyplot as plt

filename = '1.wav'

if __name__ == '__main__':
    # Open the wave file and get info
    wave_file = wave.open(filename, 'r')
    data_size = wave_file.getnframes()
    sample_rate = wave_file.getframerate()
    sample_width = wave_file.getsampwidth()
    duration = data_size / float(sample_rate)

    # Read in sample data
    sound_data = wave_file.readframes(data_size)

    # Close the file, as we don't need it any more
    wave_file.close()

    # Unpack the binary data into an array
    unpack_fmt = '%dh' % (data_size)
    sound_data = struct.unpack(unpack_fmt, sound_data)

    # Process many samples
    fouriers_per_second = 24 # Frames per second
    fourier_spread = 1.0/fouriers_per_second
    fourier_width = fourier_spread
    fourier_width_index = fourier_width * float(sample_rate)

    if len(sys.argv) < 3:
        length_to_process = int(duration)-1
    else:
        length_to_process = float(sys.argv[2])



    total_transforms = int(round(length_to_process * fouriers_per_second))
    fourier_spacing = round(fourier_spread * float(sample_rate))


    print ("For Fourier width of "+str(fourier_width)+" need "+str(fourier_width_index)+" samples each FFT")
    print ("Doing "+str(fouriers_per_second)+" Fouriers per second")
    print ("Total " + str(total_transforms * fourier_spread))
    print ("Spacing: "+str(fourier_spacing))
    print ("Total transforms "+str(total_transforms))

    lastpoint=int(round(length_to_process*float(sample_rate)+fourier_width_index))-1

    sample_size = fourier_width_index
    freq = sample_rate / sample_size * np.arange(sample_size)

    x_axis = range(0, 12)

    def getBandWidth():
        return (2.0/sample_size) * (sample_rate / 2.0)

    def freqToIndex(f):
        # If f (frequency is lower than the bandwidth of spectrum[0]
        if f < getBandWidth()/2:
            return 0
        if f > (sample_rate / 2) - (getBandWidth() / 2):
            return sample_size -1
        fraction = float(f) / float(sample_rate)
        index = round(sample_size * fraction)
        return index

    fft_averages = []

    def average_fft_bands(fft_array):
        num_bands = 12 # The number of frequency bands (12 = 1 octave)
        del fft_averages[:]
        for band in range(0, num_bands):
            avg = 0.0

            if band == 0:
                lowFreq = int(0)
            else:
                lowFreq = int(int(sample_rate / 2) / float(2 ** (num_bands - band)))
            hiFreq = int((sample_rate / 2) / float(2 ** ((num_bands-1) - band)))
            lowBound = int(freqToIndex(lowFreq))
            hiBound = int(freqToIndex(hiFreq))
            for j in range(lowBound, hiBound):
                avg += fft_array[j]

            avg /= (hiBound - lowBound + 1)
            fft_averages.append(avg)

    for offset in range(0, total_transforms):
        start = int(offset * sample_size)
        end = int((offset * sample_size) + sample_size -1)

        print ("Processing sample %i of %i (%d seconds)" % (offset + 1, total_transforms, end/float(sample_rate)))
        sample_range = sound_data[start:end]
        ## FFT the data
        fft_data = abs(np.fft.fft(sample_range))
        # Normalise the data a second time, to make numbers sensible
        fft_data *= ((2**.5)/sample_size)
        plt.ylim(0, 1000)
        average_fft_bands(fft_data)
        y_axis = fft_averages
        """Stuff for bar graph"""
        width = 0.35
        p1 = plt.bar(x_axis, y_axis, width, color='r')
        """End bar graph stuff"""
        filename = str('frame_%05d' % offset) + '.png'
        plt.savefig(filename, dpi=100)
        plt.close()
    print ("DONE!")