Music_Audio_Analysis_and_Recommendation.md

import json
import os
import math
import librosa
 
DATASET_PATH = "/content/drive/MyDrive/genres"
JSON_PATH = "data_10.json"
SAMPLE_RATE = 22050
TRACK_DURATION = 30 # measured in seconds
SAMPLES_PER_TRACK = SAMPLE_RATE * TRACK_DURATION
 
 
def save_mfcc(dataset_path, json_path, num_mfcc=13, n_fft=2048, hop_length=512, num_segments=5):
    """Extracts MFCCs from music dataset and saves them into a json file along witgh genre labels.
        :param dataset_path (str): Path to dataset
        :param json_path (str): Path to json file used to save MFCCs
        :param num_mfcc (int): Number of coefficients to extract
        :param n_fft (int): Interval we consider to apply FFT. Measured in # of samples
        :param hop_length (int): Sliding window for FFT. Measured in # of samples
        :param: num_segments (int): Number of segments we want to divide sample tracks into
        :return:
        """
 
    # dictionary to store mapping, labels, and MFCCs
    data = {
        "mapping": [],
        "labels": [],
        "mfcc": []
    }
 
    samples_per_segment = int(SAMPLES_PER_TRACK / num_segments)
    num_mfcc_vectors_per_segment = math.ceil(samples_per_segment / hop_length)
 
    # loop through all genre sub-folder
    for i, (dirpath, dirnames, filenames) in enumerate(os.walk(dataset_path)):
 
        # ensure we're processing a genre sub-folder level
        if dirpath is not dataset_path:
 
            # save genre label (i.e., sub-folder name) in the mapping
            semantic_label = dirpath.split("/")[-1]
            data["mapping"].append(semantic_label)
            print("\nProcessing: {}".format(semantic_label))
 
            # process all audio files in genre sub-dir
            for f in filenames:
 
        # load audio file
                file_path = os.path.join(dirpath, f)
                signal, sample_rate = librosa.load(file_path, sr=SAMPLE_RATE)
 
                # process all segments of audio file
                for d in range(num_segments):
 
                    # calculate start and finish sample for current segment
                    start = samples_per_segment * d
                    finish = start + samples_per_segment
 
                    # extract mfcc
                    mfcc = librosa.feature.mfcc(signal[start:finish], sample_rate, n_mfcc=num_mfcc, n_fft=n_fft, hop_length=hop_length)
                    mfcc = mfcc.T
 
                    # store only mfcc feature with expected number of vectors
                    if len(mfcc) == num_mfcc_vectors_per_segment:
                        data["mfcc"].append(mfcc.tolist())
                        data["labels"].append(i-1)
                        print("{}, segment:{}".format(file_path, d+1))
 
    # save MFCCs to json file
    with open(json_path, "w") as fp:
        json.dump(data, fp, indent=4)
        
        
if __name__ == "__main__":
    save_mfcc(DATASET_PATH, JSON_PATH, num_segments=10)

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Processing: hiphop
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Processing: classical
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import json
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow.keras as keras
import matplotlib.pyplot as plt

# path to json file that stores MFCCs and genre labels for each processed segment
DATA_PATH = "/content/data_10.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)

    # convert lists to numpy arrays
    X = np.array(data["mfcc"])
    y = np.array(data["labels"])

    print("Data succesfully loaded!")

    return  X, y

def plot_history(history):

      fig, axs = plt.subplots(2)

      # create accuracy sublpot
      axs[0].plot(history.history["accuracy"], label="train accuracy")
      axs[0].plot(history.history["val_accuracy"], label="test accuracy")
      axs[0].set_ylabel("Accuracy")
      axs[0].legend(loc="lower right")
      axs[0].set_title("Accuracy eval")

      # create error sublpot
      axs[1].plot(history.history["loss"], label="train error")
      axs[1].plot(history.history["val_loss"], label="test error")
      axs[1].set_ylabel("Error")
      axs[1].set_xlabel("Epoch")
      axs[1].legend(loc="upper right")
      axs[1].set_title("Error eval")

      plt.show()


if __name__ == "__main__":

    # load data
    X, y = load_data(DATA_PATH)

    # create train/test split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

    # build network topology
    model = keras.Sequential([

        # input layer
        keras.layers.Flatten(input_shape=(X.shape[1], X.shape[2])),

        # 1st dense layer
        keras.layers.Dense(512, activation='relu'),

        # 2nd dense layer
        keras.layers.Dense(256, activation='relu'),

        # 3rd dense layer
        keras.layers.Dense(64, activation='relu'),

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

    # 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_test, y_test), batch_size=32, epochs=50)
    plot_history(history)

