# np.var(map(lambda x: (mel[x,i] - moy[j]) / np.sqrt(var[j]), times)) features["moy_loc_%s" %j] =\ np.mean(map(lambda x: (mel[x,i] - mel_min) / (mel_max - mel_min), times)) features["var_loc_%s" %j] =\ np.var(map(lambda x: (mel[x,i] - mel_min) / (mel_max - mel_min), times)) try: text += words[2]+'\t'+str(features)+'\n' except BaseException, e: print e return text f = open('transcript_test', 'Ur') text = f.read() turns = text.split('\n.\n') turn = turns[1] turn_formated = turn.split('\n')[1:] dump = read_turn(mel, moy, var, turn_formated) #%% Fourth step : read a file def normalize_signal(path1, path2): """attention, retourne directement le tableau du signal.""" f = Sndfile(path1) n = int(f.nframes) fs = f.samplerate nc = f.channels # checker que c'est bien 1 if nc != 1: