window_len = int(1.0/winstep)
x = model.predict(data, batch_size=32, verbose=0)
-s = np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]
-w = np.hanning(window_len)
-
-smoothed = np.convolve(w/w.sum(), s[:,0], mode='valid')
-
-wavdata = np.uint8(list(map(int,
- smoothed*255))[int(window_len/2):-1*(int(window_len/2))])
-
+#s = np.r_[x[window_len-1:0:-1],x,x[-2:-window_len-1:-1]]
+#w = np.hanning(window_len)
+#
+#smoothed = np.convolve(w/w.sum(), s[:,0], mode='valid')
+#wavdata = np.uint8(list(map(int,
+# smoothed*255))[int(window_len/2):-1*(int(window_len/2))])
+wavdata = np.uint8(x*255)
print('sr: ', int(1.0/winstep))
print("len(wavdata): ", len(wavdata))