example_multi_spectrogram_classificaiton_phoneme_multi_resolution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example: phoneme classification with multiple spectrogram\n",
    "Experimental notebook by @ste\n",
    "\n",
    "TL;DR\n",
    "\n",
    "The current dataset has a lot of short words and some long words.\n",
    "If you look at the samples of \"single spectrogram\" notebook you'll see that for short sounds almost all the resulting spectrogram is \"silence\".\n",
    "\n",
    "So I've tried to create a sample notebook that, instead of a single channel, transform the \"augmented\" sound (volume, noise, ...) into a three channel image where:\n",
    " - channel 0: is the complete spectrogram (like single spectrogram notebook)\n",
    " - channel 1: contains a spectrogram created with the first 50% of the sound (for medium lenght sounds)\n",
    " - channel 2: is the spectrogram for short sound \n",
    " \n",
    "**With the single spectrogram approach (using 10% of train/valid) I've got 29% accouracy, with multi spectrogram approach I've got 37%.**"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The key point is understanding the \"chain of transformations\" that takes the original data (a \"sound\") and converts them it into an suitable input for the model.\n",
    "This chain comprises two main parts:\n",
    "* DATA AUGMENTATION: f(sound) -> sound \n",
    "* STRUCTURE TRANSFORM: f(sound) -> tensor"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NB: theese images are part of a coming post on that matter... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Notebook settings\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## fastai imports\n",
    "from fastai.basics import *\n",
    "from fastai.data_block import ItemList\n",
    "from fastai.vision import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import FastAI Audio modules here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from exp.nb_AudioCommon import *\n",
    "from exp.nb_DataBlock import *\n",
    "from exp.nb_DataAugmentation import *\n",
    "from exp.nb_AudioTransformsManager import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other library imports"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this cell fails, you probably haven't installed the correct system/Python dependencies. You might need to re-run the \"System setup\" cell again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from pdb import set_trace\n",
    "from IPython.display import Audio\n",
    "from pydub import AudioSegment, silence\n",
    "import torchaudio\n",
    "from torchaudio import transforms\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Options\n",
    "pd.set_option('display.max_colwidth', -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to do **phoneme classification** using the TIMIT dataset.\n",
    "\n",
    "The copyright on TIMIT isn't clear, and the data is hard to download, so we assume that the user has access to this dataset.\n",
    "\n",
    "**IMPORTANT:** the dataset should be located in the standard fast.ai location:\n",
    "> $HOME/.fastai/data/timit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fast.ai way to get folder for your dataset\n",
    "path = datapath4file('timit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parsing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEST PASED\n"
     ]
    }
   ],
   "source": [
    "def extractInformationsFromAbsolutePath(s: str):\n",
    "    items = list(filter(lambda x: len(x)>0,s.split('/')))\n",
    "    USAGE = items[0]\n",
    "    DIALECT = items[1]\n",
    "    SEX = items[2][0]\n",
    "    SPEAKER_ID = items[2][1:]\n",
    "    SENTENCE_ID = items[3]\n",
    "    return USAGE, DIALECT, SEX, SPEAKER_ID, SENTENCE_ID\n",
    "    \n",
    "def test_extractInformationsFromAbsolutePath():\n",
    "    USAGE, DIALECT, SEX, SPEAKER_ID, SENTENCE_ID = extractInformationsFromAbsolutePath('/TRAIN/DR7/MPAR0/SI2206')\n",
    "    assert 'TRAIN'==USAGE\n",
    "    assert 'DR7'==DIALECT\n",
    "    assert 'M'==SEX\n",
    "    assert 'PAR0'==SPEAKER_ID\n",
    "    assert 'SI2206'==SENTENCE_ID\n",
    "    print('TEST PASED')\n",
    "\n",
