def MLP(file_path: str):
FLATTEN = True
LOAD_MODEL = "ML"
NUM_LABELS = len(CLASS_LABELS)
SVM = False
'''
x_train, x_test, y_train, y_test = get_data(DATA_PATH, class_labels = CLASS_LABELS, flatten = FLATTEN, _svm = SVM)
model = MLP_Model()
print('-------------------------------- Start --------------------------------')
model.train(x_train, y_train)
model.evaluate(x_test, y_test)
model.save_model("MLP1")
result, result_prob = model.recognize_one(get_feature(file_path, flatten = FLATTEN))
print('Recogntion: ', result)
print('Probability: ', result_prob)
Radar(result_prob, CLASS_LABELS, NUM_LABELS)
print('---------------------------------- End ----------------------------------')
'''
'''
---------------------------- 加载模型 ----------------------------
'''
model = load_model(model_name = "MLP1", load_model = LOAD_MODEL)
result = model.predict(np.array([get_feature(file_path, flatten = FLATTEN)]))
result_prob = model.predict_proba(np.array([get_feature(file_path, flatten = FLATTEN)]))[0]
print('Recogntion: ', CLASS_LABELS[result[0]])
print('Probability: ', result_prob)
Radar(result_prob, CLASS_LABELS, NUM_LABELS)
def SVM(file_path: str):
FLATTEN = True
LOAD_MODEL = "ML"
NUM_LABELS = len(CLASS_LABELS)
SVM = True
'''
x_train, x_test, y_train, y_test = get_data(DATA_PATH, mfcc_len = 48, class_labels = CLASS_LABELS, flatten = FLATTEN, _svm = SVM)
model = SVM_Model()
print('-------------------------------- Start --------------------------------')
model.train(x_train, y_train)
model.evaluate(x_test, y_test)
model.save_model("SVM1")
result, result_prob = model.recognize_one(get_feature_svm(file_path, mfcc_len = 48))
print('Recogntion: ', result)
print('Probability: ', result_prob)
Radar(result_prob, CLASS_LABELS, NUM_LABELS)
print('---------------------------------- End ----------------------------------')
'''
'''
---------------------------- 加载模型 ----------------------------
'''
model = load_model(model_name = "SVM1", load_model = LOAD_MODEL)
result = model.predict(np.array([get_feature_svm(file_path, mfcc_len = 48)]))
result_prob = model.predict_proba(np.array([get_feature_svm(file_path, mfcc_len = 48)]))[0]
print('Recogntion: ', result)
print('Probability: ', result_prob)
Radar(result_prob, CLASS_LABELS, NUM_LABELS)#SVM("/Users/cemergin/PycharmProjects/audio-classification/data/03a01Nc.wav"
def LSTM(file_path: str):
FLATTEN = False
LOAD_MODEL = 'DNN'
NUM_LABELS = len(CLASS_LABELS)
SVM = False
'''
x_train, x_test, y_train, y_test = get_data(DATA_PATH, class_labels = CLASS_LABELS, flatten = FLATTEN, _svm = SVM)
y_train = np_utils.to_categorical(y_train)
y_test_train = np_utils.to_categorical(y_test)
print('-------------------------------- LSTM Start --------------------------------')
model = LSTM_Model(input_shape = x_train[0].shape, num_classes = NUM_LABELS)
model.train(x_train, y_train, x_test, y_test_train, n_epochs = 100)
model.evaluate(x_test, y_test)
model.save_model("LSTM1")
result, result_prob = model.recognize_one(get_feature(file_path))
print('Recogntion: ', CLASS_LABELS[result])
print('Probability: ', result_prob)
Radar(result_prob, CLASS_LABELS, NUM_LABELS)
print('--------------------------------- LSTM End ---------------------------------')
'''
'''
---------------------------- 加载模型 ----------------------------
'''
# 加载json
model = load_model(model_name = "LSTM1", load_model = LOAD_MODEL)
result = np.argmax(model.predict(np.array([get_feature(file_path)])))
result_prob = model.predict(np.array([get_feature(file_path)]))[0]
print('Recogntion: ', CLASS_LABELS[result])
print('Probability: ', result_prob)
Radar(result_prob, CLASS_LABELS, NUM_LABELS)
def CNN(file_path: str):
FLATTEN = False
LOAD_MODEL = "DNN"
NUM_LABELS = len(CLASS_LABELS)
SVM = False
'''
x_train, x_test, y_train, y_test = get_data(DATA_PATH, class_labels = CLASS_LABELS, flatten = FLATTEN, _svm = SVM)
y_train = np_utils.to_categorical(y_train)
y_test_train = np_utils.to_categorical(y_test)
in_shape = x_train[0].shape
x_train = x_train.reshape(x_train.shape[0], in_shape[0], in_shape[1], 1)
x_test = x_test.reshape(x_test.shape[0], in_shape[0], in_shape[1], 1)
print('-------------------------------- CNN Start --------------------------------')
model = CNN_Model(input_shape = x_train[0].shape, num_classes = NUM_LABELS)
model.train(x_train, y_train, x_test, y_test_train, n_epochs = 50)
model.evaluate(x_test, y_test)
model.save_model("CNN1")
print('-------------------------------- CNN Start --------------------------------')
'''
'''
---------------------------- 加载模型 ----------------------------
'''
# 加载json
model = load_model(model_name = "CNN1", load_model = LOAD_MODEL)
test = np.array([get_feature(file_path, flatten = FLATTEN)])
in_shape = test[0].shape
test = test.reshape(test.shape[0], in_shape[0], in_shape[1], 1)
result = np.argmax(model.predict(test))
result_prob = model.predict(test)[0]
print('Recogntion: ', CLASS_LABELS[result])
print('Probability: ', result_prob)
Radar(result_prob, CLASS_LABELS, NUM_LABELS)
def __init__(self, tf_idf_model_file, complaints_with_sentiment_file, narrative_preprocessed_file):
print("Loading tf_idf_model...")
self.tf_idf_vectorizer = load_model(tf_idf_model_file)
self.complaints_with_sentiment_file = complaints_with_sentiment_file
self.narrative_preprocessed_file = narrative_preprocessed_file