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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import cv2 as cv\n",
"import os\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"#from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"import tensorflow as tf\n",
"\n",
"from keras import Input as kInput\n",
"from keras.models import Sequential, save_model, load_model\n",
"from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Loads image data from a directory into an list using open CV\n",
"# THIS FUNCTION WAS MADE IN MY COMPUTER VISION MODULE\n",
"# also returns number of images loaded\n",
"def loadImgs(path):\n",
" images = []\n",
" for file in os.listdir(path):\n",
" img = cv.imread(os.path.join(path,file))\n",
"\n",
" img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n",
" #img = cv.resize(img, (100,100))\n",
" \n",
" if img is not None:\n",
" images.append(img)\n",
"\n",
" return images, len(os.listdir(path))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Load the mel spectrograms for each language as a dataset\n",
"def loadDataset(path, languages = []):\n",
" \n",
" # X and Y axis for classification\n",
" x = [] # For the image data\n",
" y = [] # For the class\n",
"\n",
" # Load each language passed in the languages parameter\n",
" for i in range(len(languages)):\n",
" lang = languages[i]\n",
" print(lang)\n",
" # Data = Array of images loaded\n",
" # Samples = Number of files loaded\n",
" data, samples = loadImgs(path+lang)\n",
"\n",
" # Merge tables\n",
" x = x + data\n",
"\n",
" # Populate the classes\n",
" for j in range(samples):\n",
" y.append(i)\n",
" #y.append(lang)\n",
" \n",
" \n",
" # Return number arrays\n",
" return np.array(x), np.array(y)\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"en\n",
"ja\n",
"vi\n"
]
}
],
"source": [
"# Load whole dataset (x = images, y = classes(languages))\n",
"#langArray = [\"en\", \"es\", \"ja\", \"ro\", \"vi\"]\n",
"langArray = [\"en\", \"ja\", \"vi\"]\n",
"x, y = loadDataset(\"datasets/mel_spectrograms/\", languages=langArray)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(6000, 231, 349)\n",
"(6000,)\n"
]
}
],
"source": [
"print(x.shape)\n",
"print(y.shape)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 ... 2 2 2]\n"
]
}
],
"source": [
"print(y)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Split the dataset into training & testing datasets using SKLEARN TRAIN_TEST_SPLIT\n",
"x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=0, test_size=0.25, shuffle=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x25effc2ddc8>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(x_train[0])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(231, 349, 1)\n"
]
}
],
"source": [
"#inputShape = x_train[0].shape + (1,)\n",
"print(x_train[0].shape + (1,))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# Building CNN Model for analysis of mel spectrograms\n",
"\n",
"# Creating a neural network model with a sequential layer stack\n",
"model = Sequential()\n",
"# Add the input shape. Spectrogram Width x Height x Depth(1)\n",
"model.add(kInput(shape = x_train[0].shape + (1,)))\n",
"\n",
"# Extracting 64 features using a 3x3 kernal with relu activation function and then maxpooling with a 2x2 kernal\n",
"model.add(Conv2D(64, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"\n",
"# Extracting 128 features using a 3x3 kernal with relu activation function and then maxpooling with a 2x2 kernal\n",
"model.add(Conv2D(128, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"\n",
"# Extracting 256 features using a 3x3 kernal with relu activation function and then maxpooling with a 2x2 kernal\n",
"model.add(Conv2D(256, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"\n",
"# Extracting 512 features using a 3x3 kernal with relu activation function and then maxpooling with a 2x2 kernal\n",
"model.add(Conv2D(512, (3, 3), activation='relu'))\n",
"model.add(MaxPooling2D((2, 2)))\n",
"\n",
"# Turn the 2D array into a 1D array\n",
"model.add(Flatten())\n",
"\n",
"# Dense and connect layers using the relu activation function\n",
"# with dropouts to avoid overfitting the model\n",