    "test_extractInformationsFromAbsolutePath()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>relPath</th>\n",
       "      <th>USAGE</th>\n",
       "      <th>DIALECT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>SPEAKER_ID</th>\n",
       "      <th>SENTENCE_ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDX</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MPAR0:SI2206</th>\n",
       "      <td>/TRAIN/DR7/MPAR0/SI2206</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR7</td>\n",
       "      <td>M</td>\n",
       "      <td>PAR0</td>\n",
       "      <td>SI2206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPAR0:SX316</th>\n",
       "      <td>/TRAIN/DR7/MPAR0/SX316</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR7</td>\n",
       "      <td>M</td>\n",
       "      <td>PAR0</td>\n",
       "      <td>SX316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPAR0:SI1576</th>\n",
       "      <td>/TRAIN/DR7/MPAR0/SI1576</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR7</td>\n",
       "      <td>M</td>\n",
       "      <td>PAR0</td>\n",
       "      <td>SI1576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPAR0:SA1</th>\n",
       "      <td>/TRAIN/DR7/MPAR0/SA1</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR7</td>\n",
       "      <td>M</td>\n",
       "      <td>PAR0</td>\n",
       "      <td>SA1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MPAR0:SX46</th>\n",
       "      <td>/TRAIN/DR7/MPAR0/SX46</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR7</td>\n",
       "      <td>M</td>\n",
       "      <td>PAR0</td>\n",
       "      <td>SX46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              relPath  USAGE DIALECT SEX SPEAKER_ID  \\\n",
       "IDX                                                                   \n",
       "MPAR0:SI2206  /TRAIN/DR7/MPAR0/SI2206  TRAIN  DR7     M   PAR0        \n",
       "MPAR0:SX316   /TRAIN/DR7/MPAR0/SX316   TRAIN  DR7     M   PAR0        \n",
       "MPAR0:SI1576  /TRAIN/DR7/MPAR0/SI1576  TRAIN  DR7     M   PAR0        \n",
       "MPAR0:SA1     /TRAIN/DR7/MPAR0/SA1     TRAIN  DR7     M   PAR0        \n",
       "MPAR0:SX46    /TRAIN/DR7/MPAR0/SX46    TRAIN  DR7     M   PAR0        \n",
       "\n",
       "             SENTENCE_ID  \n",
       "IDX                       \n",
       "MPAR0:SI2206  SI2206      \n",
       "MPAR0:SX316   SX316       \n",
       "MPAR0:SI1576  SI1576      \n",
       "MPAR0:SA1     SA1         \n",
       "MPAR0:SX46    SX46        "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def extractTimitDatasetInformation(path):\n",
    "    '''Put in a dataframe all the information about samples'''\n",
    "    import glob\n",
    "    files = glob.glob(str(path)+ '/**/*.WAV', recursive=True)\n",
    "    ret = pd.DataFrame(files, columns=['relPath'])\n",
    "    #Remove path and extension\n",
    "    ret['relPath'] = ret['relPath'].apply(lambda x: x.replace(str(path),'').replace('.WAV',''))\n",
    "\n",
    "    def extractIntoSeries(s):\n",
    "        USAGE, DIALECT, SEX, SPEAKER_ID, SENTENCE_ID = extractInformationsFromAbsolutePath(s)\n",
    "        return pd.Series({\n",
    "            'USAGE': USAGE,\n",
    "            'DIALECT': DIALECT,\n",
    "            'SEX': SEX,\n",
    "            'SPEAKER_ID': SPEAKER_ID,\n",
    "            'SENTENCE_ID': SENTENCE_ID,\n",
    "            'IDX': SEX + SPEAKER_ID + ':' + SENTENCE_ID})\n",
    "\n",
    "    #IMPORTANT: Apply multiple transformations at one\n",
    "    ret = ret.merge(ret['relPath'].apply(lambda s: extractIntoSeries(s)), left_index=True, right_index=True)    \n",
    "    ret = ret.set_index(['IDX']) # Add index\n",
    "    return ret\n",
    "                \n",
    "all_files_df = extractTimitDatasetInformation(path)\n",
    "all_files_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of files: 6300\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of files: {len(all_files_df)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### OPTIONAL Decimate dataset\n",
    "To speedup computation we reduce train/valid set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USAGE\n",
       "TEST     168\n",
