Amplitude_Training_Completed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "The purpose of this code is to create a TensorFlow Lite model. To do this, we take in a data set, create a TensorFlow model, train the model on that data set, then convert the TF model into a TF Lite model. This TF Lite model can then be loaded onto a mobile device for audio classification. \n",
    "\n",
    "There are a variety of models that can be trained on a variety of data sets. This code is for training amplitude models, which are the most straight foraward. This guide aims to describe all of the parameters in the training process, so that you can change them and build your own models. Remember, the end goal is to acheive the highest validation accuracy possible before loading that model onto a phone for use.\n",
    "\n",
    "**Numbers to beat for our l6-data:** <br>\n",
    "*A good goal--* 85% val_acc <br>\n",
    "*Our best model--* 91% val_acc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports\n",
    "We use Keras (which is built on top of Tensorflow) to build and train our models. Librosa is used for audio processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# make sure kernel matches pip version\n",
    "!pip3 install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "# Tensorflow\n",
    "import tensorflow as tf\n",
    "from tensorflow.python.tools import freeze_graph\n",
    "from tensorflow.python.tools import optimize_for_inference_lib\n",
    "\n",
    "# Keras\n",
    "import keras\n",
    "from keras import regularizers\n",
    "from keras.models import Sequential\n",
    "from keras.layers import (Activation, Dense, Dropout, Flatten, Conv2D, Conv1D, \n",
    "                          MaxPooling2D, GlobalAveragePooling2D, MaxPooling1D, Lambda)\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.callbacks import Callback, ReduceLROnPlateau, ModelCheckpoint\n",
    "from keras.utils import to_categorical, multi_gpu_model\n",
    "import keras.backend as K\n",
    "\n",
    "import librosa\n",
    "import multiprocessing as mp\n",
    "import numpy as np\n",
    "import scipy.io.wavfile\n",
    "from scipy.fftpack import dct\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tqdm import tqdm as tqdm\n",
    "import time\n",
    "from pprint import pprint\n",
    "import uuid\n",
    "import glob\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Constants\n",
    "\n",
    "## General\n",
    "\n",
    "**RAW_DATA_DIR:** Where the raw training data is located. In the directory each folder name is a label, and its contents are .wav files corresponding to that label. See the \"l6-data\" directory for an example. \n",
    "\n",
    "**AMP_PROCESSED_DATA_DIR:** Where the processed training data is to be stored. After processing the data, it will be populated with numpy files. Each numpy file is named after a label, and contains all of the training data for that label stored as a 3d numpy array. More info on this later.\n",
    "\n",
    "**AUDIO_LENGTH:** The desired input size for the model. An input size of 44100 at 44100 Hz would be a one second input. \n",
    "\n",
    "**SAMPLE_RATE:** The sample rate of the microphone.\n",
    "\n",
    "## Training\n",
    "\n",
    "**channel:** We use a one channel audio input\n",
    "\n",
    "**epochs:** This is how many times the model will train on your data. 200 is usually a good number, there are diminishing returns after a certain point. \n",
    "\n",
    "**batch_size:** I don't even know what this is.\n",
    "\n",
    "**verbose:** 1 is true, 0 is false. Its always a good idea to have this on. \n",
    "\n",
    "**num_classes:** The number of classes (also referred to as labels) in your data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "RAW_DATA_DIR = \"data/\"\n",
    "AMP_PROCESSED_DATA_DIR = \"amp-processed-data/\"\n",
    "MODEL_NAME = 'Audio_Recorder'\n",
    "AUDIO_LENGTH = 44100\n",
    "SAMPLE_RATE = 44100\n",
    "\n",
    "channel = 1\n",
    "epochs = 1\n",
    "batch_size = 128\n",
    "verbose = 1\n",
    "num_classes = 6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Processing\n",
    "\n",
    "### get_labels(path)\n",
    "**Input:** `RAW_DATA_DIR` <br>\n",
    "**Output:** `Tuple (Labels, Indices of the labels, one-hot encoded labels)` <br>\n",
    "**Description:** Gets all labels (aka filenames) inside your raw data directory. The indices are the positions of these labels, and the hot encoded vector is a vector of zeros the length of the number of labels, with a 1 at the corresponding label's index. More info can be found about these online.\n",
    "\n",
    "### label_to_amplitude_vecs(args)\n",
    "**Input:** `Tuple (label, input_path, output_path, tqdm_position)`<br>\n",
    "**Output:** Shape of the generated numpy file (stored under `output_path`)<br>\n",
    "**Description:** This function is called by `process_data_amplitude()` in parallel to convert a label's raw .wav files to a single numpy file. This can take a while, so David made a super fancy tdqm display (hence the tqdm_position parameter). **This function will split the training data into overlapping arrays the size of input length, then store them in a 3d numpy array under the `output_path` dir.** It will move though the clip at a rate of AUDIO_LENGTH / 2, so if a clip is 10.1 seconds long, and the input length is 44100 with a 44100 Hz sampling rate, it will be processed as 21 one second clips. The final piece of the clip is padded with zeros to match the input length. This is done for all the .wav files in a label, which are converted to amplitude arrays with `librosa.load()`. The resulting numpy array contains the numbers that are actually trained on.\n",
    "\n",
    "### process_data_amplitude(input_path, output_path)\n",
    "**Input:** RAW_DATA_DIR, AMP_PROCESSED_DATA_DIR<br>\n",
    "**Output:** Shape of the numpy files stored in output_path<br>\n",
    "**Description:** Calls ` label_to_amplitude_vecs()`, and populates output_path with numpy files. Each numpy file is named after a label, and its content is a 3d numpy array with all the training data for that label. The shape of this array is (number_of_files, AUDIO_LENGTH, 1). The 3rd empty dimension is required by Keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_labels(path):\n",
    "    labels = [i for i in sorted(os.listdir(path)) if i[0] != \".\"]\n",
    "    label_indices = np.arange(0, len(labels))\n",
    "    return labels, label_indices, to_categorical(label_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def label_to_amplitude_vecs(args) -> None:\n",
    "    label, input_path, output_path, tqdm_position = args\n",
