def getTeacherStudentRecordings():
"""get teacher and student phoneme log mel features"""
trainNacta2017_teacher, trainNacta_teacher, trainSepa_teacher, trainPrimarySchool_teacher = getTeacherRecordings()
valPrimarySchool_student, trainPrimarySchool_student = getStudentRecordings()
dic_pho_embedding_nacta2017_teacher = dumpFeaturePho(wav_path=nacta2017_wav_path,
textgrid_path=nacta2017_textgrid_path,
recordings=trainNacta2017_teacher,
syllableTierName='line',
phonemeTierName='details')
dic_pho_embedding_nacta_teacher = dumpFeaturePho(wav_path=nacta_wav_path,
textgrid_path=nacta_textgrid_path,
recordings=trainNacta_teacher,
syllableTierName='line',
phonemeTierName='details')
dic_pho_embedding_primarySchool_teacher = dumpFeaturePho(wav_path=primarySchool_wav_path,
textgrid_path=primarySchool_textgrid_path,
recordings=trainPrimarySchool_teacher,
syllableTierName='line',
phonemeTierName='details')
dic_pho_embedding_sepa_teacher = dumpFeaturePho(wav_path=nacta_wav_path,
textgrid_path=nacta_textgrid_path,
recordings=trainSepa_teacher,
syllableTierName='line',
phonemeTierName='details')
dic_pho_embedding_primarySchool_student = dumpFeaturePho(wav_path=primarySchool_wav_path,
textgrid_path=primarySchool_textgrid_path,
recordings=valPrimarySchool_student+trainPrimarySchool_student,
syllableTierName='line',
phonemeTierName='details')
# fuse two dictionaries
list_key_teacher = list(set(list(dic_pho_embedding_nacta_teacher.keys()) + list(dic_pho_embedding_nacta2017_teacher.keys()) +
list(dic_pho_embedding_primarySchool_teacher.keys()) + list(dic_pho_embedding_sepa_teacher.keys())))
list_key_student = list(set(list(dic_pho_embedding_primarySchool_student.keys())))
print(list_key_teacher)
print(list_key_student)
# teacher's part
dic_pho_embedding_all = {}
dic_pho_embedding_train = {}
dic_pho_embedding_val = {}
dic_pho_embedding_test = {}
dic_pho_feature_train = {}
feature_phn_train = []
feature_phn_val = []
feature_phn_test = []
for key in list_key_teacher:
dic_pho_embedding_all[key] = dic_pho_embedding_nacta2017_teacher[key] + dic_pho_embedding_nacta_teacher[key] + \
dic_pho_embedding_primarySchool_teacher[key] + dic_pho_embedding_sepa_teacher[key]
# split 20 percent for test
dic_pho_embedding_train[key], dic_pho_embedding_test[key] = \
train_test_split(dic_pho_embedding_all[key], test_size=0.2)
dic_pho_embedding_train[key], dic_pho_embedding_val[key] = \
train_test_split(dic_pho_embedding_train[key], test_size=0.2)
feature_phn_train.append(dic_pho_embedding_train[key])
feature_phn_val.append(dic_pho_embedding_val[key])
feature_phn_test.append(dic_pho_embedding_test[key])
dic_pho_feature_train[key] = np.vstack(dic_pho_embedding_train[key])
# save feature label scaler
# training and test data on the phone level
filename_pho_embedding_train = join(data_path, 'feature_phn_embedding_train_teacher.pkl')
pickle.dump(feature_phn_train, open(filename_pho_embedding_train, 'wb'), protocol=2)
filename_pho_embedding_val = join(data_path, 'feature_phn_embedding_val_teacher.pkl')
pickle.dump(feature_phn_val, open(filename_pho_embedding_val, 'wb'), protocol=2)
filename_pho_embedding_test = join(data_path, 'feature_phn_embedding_test_teacher.pkl')
pickle.dump(feature_phn_test, open(filename_pho_embedding_test, 'wb'), protocol=2)
pickle.dump(list_key_teacher,
open(join(data_path, 'list_key_teacher.pkl'), 'wb'), protocol=2)
# student part
dic_pho_embedding_train = {}
dic_pho_embedding_val = {}
dic_pho_embedding_test = {}
feature_phn_train = []
feature_phn_val = []
feature_phn_test = []
for key in list_key_student:
# split 20 percent for test
dic_pho_embedding_train[key], dic_pho_embedding_test[key] = \
train_test_split(dic_pho_embedding_primarySchool_student[key], test_size=0.2)