       "TRAIN    462\n",
       "Name: relPath, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#DON'T RUN THIS CELL IN YOU WANT TOI USE ALL DATA\n",
    "def decimate(train_df:pd.DataFrame, valid_df:pd.DataFrame, pct=.1)->pd.DataFrame:\n",
    "    t1=train_df.sample(frac=pct, random_state=1)\n",
    "    t2=valid_df.sample(frac=pct, random_state=1)\n",
    "    return pd.concat([t1,t2],axis=0)\n",
    "\n",
    "all_files_df=decimate(all_files_df[all_files_df.USAGE=='TRAIN'],all_files_df[all_files_df.USAGE=='TEST'],.1)\n",
    "all_files_df.groupby(['USAGE'])['relPath'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phoneme classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create data bunch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_phn_file(idx:str, phn_file, sig, sr, ext, delimiter=' ')->pd.DataFrame:\n",
    "    df = pd.read_csv(phn_file, delimiter=delimiter, header=None)\n",
    "    df.columns = ['START', 'END', ext]\n",
    "    df['SIG'] = df.apply(lambda x : torch.Tensor(sig[x['START']: x['END']]), axis=1)\n",
    "    df['SR'] = sr\n",
    "    df['IDX'] = idx\n",
    "    df['IDXN'] = pd.Series(map(lambda x: idx+':'+str(x), range(len(df))))   \n",
    "    df = df.set_index(['IDXN'])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ste/.fastai/data/timit/TRAIN/DR2/MJMD0/SX308.WAV \tExists: True\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>START</th>\n",
       "      <th>END</th>\n",
       "      <th>PHN</th>\n",
       "      <th>SIG</th>\n",
       "      <th>SR</th>\n",
       "      <th>IDX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDXN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MJMD0:SX308:0</th>\n",
       "      <td>0</td>\n",
       "      <td>2320</td>\n",
       "      <td>h#</td>\n",
       "      <td>[tensor(0.0001), tensor(0.0002), tensor(-3.0518e-05), tensor(6.1035e-05), tensor(-3.0518e-05), tensor(6.1035e-05), tensor(9.1553e-05), tensor(0.0001), tensor(0.0001), tensor(3.0518e-05), tensor(9.1553e-05), tensor(0.), tensor(-3.0518e-05), tensor(-9.1553e-05), tensor(0.0002), tensor(0.0001), tensor(0.0002), tensor(9.1553e-05), tensor(6.1035e-05), tensor(9.1553e-05), tensor(0.), tensor(0.0002), tensor(9.1553e-05), tensor(0.0001), tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(0.0001), tensor(6.1035e-05), tensor(9.1553e-05), tensor(-9.1553e-05), tensor(3.0518e-05), tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(6.1035e-05), tensor(0.0002), tensor(0.), tensor(0.0001), tensor(9.1553e-05), tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(0.0001), tensor(0.0002), tensor(6.1035e-05), tensor(9.1553e-05), tensor(6.1035e-05), tensor(6.1035e-05), tensor(0.0002), tensor(0.), tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(9.1553e-05), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.0001), tensor(0.0001), tensor(0.0002), tensor(0.0002), tensor(0.0001), tensor(9.1553e-05), tensor(3.0518e-05), tensor(9.1553e-05), tensor(-6.1035e-05), tensor(-6.1035e-05), tensor(6.1035e-05), tensor(0.0001), tensor(3.0518e-05), tensor(3.0518e-05), tensor(6.1035e-05), tensor(3.0518e-05), tensor(-6.1035e-05), tensor(-3.0518e-05), tensor(3.0518e-05), tensor(0.0001), tensor(-3.0518e-05), tensor(0.0001), tensor(3.0518e-05), tensor(0.0001), tensor(0.), tensor(3.0518e-05), tensor(0.), tensor(0.), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.0001), tensor(0.0002), tensor(9.1553e-05), tensor(3.0518e-05), tensor(0.0002), tensor(0.), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.0001), tensor(0.0002), tensor(0.0001), tensor(9.1553e-05), ...]</td>\n",
       "      <td>16000</td>\n",
       "      <td>MJMD0:SX308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MJMD0:SX308:1</th>\n",
       "      <td>2320</td>\n",
       "      <td>4803</td>\n",
       "      <td>sh</td>\n",