    "\n",
    "    # Get all audio files for this label\n",
    "    wavfiles = [os.path.join(input_path, label, wavfile) for wavfile in os.listdir(os.path.join(input_path, label))]\n",
    "\n",
    "    # tqdm is amazing, so print all the things this way\n",
    "    print(\" \", end=\"\", flush=True)\n",
    "    twavs = tqdm(wavfiles, position=tqdm_position)\n",
    "    \n",
    "    vectors = []\n",
    "    for i, wavfile in enumerate(twavs):\n",
    "        # Load the audio file; this also works for .flac files\n",
    "        audio_buf, _ = librosa.load(wavfile, mono=True, sr=SAMPLE_RATE)\n",
    "        audio_buf = audio_buf.reshape(-1, 1)\n",
    "        audio_buf = (audio_buf - np.mean(audio_buf)) / np.std(audio_buf)            \n",
    "        remaining_buf = audio_buf.copy()\n",
    "        while remaining_buf.shape[0] > AUDIO_LENGTH:\n",
    "            # Add the first AUDIO_LENGTH of the buffer as a new vector to train on\n",
    "            new_buf = remaining_buf[ : AUDIO_LENGTH ]\n",
    "            vectors.append(new_buf)\n",
    "            \n",
    "            # Remove 1/2 * AUDIO_LENGTH from the front of the buffer\n",
    "            remaining_buf = remaining_buf[ int(AUDIO_LENGTH / 2) : ]\n",
    "            \n",
    "        # Whatever is left, pad and stick in the training data\n",
    "        remaining_buf = np.concatenate((remaining_buf, np.zeros(shape=(AUDIO_LENGTH - len(remaining_buf), 1))))\n",
    "        vectors.append(remaining_buf)\n",
    "        \n",
    "        # Update tqdm\n",
    "        twavs.set_description(\"Label - '{}'\".format(label))\n",
    "        twavs.refresh()\n",
    "    np_vectors = np.array(vectors)\n",
    "    np.save(os.path.join(output_path, label + '.npy'), np_vectors)\n",
    "    return np_vectors.shape\n",
    "    \n",
    "def process_data_amplitude(input_path, output_path):\n",
    "    \n",
    "    labels, _, _ = get_labels(input_path)\n",
    "    pool = mp.Pool()\n",
    "    result = pool.map(label_to_amplitude_vecs, \n",
    "                     [(label, input_path, output_path, tqdm_position) \n",
    "                          for tqdm_position, label in enumerate(labels)])\n",
    "    pool.close()\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ls amp-processed-data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      "
     ]
    },
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      "Label - 'Knock':   0%|          | 2/407 [00:00<00:37, 10.82it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Computer_keyboard':   0%|          | 1/202 [00:00<00:38,  5.22it/s][A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Laughing':   1%|          | 2/398 [00:00<00:49,  8.02it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "Label - 'Knock':   0%|          | 2/407 [00:00<00:37, 10.82it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Label - 'Finger_snapping':   1%|          | 2/169 [00:00<00:13, 12.72it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Computer_keyboard':   0%|          | 1/202 [00:00<00:38,  5.22it/s][A\u001b[A\n",
      "\n",
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      "Label - 'Coughing':   1%|          | 3/286 [00:00<00:24, 11.75it/s]\u001b[A\n",
      "\n",
      "Label - 'Finger_snapping':   1%|          | 2/169 [00:00<00:13, 12.72it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Computer_keyboard':   1%|          | 2/202 [00:00<00:36,  5.51it/s]\u001b[A\u001b[A\n",
      "Label - 'Coughing':   1%|          | 3/286 [00:00<00:24, 11.75it/s]\u001b[A\n",
      "\n",
      "Label - 'Finger_snapping':   2%|▏         | 4/169 [00:00<00:13, 11.84it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "Label - 'Knock':   1%|          | 3/407 [00:00<00:40,  9.97it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
      "Label - 'Laughing':   1%|          | 2/398 [00:00<00:49,  8.02it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':   1%|▏         | 4/286 [00:00<00:27, 10.12it/s]\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Computer_keyboard':   1%|          | 2/202 [00:00<00:36,  5.51it/s]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Laughing':   1%|          | 3/398 [00:00<00:47,  8.25it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Computer_keyboard':   1%|          | 2/202 [00:00<00:36,  5.51it/s][A\u001b[A\n",
      "Label - 'Coughing':   1%|▏         | 4/286 [00:00<00:27, 10.12it/s]\u001b[A\n",
      "\n",
      "\n",
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      "\n",
      "\n",
      "Label - 'Computer_keyboard':   1%|▏         | 3/202 [00:00<00:31,  6.36it/s][A\u001b[A\n",
      "Label - 'Coughing':   1%|▏         | 4/286 [00:00<00:27, 10.12it/s]\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Label - 'Keys_jangling':   2%|▏         | 3/146 [00:00<00:23,  6.15it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Label - 'Laughing':   1%|          | 3/398 [00:00<00:47,  8.25it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Computer_keyboard':   1%|▏         | 3/202 [00:00<00:31,  6.36it/s]\n",
      "\n",
      "\n",
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      "\n",
      "\n",
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      "Label - 'Laughing':   1%|          | 4/398 [00:00<00:47,  8.29it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':   1%|▏         | 4/286 [00:00<00:27, 10.12it/s]\u001b[A\n",
      "\n",
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      "\n",
      "\n",
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      "Label - 'Coughing':   2%|▏         | 6/286 [00:00<00:26, 10.41it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Coughing':   2%|▏         | 6/286 [00:00<00:26, 10.41it/s]\u001b[A\n",
      "\n",
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      "Label - 'Coughing':   2%|▏         | 6/286 [00:00<00:26, 10.41it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':   1%|▏         | 5/398 [00:00<00:46,  8.38it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':   2%|▏         | 6/286 [00:00<00:26, 10.41it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Label - 'Laughing':   1%|▏         | 5/398 [00:00<00:46,  8.38it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':   3%|▎         | 8/286 [00:00<00:25, 10.76it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "Label - 'Laughing':   2%|▏         | 6/398 [00:00<00:46,  8.40it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "Label - 'Coughing':   3%|▎         | 8/286 [00:00<00:25, 10.76it/s]\u001b[A\n",
      "\n",
      "\n",
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      "\n",
