for item in training_list:
for q in range(6, 40, 4):
for iteration in range(0, 2):
folder_name = os.path.split(item[1])[-1]
hmm_path = "%s_%d_%d.hmm" % (os.path.splitext(
item[2])[0], q, iteration)
if os.path.exists(hmm_path):
print("Skipping already trained %s for q=%d and i=%d..." %
(folder_name, q, iteration))
continue
print("Training %s for q=%d and i=%d..." %
(folder_name, q, iteration))
speech_hmm = em.build_hmm_from_folder(item[1],
q,
max_iterations=250,
show_plots=False,
convergence_threshold=1)
speech_hmm.save(hmm_path)
matches = speech_hmm.match_from_folder(item[1])
for i in range(0, len(matches)):
print("\ttraining match: %.3f" % matches[i])
metrics = [
max(matches),
min(matches),
np.mean(matches),
np.std(matches),
np.percentile(matches, 5),
np.percentile(matches, 10),
np.percentile(matches, 15),