Data succesfully loaded!
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten (Flatten)            (None, 1690)              0         
_________________________________________________________________
dense (Dense)                (None, 512)               865792    
_________________________________________________________________
dense_1 (Dense)              (None, 256)               131328    
_________________________________________________________________
dense_2 (Dense)              (None, 64)                16448     
_________________________________________________________________
dense_3 (Dense)              (None, 10)                650       
=================================================================
Total params: 1,014,218
Trainable params: 1,014,218
Non-trainable params: 0
_________________________________________________________________
Epoch 1/50
219/219 [==============================] - 6s 23ms/step - loss: 14.9953 - accuracy: 0.2576 - val_loss: 3.7401 - val_accuracy: 0.3655
Epoch 2/50
219/219 [==============================] - 4s 17ms/step - loss: 3.0190 - accuracy: 0.4233 - val_loss: 2.9667 - val_accuracy: 0.3865
Epoch 3/50
219/219 [==============================] - 5s 22ms/step - loss: 2.0844 - accuracy: 0.4923 - val_loss: 2.5540 - val_accuracy: 0.4328
Epoch 4/50
219/219 [==============================] - 5s 21ms/step - loss: 1.6175 - accuracy: 0.5605 - val_loss: 2.3279 - val_accuracy: 0.4525
Epoch 5/50
219/219 [==============================] - 5s 21ms/step - loss: 1.3194 - accuracy: 0.6150 - val_loss: 2.3461 - val_accuracy: 0.4622
Epoch 6/50
219/219 [==============================] - 5s 24ms/step - loss: 1.1166 - accuracy: 0.6550 - val_loss: 2.3113 - val_accuracy: 0.4628
Epoch 7/50
219/219 [==============================] - 5s 22ms/step - loss: 1.0102 - accuracy: 0.6845 - val_loss: 2.2045 - val_accuracy: 0.4882
Epoch 8/50
219/219 [==============================] - 5s 21ms/step - loss: 0.8334 - accuracy: 0.7355 - val_loss: 2.1482 - val_accuracy: 0.4842
Epoch 9/50
219/219 [==============================] - 5s 21ms/step - loss: 0.7378 - accuracy: 0.7669 - val_loss: 2.1706 - val_accuracy: 0.4858
Epoch 10/50
219/219 [==============================] - 4s 18ms/step - loss: 0.6690 - accuracy: 0.7815 - val_loss: 2.1967 - val_accuracy: 0.5005
Epoch 11/50
219/219 [==============================] - 5s 21ms/step - loss: 0.6045 - accuracy: 0.7966 - val_loss: 2.1471 - val_accuracy: 0.5135
Epoch 12/50
219/219 [==============================] - 4s 21ms/step - loss: 0.5035 - accuracy: 0.8303 - val_loss: 2.1265 - val_accuracy: 0.5038
Epoch 13/50
219/219 [==============================] - 4s 20ms/step - loss: 0.4113 - accuracy: 0.8634 - val_loss: 2.1273 - val_accuracy: 0.5195
Epoch 14/50
219/219 [==============================] - 5s 21ms/step - loss: 0.3854 - accuracy: 0.8732 - val_loss: 2.1707 - val_accuracy: 0.5192
Epoch 15/50
219/219 [==============================] - 4s 20ms/step - loss: 0.3742 - accuracy: 0.8809 - val_loss: 2.1721 - val_accuracy: 0.5395
Epoch 16/50
219/219 [==============================] - 5s 23ms/step - loss: 0.3318 - accuracy: 0.8865 - val_loss: 2.1275 - val_accuracy: 0.5175
Epoch 17/50
219/219 [==============================] - 5s 21ms/step - loss: 0.3086 - accuracy: 0.8939 - val_loss: 2.1870 - val_accuracy: 0.5485
Epoch 18/50
219/219 [==============================] - 4s 20ms/step - loss: 0.2746 - accuracy: 0.9068 - val_loss: 2.3332 - val_accuracy: 0.5315
Epoch 19/50
219/219 [==============================] - 3s 15ms/step - loss: 0.2366 - accuracy: 0.9240 - val_loss: 2.1780 - val_accuracy: 0.5475
Epoch 20/50
219/219 [==============================] - 3s 15ms/step - loss: 0.2223 - accuracy: 0.9344 - val_loss: 2.1687 - val_accuracy: 0.5589
Epoch 21/50
219/219 [==============================] - 3s 12ms/step - loss: 0.2078 - accuracy: 0.9387 - val_loss: 2.2930 - val_accuracy: 0.5552
Epoch 22/50
219/219 [==============================] - 4s 16ms/step - loss: 0.1992 - accuracy: 0.9319 - val_loss: 2.3534 - val_accuracy: 0.5509
Epoch 23/50
219/219 [==============================] - 3s 15ms/step - loss: 0.1926 - accuracy: 0.9412 - val_loss: 2.3786 - val_accuracy: 0.5375
Epoch 24/50
219/219 [==============================] - 3s 13ms/step - loss: 0.1858 - accuracy: 0.9402 - val_loss: 2.2873 - val_accuracy: 0.5622
Epoch 25/50
219/219 [==============================] - 4s 17ms/step - loss: 0.2173 - accuracy: 0.9272 - val_loss: 2.3705 - val_accuracy: 0.5602
Epoch 26/50
219/219 [==============================] - 4s 17ms/step - loss: 0.1225 - accuracy: 0.9598 - val_loss: 2.3050 - val_accuracy: 0.5635
Epoch 27/50
219/219 [==============================] - 4s 20ms/step - loss: 0.0965 - accuracy: 0.9696 - val_loss: 2.2792 - val_accuracy: 0.5632
Epoch 28/50
219/219 [==============================] - 4s 17ms/step - loss: 0.1525 - accuracy: 0.9523 - val_loss: 2.4601 - val_accuracy: 0.5579
Epoch 29/50
219/219 [==============================] - 4s 18ms/step - loss: 0.1727 - accuracy: 0.9468 - val_loss: 2.3596 - val_accuracy: 0.5682
Epoch 30/50
219/219 [==============================] - 3s 15ms/step - loss: 0.2213 - accuracy: 0.9309 - val_loss: 2.5039 - val_accuracy: 0.5662
Epoch 31/50
219/219 [==============================] - 4s 16ms/step - loss: 0.1248 - accuracy: 0.9593 - val_loss: 2.5191 - val_accuracy: 0.5599
Epoch 32/50
219/219 [==============================] - 3s 15ms/step - loss: 0.1046 - accuracy: 0.9653 - val_loss: 2.4587 - val_accuracy: 0.5729
Epoch 33/50
219/219 [==============================] - 4s 16ms/step - loss: 0.0822 - accuracy: 0.9730 - val_loss: 2.6393 - val_accuracy: 0.5612
Epoch 34/50
219/219 [==============================] - 4s 20ms/step - loss: 0.1023 - accuracy: 0.9669 - val_loss: 2.7935 - val_accuracy: 0.5418
Epoch 35/50
219/219 [==============================] - 5s 22ms/step - loss: 0.1990 - accuracy: 0.9327 - val_loss: 2.5872 - val_accuracy: 0.5765
Epoch 36/50
219/219 [==============================] - 5s 21ms/step - loss: 0.0900 - accuracy: 0.9711 - val_loss: 2.8621 - val_accuracy: 0.5478
Epoch 37/50
219/219 [==============================] - 5s 21ms/step - loss: 0.1217 - accuracy: 0.9593 - val_loss: 2.4877 - val_accuracy: 0.5722
Epoch 38/50
219/219 [==============================] - 5s 21ms/step - loss: 0.0590 - accuracy: 0.9826 - val_loss: 2.5436 - val_accuracy: 0.5865
Epoch 39/50
219/219 [==============================] - 5s 21ms/step - loss: 0.0630 - accuracy: 0.9804 - val_loss: 2.7660 - val_accuracy: 0.5729
Epoch 40/50
219/219 [==============================] - 5s 23ms/step - loss: 0.0925 - accuracy: 0.9715 - val_loss: 2.8200 - val_accuracy: 0.5438
Epoch 41/50
219/219 [==============================] - 4s 20ms/step - loss: 0.2166 - accuracy: 0.9276 - val_loss: 2.8091 - val_accuracy: 0.5799
Epoch 42/50
219/219 [==============================] - 5s 24ms/step - loss: 0.1105 - accuracy: 0.9651 - val_loss: 2.9876 - val_accuracy: 0.5522
Epoch 43/50
219/219 [==============================] - 4s 20ms/step - loss: 0.1371 - accuracy: 0.9559 - val_loss: 2.9212 - val_accuracy: 0.5692
Epoch 44/50
219/219 [==============================] - 5s 21ms/step - loss: 0.0646 - accuracy: 0.9806 - val_loss: 2.7093 - val_accuracy: 0.5865
Epoch 45/50
219/219 [==============================] - 5s 21ms/step - loss: 0.0555 - accuracy: 0.9837 - val_loss: 2.7523 - val_accuracy: 0.5969
Epoch 46/50
219/219 [==============================] - 5s 21ms/step - loss: 0.0132 - accuracy: 0.9970 - val_loss: 2.6319 - val_accuracy: 0.5952
Epoch 47/50
219/219 [==============================] - 5s 21ms/step - loss: 0.0332 - accuracy: 0.9917 - val_loss: 2.6610 - val_accuracy: 0.6029
Epoch 48/50
219/219 [==============================] - 5s 21ms/step - loss: 0.0449 - accuracy: 0.9874 - val_loss: 3.2838 - val_accuracy: 0.5442
Epoch 49/50
219/219 [==============================] - 4s 17ms/step - loss: 0.3712 - accuracy: 0.8864 - val_loss: 3.1611 - val_accuracy: 0.5275
Epoch 50/50
219/219 [==============================] - 3s 15ms/step - loss: 0.1423 - accuracy: 0.9536 - val_loss: 2.7352 - val_accuracy: 0.5909

