def multi_DTW_process(ns1, Seeds_B1, Seeds_B2, nseeds_b, **kwargs):
#SETUP OPTIONS
info = kwargs.get('info', True)
DTW_max_samp = kwargs.get('DTW_max_samp', 15)
corecount = kwargs.get('corecount', 2)
time.sleep(.1)
with counter.get_lock():
counter.value += 1
if info:
print('\r(%i-core(s))Calculating DTW %i/%i ' %
(corecount, counter.value, nseeds_b),
end='\r')
DTW_matrix = np.zeros(nseeds_b)
Traj1 = np.zeros((Seeds_B1.shape[2], Seeds_B1.shape[1]))
Traj2 = np.zeros((Seeds_B2.shape[2], Seeds_B2.shape[1]))
for f in range(Seeds_B1.shape[2]):
Traj1[f] = Seeds_B1[ns1, :, f]
for ns2 in range(nseeds_b):
for f in range(Seeds_B2.shape[2]):
Traj2[f] = Seeds_B2[ns2, :, f]
cost_matrix, cost, alignmend_a, alignmend_b = dtw2d(Traj1, Traj2)
DTW_matrix[ns2] = cost
return DTW_matrix
def test_dtw2d(self):
a = np.array(
[[0.30590702, 0.71122566, 0.87785326, 0.41896773, 0.27411357],
[0.78599928, 0.13459698, 0.7441022, 0.79060971, 0.9681483],
[0.67467294, 0.65713991, 0.06253549, 0.2692394, 0.34000727],
[0.79171092, 0.05383535, 0.85633251, 0.79434776, 0.07521822],
[0.04116069, 0.93189229, 0.56063383, 0.15773727, 0.41580332],
[0.88198997, 0.70688883, 0.04622127, 0.6324347, 0.94977589],
[0.45343914, 0.58697004, 0.56443057, 0.53493055, 0.03360396],
[0.53693851, 0.34162195, 0.26667218, 0.44254594, 0.08195404],
[0.63299858, 0.54636604, 0.15679107, 0.53235207, 0.10908979],
[0.94692708, 0.03055877, 0.52066543, 0.15592486, 0.90842773],
[0.63713889, 0.13715326, 0.12382141, 0.18114644, 0.64222287],
[0.18344581, 0.36095063, 0.04299809, 0.93636622, 0.97176932],
[0.46324373, 0.03592446, 0.2815672, 0.024673, 0.03278622],
[0.40219816, 0.37011199, 0.89306112, 0.69104287, 0.65363817],
[0.12373541, 0.66056791, 0.72306298, 0.51247937, 0.89520386],
[0.62731672, 0.06151905, 0.20113791, 0.54437935, 0.24038752],
[0.6650012, 0.93281123, 0.45220594, 0.5920064, 0.84662455],
[0.35575232, 0.53527631, 0.26486243, 0.05665991, 0.37931886],
[0.7680193, 0.38466873, 0.42141126, 0.43820938, 0.18742516],
[0.02902629, 0.61636535, 0.14813542, 0.80206397, 0.96151947]])
b = np.array(
[[0.37700537, 0.06839722, 0.90342088, 0.67260341, 0.21236238],
[0.81431271, 0.30039987, 0.7422115, 0.81856106, 0.73533936],
[0.0785568, 0.20518932, 0.37183274, 0.66525787, 0.91777226],
[0.26032762, 0.43689793, 0.50414443, 0.62953974, 0.26879355],
[0.0772129, 0.6953331, 0.94603823, 0.04144669, 0.64313459],
[0.33403999, 0.58964715, 0.85784944, 0.1092154, 0.90425047],
[0.57409039, 0.99600997, 0.5179884, 0.39531699, 0.14252428],
[0.79165219, 0.57988299, 0.62778483, 0.69332613, 0.63002323],
[0.0249018, 0.73852518, 0.98462038, 0.24437005, 0.89890168],
[0.37329552, 0.7420235, 0.51391262, 0.18437844, 0.34257216],
[0.7842619, 0.21165934, 0.95643329, 0.399339, 0.93537423],
[0.13890876, 0.32683409, 0.90477286, 0.82666108, 0.01006905],
[0.90557923, 0.28472431, 0.09018984, 0.86671066, 0.90903727],
[0.54199152, 0.83542679, 0.65840321, 0.95418474, 0.87883887],
[0.18411973, 0.78678018, 0.39588103, 0.92501485, 0.87820573],
[0.83208939, 0.70154756, 0.96767817, 0.16727018, 0.49564328],
[0.47970899, 0.59563789, 0.25823924, 0.43123425, 0.16967742],
[0.83565957, 0.35983831, 0.73003322, 0.38112664, 0.61784956],
[0.89832668, 0.45160468, 0.56442061, 0.47974733, 0.39944765],
[0.68100611, 0.26754648, 0.79144153, 0.14882468, 0.29260584],
[0.49658543, 0.88300153, 0.4789712, 0.75601603, 0.64980613],
[0.98528348, 0.4945167, 0.60385308, 0.64908159, 0.40022932],
[0.7663773, 0.73506873, 0.94322472, 0.72975117, 0.23751136],
[0.00794367, 0.72420966, 0.07036621, 0.5213072, 0.60074303],
[0.78067221, 0.48692555, 0.38134388, 0.16889658, 0.47186969]])
cost, al_a, al_b = dtw2d(a, b)
assert (al_a == np.array([
0, 0, 1, 2, 3, 4, 5, 6, 6, 7, 8, 9, 10, 11, 11, 11, 12, 12, 13, 14,
15, 16, 17, 18, 19, 19
])).all(), al_a
assert (al_b == np.array([
0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24
])).all(), al_b
def recog():
print 'Please speak \'Open Sesame\' in the mic within 3 seconds of the prompt \'Begin\' !'