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      "Label - 'Coughing':   3%|▎         | 9/286 [00:00<00:26, 10.43it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Coughing':   3%|▎         | 9/286 [00:00<00:26, 10.43it/s]\u001b[A\n",
      "\n",
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      "Label - 'Coughing':   3%|▎         | 9/286 [00:00<00:26, 10.43it/s]\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "Label - 'Knock':   2%|▏         | 9/407 [00:00<00:40,  9.92it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Computer_keyboard':   3%|▎         | 7/202 [00:00<00:27,  7.20it/s][A\u001b[A\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Knock':   2%|▏         | 9/407 [00:00<00:40,  9.92it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':   3%|▎         | 9/286 [00:00<00:26, 10.43it/s]\u001b[A\n",
      "Label - 'Coughing':   3%|▎         | 9/286 [00:00<00:26, 10.43it/s]\u001b[A\n",
      "Label - 'Coughing':   4%|▍         | 11/286 [00:01<00:25, 10.99it/s]\u001b[A\n",
      "\n",
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      "Label - 'Keys_jangling':   5%|▍         | 7/146 [00:01<00:17,  7.79it/s]\u001b[A\u001b[A\u001b[A\n",
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      "Label - 'Keys_jangling':   5%|▍         | 7/146 [00:01<00:17,  7.79it/s]\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':   4%|▍         | 11/286 [00:01<00:25, 10.99it/s]\u001b[A\n",
      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Keys_jangling':   6%|▌         | 9/146 [00:01<00:15,  8.79it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Coughing':   5%|▍         | 13/286 [00:01<00:23, 11.67it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Coughing':   5%|▍         | 13/286 [00:01<00:23, 11.67it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "Label - 'Coughing':   5%|▌         | 15/286 [00:01<00:25, 10.77it/s]\u001b[A\n",
      "\n",
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      "Label - 'Keys_jangling':   8%|▊         | 11/146 [00:01<00:15,  8.58it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Keys_jangling':   8%|▊         | 11/146 [00:01<00:15,  8.58it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "\n",
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      "Label - 'Knock':   4%|▍         | 17/407 [00:01<00:29, 13.23it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Coughing':   6%|▌         | 17/286 [00:01<00:24, 10.82it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Coughing':   7%|▋         | 21/286 [00:01<00:21, 12.37it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
      "Label - 'Finger_snapping':   2%|▏         | 4/169 [00:01<00:13, 11.84it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Computer_keyboard':   9%|▉         | 19/202 [00:01<00:14, 12.50it/s]\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  13%|█▎        | 19/146 [00:01<00:10, 12.23it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Coughing':   8%|▊         | 23/286 [00:02<00:19, 13.58it/s]\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  13%|█▎        | 19/146 [00:02<00:10, 12.23it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Label - 'Finger_snapping':   3%|▎         | 5/169 [00:02<01:30,  1.81it/s]\u001b[A\u001b[A\n",
      "Label - 'Coughing':   8%|▊         | 23/286 [00:02<00:19, 13.58it/s]\u001b[A\n",
      "\n",
      "\n",
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      "\n",
      "Label - 'Computer_keyboard':  10%|█         | 21/202 [00:02<00:13, 12.99it/s]\u001b[A\n",
      "\n",
      "Label - 'Finger_snapping':   3%|▎         | 5/169 [00:02<01:30,  1.81it/s]\u001b[A\u001b[A\n",
      "Label - 'Coughing':   8%|▊         | 23/286 [00:02<00:19, 13.58it/s]\u001b[A\n",
      "Label - 'Coughing':   8%|▊         | 23/286 [00:02<00:19, 13.58it/s]\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  14%|█▍        | 21/146 [00:02<00:09, 13.13it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "Label - 'Finger_snapping':   3%|▎         | 5/169 [00:02<01:30,  1.81it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
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      "Label - 'Coughing':   9%|▊         | 25/286 [00:02<00:19, 13.11it/s]\u001b[A\n",
      "\n",
      "Label - 'Finger_snapping':   4%|▍         | 7/169 [00:02<01:06,  2.45it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "Label - 'Coughing':   9%|▊         | 25/286 [00:02<00:19, 13.11it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Coughing':   9%|▊         | 25/286 [00:02<00:19, 13.11it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Coughing':   9%|▊         | 25/286 [00:02<00:19, 13.11it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "Label - 'Keys_jangling':  16%|█▌        | 23/146 [00:02<00:10, 12.01it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Coughing':   9%|▉         | 27/286 [00:02<00:21, 11.89it/s]\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  17%|█▋        | 25/146 [00:02<00:09, 12.36it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Label - 'Keys_jangling':  17%|█▋        | 25/146 [00:02<00:09, 12.36it/s]\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':   9%|▉         | 27/286 [00:02<00:21, 11.89it/s]\u001b[A\n",
      "\n",
      "\n",
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      "Label - 'Coughing':   9%|▉         | 27/286 [00:02<00:21, 11.89it/s]\u001b[A\n",
      "\n",
      "Label - 'Finger_snapping':   6%|▌         | 10/169 [00:02<00:38,  4.08it/s]\u001b[A\u001b[A\n",
      "Label - 'Computer_keyboard':  12%|█▏        | 25/202 [00:02<00:14, 12.09it/s]\n",
      "\n",
      "Label - 'Computer_keyboard':  12%|█▏        | 25/202 [00:02<00:14, 12.09it/s]A\u001b[A\n",
      "Label - 'Coughing':  10%|█         | 29/286 [00:02<00:21, 12.20it/s]\u001b[A\n",
      "Label - 'Coughing':  10%|█         | 29/286 [00:02<00:21, 12.20it/s]\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Laughing':   2%|▏         | 6/398 [00:02<00:46,  8.40it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Computer_keyboard':  12%|█▏        | 25/202 [00:02<00:14, 12.09it/s][A\u001b[A\n",
      "\n",
      "Label - 'Finger_snapping':   6%|▌         | 10/169 [00:02<00:38,  4.08it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Laughing':   2%|▏         | 6/398 [00:02<00:46,  8.40it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "Label - 'Computer_keyboard':  12%|█▏        | 25/202 [00:02<00:14, 12.09it/s]\n",
      "\n",
      "Label - 'Finger_snapping':   6%|▌         | 10/169 [00:02<00:38,  4.08it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Laughing':   2%|▏         | 7/398 [00:02<04:02,  1.61it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Computer_keyboard':  13%|█▎        | 27/202 [00:02<00:13, 12.52it/s]\n",
