resolution_factor = int(input('Resolution Factor (recommended=12):')) #24: 1/8 Resolution, 12: 1/16 Resolution, 6: 1/32 Resolution
#Preprocessing: Get highest and lowest notes + maximum midi_ticks overall midi files
chord_lowest_note, chord_highest_note, chord_ticks = data_utils_train.getNoteRangeAndTicks(chord_train_files, res_factor=resolution_factor)
mel_lowest_note, mel_highest_note, mel_ticks = data_utils_train.getNoteRangeAndTicks(mel_train_files, res_factor=resolution_factor)
#Create Piano Roll Representation of the MIDI files. Return: 3-dimensional array or shape (num_midi_files, maximum num of ticks, note range)
chord_roll = data_utils_train.fromMidiCreatePianoRoll(chord_train_files, chord_ticks, chord_lowest_note, chord_highest_note,
res_factor=resolution_factor)
mel_roll = data_utils_train.fromMidiCreatePianoRoll(mel_train_files, mel_ticks, mel_lowest_note, mel_highest_note,
res_factor=resolution_factor)
#Double each chord_roll and mel_roll. Preprocessing to create Input and Target Vector for Network
double_chord_roll = data_utils_train.doubleRoll(chord_roll)
double_mel_roll = data_utils_train.doubleRoll(mel_roll)
#Create Network Inputs:
#Input_data Shape: (num of training samples, num of timesteps=sequence length, note range)
#Target_data Shape: (num of training samples, note range)
input_data, target_data = data_utils_train.createNetInputs(double_chord_roll, double_mel_roll, seq_length=chord_ticks)
input_data = input_data.astype(np.bool)
target_data = target_data.astype(np.bool)
input_dim = input_data.shape[2]
output_dim = target_data.shape[1]
print()
#Create Piano Roll Representation of the MIDI files. Return: 3-dimensional array or shape (num_midi_files, maximum num of ticks, note range)
chord_roll = data_utils_train.fromMidiCreatePianoRoll(
chord_train_files,
chord_ticks,
chord_lowest_note,
chord_highest_note,
res_factor=resolution_factor)
mel_roll = data_utils_train.fromMidiCreatePianoRoll(
mel_train_files,
mel_ticks,
mel_lowest_note,
mel_highest_note,
res_factor=resolution_factor)
#Double each chord_roll and mel_roll. Preprocessing to create Input and Target Vector for Network
double_chord_roll = data_utils_train.doubleRoll(chord_roll)
double_mel_roll = data_utils_train.doubleRoll(mel_roll)
#Create Network Inputs:
#Input_data Shape: (num of training samples, num of timesteps=sequence length, note range)
#Target_data Shape: (num of training samples, note range)
input_data, target_data = data_utils_train.createNetInputs(
double_chord_roll, double_mel_roll, seq_length=chord_ticks)
input_data = input_data.astype(np.bool)
target_data = target_data.astype(np.bool)
input_dim = input_data.shape[2]
output_dim = target_data.shape[1]
print()
print("For how many epochs do you wanna train?")
# remove old test input
old_test = glob.glob("%s*.mid" % (composition_dir))
if len(old_test) > 0:
for i in old_test:
os.remove(i)
mel_files = glob.glob("%s*.mid" % (mel_dir))
composition_files = []
for i in range(len(mel_files)):
composition_files.append('%d' % (i + 1))
mel_lowest_note = 60
mel_roll = roll(train)
double_mel_roll = data_utils_train.doubleRoll(mel_roll)
test_data = data_utils_compose.createNetInputs(double_mel_roll, 256)
batch_size = 128
thresh = float(input('Threshold (recommended ~ 0.1):'))
print("Loading Model and Weights...")
#Load model file
model_dir = 'models/models json/'
model_files = listdir(model_dir)
print("Choose a file for the json model file:")
print("---------------------------------------")
for i, file in enumerate(model_files):
print(str(i) + " : " + file)
print("---------------------------------------")
file_number_model = int(