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

# path to json file that stores MFCCs and genre labels for each processed segment
DATA_PATH = "/content/data_10.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)

    # convert lists to numpy arrays
    X = np.array(data["mfcc"])
    y = np.array(data["labels"])

    print("Data succesfully loaded!")

    return X, y


def plot_history(history):
    """Plots accuracy/loss for training/validation set as a function of the epochs
        :param history: Training history of model
        :return:
    """

    fig, axs = plt.subplots(2)

    # create accuracy sublpot
    axs[0].plot(history.history["accuracy"], label="train accuracy")
    axs[0].plot(history.history["val_accuracy"], label="test accuracy")
    axs[0].set_ylabel("Accuracy")
    axs[0].legend(loc="lower right")
    axs[0].set_title("Accuracy eval")

    # create error sublpot
    axs[1].plot(history.history["loss"], label="train error")
    axs[1].plot(history.history["val_loss"], label="test error")
    axs[1].set_ylabel("Error")
    axs[1].set_xlabel("Epoch")
    axs[1].legend(loc="upper right")
    axs[1].set_title("Error eval")

    plt.show()


if __name__ == "__main__":

    # load data
    X, y = load_data(DATA_PATH)

    # create train/test split
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

    # build network topology
    model = keras.Sequential([

        # input layer
        keras.layers.Flatten(input_shape=(X.shape[1], X.shape[2])),

        # 1st dense layer
        keras.layers.Dense(512, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
        keras.layers.Dropout(0.3),

        # 2nd dense layer
        keras.layers.Dense(256, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
        keras.layers.Dropout(0.3),

        # 3rd dense layer
        keras.layers.Dense(64, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001)),
        keras.layers.Dropout(0.3),