      "\n",
      "Label - 'Finger_snapping':   7%|▋         | 12/169 [00:02<00:30,  5.13it/s]\u001b[A\u001b[A\n",
      "Label - 'Coughing':  11%|█         | 31/286 [00:02<00:19, 13.11it/s]\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  18%|█▊        | 27/146 [00:02<00:10, 11.84it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  18%|█▊        | 27/146 [00:02<00:10, 11.84it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Laughing':   2%|▏         | 7/398 [00:02<04:02,  1.61it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Coughing':  11%|█         | 31/286 [00:02<00:19, 13.11it/s]\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Laughing':   2%|▏         | 7/398 [00:02<04:02,  1.61it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Coughing':  11%|█         | 31/286 [00:02<00:19, 13.11it/s]\u001b[A\n",
      "\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Laughing':   2%|▏         | 8/398 [00:02<03:03,  2.12it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  18%|█▊        | 27/146 [00:02<00:10, 11.84it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Label - 'Keys_jangling':  20%|█▉        | 29/146 [00:02<00:09, 12.01it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Label - 'Finger_snapping':   7%|▋         | 12/169 [00:02<00:30,  5.13it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
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      "\n",
      "Label - 'Finger_snapping':   8%|▊         | 14/169 [00:02<00:25,  6.17it/s]\u001b[A\u001b[A\n",
      "Label - 'Coughing':  11%|█         | 31/286 [00:02<00:19, 13.11it/s]\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Computer_keyboard':  14%|█▍        | 29/202 [00:02<00:14, 11.83it/s][A\u001b[A\n",
      "Label - 'Coughing':  11%|█         | 31/286 [00:02<00:19, 13.11it/s]\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Laughing':   2%|▏         | 9/398 [00:02<02:20,  2.76it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Computer_keyboard':  15%|█▌        | 31/202 [00:02<00:14, 11.83it/s]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
      "\n",
      "Label - 'Keys_jangling':  27%|██▋       | 39/146 [00:03<00:09, 11.57it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Label - 'Finger_snapping':  15%|█▌        | 26/169 [00:03<00:13, 10.50it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':   4%|▍         | 17/398 [00:03<00:57,  6.68it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Label - 'Computer_keyboard':  20%|██        | 41/202 [00:03<00:14, 11.15it/s]A\u001b[A\n",
      "Label - 'Coughing':  15%|█▌        | 43/286 [00:03<00:23, 10.55it/s]\u001b[A\n",
      "\n",
      "\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':   4%|▍         | 17/398 [00:03<00:57,  6.68it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':  15%|█▌        | 43/286 [00:03<00:23, 10.55it/s]\u001b[A\n",
      "\n",
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      "Label - 'Keys_jangling':  28%|██▊       | 41/146 [00:03<00:09, 11.12it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Laughing':   5%|▍         | 18/398 [00:03<00:58,  6.44it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Coughing':  16%|█▌        | 45/286 [00:03<00:21, 11.01it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':   5%|▍         | 18/398 [00:03<00:58,  6.44it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Coughing':  16%|█▌        | 45/286 [00:03<00:21, 11.01it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Laughing':   5%|▍         | 19/398 [00:03<00:53,  7.03it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "Label - 'Coughing':  16%|█▌        | 45/286 [00:04<00:21, 11.01it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Label - 'Laughing':   5%|▍         | 19/398 [00:04<00:53,  7.03it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':  16%|█▋        | 47/286 [00:04<00:22, 10.48it/s]\u001b[A\n",
      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':   5%|▌         | 20/398 [00:04<00:56,  6.72it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Label - 'Laughing':   5%|▌         | 20/398 [00:04<00:56,  6.72it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
      "Label - 'Laughing':   5%|▌         | 20/398 [00:04<00:56,  6.72it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':   5%|▌         | 21/398 [00:04<00:50,  7.41it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Coughing':  17%|█▋        | 49/286 [00:04<00:22, 10.69it/s]\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  31%|███       | 45/146 [00:04<00:09, 10.90it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "Label - 'Keys_jangling':  32%|███▏      | 47/146 [00:04<00:08, 11.00it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Computer_keyboard':  23%|██▎       | 47/202 [00:04<00:13, 11.26it/s]\n",
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      "\n",
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      "\n",
      "\n",
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      "Label - 'Coughing':  18%|█▊        | 51/286 [00:04<00:19, 11.85it/s]\u001b[A\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "Label - 'Knock':   8%|▊         | 33/407 [00:04<00:50,  7.35it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Coughing':  18%|█▊        | 51/286 [00:04<00:19, 11.85it/s]\u001b[A\n",
      "\n",
      "\n",
      "\n",
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      "\n",
      "\n",
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      "Label - 'Coughing':  18%|█▊        | 51/286 [00:04<00:19, 11.85it/s]\u001b[A\n",
      "\n",
      "\n",
      "\n",
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      "\n",
      "Label - 'Finger_snapping':  20%|██        | 34/169 [00:04<00:11, 11.69it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "Label - 'Finger_snapping':  20%|██        | 34/169 [00:04<00:11, 11.69it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Computer_keyboard':  23%|██▎       | 47/202 [00:04<00:13, 11.26it/s][A\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Knock':   9%|▊         | 35/407 [00:04<00:54,  6.87it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Computer_keyboard':  24%|██▍       | 49/202 [00:04<00:12, 12.09it/s]\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Knock':   9%|▊         | 35/407 [00:04<00:54,  6.87it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':  18%|█▊        | 51/286 [00:04<00:19, 11.85it/s]\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  34%|███▎      | 49/146 [00:04<00:08, 11.50it/s]\u001b[A\u001b[A\u001b[A\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Computer_keyboard':  24%|██▍       | 49/202 [00:04<00:12, 12.09it/s]\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  34%|███▎      | 49/146 [00:04<00:08, 11.50it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "Label - 'Knock':   9%|▊         | 35/407 [00:04<00:54,  6.87it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