       "      <td>[tensor(3.0518e-05), tensor(-0.0002), tensor(-3.0518e-05), tensor(0.0004), tensor(0.0003), tensor(0.0002), tensor(-0.0002), tensor(6.1035e-05), tensor(0.0002), tensor(0.0002), tensor(9.1553e-05), tensor(-0.0004), tensor(0.0002), tensor(0.0006), tensor(0.), tensor(-0.0002), tensor(-6.1035e-05), tensor(9.1553e-05), tensor(0.0003), tensor(0.0003), tensor(0.0002), tensor(-0.0005), tensor(-0.0005), tensor(0.0001), tensor(0.0003), tensor(0.0005), tensor(0.0005), tensor(-0.0007), tensor(-0.0005), tensor(0.0007), tensor(0.0005), tensor(-0.0002), tensor(-0.0002), tensor(0.0003), tensor(-0.0001), tensor(-0.0005), tensor(0.0005), tensor(0.0006), tensor(-0.0004), tensor(-0.0003), tensor(-3.0518e-05), tensor(-0.0002), tensor(0.0007), tensor(0.0005), tensor(-0.0007), tensor(-0.0006), tensor(-0.0002), tensor(0.0005), tensor(0.0008), tensor(-0.0002), tensor(-0.0008), tensor(-9.1553e-05), tensor(0.0006), tensor(0.0006), tensor(0.0002), tensor(-0.0003), tensor(-3.0518e-05), tensor(6.1035e-05), tensor(-0.0005), tensor(-0.0003), tensor(3.0518e-05), tensor(-0.0004), tensor(-0.0005), tensor(-6.1035e-05), tensor(9.1553e-05), tensor(0.0003), tensor(0.0006), tensor(-0.0005), tensor(-0.0005), tensor(0.), tensor(-0.0002), tensor(0.0004), tensor(0.0005), tensor(-0.0003), tensor(-0.0003), tensor(0.0003), tensor(0.0006), tensor(0.0005), tensor(-0.0002), tensor(-0.0005), tensor(-3.0518e-05), tensor(-3.0518e-05), tensor(0.0005), tensor(-0.0004), tensor(-0.0005), tensor(0.0002), tensor(0.0002), tensor(0.0006), tensor(0.0003), tensor(-0.0004), tensor(-0.0003), tensor(0.0003), tensor(0.0004), tensor(0.0001), tensor(-0.0011), tensor(-0.0003), tensor(0.0006), tensor(0.0005), tensor(0.0009), tensor(0.0004), ...]</td>\n",
       "      <td>16000</td>\n",
       "      <td>MJMD0:SX308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               START   END PHN  \\\n",
       "IDXN                             \n",
       "MJMD0:SX308:0  0      2320  h#   \n",
       "MJMD0:SX308:1  2320   4803  sh   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               SIG  \\\n",
       "IDXN                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 \n",
       "MJMD0:SX308:0  [tensor(0.0001), tensor(0.0002), tensor(-3.0518e-05), tensor(6.1035e-05), tensor(-3.0518e-05), tensor(6.1035e-05), tensor(9.1553e-05), tensor(0.0001), tensor(0.0001), tensor(3.0518e-05), tensor(9.1553e-05), tensor(0.), tensor(-3.0518e-05), tensor(-9.1553e-05), tensor(0.0002), tensor(0.0001), tensor(0.0002), tensor(9.1553e-05), tensor(6.1035e-05), tensor(9.1553e-05), tensor(0.), tensor(0.0002), tensor(9.1553e-05), tensor(0.0001), tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(0.0001), tensor(6.1035e-05), tensor(9.1553e-05), tensor(-9.1553e-05), tensor(3.0518e-05), tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(6.1035e-05), tensor(0.0002), tensor(0.), tensor(0.0001), tensor(9.1553e-05), tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(0.0001), tensor(0.0002), tensor(6.1035e-05), tensor(9.1553e-05), tensor(6.1035e-05), tensor(6.1035e-05), tensor(0.0002), tensor(0.), tensor(0.0002), tensor(0.0002), tensor(0.0002), tensor(9.1553e-05), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.0001), tensor(0.0001), tensor(0.0002), tensor(0.0002), tensor(0.0001), tensor(9.1553e-05), tensor(3.0518e-05), tensor(9.1553e-05), tensor(-6.1035e-05), tensor(-6.1035e-05), tensor(6.1035e-05), tensor(0.0001), tensor(3.0518e-05), tensor(3.0518e-05), tensor(6.1035e-05), tensor(3.0518e-05), tensor(-6.1035e-05), tensor(-3.0518e-05), tensor(3.0518e-05), tensor(0.0001), tensor(-3.0518e-05), tensor(0.0001), tensor(3.0518e-05), tensor(0.0001), tensor(0.), tensor(3.0518e-05), tensor(0.), tensor(0.), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.0001), tensor(0.0002), tensor(9.1553e-05), tensor(3.0518e-05), tensor(0.0002), tensor(0.), tensor(9.1553e-05), tensor(9.1553e-05), tensor(0.0001), tensor(0.0002), tensor(0.0001), tensor(9.1553e-05), ...]   \n",
       "MJMD0:SX308:1  [tensor(3.0518e-05), tensor(-0.0002), tensor(-3.0518e-05), tensor(0.0004), tensor(0.0003), tensor(0.0002), tensor(-0.0002), tensor(6.1035e-05), tensor(0.0002), tensor(0.0002), tensor(9.1553e-05), tensor(-0.0004), tensor(0.0002), tensor(0.0006), tensor(0.), tensor(-0.0002), tensor(-6.1035e-05), tensor(9.1553e-05), tensor(0.0003), tensor(0.0003), tensor(0.0002), tensor(-0.0005), tensor(-0.0005), tensor(0.0001), tensor(0.0003), tensor(0.0005), tensor(0.0005), tensor(-0.0007), tensor(-0.0005), tensor(0.0007), tensor(0.0005), tensor(-0.0002), tensor(-0.0002), tensor(0.0003), tensor(-0.0001), tensor(-0.0005), tensor(0.0005), tensor(0.0006), tensor(-0.0004), tensor(-0.0003), tensor(-3.0518e-05), tensor(-0.0002), tensor(0.0007), tensor(0.0005), tensor(-0.0007), tensor(-0.0006), tensor(-0.0002), tensor(0.0005), tensor(0.0008), tensor(-0.0002), tensor(-0.0008), tensor(-9.1553e-05), tensor(0.0006), tensor(0.0006), tensor(0.0002), tensor(-0.0003), tensor(-3.0518e-05), tensor(6.1035e-05), tensor(-0.0005), tensor(-0.0003), tensor(3.0518e-05), tensor(-0.0004), tensor(-0.0005), tensor(-6.1035e-05), tensor(9.1553e-05), tensor(0.0003), tensor(0.0006), tensor(-0.0005), tensor(-0.0005), tensor(0.), tensor(-0.0002), tensor(0.0004), tensor(0.0005), tensor(-0.0003), tensor(-0.0003), tensor(0.0003), tensor(0.0006), tensor(0.0005), tensor(-0.0002), tensor(-0.0005), tensor(-3.0518e-05), tensor(-3.0518e-05), tensor(0.0005), tensor(-0.0004), tensor(-0.0005), tensor(0.0002), tensor(0.0002), tensor(0.0006), tensor(0.0003), tensor(-0.0004), tensor(-0.0003), tensor(0.0003), tensor(0.0004), tensor(0.0001), tensor(-0.0011), tensor(-0.0003), tensor(0.0006), tensor(0.0005), tensor(0.0009), tensor(0.0004), ...]                                                                               \n",
       "\n",
       "                  SR          IDX  \n",
       "IDXN                               \n",
       "MJMD0:SX308:0  16000  MJMD0:SX308  \n",
       "MJMD0:SX308:1  16000  MJMD0:SX308  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 34\n",
      "TEST DONE\n"
     ]
    }
   ],
   "source": [
    "def extractItems(path, idx, relativePath, ext, verbose=False)->pd.DataFrame:\n",
    "    base_path = str(path) + relativePath\n",
    "    wav_file = base_path + \".WAV\"\n",
    "    wav_exists = os.path.isfile(wav_file)\n",
    "    if verbose: print(wav_file, f'\\tExists: {wav_exists}')\n",
    "    #Sanity check\n",
    "    if not wav_exists: return none\n",
    "    #Proceed\n",
    "    sigTwoChannels,sr = torchaudio.load(wav_file)\n",
    "    sig = sigTwoChannels[0] # MONO NO STEREO - convert to \"one dimension\"\n",
    "    phn_file = base_path + '.' + ext\n",
    "            \n",
    "    ret = process_phn_file(idx, phn_file, sig, sr, ext, delimiter=' ')\n",
    "    \n",
    "    return ret\n",
    "            \n",
    "def test_extractItems_PHN():\n",
    "    row = all_files_df.iloc[0]\n",
    "    ret_df = extractItems(path, row.name, row['relPath'], 'PHN', verbose=True)\n",
    "    display(ret_df.head(2))\n",
    "    print(f'Number of rows: {len(ret_df)}')\n",
    "#    assert 54==len(ret_df)\n",
    "    print('TEST DONE')\n",
    "\n",
    "test_extractItems_PHN()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/ste/.fastai/data/timit/TRAIN/DR2/MJMD0/SX308.WAV \tExists: True\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>START</th>\n",
       "      <th>END</th>\n",
       "      <th>WRD</th>\n",
       "      <th>SIG</th>\n",
       "      <th>SR</th>\n",
       "      <th>IDX</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDXN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MJMD0:SX308:0</th>\n",
       "      <td>2320</td>\n",
       "      <td>11400</td>\n",
       "      <td>shipbuilding</td>\n",
       "      <td>[tensor(3.0518e-05), tensor(-0.0002), tensor(-3.0518e-05), tensor(0.0004), tensor(0.0003), tensor(0.0002), tensor(-0.0002), tensor(6.1035e-05), tensor(0.0002), tensor(0.0002), tensor(9.1553e-05), tensor(-0.0004), tensor(0.0002), tensor(0.0006), tensor(0.), tensor(-0.0002), tensor(-6.1035e-05), tensor(9.1553e-05), tensor(0.0003), tensor(0.0003), tensor(0.0002), tensor(-0.0005), tensor(-0.0005), tensor(0.0001), tensor(0.0003), tensor(0.0005), tensor(0.0005), tensor(-0.0007), tensor(-0.0005), tensor(0.0007), tensor(0.0005), tensor(-0.0002), tensor(-0.0002), tensor(0.0003), tensor(-0.0001), tensor(-0.0005), tensor(0.0005), tensor(0.0006), tensor(-0.0004), tensor(-0.0003), tensor(-3.0518e-05), tensor(-0.0002), tensor(0.0007), tensor(0.0005), tensor(-0.0007), tensor(-0.0006), tensor(-0.0002), tensor(0.0005), tensor(0.0008), tensor(-0.0002), tensor(-0.0008), tensor(-9.1553e-05), tensor(0.0006), tensor(0.0006), tensor(0.0002), tensor(-0.0003), tensor(-3.0518e-05), tensor(6.1035e-05), tensor(-0.0005), tensor(-0.0003), tensor(3.0518