texp = template_exp.\
Experiment(pattern=np.array(('l','iy')),
data_paths_file=root_path+'Data/WavFilesTrainPaths_feverfew',
spread_length=3,
abst_threshold=.0001*np.ones(8),
fft_length=512,num_window_step_samples=80,
freq_cutoff=3000,sample_rate=16000,
num_window_samples=320,kernel_length=7
)
train_data_iter, tune_data_iter =\
template_exp.get_exp_iterator(texp,train_percent=.6)
liy_patterns = []
train_data_iter.reset_exp()
for datum_id in xrange(train_data_iter.num_data):
if datum_id % 10 == 0:
print datum_id
if train_data_iter.next(wait_for_positive_example=True,
compute_patterns=True,
max_template_length=40):
# the context length is 11
for p in train_data_iter.patterns:
pattern = p.copy()
esp.threshold_edgemap(pattern,.30,edge_feature_row_breaks,report_level=False,abst_threshold=abst_threshold)
esp.spread_edgemap(pattern,edge_feature_row_breaks,edge_orientations,spread_length=3)
liy_patterns.append(pattern)
reload(template_exp)
texp = template_exp.\
Experiment(pattern=np.array(('p','iy')),
data_paths_file=root_path+'Data/WavFilesTrainPaths',
spread_length=3,
abst_threshold=.0001*np.ones(8),
fft_length=512,num_window_step_samples=80,
freq_cutoff=3000,sample_rate=16000,
num_window_samples=320,kernel_length=7
)
train_data_iter, tune_data_iter =\
template_exp.get_exp_iterator(texp,train_percent=.7)
output = open('data_iter_piy050912.pkl','wb')
cPickle.dump(train_data_iter,output)
cPickle.dump(tune_data_iter,output)
output.close()
all_patterns_context = []
all_patterns = []
E_avg = template_exp.AverageBackground()
train_data_iter.spread_length = 5
for datum_id in xrange(train_data_iter.num_data):
if datum_id % 10 == 0:
print datum_id
texp = template_exp.Experiment(
patterns=[np.array(("aa", "r")), np.array(("ah", "r"))],
data_paths_file=root_path + "Data/WavFilesTestPaths_feverfew",
# data_paths_file=root_path+'Data/WavFilesTrainPaths',
spread_length=3,
abst_threshold=abst_threshold,
fft_length=512,
num_window_step_samples=80,
freq_cutoff=3000,
sample_rate=16000,
num_window_samples=320,
kernel_length=7,
offset=3,
)
test_data_iter, _ = template_exp.get_exp_iterator(texp, train_percent=1)
data_dir = root_path + "Data/Test/"
num_test_data = 0
while test_data_iter.next():
num_test_data += 1
np.save(data_dir + str(test_data_iter.cur_data_pointer) + "tune_E.npy", test_data_iter.E)
np.save(
data_dir + str(test_data_iter.cur_data_pointer) + "feature_label_transitions.npy",
test_data_iter.feature_label_transitions,
)
np.save(data_dir + str(test_data_iter.cur_data_pointer) + "phns.npy", test_data_iter.phns)
np.save(data_dir + str(test_data_iter.cur_data_pointer) + "s.npy", test_data_iter.s)
num_target_phns = np.zeros(len(phn_list))