      "Label - 'Laughing':   5%|▌         | 21/398 [00:04<00:50,  7.41it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':  19%|█▊        | 53/286 [00:04<00:19, 12.23it/s]\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  34%|███▎      | 49/146 [00:04<00:08, 11.50it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n",
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      "Label - 'Coughing':  19%|█▊        | 53/286 [00:04<00:19, 12.23it/s]\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  34%|███▎      | 49/146 [00:04<00:08, 11.50it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
      "\n",
      "Label - 'Keys_jangling':  35%|███▍      | 51/146 [00:04<00:07, 12.14it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Label - 'Laughing':   6%|▌         | 22/398 [00:04<01:17,  4.85it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
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      "Label - 'Coughing':  19%|█▉        | 55/286 [00:04<00:18, 12.74it/s]\u001b[A\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Label - 'Laughing':   6%|▌         | 23/398 [00:04<01:05,  5.68it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':  19%|█▉        | 55/286 [00:04<00:18, 12.74it/s]\u001b[A\n",
      "\n",
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      "Label - 'Keys_jangling':  35%|███▍      | 51/146 [00:04<00:07, 12.14it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Coughing':  20%|█▉        | 57/286 [00:04<00:17, 13.26it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':   7%|▋         | 27/398 [00:05<01:00,  6.16it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Laughing':   7%|▋         | 27/398 [00:05<01:00,  6.16it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Keys_jangling':  39%|███▉      | 57/146 [00:05<00:07, 12.11it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "Label - 'Laughing':   7%|▋         | 28/398 [00:05<00:55,  6.63it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Knock':  10%|█         | 41/407 [00:05<00:52,  6.97it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  40%|████      | 59/146 [00:05<00:08, 10.56it/s]\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':  21%|██▏       | 61/286 [00:05<00:20, 11.23it/s]\u001b[A\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Finger_snapping':  24%|██▎       | 40/169 [00:05<00:14,  8.63it/s]\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':   7%|▋         | 28/398 [00:05<00:55,  6.63it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Keys_jangling':  43%|████▎     | 63/146 [00:05<00:07, 11.10it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "Label - 'Keys_jangling':  43%|████▎     | 63/146 [00:05<00:07, 11.10it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Keys_jangling':  43%|████▎     | 63/146 [00:05<00:07, 11.10it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':   8%|▊         | 30/398 [00:06<00:50,  7.22it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':   8%|▊         | 31/398 [00:06<01:34,  3.90it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Coughing':  26%|██▌       | 73/286 [00:06<00:14, 14.96it/s]\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  48%|████▊     | 70/146 [00:06<00:05, 14.25it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Laughing':   8%|▊         | 31/398 [00:06<01:34,  3.90it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Knock':  12%|█▏        | 48/407 [00:06<00:54,  6.58it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Keys_jangling':  48%|████▊     | 70/146 [00:06<00:05, 14.25it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Coughing':  26%|██▌       | 73/286 [00:06<00:14, 14.96it/s]\u001b[A\n",
      "\n",
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      "Label - 'Laughing':   8%|▊         | 31/398 [00:06<01:34,  3.90it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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      "Label - 'Coughing':  26%|██▌       | 73/286 [00:06<00:14, 14.96it/s]\u001b[A\n",
      "\n",
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      "Label - 'Keys_jangling':  48%|████▊     | 70/146 [00:06<00:05, 14.25it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Label - 'Computer_keyboard':  25%|██▌       | 51/202 [00:06<00:11, 12.65it/s]A\u001b[A\n",
      "\n",
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      "Label - 'Laughing':   8%|▊         | 32/398 [00:06<01:21,  4.49it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':   8%|▊         | 33/398 [00:06<01:08,  5.34it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':   8%|▊         | 33/398 [00:06<01:08,  5.34it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':  27%|██▋       | 77/286 [00:06<00:17, 11.97it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "Label - 'Knock':  13%|█▎        | 51/407 [00:06<00:47,  7.49it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Laughing':   9%|▊         | 34/398 [00:06<01:00,  6.01it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Knock':  14%|█▎        | 55/407 [00:06<00:52,  6.75it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Coughing':  33%|███▎      | 94/286 [00:07<00:15, 12.39it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Coughing':  33%|███▎      | 94/286 [00:07<00:15, 12.39it/s]\u001b[A\n",
      "\n",
      "Label - 'Computer_keyboard':  29%|██▉       | 59/202 [00:07<00:26,  5.38it/s]A\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Coughing':  33%|███▎      | 94/286 [00:07<00:15, 12.39it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Laughing':  11%|█▏        | 45/398 [00:07<00:41,  8.55it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Coughing':  33%|███▎      | 94/286 [00:07<00:15, 12.39it/s]\u001b[A\n",
      "\n",
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      "Label - 'Laughing':  11%|█▏        | 45/398 [00:07<00:41,  8.55it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Knock':  15%|█▍        | 60/407 [00:07<00:46,  7.53it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':  34%|███▎      | 96/286 [00:07<00:15, 12.59it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "Label - 'Coughing':  36%|███▋      | 104/286 [00:08<00:15, 11.39it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Laughing':  14%|█▎        | 54/398 [00:08<00:33, 10.38it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Computer_keyboard':  35%|███▌      | 71/202 [00:09<00:19,  6.88it/s]\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Label - 'Finger_snapping':  40%|████      | 68/169 [00:09<00:18,  5.41it/s]\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Coughing':  40%|███▉      | 114/286 [00:09<00:18,  9.43it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Coughing':  43%|████▎     | 122/286 [00:10<00:13, 12.53it/s]\u001b[A\n",
