def main():
# Case of loading pre-extracted features and/or pre-trained feature
pre_trained_model_file = ""
# Set Conditions
run_feature_extraction = False
run_training = True
# Instantiate mgc main class
MGC = MusicGenreClassification(AudioDatasetMaker,
AudioFeatureExtraction,
Classifier,
music_dataset_path="processed_data",
setting_file="config/master_config.ini")
# Make label from genre names in processed_music_data
MGC.make_label()
# Feature extraction/ Load pre-extracted feature
if run_feature_extraction is True:
# Apply feature extraction
directory_files_feature_dict, label_list = MGC.feature_extraction()
print("Cleaning extracted feature...")
# Apply data processing to extracted feature
expert_feature_array, mel_spectrogram_array, label_array = MGC.dict2array(directory_files_feature_dict,
label_list,
MGC.cfg.normalize)
# Save extracted data
expert_feature_array, mel_spectrogram_array, label_array = MGC.save_data(expert_feature_array,
mel_spectrogram_array,
label_array)
print("Saved feature!")
# Load pre-extracted feature. Train/Test separation
if MGC.CLF.selected_classifier == 'cnn' or MGC.CLF.selected_classifier == 'resnet' or MGC.CLF.selected_classifier == 'rnn':
data_array, label_array = DataProcess.read_data_from_array("backend/feature/mel_spectrogram")
train_data, test_data, train_label, test_label = MGC.make_dataset_from_array(data_array, label_array)
else:
data_array, label_array = DataProcess.read_data_from_array("backend/feature/expert")
train_data, test_data, train_label, test_label = MGC.make_dataset_from_array(data_array, label_array)
# Run training or load pre-trained model
print("Training model...")
model = MGC.training(run_training,
train_data,
train_label,
pre_trained_model_file,
output_model_directory_path="backend/model")
print("Trained model!")
# Write metadata json file
metadata = {"words": [label for label in os.listdir('processed_data')]}
FileUtil.dict2json(metadata, 'backend/model/metadata.json')
# Test model performance
loss, accuracy = MGC.test(model, test_data, test_label)
def main():
# Case of loading pre-extracted features and/or pre-trained feature
pre_trained_model_file = ""
# Set Conditions
run_feature_extraction = True
run_training = True
# Instantiate mgc main class
MGC = MusicGenreClassification(AudioDatasetMaker, AudioFeatureExtraction, Classifier,
music_dataset_path="processed_music_data_small",
setting_file="config/master_config.ini")
# Make label from genre names in processed_music_data
MGC.make_label()
# Feature extraction/ Load pre-extracted feature
if run_feature_extraction is True:
# Apply feature extraction
directory_files_feature_dict, label_list = MGC.feature_extraction()
# Apply data processing to extracted feature
expert_feature_array, mel_spectrogram_array, label_array = MGC.dict2array(directory_files_feature_dict,
label_list,
MGC.cfg.normalize)
# Save extracted data
expert_feature_array, mel_spectrogram_array, label_array = MGC.save_data(expert_feature_array, mel_spectrogram_array, label_array)
# Load pre-extracted feature. Train/Test separation
if MGC.CLF.selected_classifier == 'cnn' or MGC.CLF.selected_classifier == 'resnet' or MGC.CLF.selected_classifier == 'rnn':
data_array, label_array = DataProcess.read_data_from_array("backend/feature/mel_spectrogram")
train_data, test_data, train_label, test_label = MGC.make_dataset_from_array(data_array, label_array)
else:
data_array, label_array = DataProcess.read_data_from_array("backend/feature/expert")
train_data, test_data, train_label, test_label = MGC.make_dataset_from_array(data_array, label_array)
# Run training or load pre-trained model
model = MGC.training(run_training, train_data, train_label, pre_trained_model_file, output_model_directory_path="backend/model")
# Test model performance
accuracy = MGC.test(model, test_data, test_label)
print("Test accuracy is {0}% \n".format(accuracy))