def generate_train(self):
'''Generate mini-batch data for training.
Returns:
batch_data_dict: dict containing audio_name, feature and target
'''
batch_size = self.batch_size
audio_indexes = np.array(self.train_audio_indexes)
self.random_state.shuffle(audio_indexes)
pointer = 0
while True:
# Reset pointer
if pointer >= len(audio_indexes):
pointer = 0
self.random_state.shuffle(audio_indexes)
# Get batch audio_indexes
batch_audio_indexes = audio_indexes[pointer: pointer + batch_size]
pointer += batch_size
batch_data_dict = {}
batch_data_dict['audio_name'] = \
self.data_dict['audio_name'][batch_audio_indexes]
batch_feature = self.data_dict['feature'][batch_audio_indexes]
batch_feature = self.transform(batch_feature)
batch_data_dict['feature'] = batch_feature
sparse_target = self.data_dict['target'][batch_audio_indexes]
batch_data_dict['target'] = sparse_to_categorical(
sparse_target, self.in_domain_classes_num)
yield batch_data_dict
def generate_validate(self, data_type, source, max_iteration=None):
'''Generate mini-batch data for training.
Args:
data_type: 'train' | 'validate'
source: 'a' | 'b' | 'c'
max_iteration: int, maximum iteration to validate to speed up validation
Returns:
batch_data_dict: dict containing audio_name, feature and target
'''
batch_size = self.batch_size
if data_type == 'train':
audio_indexes = np.array(self.train_audio_indexes)
elif data_type == 'validate':
audio_indexes = np.array(self.validate_audio_indexes)
else:
raise Exception('Incorrect argument!')
audio_indexes = self.get_source_indexes(
audio_indexes, self.data_dict, source)
iteration = 0
pointer = 0
while True:
if iteration == max_iteration:
break
# Reset pointer
if pointer >= len(audio_indexes):
break
# Get batch audio_indexes
batch_audio_indexes = audio_indexes[pointer: pointer + batch_size]
pointer += batch_size
iteration += 1
batch_data_dict = {}
batch_data_dict['audio_name'] = \
self.data_dict['audio_name'][batch_audio_indexes]
batch_feature = self.data_dict['feature'][batch_audio_indexes]
batch_feature = self.transform(batch_feature)
batch_data_dict['feature'] = batch_feature
sparse_target = self.data_dict['target'][batch_audio_indexes]
batch_data_dict['target'] = sparse_to_categorical(
sparse_target, self.all_classes_num)
yield batch_data_dict
def generate_train(self):
'''Generate mini-batch data for training.
Returns:
batch_data_dict: dict containing audio_name, feature and target
'''
while True:
# Reset pointer
if self.pointer >= len(self.train_audio_indexes):
self.pointer = 0
self.random_state.shuffle(self.train_audio_indexes)
# Get batch audio_indexes
batch_audio_indexes = self.train_audio_indexes[self.pointer:self.
pointer +
self.batch_size]
self.pointer += self.batch_size
batch_data_dict = {}
batch_data_dict['audio_name'] = \
self.data_dict['audio_name'][batch_audio_indexes]
batch_feature = self.data_dict['feature'][batch_audio_indexes]
batch_feature = self.transform(batch_feature)
batch_data_dict['feature'] = batch_feature
batch_feature_gamm = self.data_dict['feature_gamm'][
batch_audio_indexes]
batch_feature_gamm = self.transform_gamm(batch_feature_gamm)
batch_data_dict['feature_gamm'] = batch_feature_gamm
batch_feature_mfcc = self.data_dict['feature_mfcc'][
batch_audio_indexes]
batch_feature_mfcc = self.transform_mfcc(batch_feature_mfcc)
batch_data_dict['feature_mfcc'] = batch_feature_mfcc
batch_feature_panns = self.data_dict['feature_panns'][
batch_audio_indexes]
batch_feature_panns = self.transform_panns(batch_feature_panns)
batch_data_dict['feature_panns'] = batch_feature_panns
sparse_target = self.data_dict['target'][batch_audio_indexes]
batch_data_dict['target'] = sparse_to_categorical(
sparse_target, self.in_domain_classes_num)
yield batch_data_dict
def generate_train(self):
'''Generate mini-batch data for training.
Returns:
batch_data_dict: dict containing audio_name, feature and target
'''
batch_size = self.batch_size
audio_indexes = np.array(self.evaluation_audio_indexes)
self.random_state.shuffle(audio_indexes)
pointer = 0
while True:
# Reset pointer
if pointer >= len(audio_indexes):
pointer = 0
self.random_state.shuffle(audio_indexes)
# Get batch audio_indexes
batch_audio_indexes = audio_indexes[pointer:pointer + batch_size]
pointer += batch_size
batch_data_dict = {}
batch_data_dict_left = {}
batch_data_dict_right = {}
batch_data_dict_side = {}
batch_data_dict_harmonic = {}
batch_data_dict_percussive = {}
batch_data_dict['audio_name'] = \
self.data_dict['audio_name'][batch_audio_indexes]
batch_data_dict_left['audio_name'] = \
self.data_dict_left['audio_name'][batch_audio_indexes]
batch_data_dict_right['audio_name'] = \
self.data_dict_right['audio_name'][batch_audio_indexes]
batch_data_dict_side['audio_name'] = \
self.data_dict_side['audio_name'][batch_audio_indexes]
batch_data_dict_harmonic['audio_name'] = \
self.data_dict_harmonic['audio_name'][batch_audio_indexes]
batch_data_dict_percussive['audio_name'] = \
self.data_dict_percussive['audio_name'][batch_audio_indexes]
batch_feature = self.data_dict['feature'][batch_audio_indexes]
batch_feature = self.transform(batch_feature)
batch_data_dict['feature'] = batch_feature
batch_feature_left = self.data_dict_left['feature_left'][
batch_audio_indexes]
batch_feature_left = self.transform_left(batch_feature_left)
batch_data_dict_left['feature_left'] = batch_feature_left
batch_feature_right = self.data_dict_right['feature_right'][
batch_audio_indexes]
batch_feature_right = self.transform_right(batch_feature_right)
batch_data_dict_right['feature_right'] = batch_feature_right
batch_feature_side = self.data_dict_side['feature_side'][
batch_audio_indexes]
batch_feature_side = self.transform_side(batch_feature_side)
batch_data_dict_side['feature_side'] = batch_feature_side
batch_feature_harmonic = self.data_dict_harmonic[
'feature_harmonic'][batch_audio_indexes]
batch_feature_harmonic = self.transform_harmonic(
batch_feature_harmonic)
batch_data_dict_harmonic[
'feature_harmonic'] = batch_feature_harmonic
batch_feature_percussive = self.data_dict_percussive[
'feature_percussive'][batch_audio_indexes]
batch_feature_percussive = self.transform_percussive(
batch_feature_percussive)
batch_data_dict_percussive[
'feature_percussive'] = batch_feature_percussive
sparse_target = self.data_dict['target'][batch_audio_indexes]
batch_data_dict['target'] = sparse_to_categorical(
sparse_target, self.in_domain_classes_num)
yield batch_data_dict, batch_data_dict_left, batch_data_dict_right, batch_data_dict_side, batch_data_dict_harmonic, batch_data_dict_percussive