Added TCN model prototype

AnanthVivekanand · Nov 2, 2020 · 676d911 · 676d911
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diff --git a/training/.ipynb_checkpoints/WaveNet-classifier-checkpoint.ipynb b/training/.ipynb_checkpoints/WaveNet-classifier-checkpoint.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "!{sys.executable} -m pip install librosa\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import librosa\n",
+    "import librosa.display\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from keras.utils import to_categorical\n",
+    "import h5py\n",
+    "import math\n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import sys\n",
+    "import tensorflow as tf\n",
+    "from urllib.request import urlopen\n",
+    "from keras.layers import Input, Dense, Lambda, Flatten, Reshape, Activation, Dropout, Add, TimeDistributed, Multiply, Conv1D, Conv2D, MaxPooling1D, AveragePooling1D\n",
+    "from keras.models import Model, Sequential, load_model\n",
+    "from keras import backend as K\n",
+    "from keras import metrics\n",
+    "from keras import optimizers\n",
+    "from keras.callbacks import History, ModelCheckpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_X = []\n",
+    "all_Y = []\n",
+    "fulldatasetpath = 'http://76.213.149.126:8080/audio/'\n",
+    "metadata = pd.read_csv('http://76.213.149.126:8080/metadata/UrbanSound8K.csv')\n",
+    "print(metadata)\n",
+    "for index, row in metadata.iterrows():\n",
+    "    \n",
+    "    file_name = (fulldatasetpath) + 'fold'+str(row[\"fold\"])+'/' + str(row[\"slice_file_name\"])\n",
+    "    all_X.append(file_name.replace(\".wav\", \"_x.pkl\"))\n",
+    "    all_Y.append(file_name.replace(\".wav\", \"_y.pkl\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split \n",
+    "x_train, x_test, y_train, y_test = train_test_split(all_X, all_Y, test_size=0.2, random_state = 42)\n",
+    "\n",
+    "shapes = (tf.TensorShape([88200,1]), tf.TensorShape([10,1]))\n",
+    "train_iter = zip(x_train, y_train)\n",
+    "def gen_train():\n",
+    "    sample = next(train_iter)\n",
+    "    print (sample)\n",
+    "    return (list(np.array(pickle.load(urlopen(sample[0]))).reshape((1,88200))), np.array(pickle.load(urlopen(sample[1]))).reshape((1,10)))\n",
+    "\n",
+    "test_iter = zip(x_test, y_test)\n",
+    "def gen_test():\n",
+    "    sample = next(test_iter)\n",
+    "    return (pickle.load(urlopen(sample[0])), pickle.load(urlopen(sample[1])))\n",
+    "\n",
+    "train_dataset = tf.data.Dataset.from_generator(generator=gen_train,output_types=(tf.float32,tf.float32),output_shapes=shapes)\n",
+    "test_dataset  = tf.data.Dataset.from_generator(generator=gen_test,output_types=(tf.float32,tf.float32),output_shapes=shapes)\n",
+    "print(gen_train())"
+   ]
+  }
+ ],
+ "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}