def create_overview(files,
start,
end,
num_files_per_group=8,
type_output='B',
to_mp3=False,
artist='Glossika',
album='Glossika Training',
prefix=''):
if num_files_per_group == 0:
num_files_per_group = get_num_files(len(files))
result = []
old_start = start
old_end = end
start = 0
end = old_end - old_start + 1
for i in range(math.ceil((end - start + 1) / num_files_per_group)):
sub_list = files[start:min(start + num_files_per_group, end)][:]
result = result + sub_list + sub_list
start = start + num_files_per_group
type_num = '1' if type_output == 'B' else '2'
dir_name = OUTPUT_ALL + '(wav)/' + sub_directory()
name = _get_name(prefix, type_num, dir_name, old_start, old_end)
makedir(dir_name)
make_track(result, name)
convert_mp3(to_mp3, name, dir_name.replace('wav', 'mp3'), artist, album)
print('Shuffle Files: Done')
def create_accent_grammar(list_of_tracks,
num_files_per_group,
num_plays,
num_copies=1,
prefix='',
to_mp3=False,
artist='Accent',
album='Accent Training',
shuffled='',
grammar=False):
type_file = 'Accent' if not grammar else 'Grammar'
artist = 'Accent' if not grammar else 'Grammar'
album = 'Accent Training' if not grammar else 'Grammar Training'
input_files = []
for track in list_of_tracks:
sub_input_files = []
for f in sorted(os.listdir(type_file + '/' + type_file + 'EN/')):
if not (f[-3:] == 'mp3' or f[-3:] == 'wav'): continue
if grammar: u = f[1:4]
else: u = f[6:9]
if u == '%03d' % (track):
sub_input_files.append(type_file + '/' + type_file + 'EN/' + f)
if shuffled == "group":
shuffle(sub_input_files)
input_files.extend(sub_input_files)
if shuffled == "all": shuffle(input_files)
if prefix == '' or prefix == None:
prefix = get_prefix(list_of_tracks, grammar)
if num_files_per_group == 0:
num_files_per_group = get_num_files(len(input_files))
generate_from_list_of_files(input_files,
type_file + '/' + type_file + 'VN/', type_file,
False)
files = [
'output' + type_file + '/' + f.split('/')[-1][:-6] + type_file +
f.split('/')[-1][-4:] for f in input_files
]
# Shuffle files
for copies in range(int(num_copies)):
result = shuffle_track(files, num_plays, num_files_per_group)
dir_name = OUTPUT_ALL + '(wav)/' + sub_directory()
makedir(dir_name)
name = get_name(dir_name, prefix, num_plays)
make_track(result, name)
convert_mp3(to_mp3, name, dir_name.replace("wav", "mp3"), artist,
album)
rmtree('output' + type_file)
def create_review(files,
start,
end,
num_plays,
num_files_per_group,
log=False,
log_tracks=0,
num_copies=1,
to_mp3=False,
artist='Glossika',
album='Glossika Training',
name=None):
'''
Combine files to make them useful for Glossika Traning
numPlays: each track is played numPlays times
numFilesPerTrack: the number of tracks per playlist
start: start track number
end: end track number
log: set True to print debug information
logTracks: use in debug mode
numCopies: number of copies of output file
toMP3: set True to convert output file to .mp3
artist:
album: if toMP3=True, use these values to set meta information
'''
makedir('outputB')
# if shuffled == 'all': shuffle(files)
if num_files_per_group == 0:
num_files_per_group = get_num_files(len(input_files))
# Shuffle files
prefix = 'Review_%04d_%04d' % (start, end)
if name is not None and not name == '':
prefix = name
for copies in range(int(num_copies)):
result = shuffle_track(files, num_plays, num_files_per_group)
dir_name = OUTPUT_ALL + '(wav)/' + sub_directory()
makedir(dir_name)
name = get_name(dir_name, prefix, num_plays)
make_track(result, name)
convert_mp3(to_mp3, name, dir_name.replace("wav", "mp3"), artist,
album)
print_log(log, log_tracks, result)
print('Shuffle Files: Done')
finetune_model = utils.build_finetune_model(base_model,
dropout=args.dropout,
fc_layers=FC_LAYERS,
num_classes=len(class_list))
if args.continue_training:
finetune_model.load_weights("./checkpoints/" + args.model +
"_model_weights.h5")
print("load success!")
adam = Adam(lr=0.00001)
finetune_model.compile(adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
num_train_images = utils.get_num_files(TRAIN_DIR)
num_val_images = utils.get_num_files(VAL_DIR)
def lr_decay(epoch):
if epoch % 20 == 0 and epoch != 0:
lr = K.get_value(model.optimizer.lr)
K.set_value(model.optimizer.lr, lr / 2)
print("LR changed to {}".format(lr / 2))
return K.get_value(model.optimizer.lr)
learning_rate_schedule = LearningRateScheduler(lr_decay)
filepath = "./checkpoints/" + args.model + "_model_weights.h5"
checkpoint = ModelCheckpoint(filepath,
monitor=["acc"],
verbose=1,
finetune_model = utils.build_finetune_model(base_model,
dropout=args.dropout,
fc_layers=FC_LAYERS,
num_classes=len(class_list))
if args.continue_training:
finetune_model.load_weights("./checkpoints/" + args.model +
"_model_weights.h5")
adam = Adam(lr=0.00001)
finetune_model.compile(adam,
loss='categorical_crossentropy',
metrics=['accuracy'])
num_train_images = utils.get_num_files(BASE_IMG_DIR + TRAIN_DIR)
num_val_images = utils.get_num_files(BASE_IMG_DIR + VAL_DIR)
def lr_decay(epoch):
if epoch % 20 == 0 and epoch != 0:
lr = K.get_value(model.optimizer.lr)
K.set_value(model.optimizer.lr, lr / 2)
print("LR changed to {}".format(lr / 2))
return K.get_value(model.optimizer.lr)
learning_rate_schedule = LearningRateScheduler(lr_decay)
filepath = "./checkpoints/" + args.model + "_model_weights.h5"
checkpoint = ModelCheckpoint(filepath,
monitor=["acc"],
verbose=1,