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

    # 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_test, y_test), batch_size=32, epochs=100)

    # plot accuracy and error as a function of the epochs
    plot_history(history)

Data succesfully loaded!
Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_1 (Flatten)          (None, 1690)              0         
_________________________________________________________________
dense_4 (Dense)              (None, 512)               865792    
_________________________________________________________________
dropout (Dropout)            (None, 512)               0         
_________________________________________________________________
dense_5 (Dense)              (None, 256)               131328    
_________________________________________________________________
dropout_1 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_6 (Dense)              (None, 64)                16448     
_________________________________________________________________
dropout_2 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_7 (Dense)              (None, 10)                650       
=================================================================
Total params: 1,014,218
Trainable params: 1,014,218
Non-trainable params: 0
_________________________________________________________________
Epoch 1/100
219/219 [==============================] - 3s 13ms/step - loss: 42.4615 - accuracy: 0.1413 - val_loss: 3.8154 - val_accuracy: 0.2744
Epoch 2/100
219/219 [==============================] - 3s 12ms/step - loss: 8.5900 - accuracy: 0.1913 - val_loss: 3.3918 - val_accuracy: 0.2184
Epoch 3/100
219/219 [==============================] - 3s 13ms/step - loss: 5.1873 - accuracy: 0.1744 - val_loss: 3.3716 - val_accuracy: 0.1791
Epoch 4/100
219/219 [==============================] - 3s 12ms/step - loss: 4.1783 - accuracy: 0.1704 - val_loss: 3.3870 - val_accuracy: 0.1657
Epoch 5/100
219/219 [==============================] - 3s 12ms/step - loss: 3.8840 - accuracy: 0.1743 - val_loss: 3.3031 - val_accuracy: 0.2184
Epoch 6/100
219/219 [==============================] - 3s 12ms/step - loss: 3.6202 - accuracy: 0.1921 - val_loss: 3.2958 - val_accuracy: 0.2317
Epoch 7/100
219/219 [==============================] - 3s 12ms/step - loss: 3.5195 - accuracy: 0.2031 - val_loss: 3.2370 - val_accuracy: 0.2814
Epoch 8/100
219/219 [==============================] - 3s 12ms/step - loss: 3.4066 - accuracy: 0.2358 - val_loss: 3.1855 - val_accuracy: 0.2954
Epoch 9/100
219/219 [==============================] - 3s 12ms/step - loss: 3.3676 - accuracy: 0.2546 - val_loss: 3.1560 - val_accuracy: 0.2961
Epoch 10/100
219/219 [==============================] - 3s 12ms/step - loss: 3.2927 - accuracy: 0.2632 - val_loss: 3.0984 - val_accuracy: 0.3104
Epoch 11/100
219/219 [==============================] - 3s 12ms/step - loss: 3.2510 - accuracy: 0.2610 - val_loss: 3.0870 - val_accuracy: 0.3161
Epoch 12/100
219/219 [==============================] - 3s 12ms/step - loss: 3.2431 - accuracy: 0.2651 - val_loss: 3.0472 - val_accuracy: 0.3161
Epoch 13/100
219/219 [==============================] - 3s 12ms/step - loss: 3.1792 - accuracy: 0.2908 - val_loss: 3.0640 - val_accuracy: 0.3128
Epoch 14/100
219/219 [==============================] - 3s 12ms/step - loss: 3.1718 - accuracy: 0.2912 - val_loss: 2.9608 - val_accuracy: 0.3438
Epoch 15/100
219/219 [==============================] - 3s 12ms/step - loss: 3.1297 - accuracy: 0.2923 - val_loss: 2.9957 - val_accuracy: 0.3354
Epoch 16/100
219/219 [==============================] - 3s 13ms/step - loss: 3.0968 - accuracy: 0.3004 - val_loss: 2.9319 - val_accuracy: 0.3501
Epoch 17/100
219/219 [==============================] - 3s 12ms/step - loss: 3.0555 - accuracy: 0.3162 - val_loss: 2.9151 - val_accuracy: 0.3661
Epoch 18/100
219/219 [==============================] - 3s 13ms/step - loss: 3.0321 - accuracy: 0.3241 - val_loss: 2.8980 - val_accuracy: 0.3551
Epoch 19/100
219/219 [==============================] - 3s 13ms/step - loss: 2.9862 - accuracy: 0.3278 - val_loss: 2.9103 - val_accuracy: 0.3431
Epoch 20/100
219/219 [==============================] - 3s 13ms/step - loss: 2.9716 - accuracy: 0.3365 - val_loss: 2.8426 - val_accuracy: 0.3631
Epoch 21/100
219/219 [==============================] - 3s 12ms/step - loss: 2.9385 - accuracy: 0.3307 - val_loss: 2.8430 - val_accuracy: 0.3731
Epoch 22/100
219/219 [==============================] - 3s 12ms/step - loss: 2.8965 - accuracy: 0.3487 - val_loss: 2.7912 - val_accuracy: 0.3831
Epoch 23/100
219/219 [==============================] - 3s 12ms/step - loss: 2.8810 - accuracy: 0.3454 - val_loss: 2.7583 - val_accuracy: 0.3821
Epoch 24/100
219/219 [==============================] - 3s 12ms/step - loss: 2.8300 - accuracy: 0.3576 - val_loss: 2.7452 - val_accuracy: 0.4068
Epoch 25/100
219/219 [==============================] - 3s 12ms/step - loss: 2.8237 - accuracy: 0.3654 - val_loss: 2.7263 - val_accuracy: 0.4021
Epoch 26/100
219/219 [==============================] - 3s 12ms/step - loss: 2.7492 - accuracy: 0.3856 - val_loss: 2.6974 - val_accuracy: 0.3995
Epoch 27/100
219/219 [==============================] - 3s 12ms/step - loss: 2.7497 - accuracy: 0.3661 - val_loss: 2.7013 - val_accuracy: 0.3935
Epoch 28/100