e-05), tensor(-0.0004), tensor(-0.0005), tensor(-6.1035e-05), tensor(9.1553e-05), tensor(0.0003), tensor(0.0006), tensor(-0.0005), tensor(-0.0005), tensor(0.), tensor(-0.0002), tensor(0.0004), tensor(0.0005), tensor(-0.0003), tensor(-0.0003), tensor(0.0003), tensor(0.0006), tensor(0.0005), tensor(-0.0002), tensor(-0.0005), tensor(-3.0518e-05), tensor(-3.0518e-05), tensor(0.0005), tensor(-0.0004), tensor(-0.0005), tensor(0.0002), tensor(0.0002), tensor(0.0006), tensor(0.0003), tensor(-0.0004), tensor(-0.0003), tensor(0.0003), tensor(0.0004), tensor(0.0001), tensor(-0.0011), tensor(-0.0003), tensor(0.0006), tensor(0.0005), tensor(0.0009), tensor(0.0004), ...]</td>\n",
       "      <td>16000</td>\n",
       "      <td>MJMD0:SX308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               START    END           WRD  \\\n",
       "IDXN                                        \n",
       "MJMD0:SX308:0  2320   11400  shipbuilding   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   SIG  \\\n",
       "IDXN                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     \n",
       "MJMD0:SX308:0  [tensor(3.0518e-05), tensor(-0.0002), tensor(-3.0518e-05), tensor(0.0004), tensor(0.0003), tensor(0.0002), tensor(-0.0002), tensor(6.1035e-05), tensor(0.0002), tensor(0.0002), tensor(9.1553e-05), tensor(-0.0004), tensor(0.0002), tensor(0.0006), tensor(0.), tensor(-0.0002), tensor(-6.1035e-05), tensor(9.1553e-05), tensor(0.0003), tensor(0.0003), tensor(0.0002), tensor(-0.0005), tensor(-0.0005), tensor(0.0001), tensor(0.0003), tensor(0.0005), tensor(0.0005), tensor(-0.0007), tensor(-0.0005), tensor(0.0007), tensor(0.0005), tensor(-0.0002), tensor(-0.0002), tensor(0.0003), tensor(-0.0001), tensor(-0.0005), tensor(0.0005), tensor(0.0006), tensor(-0.0004), tensor(-0.0003), tensor(-3.0518e-05), tensor(-0.0002), tensor(0.0007), tensor(0.0005), tensor(-0.0007), tensor(-0.0006), tensor(-0.0002), tensor(0.0005), tensor(0.0008), tensor(-0.0002), tensor(-0.0008), tensor(-9.1553e-05), tensor(0.0006), tensor(0.0006), tensor(0.0002), tensor(-0.0003), tensor(-3.0518e-05), tensor(6.1035e-05), tensor(-0.0005), tensor(-0.0003), tensor(3.0518e-05), tensor(-0.0004), tensor(-0.0005), tensor(-6.1035e-05), tensor(9.1553e-05), tensor(0.0003), tensor(0.0006), tensor(-0.0005), tensor(-0.0005), tensor(0.), tensor(-0.0002), tensor(0.0004), tensor(0.0005), tensor(-0.0003), tensor(-0.0003), tensor(0.0003), tensor(0.0006), tensor(0.0005), tensor(-0.0002), tensor(-0.0005), tensor(-3.0518e-05), tensor(-3.0518e-05), tensor(0.0005), tensor(-0.0004), tensor(-0.0005), tensor(0.0002), tensor(0.0002), tensor(0.0006), tensor(0.0003), tensor(-0.0004), tensor(-0.0003), tensor(0.0003), tensor(0.0004), tensor(0.0001), tensor(-0.0011), tensor(-0.0003), tensor(0.0006), tensor(0.0005), tensor(0.0009), tensor(0.0004), ...]   \n",
       "\n",
       "                  SR          IDX  \n",
       "IDXN                               \n",
       "MJMD0:SX308:0  16000  MJMD0:SX308  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows: 6\n",
      "TEST DONE\n"
     ]
    }
   ],
   "source": [
    "def test_extractItems_WRD():\n",
    "    row = all_files_df.iloc[0]\n",
    "    ret_df = extractItems(path, row.name, row['relPath'], 'WRD', verbose=True)\n",
    "    display(ret_df.head(1))\n",
    "    print(f'Number of rows: {len(ret_df)}')\n",
    "#    assert 14==len(ret_df)\n",
    "    print('TEST DONE')\n",
    "\n",
    "test_extractItems_WRD()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>relPath</th>\n",
       "      <th>USAGE</th>\n",
       "      <th>DIALECT</th>\n",
       "      <th>SEX</th>\n",
       "      <th>SPEAKER_ID</th>\n",
       "      <th>SENTENCE_ID</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IDX</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MJMD0:SX308</th>\n",
       "      <td>/TRAIN/DR2/MJMD0/SX308</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR2</td>\n",
       "      <td>M</td>\n",
       "      <td>JMD0</td>\n",
       "      <td>SX308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLEH0:SI2311</th>\n",
       "      <td>/TRAIN/DR7/FLEH0/SI2311</td>\n",
       "      <td>TRAIN</td>\n",
       "      <td>DR7</td>\n",