      "\n",
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      "Label - 'Laughing':  17%|█▋        | 67/398 [00:10<00:49,  6.65it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
      "\n",
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      "Label - 'Coughing':  43%|████▎     | 122/286 [00:10<00:13, 12.53it/s]\u001b[A\n",
      "\n",
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      "Label - 'Laughing':  17%|█▋        | 69/398 [00:10<00:42,  7.70it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':  43%|████▎     | 122/286 [00:10<00:13, 12.53it/s]\u001b[A\n",
      "Label - 'Coughing':  43%|████▎     | 124/286 [00:10<00:12, 13.06it/s]\u001b[A\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Keys_jangling':  77%|███████▋  | 113/146 [00:10<00:02, 11.95it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "\n",
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      "\n",
      "\n",
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      "\n",
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      "Label - 'Laughing':  17%|█▋        | 69/398 [00:10<00:42,  7.70it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Laughing':  17%|█▋        | 69/398 [00:10<00:42,  7.70it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Keys_jangling':  79%|███████▉  | 115/146 [00:10<00:02, 12.91it/s]\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':  19%|█▉        | 76/398 [00:10<00:34,  9.40it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Coughing':  46%|████▌     | 132/286 [00:11<00:12, 12.13it/s]\u001b[A\n",
      "\n",
      "\n",
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      "\n",
      "\n",
      "Label - 'Keys_jangling':  83%|████████▎ | 121/146 [00:11<00:02, 10.82it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Knock':  22%|██▏       | 88/407 [00:11<00:43,  7.31it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Keys_jangling':  86%|████████▌ | 125/146 [00:11<00:01, 11.33it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
      "Label - 'Finger_snapping':  51%|█████     | 86/169 [00:11<00:07, 10.96it/s]\u001b[A\u001b[A\n",
      "\n",
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      "Label - 'Laughing':  20%|██        | 80/398 [00:11<00:28, 11.33it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "Label - 'Coughing':  48%|████▊     | 136/286 [00:11<00:11, 12.87it/s]\u001b[A\n",
      "\n",
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      "Label - 'Laughing':  20%|██        | 80/398 [00:11<00:28, 11.33it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Keys_jangling':  87%|████████▋ | 127/146 [00:11<00:01, 12.02it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "Label - 'Knock':  22%|██▏       | 89/407 [00:11<00:40,  7.92it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Laughing':  21%|██        | 82/398 [00:11<00:41,  7.69it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
      "Label - 'Keys_jangling':  90%|████████▉ | 131/146 [00:11<00:01, 10.81it/s]\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Coughing':  50%|████▉     | 142/286 [00:11<00:12, 11.79it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "Label - 'Laughing':  21%|██        | 82/398 [00:11<00:41,  7.69it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
      "Label - 'Finger_snapping':  53%|█████▎    | 90/169 [00:11<00:07, 10.62it/s]\u001b[A\u001b[A\n",
      "\n",
      "\n",
      "Label - 'Keys_jangling':  90%|████████▉ | 131/146 [00:11<00:01, 10.81it/s]\u001b[A\u001b[A\u001b[A\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Coughing':  50%|████▉     | 142/286 [00:11<00:12, 11.79it/s]\u001b[A\n",
      "\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "\n",
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      "Label - 'Laughing':  22%|██▏       | 86/398 [00:12<00:33,  9.18it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
      "Label - 'Finger_snapping':  56%|█████▌    | 94/169 [00:12<00:09,  8.24it/s]\u001b[A\u001b[A\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Coughing':  53%|█████▎    | 152/286 [00:12<00:10, 12.53it/s]\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "Label - 'Computer_keyboard':  44%|████▍     | 89/202 [00:13<00:16,  6.92it/s]\n",
      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Coughing':  57%|█████▋    | 162/286 [00:13<00:10, 11.68it/s]\u001b[A\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Coughing':  57%|█████▋    | 162/286 [00:13<00:10, 11.68it/s]\u001b[A\n",
      "\n",
      "Label - 'Finger_snapping':  59%|█████▉    | 100/169 [00:13<00:08,  7.99it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
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      "\n",
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      "\n",
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      "Label - 'Computer_keyboard':  45%|████▌     | 91/202 [00:14<00:22,  4.98it/s]\n",
      "\n",
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      "\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Computer_keyboard':  45%|████▌     | 91/202 [00:14<00:22,  4.98it/s]A\u001b[A\u001b[A\n",
      "\n",
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      "\n",
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      "\n",
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      "\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
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      "\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Finger_snapping':  78%|███████▊  | 132/169 [00:17<00:05,  6.40it/s]\u001b[A\u001b[A\n",
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      "\n",
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      "\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Laughing':  40%|███▉      | 158/398 [00:17<00:19, 12.56it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Coughing':  84%|████████▍ | 241/286 [00:18<00:02, 15.81it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Coughing':  89%|████████▉ | 255/286 [00:19<00:02, 15.28it/s]\u001b[A\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Coughing':  94%|█████████▍| 270/286 [00:20<00:01, 15.05it/s]\u001b[A\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Laughing':  51%|█████     | 202/398 [00:26<01:17,  2.54it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