219/219 [==============================] - 3s 12ms/step - loss: 2.7235 - accuracy: 0.3809 - val_loss: 2.6358 - val_accuracy: 0.4171
Epoch 29/100
219/219 [==============================] - 3s 12ms/step - loss: 2.6851 - accuracy: 0.3772 - val_loss: 2.5979 - val_accuracy: 0.4321
Epoch 30/100
219/219 [==============================] - 3s 12ms/step - loss: 2.6070 - accuracy: 0.3980 - val_loss: 2.5281 - val_accuracy: 0.4461
Epoch 31/100
219/219 [==============================] - 3s 12ms/step - loss: 2.6097 - accuracy: 0.4016 - val_loss: 2.5114 - val_accuracy: 0.4518
Epoch 32/100
219/219 [==============================] - 3s 12ms/step - loss: 2.5565 - accuracy: 0.4189 - val_loss: 2.5062 - val_accuracy: 0.4585
Epoch 33/100
219/219 [==============================] - 3s 13ms/step - loss: 2.5320 - accuracy: 0.4210 - val_loss: 2.4841 - val_accuracy: 0.4525
Epoch 34/100
219/219 [==============================] - 3s 13ms/step - loss: 2.5044 - accuracy: 0.4206 - val_loss: 2.4128 - val_accuracy: 0.4842
Epoch 35/100
219/219 [==============================] - 3s 13ms/step - loss: 2.4410 - accuracy: 0.4422 - val_loss: 2.4233 - val_accuracy: 0.4622
Epoch 36/100
219/219 [==============================] - 3s 13ms/step - loss: 2.4159 - accuracy: 0.4380 - val_loss: 2.3811 - val_accuracy: 0.4818
Epoch 37/100
219/219 [==============================] - 3s 13ms/step - loss: 2.3724 - accuracy: 0.4395 - val_loss: 2.3720 - val_accuracy: 0.4725
Epoch 38/100
219/219 [==============================] - 3s 12ms/step - loss: 2.3332 - accuracy: 0.4547 - val_loss: 2.2977 - val_accuracy: 0.4915
Epoch 39/100
219/219 [==============================] - 3s 12ms/step - loss: 2.2986 - accuracy: 0.4634 - val_loss: 2.2889 - val_accuracy: 0.4955
Epoch 40/100
219/219 [==============================] - 3s 13ms/step - loss: 2.2507 - accuracy: 0.4739 - val_loss: 2.2718 - val_accuracy: 0.4858
Epoch 41/100
219/219 [==============================] - 3s 12ms/step - loss: 2.2158 - accuracy: 0.4675 - val_loss: 2.2288 - val_accuracy: 0.5055
Epoch 42/100
219/219 [==============================] - 3s 12ms/step - loss: 2.1836 - accuracy: 0.4890 - val_loss: 2.2003 - val_accuracy: 0.4908
Epoch 43/100
219/219 [==============================] - 3s 13ms/step - loss: 2.1223 - accuracy: 0.4950 - val_loss: 2.1766 - val_accuracy: 0.4932
Epoch 44/100
219/219 [==============================] - 3s 13ms/step - loss: 2.1100 - accuracy: 0.4951 - val_loss: 2.1442 - val_accuracy: 0.5015
Epoch 45/100
219/219 [==============================] - 3s 13ms/step - loss: 2.0855 - accuracy: 0.4983 - val_loss: 2.1531 - val_accuracy: 0.5052
Epoch 46/100
219/219 [==============================] - 3s 13ms/step - loss: 2.0284 - accuracy: 0.5136 - val_loss: 2.0957 - val_accuracy: 0.5248
Epoch 47/100
219/219 [==============================] - 3s 13ms/step - loss: 1.9889 - accuracy: 0.5175 - val_loss: 2.0682 - val_accuracy: 0.5208
Epoch 48/100
219/219 [==============================] - 3s 13ms/step - loss: 2.0196 - accuracy: 0.5121 - val_loss: 2.0435 - val_accuracy: 0.5105
Epoch 49/100
219/219 [==============================] - 3s 13ms/step - loss: 1.9763 - accuracy: 0.5073 - val_loss: 2.0367 - val_accuracy: 0.5262
Epoch 50/100
219/219 [==============================] - 3s 12ms/step - loss: 1.9135 - accuracy: 0.5339 - val_loss: 2.0335 - val_accuracy: 0.5092
Epoch 51/100
219/219 [==============================] - 3s 13ms/step - loss: 1.8668 - accuracy: 0.5412 - val_loss: 2.0045 - val_accuracy: 0.5228
Epoch 52/100
219/219 [==============================] - 3s 13ms/step - loss: 1.8437 - accuracy: 0.5539 - val_loss: 2.0183 - val_accuracy: 0.5075
Epoch 53/100
219/219 [==============================] - 3s 12ms/step - loss: 1.8239 - accuracy: 0.5523 - val_loss: 1.9976 - val_accuracy: 0.5262
Epoch 54/100
219/219 [==============================] - 3s 13ms/step - loss: 1.7744 - accuracy: 0.5474 - val_loss: 1.9818 - val_accuracy: 0.5232
Epoch 55/100
219/219 [==============================] - 3s 12ms/step - loss: 1.7685 - accuracy: 0.5592 - val_loss: 1.9507 - val_accuracy: 0.5295
Epoch 56/100
219/219 [==============================] - 3s 13ms/step - loss: 1.7405 - accuracy: 0.5633 - val_loss: 1.9643 - val_accuracy: 0.5278
Epoch 57/100
219/219 [==============================] - 3s 13ms/step - loss: 1.7294 - accuracy: 0.5679 - val_loss: 1.9237 - val_accuracy: 0.5325
Epoch 58/100
219/219 [==============================] - 3s 13ms/step - loss: 1.7099 - accuracy: 0.5758 - val_loss: 1.9351 - val_accuracy: 0.5362
Epoch 59/100
219/219 [==============================] - 3s 13ms/step - loss: 1.6662 - accuracy: 0.5620 - val_loss: 1.8790 - val_accuracy: 0.5255
Epoch 60/100
219/219 [==============================] - 3s 13ms/step - loss: 1.6173 - accuracy: 0.5972 - val_loss: 1.9059 - val_accuracy: 0.5382
Epoch 61/100
219/219 [==============================] - 3s 13ms/step - loss: 1.6262 - accuracy: 0.5840 - val_loss: 1.8799 - val_accuracy: 0.5388
Epoch 62/100
219/219 [==============================] - 3s 13ms/step - loss: 1.5948 - accuracy: 0.5934 - val_loss: 1.8864 - val_accuracy: 0.5242
Epoch 63/100
219/219 [==============================] - 3s 13ms/step - loss: 1.5720 - accuracy: 0.5947 - val_loss: 1.8542 - val_accuracy: 0.5345
Epoch 64/100
219/219 [==============================] - 3s 12ms/step - loss: 1.5563 - accuracy: 0.6049 - val_loss: 1.8281 - val_accuracy: 0.5392
