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Patrick authored99033d7d
import librosa.feature
import pandas as pd
import numpy as np
from pathlib import Path
from os import chdir
import os
import csv
from tensorflow import keras
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from keras import models
from keras import layers
import matplotlib.pyplot as plt
from sklearn.metrics import classification_report
import constants
import json
from openpyxl import Workbook
import time
def create_csv_header():
header = 'filename '
for i in range(constants.MFCC_FEATURE_START, constants.MFCC_FEATURE_END):
header += f' mfcc{i}'
header += ' label'
header = header.split()
file = open(constants.FEATURES_CSV_NAME, 'w', newline='')
with file:
writer = csv.writer(file)
writer.writerow(header)
def extract_features(trainingDataDir, trainingDataSubDirs):
create_csv_header()
# Looping over every file inside the subdirectories for feature extraction
for trainingDataSubDir in trainingDataSubDirs:
for fileName in os.listdir(trainingDataDir/f'{trainingDataSubDir}'):
if fileName.endswith(".wav"):
audioFile = trainingDataDir/f'{trainingDataSubDir}/{fileName}'
print("Extracting Features from Directory "+trainingDataSubDir+" and file "+audioFile.name)
y, sr = librosa.load(audioFile, mono=True)
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=(constants.MFCC_FEATURE_END - constants.MFCC_FEATURE_START))
to_append = f'{audioFile.name}'
for g in mfcc:
to_append += f' {np.mean(g)}'
if trainingDataSubDir == constants.CAR:
to_append += f' {constants.LIGHT_WEIGHT}'
elif trainingDataSubDir == constants.BUS:
to_append += f' {constants.MEDIUM_WEIGHT}'
elif trainingDataSubDir == constants.TRUCK:
to_append += f' {constants.HEAVY_WEIGHT}'
elif trainingDataSubDir == constants.MOTORCYCLE:
to_append += f' {constants.TWO_WHEELED}'
elif trainingDataSubDir == constants.TRAM:
to_append += f' {constants.RAIL_BOUND}'
file = open(constants.FEATURES_CSV_NAME, 'a', newline='')
with file:
writer = csv.writer(file)
writer.writerow(to_append.split())
def preprocessing_csv_data():
print("Reading Features... ")
data = pd.read_csv(constants.FEATURES_CSV_NAME)
# Dropping unnecessary columns (Column Filename is dropped)
data = data.drop(['filename'], axis=1)
return data
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def encode_labels(data):
# Extracting classes/label column as y from csv and converting string labels to numbers using LabelEncoder
audio_list = data.iloc[:, -1]
encoder = LabelEncoder()
target_labels = encoder.fit_transform(audio_list)
return target_labels, encoder
def normalize_data(data):
# normalizing - Extracting Remaining Columns as X and normalizing them to a common scale
scaler = StandardScaler()
X = scaler.fit_transform(np.array(data.iloc[:, :-1], dtype=float))
return X
def train_test_data_split(X, y):
# splitting of dataset into train and test dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
return X_train, X_test, y_train, y_test
def create_and_compile_model(X, hidden_layer_dimensions):
print("Creating a Model")
# creating a model
model = models.Sequential()
for i, layer_dimension in enumerate(hidden_layer_dimensions):
if i == 0:
model.add(layers.Dense(layer_dimension, activation=constants.ACTIVATION_RELU, input_shape=(X.shape[1],)))
else:
model.add(layers.Dense(layer_dimension, activation=constants.ACTIVATION_RELU))
model.add(layers.Dense(constants.OUTPUT_LAYER_DIMENSIONS, activation=constants.ACTIVATION_SOFTMAX))
print("Compiling a Model")
model.compile(optimizer= constants.OPTIMIZER_ADAM, loss= constants.LOSS_FUNCTION_SPARSE, metrics=[constants.ACCURACY_METRICS])
return model
def train_and_save_model(model, X_train, y_train, X_test, y_test):
#logdir = constants.LOG_DIR_PATH
#tensorboard_callback = keras.callbacks.TensorBoard(log_dir=logdir)
print("Start Training...")
#history = model.fit(X_train, y_train, epochs=35, validation_data=(X_test, y_test), callbacks=[tensorboard_callback])
history = model.fit(X_train, y_train, epochs=35, validation_data=(X_test, y_test))
# Saving the trained model to avoid re-training
#model.save(constants.TRAINED_MODEL)
return history
def model_predict(model, X_test, y_test):
test_loss, test_acc = model.evaluate(X_test, y_test)
print('test_acc: ', test_acc)
y_predicted = np.argmax(model.predict(X_test), axis=-1)
accuracy = np.mean(y_test == y_predicted)
print(accuracy)
return accuracy
def predict(model, X_test, y_test):
print("Predictions.....")
predictions = np.argmax(model.predict(X_test), axis=-1)
target_names = [constants.LIGHT_WEIGHT, constants.MEDIUM_WEIGHT, constants.HEAVY_WEIGHT,constants.TWO_WHEELED, constants.RAIL_BOUND]
print(classification_report(y_test, predictions, target_names=target_names))
def plot_model_accuracy(history):
# Plot graph Model Accuracy
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model Accuracy')
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plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper left')
plt.show()
def plot_model_loss(history):
# Plot graph Model Loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper right')
plt.show()
def construct_and_apply_network(hidden_layer_dimensions, data, target_labels, encoder, X):
X_train, X_test, y_train, y_test = train_test_data_split(X, target_labels)
model = create_and_compile_model(X, hidden_layer_dimensions)
history = train_and_save_model(model, X_train, y_train, X_test, y_test)
predict(model, X_test, y_test)
accuracy = model_predict(model, X_test, y_test)
#plot_model_accuracy(history)
#plot_model_loss(history)
return accuracy
if __name__ == "__main__":
# Changing Directory to Training Dataset Folder
chdir(constants.TRAINING_DATA_DIRECTORY_NAME)
trainingDataDir = Path.cwd()
trainingDataSubDirs = os.listdir(trainingDataDir)
chdir("..")
if os.path.isfile(constants.FEATURES_CSV_NAME):
print("already exists")
else:
extract_features(trainingDataDir, trainingDataSubDirs)
data = preprocessing_csv_data()
target_labels, encoder = encode_labels(data)
X = normalize_data(data)
neurons_increment_by = 8
start_neuron_value = 8
max_neuron_value = 32
hidden_layers = 3
hidden_layer_dimensions = []
book = Workbook()
sheet = book.active
# loop_count = int((max_neuron_value / neurons_increment_by) * 4)
row_counter = 0
for i in range(hidden_layers):
hidden_layer_dimensions.append(0)
for j in range(start_neuron_value, (max_neuron_value + 1), neurons_increment_by):
row_counter += 1
hidden_layer_dimensions[i] = j
start = time.time()
new_accuracy = construct_and_apply_network(hidden_layer_dimensions, data, target_labels, encoder, X)
end = time.time()
elapsed_time = end - start
sheet.cell(row=(row_counter), column=1).value = hidden_layer_dimensions.__str__()
sheet.cell(row=(row_counter), column=2).value = new_accuracy
sheet.cell(row=(row_counter), column=3).value = elapsed_time
book.save("sample.xlsx")