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      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Laughing':  58%|█████▊    | 232/398 [00:29<00:11, 14.29it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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     ]
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    {
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     "output_type": "stream",
     "text": [
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      "\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Laughing':  67%|██████▋   | 267/398 [00:45<00:33,  3.95it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Laughing':  77%|███████▋  | 306/398 [00:46<00:03, 24.75it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Label - 'Laughing':  87%|████████▋ | 347/398 [00:48<00:01, 27.16it/s]\u001b[A\u001b[A\u001b[A\u001b[A\u001b[A\n",
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      "\n",
      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "[(4255, 44100, 1),\n",
       " (2930, 44100, 1),\n",
       " (911, 44100, 1),\n",
       " (2204, 44100, 1),\n",
       " (3043, 44100, 1),\n",
       " (6057, 44100, 1)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "process_data_amplitude(RAW_DATA_DIR, AMP_PROCESSED_DATA_DIR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the Model\n",
    "\n",
    "### get_train_test(split_ratio: float)\n",
    "**Inputs:** `split_ratio` <br>\n",
    "**Outputs:** `X_train, X_test, y_train, y_test` <br>\n",
    "**Description:** Uses a sklearn library to split the processed data into training data and test data. We use a .75 ratio of training to test data. X_train is the training data, which is a 3d array with dimnesions (number_of_clips, input_size, 1). Again, the 3rd dimension is a requirement from Keras. y_train is a 1d array containing label indices that correspond to each clip in X_train. X_test and y_test are the same, but for testing data instead of training data. The testing data is used to calculate accuracy metrics during training.\n",
    "### get_model()\n",
    "**Inputs:** None <br>\n",
    "**Outputs:** `Tensorflow Model` <br> \n",
    "**Description:** This function is where the model itself is built. The input size must match the clip length (e.g. our input size variable), and the output must be a set of weights corresponding to each label (so an array of length labels). The highest weight will correspond to the final classification. The models we use are CNNs, which are built layer by layer. In general, a larger input size means that more layers can be used to learn on that data. Because the amplitude input is fairly large (44000 elements, compared to MFCC with just 11136), this model should be fairly large as well. Any model with the proper input and output sizes can be loaded. In our case, the input shape was (44100,1), as Keras required the extra dimension. \n",
    "\n",
    "### train(model, X_train, y_train_hot, X_test, y_test_hot)\n",
    "**Inputs:** `model, X_train, y_train_hot, X_test, y_test_hot`<br>\n",
    "**Outputs:** The given model is trained on the given data. <br>\n",
    "**Description:** We use 'adam' optimization during training, as it usually results in much higher accuracies. We also save the model at its highest validation accuracies as it trains, so that we can retrieve the model from the epoch with the highest accuracy. These are saved as \"weights-{val_acc}.hdf5\", and can be converted to TF Lite models later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def get_train_test(split_ratio: float):\n",
    "    \n",
    "    assert split_ratio < 1 and split_ratio > 0\n",
    "    \n",
    "    # Get available labels\n",
    "    labels, indices, _ = get_labels(RAW_DATA_DIR)\n",
    "\n",
    "    # Getting first arrays\n",
    "    X = np.load(AMP_PROCESSED_DATA_DIR + labels[0] + '.npy')\n",
    "    y = np.zeros(X.shape[0])\n",
    "\n",
    "    # Append all of the dataset into one single array, same goes for y\n",
    "    for i, label in tqdm(enumerate(labels[1:])):\n",
    "        \n",
    "        x = np.load(AMP_PROCESSED_DATA_DIR + label + '.npy')\n",
    "        print(AMP_PROCESSED_DATA_DIR + label + '.npy')\n",
    "        print (X.shape)\n",
    "        print (x.shape)\n",
    "        X = np.vstack((X, x))\n",
    "        y = np.append(y, np.full(x.shape[0], fill_value= (i + 1)))\n",
    "\n",
    "    assert X.shape[0] == len(y)\n",
    "\n",
    "    # # Loading train set and test set\n",
    "    return train_test_split(X, y, test_size= (1 - split_ratio), random_state=42, shuffle=True)\n",
    "\n",
    "X_train, X_test, y_train, y_test = get_train_test(0.75)\n",
    "        \n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)\n",
    "\n",
    "print(\"Converting to categorical\")\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)\n",
    "\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model():\n",
    "    m = Sequential()\n",
    "    m.add(Conv1D(64,\n",
    "                 input_shape=[AUDIO_LENGTH, 1],\n",
    "                 kernel_size=80,\n",
    "                 strides=4,\n",
    "                 padding='same',\n",
    "                 kernel_initializer='glorot_uniform',\n",
    "                 kernel_regularizer=regularizers.l2(l=0.0001),\n",
    "                 name='voice'))\n",
    "    m.add(BatchNormalization())\n",
    "    m.add(Activation('relu'))\n",
    "    m.add(MaxPooling1D(pool_size=4, strides=None))\n",
    "\n",
    "    for i in range(2):\n",
    "        m.add(Conv1D(64,\n",
    "                     kernel_size=3,\n",
    "                     strides=1,\n",
    "                     padding='same',\n",
    "                     kernel_initializer='glorot_uniform',\n",
    "                     kernel_regularizer=regularizers.l2(l=0.0001)))\n",
    "        m.add(BatchNormalization())\n",
    "        m.add(Activation('relu'))\n",
    "    m.add(MaxPooling1D(pool_size=4, strides=None))\n",
    "\n",
    "    for i in range(2):\n",
    "        m.add(Conv1D(128,\n",
    "                     kernel_size=3,\n",
    "                     strides=1,\n",
    "                     padding='same',\n",
    "                     kernel_initializer='glorot_uniform',\n",
    "                     kernel_regularizer=regularizers.l2(l=0.0001)))\n",
    "        m.add(BatchNormalization())\n",
    "        m.add(Activation('relu'))\n",
    "    m.add(MaxPooling1D(pool_size=4, strides=None))\n",
