def vad_gmm_list(audio_list, model_list, model_file, feature_type,
n_coeffs_per_frame, acc_frames, results_dir, working_dir, samp_period, win_length):
features_dir = os.path.join(working_dir,feature_type)
acc_features_dir = features_dir+'_frames'
fea_file_list = os.path.join(working_dir,'feature_files.list')
if not os.path.exists(features_dir):
os.makedirs(features_dir)
if not os.path.exists(acc_features_dir):
os.makedirs(acc_features_dir)
vef.fea_extract(audio_list,feature_type,n_coeffs_per_frame,features_dir,samp_period, win_length)
vef.create_corresponding_list_assert(audio_list,features_dir,fea_file_list,'fea')
vef.accumulate_feature_vectors(fea_file_list,acc_frames,acc_features_dir)
vef.create_corresponding_list_assert(audio_list,acc_features_dir,fea_file_list,'fea')
test_gmm_classifier(fea_file_list, model_list, model_file, results_dir)
def estimate_channel_gmm(audio_list, model_list, model_file, feature_type,
n_coeffs_per_frame, acc_frames, results_dir, working_dir):
features_dir = os.path.join(working_dir,feature_type)
acc_features_dir = features_dir+'_frames'
fea_file_list = os.path.join(working_dir,'feature_files.list')
if not os.path.exists(features_dir):
os.makedirs(features_dir)
if not os.path.exists(acc_features_dir):
os.makedirs(acc_features_dir)
vef.fea_extract(audio_list,feature_type,n_coeffs_per_frame,features_dir)
vef.create_corresponding_list_assert(audio_list,features_dir,fea_file_list,'fea')
vef.accumulate_feature_vectors(fea_file_list,acc_frames,acc_features_dir)
vef.create_corresponding_list_assert(audio_list,acc_features_dir,fea_file_list,'fea')
test_gmm_classifier.test_gmm_classifier(test_list=fea_file_list, model_list=model_list,
model_file=model_file, results_dir=results_dir,
mode='single')
def vad_gmm_list(audio_list, model_list, model_file, feature_type,
n_coeffs_per_frame, acc_frames, results_dir, working_dir,
samp_period, win_length):
features_dir = os.path.join(working_dir, feature_type)
acc_features_dir = features_dir + '_frames'
fea_file_list = os.path.join(working_dir, 'feature_files.list')
if not os.path.exists(features_dir):
os.makedirs(features_dir)
if not os.path.exists(acc_features_dir):
os.makedirs(acc_features_dir)
vef.fea_extract(audio_list, feature_type, n_coeffs_per_frame, features_dir,
samp_period, win_length)
vef.create_corresponding_list_assert(audio_list, features_dir,
fea_file_list, 'fea')
vef.accumulate_feature_vectors(fea_file_list, acc_frames, acc_features_dir)
vef.create_corresponding_list_assert(audio_list, acc_features_dir,
fea_file_list, 'fea')
test_gmm_classifier(fea_file_list, model_list, model_file, results_dir)
def estimate_channel_gmm(audio_list, model_list, model_file, feature_type,
n_coeffs_per_frame, acc_frames, results_dir,
working_dir):
features_dir = os.path.join(working_dir, feature_type)
acc_features_dir = features_dir + '_frames'
fea_file_list = os.path.join(working_dir, 'feature_files.list')
if not os.path.exists(features_dir):
os.makedirs(features_dir)
if not os.path.exists(acc_features_dir):
os.makedirs(acc_features_dir)
vef.fea_extract(audio_list, feature_type, n_coeffs_per_frame, features_dir)
vef.create_corresponding_list_assert(audio_list, features_dir,
fea_file_list, 'fea')
vef.accumulate_feature_vectors(fea_file_list, acc_frames, acc_features_dir)
vef.create_corresponding_list_assert(audio_list, acc_features_dir,
fea_file_list, 'fea')
test_gmm_classifier.test_gmm_classifier(test_list=fea_file_list,
model_list=model_list,
model_file=model_file,
results_dir=results_dir,
mode='single')
def test_gmm_classification(models=None, n_samples=10000, working_dir=os.getcwd(),
apply_hlda=False, hlda_nuisance_dims=2, do_not_generate_features=False):
test_percentage = 0.2
samp_period = 0.01
generate_features = not do_not_generate_features
if not os.path.exists(working_dir):
os.makedirs(working_dir)
fea_dir = os.path.join(working_dir, 'features')
lab_dir = os.path.join(working_dir, 'lab')
list_dir = os.path.join(working_dir, 'lists')
model_dir = os.path.join(working_dir, 'models')
results_dir = os.path.join(working_dir, 'results')
if not os.path.exists(list_dir):
os.makedirs(list_dir)
if not os.path.exists(fea_dir):
os.makedirs(fea_dir)
if not os.path.exists(lab_dir):
os.makedirs(lab_dir)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
if not os.path.exists(results_dir):
os.makedirs(results_dir)