"model.add(Dense(units = 512, activation = \"relu\"))\n",
"#model.add(Dropout(0.8))\n",
"\n",
"model.add(Dense(units = 512, activation = \"relu\"))\n",
"#model.add(Dropout(0.8))\n",
"\n",
"model.add(Dense(units = 256, activation = \"relu\"))\n",
"#model.add(Dropout(0.6))\n",
"\n",
"model.add(Dense(units = 256, activation = \"relu\"))\n",
"#model.add(Dropout(0.6))\n",
"\n",
"# Dense into the OUTPUT layer using the softmax activation function\n",
"# using the units of the number of classes to connec the rest of the layers\n",
"if len(langArray) == 2:\n",
" model.add(Dense(units = len(langArray)-1, activation = \"sigmoid\"))\n",
"else:\n",
" model.add(Dense(units = len(langArray), activation = \"softmax\"))\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model: \"sequential_2\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" conv2d_8 (Conv2D) (None, 229, 347, 64) 640 \n",
" \n",
" max_pooling2d_8 (MaxPooling (None, 114, 173, 64) 0 \n",
" 2D) \n",
" \n",
" conv2d_9 (Conv2D) (None, 112, 171, 128) 73856 \n",
" \n",
" max_pooling2d_9 (MaxPooling (None, 56, 85, 128) 0 \n",
" 2D) \n",
" \n",
" conv2d_10 (Conv2D) (None, 54, 83, 256) 295168 \n",
" \n",
" max_pooling2d_10 (MaxPoolin (None, 27, 41, 256) 0 \n",
" g2D) \n",
" \n",
" conv2d_11 (Conv2D) (None, 25, 39, 512) 1180160 \n",
" \n",
" max_pooling2d_11 (MaxPoolin (None, 12, 19, 512) 0 \n",
" g2D) \n",
" \n",
" flatten_2 (Flatten) (None, 116736) 0 \n",
" \n",
" dense_10 (Dense) (None, 512) 59769344 \n",
" \n",
" dense_11 (Dense) (None, 512) 262656 \n",
" \n",
" dense_12 (Dense) (None, 256) 131328 \n",
" \n",
" dense_13 (Dense) (None, 256) 65792 \n",
" \n",
" dense_14 (Dense) (None, 3) 771 \n",
" \n",
"=================================================================\n",
"Total params: 61,779,715\n",
"Trainable params: 61,779,715\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# Compiling the CNN\n",
"if len(langArray) == 2:\n",
"#model.compile(optimizer='adam', loss=tf.keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])\n",
" model.compile(optimizer='adam', loss=tf.keras.losses.binary_crossentropy, metrics=['accuracy'])\n",
"else:\n",
" model.compile(optimizer='adam', loss=tf.keras.losses.sparse_categorical_crossentropy, metrics=['accuracy'])\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4500, 231, 349)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_train.shape"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/5\n",
"141/141 [==============================] - 883s 6s/step - loss: 2.7568 - accuracy: 0.6209\n",
"Epoch 2/5\n",
"141/141 [==============================] - 821s 6s/step - loss: 0.5314 - accuracy: 0.7642\n",
"Epoch 3/5\n",
"141/141 [==============================] - 821s 6s/step - loss: 0.4062 - accuracy: 0.8344\n",
"Epoch 4/5\n",
"141/141 [==============================] - 821s 6s/step - loss: 0.3175 - accuracy: 0.8689\n",
"Epoch 5/5\n",
"141/141 [==============================] - 824s 6s/step - loss: 0.1926 - accuracy: 0.9238\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x25effcd22c8>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Model training\n",
"model.fit(x_train, y_train, epochs = 5)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"47/47 [==============================] - 69s 1s/step - loss: 0.6530 - accuracy: 0.8060\n"
]
},
{
"data": {
"text/plain": [
"[0.6529664397239685, 0.8059999942779541]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Evaluate model\n",
"model.evaluate(x_test, y_test)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 4 of 4). These functions will not be directly callable after loading.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: cnnEnJaVi\\assets\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO:tensorflow:Assets written to: cnnEnJaVi\\assets\n"
]
}
],
"source": [
"# Saves CNN model to a file\n",
"model.save(\"cnnEnJaVi\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
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"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "7b7a235db96fc685dcf040f3fadce312cefff1737c66335c00fd4538bc138012"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}