    "\n",
    "    for i in range(3):\n",
    "        m.add(Conv1D(256,\n",
    "                     kernel_size=3,\n",
    "                     strides=1,\n",
    "                     padding='same',\n",
    "                     kernel_initializer='glorot_uniform',\n",
    "                     kernel_regularizer=regularizers.l2(l=0.0001)))\n",
    "        m.add(BatchNormalization())\n",
    "        m.add(Activation('relu'))\n",
    "    m.add(MaxPooling1D(pool_size=4, strides=None))\n",
    "\n",
    "    for i in range(2):\n",
    "        m.add(Conv1D(512,\n",
    "                     kernel_size=3,\n",
    "                     strides=1,\n",
    "                     padding='same',\n",
    "                     kernel_initializer='glorot_uniform',\n",
    "                     kernel_regularizer=regularizers.l2(l=0.0001)))\n",
    "        m.add(BatchNormalization())\n",
    "        m.add(Activation('relu'))\n",
    "\n",
    "    m.add(Lambda(lambda x: K.mean(x, axis=1))) # Same as GAP for 1D Conv Layer\n",
    "    m.add(Dense(num_classes, activation='softmax', name='label'))\n",
    "    return m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(model, X_train, y_train_hot, X_test, y_test_hot):\n",
    "    model.compile(optimizer='adam',\n",
    "                  loss='categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "    print(model.summary())\n",
    "    \n",
    "    reduce_lr = ReduceLROnPlateau(monitor='val_acc', factor=0.5, patience=10, min_lr=0.0001, verbose=1)\n",
    "    mcp_save = ModelCheckpoint('out/weights-{val_acc:.2f}.hdf5', save_best_only=True, monitor='val_acc', mode='max')\n",
    "    model.fit(X_train, \n",
    "              y_train_hot, \n",
    "              batch_size=batch_size, \n",
    "              epochs=epochs, \n",
    "              verbose=verbose, \n",
    "              validation_data=(X_test, y_test_hot),\n",
    "              callbacks=[reduce_lr, mcp_save],\n",
    "              shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "if not os.path.exists('out'):\n",
    "    os.mkdir('out')\n",
    "        \n",
    "model = get_model()\n",
    "train(model, X_train, y_train, X_test, y_test)\n",
    "model.save(\"out/amplitude-l6.hdf5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Converting to TF Lite\n",
    "**frozen_model_name:** The frozen model to convert. These are saved automatically in the `out/` directory during training.<br>\n",
    "**tf_lite_model_name:** The desired name of your TF model. We follow the following format: `dataset_feature_'i'inputsize_'l'labelsize`. So, a model trained on 6 office sounds with 1 second of amplitude at a sample rate of 44100 Hz would be named `OfficeSounds_Amplitude_i44100_l6.tflite`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "frozen_model_name = 'out/weights-0.91.hdf5'\n",
    "tf_lite_model_name = \"amplitude-l6-acc91.tflite\"\n",
    "\n",
    "converter = tf.lite.TFLiteConverter.from_keras_model_file(frozen_model)\n",
    "tfmodel = converter.convert()\n",
    "open (tf_lite_name, \"wb\") .write(tfmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting\n",
    "A possibly unfinished, definitely never used function to actually predict labels on the computer side. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(filepath, model):\n",
    "    \"\"\"Predict the classification of a single audio sample\"\"\"\n",
    "    audio_buf, _ = librosa.load(filepath, mono=True, sr=SAMPLE_RATE)\n",
    "    audio_buf = audio_buf.reshape(-1, 1)\n",
    "    audio_buf = (audio_buf - np.mean(audio_buf)) / np.std(audio_buf)\n",
    "\n",
    "    remaining_buf = audio_buf.copy()\n",
    "    wave_vectors = []\n",
    "    while remaining_buf.shape[0] > AUDIO_LENGTH:\n",
    "        # Add the first AUDIO_LENGTH of the buffer as a new vector to train on\n",
    "        new_buf = remaining_buf[ : AUDIO_LENGTH ]\n",
    "        wave_vectors.append(new_buf)\n",
    "\n",
    "        # Shrink the buffer by AUDIO_LENGTH\n",
    "        remaining_buf = remaining_buf[ AUDIO_LENGTH : ]\n",
    "\n",
    "    # Whatever is left, pad and stick in the training data\n",
    "    remaining_buf = np.concatenate((remaining_buf, np.zeros(shape=(AUDIO_LENGTH - len(remaining_buf), 1))))\n",
    "    wave_vectors.append(remaining_buf)\n",
    "    \n",
    "    # wave_vectors now contains every segment of the provided audio input. \n",
    "    # Get the classification for the sample by predicting on each segment, \n",
    "    # and then taking a majority vote.\n",
    "    \n",
    "    def most_common(lst):\n",
    "        return max(set(lst), key=lst.count)\n",
    "    \n",
    "    def most_confident(lst):\n",
    "        \"\"\"Return the label with the highest confidence\"\"\"\n",
    "        return \n",
    "    \n",
    "    labels, _, _ = get_labels(DATA_PATH)\n",
    "    predicted_labels = []\n",
    "    predicted_confs = []\n",
    "    \n",
    "#     for i, vec in enumerate(wave_vectors):\n",
    "#         inp = np.array([vec])\n",
    "#         label_vec = model.predict(inp)\n",
    "#         best_label_idx = np.argmax(label_vec, axis=1)[0]\n",
    "#         predicted_confs.append(np.asscalar(np.max(label_vec, axis=1)))\n",
    "#         predicted_labels.append(best_label_idx)\n",
    "# #         print(\"label for {}: {}\".format(i, best_label_idx))\n",
    "\n",
    "    label_vec = model.predict(wave_vectors)\n",
    "    best_label_idx = np.argmax(label_vec, axis=1)[0]\n",
    "    predicted_confs.append(np.asscalar(np.max(label_vec, axis=1)))\n",
    "    predicted_labels.append(best_label_idx)\n",
    "#         print(\"label for {}: {}\".format(i, best_label_idx))\n",
    "        \n",
    "    print(predicted_labels)\n",
    "    print(predicted_confs)\n",
    "    \n",
    "    # returns the label recognized with the highest confidence\n",
    "    best_total_label_idx = predicted_labels[np.argmax(predicted_confs)]\n",
    "    \n",
    "    return best_total_label_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "num_correct = 0\n",
    "num_total = 0\n",
    "# files = glob.glob(\"esc50/keyboard_typing/*.wav\")\n",
    "files = glob.glob(\"keyboard.wav\")\n",
    "for f in files:\n",
    "    label_idx = predict(f, model)\n",
    "    \n",
    "    string_label = labels[label_idx]\n",
    "    print(\"Predicted label: {}\".format(string_label))\n",
    "    \n",
    "    if label_idx == 3:\n",
    "        num_correct += 1\n",
    "    num_total += 1\n",
    "print(\"Number correct: {}\".format(num_correct))\n",
    "print(\"Total files: {}\".format(num_total))"
   ]
  }
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