time.sleep(2)
print 'Begin !'
os.system(
'arecord -D sysdefault:CARD=1 -c 1 -f S16 -r 8000 -d 3 /home/pi/DPCodes/Modules/recog.wav'
)
#file_name = os.path.join(os.path.dirname(os.path.abspath(__file__)),'recog.wav')
file_name = '/home/pi/DPCodes/Modules/recog.wav'
fs, signal = wav.read(file_name)
#signal = signal[:,0]
############# Extract MFCC features #############
mfcc = speechpy.feature.mfcc(signal,
sampling_frequency=fs,
frame_length=0.020,
frame_stride=0.01,
num_filters=40,
fft_length=512,
low_frequency=0,
high_frequency=None)
mfccset = {}
mfccset['unknown'] = mfcc
with open('unknownsample.dat', 'wb') as writef:
pickle.dump(mfccset, writef, protocol=pickle.HIGHEST_PROTOCOL)
mfccset = {}
with open('mfccdataset.dat', 'rb') as readf:
mfccset = pickle.load(readf)
if (len(mfccset) == 0):
return 'No Data'
mfccdata = mfccset.values()
mfcckeys = mfccset.keys()
n = len(mfccdata)
with open('unknownsample.dat', 'rb') as readf:
aset = pickle.load(readf)
a = aset.values()[0]
mindist = 10000.000
minind = -1
for i in range(n):
b = mfccdata[i]
cost_matrix, alignmend_a, alignmend_b = dtw2d(a, b)
r, l = cost_matrix.shape
cost = cost_matrix[r - 1][l - 1]
#print "Distance between ",mfcckeys[i]," and given voice is:\t",cost_matrix[r-1][l-1]
if cost < mindist:
mindist = cost
minind = i
if mindist >= 2500 or minind == -1:
return 'Not Found'
elif mindist >= 2000:
return 'Not Accurate'
else:
return str(mfcckeys[minind])
def test_dtw2d(self):
a = np.array([[0.30590702, 0.71122566, 0.87785326, 0.41896773, 0.27411357],
[0.78599928, 0.13459698, 0.7441022, 0.79060971, 0.9681483],
[0.67467294, 0.65713991, 0.06253549, 0.2692394, 0.34000727],
[0.79171092, 0.05383535, 0.85633251, 0.79434776, 0.07521822],
[0.04116069, 0.93189229, 0.56063383, 0.15773727, 0.41580332],
[0.88198997, 0.70688883, 0.04622127, 0.6324347, 0.94977589],
[0.45343914, 0.58697004, 0.56443057, 0.53493055, 0.03360396],
[0.53693851, 0.34162195, 0.26667218, 0.44254594, 0.08195404],
[0.63299858, 0.54636604, 0.15679107, 0.53235207, 0.10908979],
[0.94692708, 0.03055877, 0.52066543, 0.15592486, 0.90842773],
[0.63713889, 0.13715326, 0.12382141, 0.18114644, 0.64222287],
[0.18344581, 0.36095063, 0.04299809, 0.93636622, 0.97176932],
[0.46324373, 0.03592446, 0.2815672, 0.024673, 0.03278622],
[0.40219816, 0.37011199, 0.89306112, 0.69104287, 0.65363817],
[0.12373541, 0.66056791, 0.72306298, 0.51247937, 0.89520386],
[0.62731672, 0.06151905, 0.20113791, 0.54437935, 0.24038752],
[0.6650012, 0.93281123, 0.45220594, 0.5920064, 0.84662455],
[0.35575232, 0.53527631, 0.26486243, 0.05665991, 0.37931886],
[0.7680193, 0.38466873, 0.42141126, 0.43820938, 0.18742516],
[0.02902629, 0.61636535, 0.14813542, 0.80206397, 0.96151947]])
b = np.array([[0.37700537, 0.06839722, 0.90342088, 0.67260341, 0.21236238],
