Raga_Model.py

import json
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow.keras as keras
import matplotlib.pyplot as plt


DATA_PATH = "4_raga_mfcc.json"


def load_data(data_path):
    """Loads training dataset from json file.

        :param data_path (str): Path to json file containing data
        :return X (ndarray): Inputs
        :return y (ndarray): Targets
    """

    with open(data_path, "r") as fp:
        data = json.load(fp)

    X = np.array(data["mfcc"])
    y = np.array(data["labels"])
    return X, y

def build_model(input_shape):
    """Generates CNN model

    :param input_shape (tuple): Shape of input set
    :return model: CNN model
    """

    # build network topology
    model = keras.Sequential()

    # 1st conv layer
    model.add(keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
    model.add(keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same'))
    model.add(keras.layers.BatchNormalization())

    # 2nd conv layer
    model.add(keras.layers.Conv2D(32, (3, 3), activation='relu'))
    model.add(keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding='same'))
    model.add(keras.layers.BatchNormalization())

    # 3rd conv layer
    model.add(keras.layers.Conv2D(32, (2, 2), activation='relu'))
    model.add(keras.layers.MaxPooling2D((2, 2), strides=(2, 2), padding='same'))
    model.add(keras.layers.BatchNormalization())

    # flatten output and feed it into dense layer
    model.add(keras.layers.Flatten())
    model.add(keras.layers.Dense(64, activation='relu'))
    model.add(keras.layers.Dropout(0.3))

    # output layer
    model.add(keras.layers.Dense(10, activation='softmax'))

    return model

def prepare_datasets(test_size, validation_size):
    """Loads data and splits it into train, validation and test sets.

    :param test_size (float): Value in [0, 1] indicating percentage of data set to allocate to test split
    :param validation_size (float): Value in [0, 1] indicating percentage of train set to allocate to validation split

    :return X_train (ndarray): Input training set
    :return X_validation (ndarray): Input validation set
    :return X_test (ndarray): Input test set
    :return y_train (ndarray): Target training set
    :return y_validation (ndarray): Target validation set
    :return y_test (ndarray): Target test set
    """

    # load data
    X, y = load_data(DATA_PATH)

    # create train, validation and test split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
    X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size)

    # add an axis to input sets
    X_train = X_train[..., np.newaxis]
    X_validation = X_validation[..., np.newaxis]
    X_test = X_test[..., np.newaxis]

    return X_train, X_validation, X_test, y_train, y_validation, y_test

def predict(model, X, y):
    """Predict a single sample using the trained model

    :param model: Trained classifier
    :param X: Input data
    :param y (int): Target
    """

    # add a dimension to input data for sample - model.predict() expects a 4d array in this case
    X = X[np.newaxis, ...] # array shape (1, 130, 13, 1)

    # perform prediction
    prediction = model.predict(X)

    # get index with max value
    predicted_index = np.argmax(prediction, axis=1)

    print("Target: {}, Predicted label: {}".format(y, predicted_index))


if __name__ == "__main__":

    # get train, validation, test splits
    X_train, X_validation, X_test, y_train, y_validation, y_test = prepare_datasets(0.25, 0.2)

    # create network
    input_shape = (X_train.shape[1], X_train.shape[2], 1)
    model = build_model(input_shape)

    # compile model
    optimiser = keras.optimizers.Adam(learning_rate=0.0001)
    model.compile(optimizer=optimiser,
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])

    model.summary()

    # train model
    history = model.fit(X_train, y_train, validation_data=(X_validation, y_validation), batch_size=32, epochs=30)
    model.save("13Mfcc_4_RAGA_MODEL.keras")


    # evaluate model on test set
    test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
    print("Accuracy on test set is:{}".format(test_acc))