def predict(m, fs, signal, da):
try:
feat = mix_feature((fs, signal))
feat = da.get_hidden_values(feat).eval()
except Exception as e:
return None
return m.gmmset.predict_one(feat)
def enroll(self, name, fs, signal):
"""
add the signal to this person's training dataset
name: person's name
"""
feat = mix_feature((fs, signal))
self.features[name].extend(feat)
def predict(self, fs, signal):
try:
feat = mix_feature((fs, signal))
except Exception as e:
print tb.format_exc()
return None
return self.gmmset.predict_one(feat)
def enroll(self, name, fs, signal):
"""
add the signal to this person's training dataset
name: person's name
"""
feat = mix_feature((fs, signal))
self.features[name].extend(feat)
def predict(self, fs, signal):
try:
feat = mix_feature((fs, signal))
except Exception as e:
print tb.format_exc()
return None
return self.gmmset.predict_one(feat)
def predict(self, fs, signal):
"""
return a label (name)
"""
try:
feat = mix_feature((fs, signal))
except Exception as e:
print(tb.format_exc())
return None
return self.gmmset.predict_one(feat)
def predict_scores(self, fs, signal):
"""
return scores
"""
try:
feat = mix_feature((fs, signal))
except Exception as e:
print tb.format_exc()
return None
return self.gmmset.predict_scores(feat)
def predict(m, fs, signal, da, up_bound, lower_bound):
try:
feat = mix_feature((fs, signal))
# put all values into -1~1
for i in xrange(len(feat)):
for j in xrange(len(feat[0])):
feat[i][j] = 2*((feat[i][j]-lower_bound[j]) / (up_bound[j]-lower_bound[j]))-1
feat = da.get_hidden_values(feat).eval()
except Exception as e:
return None
return m.gmmset.predict_one(feat)
def predict_with_score(self, fs, signal):
"""
return a label (name)
"""
try:
feat = mix_feature((fs, signal))
except Exception as e:
print tb.format_exc()
return None
# gmmset = GMMSet() = gmmset.GMMSetPyGMM
return self.gmmset.predict_one_with_score(feat)
def test_model(mods,path = "./tmp.wav"): fs, signal = read_wav(path) feat = mix_feature((fs, signal)) x = feat scores = [mods.gmmset.gmm_score(gmm, x) / len(x) for gmm in mods.gmmset.gmms] import operator p = sorted(enumerate(scores), key=operator.itemgetter(1), reverse=True) p = [(str(mods.gmmset.y[i]), y, p[0][1] - y) for i, y in p] result = [(mods.gmmset.y[index], value) for (index, value) in enumerate(scores)] p = max(result, key=operator.itemgetter(1)) return result
def predict(self, fs, signal):
"""
return a label (name)
"""
try:
feat = mix_feature((fs, signal))
except Exception as e:
print tb.format_exc()
return None
try:
return self.gmmset.predict_one_with_score(feat)
except:
print "Unexpected error:", sys.exc_info()[0]
def predict(self, fs, signal, reject=False):
from gmmset import GMMSetPyGMM
if GMMSet is not GMMSetPyGMM:
reject = False
try:
feat = mix_feature((fs, signal))
except Exception as e:
print tb.format_exc()
return None
if reject:
try:
return self.gmmset.predict_one_with_rejection(feat)
except Exception as e:
print tb.format_exc()
return self.gmmset.predict_one(feat)
def predict(self, fs, signal, reject=False):
from gmmset import GMMSetPyGMM
if GMMSet is not GMMSetPyGMM:
reject = False
try:
feat = mix_feature((fs, signal))
except Exception as e:
print str(e)
return None
if reject:
try:
l = self.gmmset.predict_one_with_rejection(feat)
return l
except Exception as e:
print str(e)
return self.gmmset.predict_one(feat)
def predict(self, fs, signal, reject=False):
from gmmset import GMMSetPyGMM
if GMMSet is not GMMSetPyGMM:
reject = False
print "Length of signal to predict:", len(signal)
try:
feat = mix_feature((fs, signal))
except Exception as e:
print str(e)
return None
if reject:
try:
l = self.gmmset.predict_one_with_rejection(feat)
return l
except Exception as e:
print str(e)
return self.gmmset.predict_one(feat)
def predict(self, fs, signal):
feat = mix_feature((fs, signal))
return self.gmmset.predict_one(feat)
def enroll(self, name, fs, signal):
feat = mix_feature((fs, signal))
self.features[name].extend(feat)
def enroll(self, name, fs, signal):
feat = mix_feature((fs, signal))
#pdb.set_trace()
self.features[name].extend(feat)
def enroll(self, name, fs, signal):
feat = mix_feature((fs, signal))
self.features[name].extend(feat)