dic_pho_embedding_train[key], dic_pho_embedding_val[key] = \
train_test_split(dic_pho_embedding_train[key], test_size=0.2)
feature_phn_train.append(dic_pho_embedding_train[key])
feature_phn_val.append(dic_pho_embedding_val[key])
feature_phn_test.append(dic_pho_embedding_test[key])
dic_pho_feature_train[key] = np.vstack(dic_pho_embedding_train[key])
feature_train, _, scaler = featureAggregator(dic_pho_feature_train)
# save feature label scaler
# training and test data on the phone level, no need to save teacher's data
filename_pho_embedding_train = join(data_path, 'feature_phn_embedding_train_student.pkl')
pickle.dump(feature_phn_train, open(filename_pho_embedding_train, 'wb'), protocol=2)
filename_pho_embedding_val = join(data_path, 'feature_phn_embedding_val_student.pkl')
pickle.dump(feature_phn_val, open(filename_pho_embedding_val, 'wb'), protocol=2)
filename_pho_embedding_test = join(data_path, 'feature_phn_embedding_test_student.pkl')
pickle.dump(feature_phn_test, open(filename_pho_embedding_test, 'wb'), protocol=2)
pickle.dump(scaler,
open(join(data_path, 'scaler_phn_embedding_train_teacher_student.pkl'), 'wb'), protocol=2)
pickle.dump(list_key_student,
open(join(data_path, 'list_key_student.pkl'), 'wb'), protocol=2)
def getTeacherStudentAudio():
"""retrieve the audio of each phoneme, and save them into .wav"""
trainNacta2017_teacher, trainNacta_teacher, trainSepa_teacher, trainPrimarySchool_teacher = getTeacherRecordings()
valPrimarySchool_student, trainPrimarySchool_student = getStudentRecordings()
dic_audio_nacta2017_teacher = dumpAudioPhn(wav_path=nacta2017_wav_path,
textgrid_path=nacta2017_textgrid_path,
recordings=trainNacta2017_teacher,
lineTierName='line',
phonemeTierName='details')
dic_audio_nacta_teacher = dumpAudioPhn(wav_path=nacta_wav_path,
textgrid_path=nacta_textgrid_path,
recordings=trainNacta_teacher,
lineTierName='line',
phonemeTierName='details')
dic_audio_primarySchool_teacher = dumpAudioPhn(wav_path=primarySchool_wav_path,
textgrid_path=primarySchool_textgrid_path,
recordings=trainPrimarySchool_teacher,
lineTierName='line',
phonemeTierName='details')
dic_audio_sepa_teacher = dumpAudioPhn(wav_path=nacta_wav_path,
textgrid_path=nacta_textgrid_path,
recordings=trainSepa_teacher,
lineTierName='line',
phonemeTierName='details')
dic_audio_primarySchool_student = dumpAudioPhn(wav_path=primarySchool_wav_path,
textgrid_path=primarySchool_textgrid_path,
recordings=valPrimarySchool_student+trainPrimarySchool_student,
lineTierName='line',
phonemeTierName='details')
dic_audio_teacher = {}
# fuse two dictionaries
list_key_teacher = list(set(list(dic_audio_nacta_teacher.keys()) + list(dic_audio_nacta2017_teacher.keys()) +
list(dic_audio_primarySchool_teacher.keys()) + list(dic_audio_sepa_teacher.keys())))
list_key_student = list(set(list(dic_audio_primarySchool_student.keys())))
for key in list_key_teacher:
dic_audio_teacher[key] = dic_audio_nacta2017_teacher[key] + dic_audio_nacta_teacher[key] + \
dic_audio_primarySchool_teacher[key] + dic_audio_sepa_teacher[key]
for ii, phn in enumerate(dic_audio_teacher[key]):
sf.write(join(phn_wav_path, "teacher", key+"_teacher_"+str(ii)+".wav"), phn, fs)
for key in list_key_student:
for ii, phn in enumerate(dic_audio_primarySchool_student[key]):
sf.write(join(phn_wav_path, "student", key+"_student_"+str(ii)+".wav"), phn, fs)
def getTeacherRecordingsTrainingData():
"""get teacher training and test log mel features"""
from src.train_test_filenames import getTeacherRecordings
# import h5py
trainNacta2017, trainNacta, trainSepa, trainPrimarySchool = getTeacherRecordings(
)
dic_pho_embedding_nacta2017 = dumpFeaturePho(
wav_path=nacta2017_wav_path,