# Create a list of the model names
model_list = os.path.join(working_dir,'models.list')
ml = open(model_list,'w')
labels = []
for md in models:
ml.write('{}\n'.format(md.name))
labels.append(md.name)
ml.close()
tst_file_list = os.path.join(list_dir,'test_files.list')
trn_file_list = os.path.join(list_dir,'train_files.list')
if generate_features:
fea_file_names = write_feature_files(models, n_samples,
fea_dir, lab_dir)
n_fea_files = len(fea_file_names)
logging.info('Number of feature files: {}'.format(n_fea_files))
n_train_files = int(round(n_fea_files*(1-test_percentage)))
trn_list = open(trn_file_list,'w')
for f_n in fea_file_names[0:n_train_files]:
trn_list.write("{}\n".format(os.path.join(fea_dir,f_n)))
trn_list.close()
tst_list = open(tst_file_list,'w')
for f_n in fea_file_names[n_train_files:n_fea_files-1]:
tst_list.write("{}\n".format(os.path.join(fea_dir,f_n)))
tst_list.close()
n_mixes = models[0].n_components
n_dims = models[0].means.shape[1]
train_gmm_classifier.train_M_sized_gmm_classifier(n_mixes, n_dims, 'USER', trn_file_list,
model_list, lab_dir, model_dir, apply_hlda,
hlda_nuisance_dims)
model_file = os.path.join(model_dir,'newMacros')
test_gmm_classifier.test_gmm_classifier(tst_file_list, model_list, model_file, results_dir)
ref_annotations_list = os.path.join(list_dir,'labels.list')
hyp_annotations_list = os.path.join(list_dir,'hyp.list')
lists.create_corresponding_list_assert(tst_file_list,lab_dir,ref_annotations_list,'lab')
lists.create_corresponding_list_assert(tst_file_list,results_dir,hyp_annotations_list,'rec')
conf_matrix = evaluate_gmm_classifier.evaluate_results_list(ref_annotations_list,
hyp_annotations_list,
samp_period, labels)
logging.info('Confusion matrix: {}'.format(conf_matrix))
def test_gmm_classification(models=None,
n_samples=10000,
working_dir=os.getcwd(),
apply_hlda=False,
hlda_nuisance_dims=2,
do_not_generate_features=False):
test_percentage = 0.2
samp_period = 0.01
generate_features = not do_not_generate_features
if not os.path.exists(working_dir):
os.makedirs(working_dir)
fea_dir = os.path.join(working_dir, 'features')
lab_dir = os.path.join(working_dir, 'lab')
list_dir = os.path.join(working_dir, 'lists')
model_dir = os.path.join(working_dir, 'models')
results_dir = os.path.join(working_dir, 'results')
if not os.path.exists(list_dir):
os.makedirs(list_dir)
if not os.path.exists(fea_dir):
os.makedirs(fea_dir)
if not os.path.exists(lab_dir):
os.makedirs(lab_dir)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
if not os.path.exists(results_dir):
os.makedirs(results_dir)
# Create a list of the model names
model_list = os.path.join(working_dir, 'models.list')
ml = open(model_list, 'w')
labels = []
for md in models:
ml.write('{}\n'.format(md.name))
labels.append(md.name)
ml.close()
tst_file_list = os.path.join(list_dir, 'test_files.list')
trn_file_list = os.path.join(list_dir, 'train_files.list')
if generate_features:
fea_file_names = write_feature_files(models, n_samples, fea_dir,
lab_dir)
n_fea_files = len(fea_file_names)
logging.info('Number of feature files: {}'.format(n_fea_files))
n_train_files = int(round(n_fea_files * (1 - test_percentage)))
trn_list = open(trn_file_list, 'w')
for f_n in fea_file_names[0:n_train_files]:
trn_list.write("{}\n".format(os.path.join(fea_dir, f_n)))
trn_list.close()
tst_list = open(tst_file_list, 'w')
for f_n in fea_file_names[n_train_files:n_fea_files - 1]:
tst_list.write("{}\n".format(os.path.join(fea_dir, f_n)))
tst_list.close()
n_mixes = models[0].n_components
n_dims = models[0].means.shape[1]
train_gmm_classifier.train_M_sized_gmm_classifier(n_mixes, n_dims, 'USER',
trn_file_list,
model_list, lab_dir,
model_dir, apply_hlda,
hlda_nuisance_dims)
model_file = os.path.join(model_dir, 'newMacros')
test_gmm_classifier.test_gmm_classifier(tst_file_list, model_list,
model_file, results_dir)
ref_annotations_list = os.path.join(list_dir, 'labels.list')
hyp_annotations_list = os.path.join(list_dir, 'hyp.list')
lists.create_corresponding_list_assert(tst_file_list, lab_dir,
ref_annotations_list, 'lab')
lists.create_corresponding_list_assert(tst_file_list, results_dir,
hyp_annotations_list, 'rec')
conf_matrix = evaluate_gmm_classifier.evaluate_results_list(
ref_annotations_list, hyp_annotations_list, samp_period, labels)
logging.info('Confusion matrix: {}'.format(conf_matrix))