       "      <td>F</td>\n",
       "      <td>LEH0</td>\n",
       "      <td>SI2311</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              relPath  USAGE DIALECT SEX SPEAKER_ID  \\\n",
       "IDX                                                                   \n",
       "MJMD0:SX308   /TRAIN/DR2/MJMD0/SX308   TRAIN  DR2     M   JMD0        \n",
       "FLEH0:SI2311  /TRAIN/DR7/FLEH0/SI2311  TRAIN  DR7     F   LEH0        \n",
       "\n",
       "             SENTENCE_ID  \n",
       "IDX                       \n",
       "MJMD0:SX308   SX308       \n",
       "FLEH0:SI2311  SI2311      "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_files_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 630 entries, MJMD0:SX308 to FCAU0:SX317\n",
      "Data columns (total 6 columns):\n",
      "relPath        630 non-null object\n",
      "USAGE          630 non-null object\n",
      "DIALECT        630 non-null object\n",
      "SEX            630 non-null object\n",
      "SPEAKER_ID     630 non-null object\n",
      "SENTENCE_ID    630 non-null object\n",
      "dtypes: object(6)\n",
      "memory usage: 34.5+ KB\n"
     ]
    }
   ],
   "source": [
    "all_files_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows: 376\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 376 entries, MJMD0:SX308:0 to MADD0:SI538:36\n",
      "Data columns (total 6 columns):\n",
      "START    376 non-null int64\n",
      "END      376 non-null int64\n",
      "PHN      376 non-null object\n",
      "SIG      376 non-null object\n",
      "SR       376 non-null int64\n",
      "IDX      376 non-null object\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 20.6+ KB\n"
     ]
    }
   ],
   "source": [
    "def createItemsDataFrame(path, files_df, ext):\n",
    "    ret = pd.DataFrame()\n",
    "    for i,row in files_df.iterrows():\n",
    "        tmp_df = extractItems(path, row.name, row['relPath'], ext)\n",
    "        ret = ret.append(tmp_df)\n",
    "        #print(len(ret))\n",
    "#    ret = files_df.apply(lambda row: ret.append(extractWords(path, row.name, row['relPath']), ignore_index=True), axis=1)\n",
    "#    files_df.apply(lambda row: print(row), axis=1)\n",
    "    return ret\n",
    "\n",
    "def test_createItemsDataFrame():\n",
    "    tdf = createItemsDataFrame(path, all_files_df.head(10), 'PHN')\n",
    "    print(f'Rows: {len(tdf)}')\n",
    "    tdf.info()\n",
    "#    assert 86==len(tdf)\n",
    "\n",
    "test_createItemsDataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 28.4 s, sys: 418 ms, total: 28.8 s\n",
      "Wall time: 4.81 s\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 24034 entries, MJMD0:SX308:0 to FCAU0:SX317:38\n",
      "Data columns (total 6 columns):\n",
      "START    24034 non-null int64\n",
      "END      24034 non-null int64\n",
      "PHN      24034 non-null object\n",
      "SIG      24034 non-null object\n",
      "SR       24034 non-null int64\n",
      "IDX      24034 non-null object\n",
      "dtypes: int64(3), object(3)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "%time all_phonemes_df = createItemsDataFrame(path, all_files_df, 'PHN')\n",
    "all_phonemes_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phonemes count: 24034\n"
     ]
    }
   ],
   "source": [
    "print(f'Phonemes count: {len(all_phonemes_df)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join data and create \"src\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Join files with words\n",
    "src = all_phonemes_df.merge(all_files_df,left_on='IDX',right_on='IDX')\n",
    "assert len(src)==len(all_phonemes_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USAGE\n",
       "TEST     6367 \n",
       "TRAIN    17667\n",
       "Name: SIG, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Optionally subsample and ensure good distribution among train and \"valid\"\n",
    "#src = src.sample(5000, random_state=1) # Uncomment this to reduce dataset\n",
    "src.groupby(['USAGE'])['SIG'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PHN</th>\n",