Epoch 65/100
219/219 [==============================] - 3s 13ms/step - loss: 1.5374 - accuracy: 0.6012 - val_loss: 1.8239 - val_accuracy: 0.5382
Epoch 66/100
219/219 [==============================] - 3s 13ms/step - loss: 1.5252 - accuracy: 0.5976 - val_loss: 1.8664 - val_accuracy: 0.5325
Epoch 67/100
219/219 [==============================] - 3s 13ms/step - loss: 1.5146 - accuracy: 0.6138 - val_loss: 1.8315 - val_accuracy: 0.5478
Epoch 68/100
219/219 [==============================] - 3s 13ms/step - loss: 1.4785 - accuracy: 0.6183 - val_loss: 1.8290 - val_accuracy: 0.5408
Epoch 69/100
219/219 [==============================] - 3s 13ms/step - loss: 1.4641 - accuracy: 0.6208 - val_loss: 1.7778 - val_accuracy: 0.5565
Epoch 70/100
219/219 [==============================] - 3s 13ms/step - loss: 1.4079 - accuracy: 0.6349 - val_loss: 1.7745 - val_accuracy: 0.5529
Epoch 71/100
219/219 [==============================] - 3s 12ms/step - loss: 1.4185 - accuracy: 0.6335 - val_loss: 1.8473 - val_accuracy: 0.5438
Epoch 72/100
219/219 [==============================] - 3s 12ms/step - loss: 1.4268 - accuracy: 0.6246 - val_loss: 1.7700 - val_accuracy: 0.5539
Epoch 73/100
219/219 [==============================] - 3s 13ms/step - loss: 1.4088 - accuracy: 0.6197 - val_loss: 1.7678 - val_accuracy: 0.5569
Epoch 74/100
219/219 [==============================] - 3s 13ms/step - loss: 1.3886 - accuracy: 0.6335 - val_loss: 1.7774 - val_accuracy: 0.5425
Epoch 75/100
219/219 [==============================] - 3s 13ms/step - loss: 1.3545 - accuracy: 0.6380 - val_loss: 1.7576 - val_accuracy: 0.5552
Epoch 76/100
219/219 [==============================] - 3s 13ms/step - loss: 1.3398 - accuracy: 0.6494 - val_loss: 1.7626 - val_accuracy: 0.5652
Epoch 77/100
219/219 [==============================] - 3s 13ms/step - loss: 1.3223 - accuracy: 0.6550 - val_loss: 1.7589 - val_accuracy: 0.5685
Epoch 78/100
219/219 [==============================] - 3s 13ms/step - loss: 1.2907 - accuracy: 0.6620 - val_loss: 1.7561 - val_accuracy: 0.5695
Epoch 79/100
219/219 [==============================] - 3s 13ms/step - loss: 1.2760 - accuracy: 0.6645 - val_loss: 1.7461 - val_accuracy: 0.5652
Epoch 80/100
219/219 [==============================] - 3s 13ms/step - loss: 1.2785 - accuracy: 0.6709 - val_loss: 1.7838 - val_accuracy: 0.5585
Epoch 81/100
219/219 [==============================] - 3s 13ms/step - loss: 1.2700 - accuracy: 0.6633 - val_loss: 1.7220 - val_accuracy: 0.5715
Epoch 82/100
219/219 [==============================] - 3s 12ms/step - loss: 1.2561 - accuracy: 0.6733 - val_loss: 1.7392 - val_accuracy: 0.5645
Epoch 83/100
219/219 [==============================] - 3s 12ms/step - loss: 1.2387 - accuracy: 0.6697 - val_loss: 1.7709 - val_accuracy: 0.5659
Epoch 84/100
219/219 [==============================] - 3s 13ms/step - loss: 1.2659 - accuracy: 0.6728 - val_loss: 1.8261 - val_accuracy: 0.5662
Epoch 85/100
219/219 [==============================] - 3s 12ms/step - loss: 1.2359 - accuracy: 0.6833 - val_loss: 1.7158 - val_accuracy: 0.5729
Epoch 86/100
219/219 [==============================] - 3s 12ms/step - loss: 1.2026 - accuracy: 0.6785 - val_loss: 1.7899 - val_accuracy: 0.5685
Epoch 87/100
219/219 [==============================] - 3s 13ms/step - loss: 1.2083 - accuracy: 0.6849 - val_loss: 1.7467 - val_accuracy: 0.5629
Epoch 88/100
219/219 [==============================] - 3s 13ms/step - loss: 1.2071 - accuracy: 0.6913 - val_loss: 1.7509 - val_accuracy: 0.5692
Epoch 89/100
219/219 [==============================] - 3s 13ms/step - loss: 1.1712 - accuracy: 0.6917 - val_loss: 1.7265 - val_accuracy: 0.5639
Epoch 90/100
219/219 [==============================] - 3s 13ms/step - loss: 1.1671 - accuracy: 0.6972 - val_loss: 1.7832 - val_accuracy: 0.5659
Epoch 91/100
219/219 [==============================] - 3s 13ms/step - loss: 1.1003 - accuracy: 0.7237 - val_loss: 1.7441 - val_accuracy: 0.5709
Epoch 92/100
219/219 [==============================] - 3s 13ms/step - loss: 1.1507 - accuracy: 0.6957 - val_loss: 1.7473 - val_accuracy: 0.5719
Epoch 93/100
219/219 [==============================] - 3s 13ms/step - loss: 1.0951 - accuracy: 0.7174 - val_loss: 1.7299 - val_accuracy: 0.5702
Epoch 94/100
219/219 [==============================] - 3s 12ms/step - loss: 1.1532 - accuracy: 0.6933 - val_loss: 1.7176 - val_accuracy: 0.5759
Epoch 95/100
219/219 [==============================] - 3s 13ms/step - loss: 1.1779 - accuracy: 0.6962 - val_loss: 1.7577 - val_accuracy: 0.5605
Epoch 96/100
219/219 [==============================] - 3s 13ms/step - loss: 1.1017 - accuracy: 0.7125 - val_loss: 1.7621 - val_accuracy: 0.5682
Epoch 97/100
219/219 [==============================] - 3s 12ms/step - loss: 1.1064 - accuracy: 0.7128 - val_loss: 1.7559 - val_accuracy: 0.5649
Epoch 98/100
219/219 [==============================] - 3s 12ms/step - loss: 1.0666 - accuracy: 0.7256 - val_loss: 1.7765 - val_accuracy: 0.5782
Epoch 99/100
219/219 [==============================] - 3s 13ms/step - loss: 1.0958 - accuracy: 0.7189 - val_loss: 1.7327 - val_accuracy: 0.5749
Epoch 100/100
219/219 [==============================] - 3s 13ms/step - loss: 1.0950 - accuracy: 0.7056 - val_loss: 1.7285 - val_accuracy: 0.5629

