plot2.py

# https://kevinsprojects.wordpress.com/2014/12/13/short-time-fourier-transform-using-python-and-numpy/

from matplotlib import pyplot as plt
import numpy as np
import librosa

data, fs = librosa.load('raw/voice/TEST_DR1_FAKS0_SA1.WAV')
print(fs)
fft_size = 1024
overlap_fac = 0.5

# data = data / np.max(data)

# hop_size = np.int32(np.floor(fft_size * (1 - overlap_fac)))
hop_size = np.int32(fs / 100)
pad_end_size = fft_size  # the last segment can overlap the end of the data array by no more than one window size
total_segments = np.int32(np.ceil(len(data) / np.float32(hop_size)))
t_max = len(data) / np.float32(fs)

window = np.hanning(fft_size)  # our half cosine window
inner_pad = np.zeros(fft_size)  # the zeros which will be used to double each segment size

proc = np.concatenate((data, np.zeros(pad_end_size)))  # the data to process
result = np.empty((total_segments, fft_size), dtype=np.float32)  # space to hold the result

for i in range(total_segments):  # for each segment
    current_hop = hop_size * i  # figure out the current segment offset
    segment = proc[current_hop:current_hop + fft_size]  # get the current segment
    windowed = segment * window  # multiply by the half cosine function

    padded = np.append(windowed, inner_pad)  # add 0s to double the length of the data

    spectrum = np.fft.fft(padded) / fft_size  # take the Fourier Transform and scale by the number of samples

    autopower = np.abs(spectrum * np.conj(spectrum))  # find the autopower spectrum

    result[i, :] = autopower[:fft_size]  # append to the results array

result = 20 * np.log10(result)  # scale to db
# result = np.clip(result, -40, 200)  # clip values
print(result.shape)
img = plt.imshow(result, origin='lower', cmap='jet', interpolation='nearest', aspect='auto')
plt.show()