[0.81431271, 0.30039987, 0.7422115, 0.81856106, 0.73533936],
[0.0785568, 0.20518932, 0.37183274, 0.66525787, 0.91777226],
[0.26032762, 0.43689793, 0.50414443, 0.62953974, 0.26879355],
[0.0772129, 0.6953331, 0.94603823, 0.04144669, 0.64313459],
[0.33403999, 0.58964715, 0.85784944, 0.1092154, 0.90425047],
[0.57409039, 0.99600997, 0.5179884, 0.39531699, 0.14252428],
[0.79165219, 0.57988299, 0.62778483, 0.69332613, 0.63002323],
[0.0249018, 0.73852518, 0.98462038, 0.24437005, 0.89890168],
[0.37329552, 0.7420235, 0.51391262, 0.18437844, 0.34257216],
[0.7842619, 0.21165934, 0.95643329, 0.399339, 0.93537423],
[0.13890876, 0.32683409, 0.90477286, 0.82666108, 0.01006905],
[0.90557923, 0.28472431, 0.09018984, 0.86671066, 0.90903727],
[0.54199152, 0.83542679, 0.65840321, 0.95418474, 0.87883887],
[0.18411973, 0.78678018, 0.39588103, 0.92501485, 0.87820573],
[0.83208939, 0.70154756, 0.96767817, 0.16727018, 0.49564328],
[0.47970899, 0.59563789, 0.25823924, 0.43123425, 0.16967742],
[0.83565957, 0.35983831, 0.73003322, 0.38112664, 0.61784956],
[0.89832668, 0.45160468, 0.56442061, 0.47974733, 0.39944765],
[0.68100611, 0.26754648, 0.79144153, 0.14882468, 0.29260584],
[0.49658543, 0.88300153, 0.4789712, 0.75601603, 0.64980613],
[0.98528348, 0.4945167, 0.60385308, 0.64908159, 0.40022932],
[0.7663773, 0.73506873, 0.94322472, 0.72975117, 0.23751136],
[0.00794367, 0.72420966, 0.07036621, 0.5213072, 0.60074303],
[0.78067221, 0.48692555, 0.38134388, 0.16889658, 0.47186969]])
cost, al_a, al_b = dtw2d(a, b)
assert (al_a == np.array([0, 0, 1, 2, 3, 4, 5, 6, 6, 7, 8, 9, 10, 11, 11,
11, 12, 12, 13, 14, 15, 16, 17, 18, 19, 19])).all(), al_a
assert (al_b == np.array([0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24])).all(), al_b
def distance_fast(Traj1, Traj2, max_step):
if Traj1.ndim == 1:
cost_matrix, cost, alignmend_a, alignmend_b = dtw1d(Traj1, Traj2)
else:
Traj1 = np.ascontiguousarray(Traj1.transpose())
Traj2 = np.ascontiguousarray(Traj2.transpose())
cost_matrix, cost, alignmend_a, alignmend_b = dtw2d(Traj1, Traj2)
return cost
# def distance_fast(s1, s2, window=None, max_dist=None,
# max_step=None, max_length_diff=None, penalty=None, psi=None):
# """Fast C version of :meth:`distance`.
# Note: the series are expected to be arrays of the type ``double``.
# Thus ``numpy.array([1,2,3], dtype=numpy.double)`` or
# ``array.array('d', [1,2,3])``
# """
# if dtw_c is None:
# _print_library_missing()
# return None
# if window is None:
# window = 0
# if max_dist is None:
# max_dist = 0
# if max_step is None:
# max_step = 0
# if max_length_diff is None:
# max_length_diff = 0
# if penalty is None:
# penalty = 0
# if psi is None:
# psi = 0
# d = dtw_c.distance_nogil(s1, s2, window,
# max_dist=max_dist,
# max_step=max_step,
# max_length_diff=max_length_diff,
# penalty=penalty,
# psi=psi)
# return d