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 = "/content/data_10.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 plot_history(history):
    """Plots accuracy/loss for training/validation set as a function of the epochs
        :param history: Training history of model
        :return:
    """

    fig, axs = plt.subplots(2)

    # create accuracy sublpot
    axs[0].plot(history.history["accuracy"], label="train accuracy")
    axs[0].plot(history.history["val_accuracy"], label="test accuracy")
    axs[0].set_ylabel("Accuracy")
    axs[0].legend(loc="lower right")
    axs[0].set_title("Accuracy eval")

    # create error sublpot
    axs[1].plot(history.history["loss"], label="train error")
    axs[1].plot(history.history["val_loss"], label="test error")
    axs[1].set_ylabel("Error")
    axs[1].set_xlabel("Epoch")
    axs[1].legend(loc="upper right")
    axs[1].set_title("Error eval")

    plt.show()


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 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 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=40)

    # plot accuracy/error for training and validation
    plot_history(history)

    # evaluate model on test set
    test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
    print('\nTest accuracy:', test_acc)

    # pick a sample to predict from the test set
    X_to_predict = X_test[100]
    y_to_predict = y_test[100]

    # predict sample
    predict(model, X_to_predict, y_to_predict)

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 128, 11, 32)       320       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 64, 6, 32)         0         
_________________________________________________________________
batch_normalization (BatchNo (None, 64, 6, 32)         128       
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 62, 4, 32)         9248      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 31, 2, 32)         0         
_________________________________________________________________
batch_normalization_1 (Batch (None, 31, 2, 32)         128       
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 30, 1, 32)         4128      
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 15, 1, 32)         0         
_________________________________________________________________
batch_normalization_2 (Batch (None, 15, 1, 32)         128       
_________________________________________________________________
flatten_2 (Flatten)          (None, 480)               0         
_________________________________________________________________
dense_8 (Dense)              (None, 64)                30784     
_________________________________________________________________
dropout_3 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_9 (Dense)              (None, 10)                650       
=================================================================
Total params: 45,514
Trainable params: 45,322
Non-trainable params: 192
_________________________________________________________________
Epoch 1/40
188/188 [==============================] - 11s 56ms/step - loss: 2.6492 - accuracy: 0.1956 - val_loss: 1.8044 - val_accuracy: 0.3640
Epoch 2/40
188/188 [==============================] - 11s 56ms/step - loss: 1.9150 - accuracy: 0.3512 - val_loss: 1.5739 - val_accuracy: 0.4220
Epoch 3/40
188/188 [==============================] - 11s 56ms/step - loss: 1.7200 - accuracy: 0.3871 - val_loss: 1.4538 - val_accuracy: 0.4593
Epoch 4/40
188/188 [==============================] - 11s 57ms/step - loss: 1.5504 - accuracy: 0.4473 - val_loss: 1.3654 - val_accuracy: 0.4940
Epoch 5/40
188/188 [==============================] - 11s 56ms/step - loss: 1.4452 - accuracy: 0.4832 - val_loss: 1.3153 - val_accuracy: 0.5153
Epoch 6/40
188/188 [==============================] - 11s 56ms/step - loss: 1.3916 - accuracy: 0.5090 - val_loss: 1.2543 - val_accuracy: 0.5380
Epoch 7/40
188/188 [==============================] - 11s 57ms/step - loss: 1.3135 - accuracy: 0.5357 - val_loss: 1.2204 - val_accuracy: 0.5540
Epoch 8/40
188/188 [==============================] - 11s 56ms/step - loss: 1.3032 - accuracy: 0.5354 - val_loss: 1.1790 - val_accuracy: 0.5680
Epoch 9/40
188/188 [==============================] - 11s 56ms/step - loss: 1.2301 - accuracy: 0.5556 - val_loss: 1.1461 - val_accuracy: 0.5820
Epoch 10/40
188/188 [==============================] - 11s 57ms/step - loss: 1.1591 - accuracy: 0.5780 - val_loss: 1.1339 - val_accuracy: 0.5820
Epoch 11/40
188/188 [==============================] - 11s 57ms/step - loss: 1.1623 - accuracy: 0.5948 - val_loss: 1.0936 - val_accuracy: 0.6113
Epoch 12/40
188/188 [==============================] - 11s 56ms/step - loss: 1.1404 - accuracy: 0.5934 - val_loss: 1.0597 - val_accuracy: 0.6200
Epoch 13/40
188/188 [==============================] - 11s 56ms/step - loss: 1.0710 - accuracy: 0.6241 - val_loss: 1.0335 - val_accuracy: 0.6280
Epoch 14/40
188/188 [==============================] - 11s 56ms/step - loss: 1.0549 - accuracy: 0.6261 - val_loss: 1.0185 - val_accuracy: 0.6433
Epoch 15/40
188/188 [==============================] - 11s 57ms/step - loss: 1.0214 - accuracy: 0.6387 - val_loss: 1.0067 - val_accuracy: 0.6513
Epoch 16/40
188/188 [==============================] - 11s 56ms/step - loss: 1.0126 - accuracy: 0.6450 - val_loss: 0.9821 - val_accuracy: 0.6653
Epoch 17/40
188/188 [==============================] - 11s 57ms/step - loss: 0.9637 - accuracy: 0.6602 - val_loss: 0.9614 - val_accuracy: 0.6573
Epoch 18/40
188/188 [==============================] - 11s 57ms/step - loss: 0.9566 - accuracy: 0.6659 - val_loss: 0.9510 - val_accuracy: 0.6753
Epoch 19/40
188/188 [==============================] - 11s 57ms/step - loss: 0.9188 - accuracy: 0.6843 - val_loss: 0.9342 - val_accuracy: 0.6747
Epoch 20/40
188/188 [==============================] - 11s 57ms/step - loss: 0.9008 - accuracy: 0.6773 - val_loss: 0.9117 - val_accuracy: 0.6873
Epoch 21/40
188/188 [==============================] - 11s 57ms/step - loss: 0.8248 - accuracy: 0.7195 - val_loss: 0.9063 - val_accuracy: 0.6940
Epoch 22/40
188/188 [==============================] - 11s 57ms/step - loss: 0.8438 - accuracy: 0.7026 - val_loss: 0.9146 - val_accuracy: 0.6840
Epoch 23/40
188/188 [==============================] - 11s 57ms/step - loss: 0.8127 - accuracy: 0.7199 - val_loss: 0.8983 - val_accuracy: 0.6953
Epoch 24/40
188/188 [==============================] - 11s 57ms/step - loss: 0.8016 - accuracy: 0.7257 - val_loss: 0.8578 - val_accuracy: 0.7047
Epoch 25/40
188/188 [==============================] - 11s 57ms/step - loss: 0.7820 - accuracy: 0.7294 - val_loss: 0.8635 - val_accuracy: 0.7080
Epoch 26/40
188/188 [==============================] - 11s 57ms/step - loss: 0.7490 - accuracy: 0.7388 - val_loss: 0.8762 - val_accuracy: 0.7047
Epoch 27/40
188/188 [==============================] - 11s 57ms/step - loss: 0.7537 - accuracy: 0.7461 - val_loss: 0.8478 - val_accuracy: 0.7120
Epoch 28/40
188/188 [==============================] - 11s 57ms/step - loss: 0.7375 - accuracy: 0.7371 - val_loss: 0.8544 - val_accuracy: 0.7193
Epoch 29/40
188/188 [==============================] - 11s 57ms/step - loss: 0.7252 - accuracy: 0.7524 - val_loss: 0.8436 - val_accuracy: 0.7080
Epoch 30/40
188/188 [==============================] - 11s 57ms/step - loss: 0.6915 - accuracy: 0.7587 - val_loss: 0.8411 - val_accuracy: 0.7173
Epoch 31/40
188/188 [==============================] - 11s 57ms/step - loss: 0.7133 - accuracy: 0.7524 - val_loss: 0.8437 - val_accuracy: 0.7060
Epoch 32/40
188/188 [==============================] - 11s 57ms/step - loss: 0.6844 - accuracy: 0.7673 - val_loss: 0.8511 - val_accuracy: 0.7060
Epoch 33/40
188/188 [==============================] - 11s 57ms/step - loss: 0.6627 - accuracy: 0.7711 - val_loss: 0.8209 - val_accuracy: 0.7300
Epoch 34/40
188/188 [==============================] - 11s 57ms/step - loss: 0.6560 - accuracy: 0.7794 - val_loss: 0.8138 - val_accuracy: 0.7280
Epoch 35/40
188/188 [==============================] - 11s 57ms/step - loss: 0.6332 - accuracy: 0.7796 - val_loss: 0.8021 - val_accuracy: 0.7260
Epoch 36/40
188/188 [==============================] - 11s 57ms/step - loss: 0.6054 - accuracy: 0.7893 - val_loss: 0.8065 - val_accuracy: 0.7187
Epoch 37/40
188/188 [==============================] - 11s 57ms/step - loss: 0.6070 - accuracy: 0.7883 - val_loss: 0.7951 - val_accuracy: 0.7213
Epoch 38/40
188/188 [==============================] - 11s 57ms/step - loss: 0.6031 - accuracy: 0.7944 - val_loss: 0.8175 - val_accuracy: 0.7240
Epoch 39/40
188/188 [==============================] - 11s 57ms/step - loss: 0.6206 - accuracy: 0.7851 - val_loss: 0.8390 - val_accuracy: 0.7193
Epoch 40/40
188/188 [==============================] - 11s 56ms/step - loss: 0.5934 - accuracy: 0.7974 - val_loss: 0.8129 - val_accuracy: 0.7300