textgrid_path=nacta2017_textgrid_path,
recordings=trainNacta2017,
syllableTierName='line',
phonemeTierName='details')
dic_pho_embedding_nacta = dumpFeaturePho(wav_path=nacta_wav_path,
textgrid_path=nacta_textgrid_path,
recordings=trainNacta,
syllableTierName='line',
phonemeTierName='details')
dic_pho_embedding_primarySchool = dumpFeaturePho(
wav_path=primarySchool_wav_path,
textgrid_path=primarySchool_textgrid_path,
recordings=trainPrimarySchool,
syllableTierName='line',
phonemeTierName='details')
dic_pho_embedding_sepa = dumpFeaturePho(wav_path=nacta_wav_path,
textgrid_path=nacta_textgrid_path,
recordings=trainSepa,
syllableTierName='line',
phonemeTierName='details')
# fuse two dictionaries
list_key = list(
set(
list(dic_pho_embedding_nacta.keys()) +
list(dic_pho_embedding_nacta2017.keys()) +
list(dic_pho_embedding_primarySchool.keys()) +
list(dic_pho_embedding_sepa.keys())))
print(list_key)
dic_pho_embedding_all = {}
dic_pho_embedding_train = {}
dic_pho_embedding_test = {}
dic_pho_feature_train = {}
feature_phn_train = []
feature_phn_test = []
for key in list_key:
dic_pho_embedding_all[key] = dic_pho_embedding_nacta2017[key] + dic_pho_embedding_nacta[key] + \
dic_pho_embedding_primarySchool[key] + dic_pho_embedding_sepa[key]
# for l in dic_pho_embedding_all[key]:
# if not len(l):
# print(key, 'empty')
# raise ValueError
# split 20 percent for test
dic_pho_embedding_train[key], dic_pho_embedding_test[key] = \
train_test_split(dic_pho_embedding_all[key], test_size=0.2)
feature_phn_train.append(dic_pho_embedding_train[key])
feature_phn_test.append(dic_pho_embedding_test[key])
# print(len(dic_pho_embedding_train[key]))
dic_pho_feature_train[key] = np.vstack(dic_pho_embedding_train[key])
feature_train, _, scaler = featureAggregator(dic_pho_feature_train)
print(feature_train.shape)
# save feature label scaler
# training and test data on the phone level
filename_pho_embedding_train = join(data_path_phone_embedding_model,
'feature_phn_embedding_train.pkl')
pickle.dump(feature_phn_train,
open(filename_pho_embedding_train, 'wb'),
protocol=2)
# h5f = h5py.File(filename_pho_embedding_train, 'w')
# h5f.create_dataset('feature_train', data=feature_phn_train)
# h5f.close()
# dd.io.save(filename_pho_embedding_train, feature_phn_train)
filename_pho_embedding_test = join(data_path_phone_embedding_model,
'feature_phn_embedding_test.pkl')
pickle.dump(feature_phn_test,
open(filename_pho_embedding_test, 'wb'),
protocol=2)
# h5f = h5py.File(filename_pho_embedding_test, 'w')
# h5f.create_dataset('feature_test', data=feature_phn_test)
# h5f.close()
# dd.io.save(filename_pho_embedding_test, feature_phn_test)
pickle.dump(scaler,
open(
join(data_path_phone_embedding_model,
'scaler_phn_embedding.pkl'), 'wb'),
protocol=2)
pickle.dump(list_key,
open(join(data_path_phone_embedding_model, 'list_key.pkl'),
'wb'),
protocol=2)
label_all = np.array(label_all, dtype='int64')
scaler = preprocessing.StandardScaler().fit(feature_all)
feature_all = scaler.transform(feature_all)
feature_all = featureReshape(feature_all, nlen=7)
return feature_all, label_all, scaler
if __name__ == '__main__':
from src.train_test_filenames import getTeacherRecordings
import h5py
import pickle
trainNacta2017, trainNacta, trainSepa, trainPrimarySchool = getTeacherRecordings(
)
dic_pho_feature_nacta2017 = dumpFeaturePho(
wav_path=nacta2017_wav_path,
textgrid_path=nacta2017_textgrid_path,
recordings=trainNacta2017,
syllableTierName='line',
phonemeTierName='details')
dic_pho_feature_nacta = dumpFeaturePho(wav_path=nacta_wav_path,
textgrid_path=nacta_textgrid_path,
recordings=trainNacta,
syllableTierName='line',
phonemeTierName='details')
dic_pho_feature_primarySchool = dumpFeaturePho(