79/79 - 1s - loss: 0.8129 - accuracy: 0.7251

Test accuracy: 0.7250900268554688
Target: 8, Predicted label: [8]

X_to_predict_2 = X_test[69]
y_to_predict_2 = y_test[69]

# predict sample
predict(model, X_to_predict_2, y_to_predict_2)

Target: 3, Predicted label: [3]

X_u = X
X_u = X_u.reshape(X.shape[0],X.shape[1],X.shape[2],1)
X_u.shape

(9996, 130, 13, 1)

predictions_array = np.zeros(shape=(X.shape[0],1,10))

X_r = X_u[4]
X_r = (X_r)[np.newaxis, ...]
X_r.shape

(1, 130, 13, 1)

for i in range(X.shape[0]):
  print("Processed "+str(i))
  X_row = X_u[i]
  X_row = (X_row)[np.newaxis, ...] # array shape (1, 130, 13, 1)
  predictions_array[i] = model.predict(X_row)

�[1;30;43mStreaming output truncated to the last 5000 lines.�[0m
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genre_dict = {0:'blues', 1:'classical', 2:'country', 3:'disco', 4:'hiphop', 5:'jazz', 6:'metal', 7:'pop', 8:'reggae', 9:'rock'}

while(True):
  print("Genres :")
  print()
  print("blues : 0")
  print("classical : 1")
  print("country : 2")
  print("disco : 3")
  print("hiphop : 4")
  print("jazz : 5")
  print("metal : 6")
  print("pop : 7")
  print("reggae : 8")
  print("rock : 9")
  print()
  print()
  x = int(input("Select your song's genre (0-9) : "))
  s_n = int(input("Select the song number of the song you like (0-99) from " + str(genre_dict[x]) +" genre and I will recommend you top 5 similar songs, you might like : "))
  d = x*1000 + s_n*10 + 5
  ex_song = predictions_array[d]
  print()
  print("Your song's genre is : ")
  print()
  print(genre_dict[y[d]])
  print()
  print("and song number :")
  print()
  print(math.floor(((d%1000)/10)))
  min_list = []
  min_index_list = []
  min = 10000
  for i in range(9996):
    if (i!=d):
      s=np.linalg.norm(predictions_array[i][0] - ex_song[0])  
                                          
    if(s<min):
      min = s
      min_list.append(s)
      min_index_list.append(i)
  print()
  print("The top 5 songs I found most similar to the given song are : ")
  print()
  res = []
  #print(min_index_list[0])
  for i in min_index_list:
      if i not in res:
          res.append(i)
  c=-1
  for i in range(5):
    genre_name = genre_dict[int(res[c]/1000)]
    song_no = math.floor(((res[c]%1000)/10))
    print(genre_name+" song number "+str(song_no))
    c = c-1
  print()
  print()
  ans = str(input(print("Do you wish to enter more : ? (y/n)")))
  if(ans=='y'):
    continue
  else:
    break

Genres :

blues : 0
classical : 1
country : 2
disco : 3
hiphop : 4
jazz : 5
metal : 6
pop : 7
reggae : 8
rock : 9


Select your song's genre (0-9) : 1
Select the song number of the song you like (0-99) from classical genre and I will recommend you top 5 similar songs, you might like : 44

Your song's genre is : 

classical

and song number :

44

The top 5 songs I found most similar to the given song are : 

classical song number 47
classical song number 34
classical song number 13
classical song number 9
classical song number 9


Do you wish to enter more : ? (y/n)
y
Genres :

blues : 0
classical : 1
country : 2
disco : 3
hiphop : 4
jazz : 5
metal : 6
pop : 7
reggae : 8
rock : 9


Select your song's genre (0-9) : 3
Select the song number of the song you like (0-99) from disco genre and I will recommend you top 5 similar songs, you might like : 77

Your song's genre is : 

disco

and song number :

77

The top 5 songs I found most similar to the given song are : 

disco song number 64
disco song number 40
disco song number 25
disco song number 8
disco song number 1


Do you wish to enter more : ? (y/n)
y
Genres :

blues : 0
classical : 1
country : 2
disco : 3
hiphop : 4
jazz : 5
metal : 6
pop : 7
reggae : 8
rock : 9


Select your song's genre (0-9) : 7
Select the song number of the song you like (0-99) from pop genre and I will recommend you top 5 similar songs, you might like : 68

Your song's genre is : 

pop

and song number :

68

The top 5 songs I found most similar to the given song are : 

hiphop song number 68
hiphop song number 68
hiphop song number 56
hiphop song number 3
classical song number 27


Do you wish to enter more : ? (y/n)
n