       "      <th>FREQ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>aa</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ae</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ah</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ao</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>aw</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PHN  FREQ\n",
       "0  aa  402 \n",
       "1  ae  548 \n",
       "2  ah  316 \n",
       "3  ao  419 \n",
       "4  aw  75  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrd_freq = src.groupby(['PHN'])['SIG'].count().reset_index(name='FREQ')\n",
    "wrd_freq.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add word frequency measure to src\n",
    "src = src.merge(wrd_freq,left_on='PHN',right_on='PHN')\n",
    "assert len(src)==len(all_phonemes_df) # does not pass if src is reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24034\n"
     ]
    }
   ],
   "source": [
    "# Keep high frequency words\n",
    "#src = src[src.FREQ>=8]\n",
    "print(len(src))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([ 1.2207e-04,  1.5259e-04, -3.0518e-05,  ..., -3.6621e-04,\n",
       "          4.8828e-04,  6.1035e-04]), 16000)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create merged column...\n",
    "src['SampleAndSr'] = src.apply(lambda row: (row.SIG,row.SR),axis=1)\n",
    "src.iloc[0]['SampleAndSr']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "is_valid\n",
       "False    17667\n",
       "True     6367 \n",
       "Name: SIG, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create validation set column from \"test\" hold out\n",
    "src['is_valid'] = src['USAGE'].apply(lambda x: not (x=='TRAIN'))\n",
    "src.groupby(['is_valid'])['SIG'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count unique words\n",
    "len(src.PHN.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Useful statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shortest item: 95\n",
      "Longest item: 74285\n"
     ]
    }
   ],
   "source": [
    "src['LENGHT'] = src.SIG.apply(lambda x: x.shape[0])\n",
    "print(f'Shortest item: {src.LENGHT.min()}')\n",
    "print(f'Longest item: {src.LENGHT.max()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb29f379e8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "src['LENGHT'].hist(bins=61)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**WARNING** there are zero lenght items that should be filtered..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shortest item: 95\n",
      "Longest item: 9971\n",
      "Number of items: 24000\n"
     ]
    }
   ],
   "source": [
    "#Discarding too short and too long sounds\n",
    "min_duration = 50 # in samples\n",
    "max_duration = 10000\n",
    "src = src[src.LENGHT>min_duration]\n",
    "src = src[src.LENGHT<=max_duration]\n",
    "print(f'Shortest item: {src.LENGHT.min()}')\n",
    "print(f'Longest item: {src.LENGHT.max()}')\n",
    "print(f'Number of items: {len(src)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classify phonemes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = src.sample(10,random_state=1)\n",
    "adsAndLbl = [(AudioData(samples.iloc[i].SIG,samples.iloc[i].SR),samples.iloc[i].PHN) for i in range(len(samples))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(adsAndLbl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio controls=\"controls\" >\n",
       "                    <source src=\"data:audio/wav;base64,UklGRlwBAABXQVZFZm10IBAAAAABAAEAgD4AAAB9AAACABAAZGF0YTgBAACSAXICZgElA3ICsQe7Fv9/jXZZx8DhF0luFyUjaFMY6Znl+1STepEIDKi7/V3yT/jf5z2zQd6ER6E0D9q30sypDsEC+ZnFV5Urrt6u7vrjK5nlWAeK8mYBgw42LxQXIviEBz8+JUOqKnoY9/AAAFlAJjzwLLv9wrqP9rMAqdhdshW+Eqz6wuLZOagApy7gAtnK8Bb3atN55tEGYw9dK+UkFwmRCD4lVDV9KiwgZxrRJgMrrRxRCiUD9fd4BvYQQf4s507fmeUk8VDxuOtk7yXqse7J997uF/CO/TkBgw6DDnX7G/sSBd4HgRVIFJ4JkgG1+d8ARQKI+Qj9L/ns6PPlp98i2Pvbddui267citLyzFjOeM3D0+nWJNGzx5W69rfJt5C2Gcl90W3F19EP2si+ytCu/M778QU=\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = adsAndLbl[4][0]\n",
    "display(Audio(s.sig, rate=s.sr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "z tensor([-0.0024,  0.0034,  0.0006, -0.0034, -0.0013])\n",
      "eh tensor([0.0106, 0.0108, 0.0137, 0.0164, 0.0186])\n",
      "s tensor([-0.0008, -0.0005, -0.0003, -0.0002,  0.0000])\n",
      "ix tensor([-0.0019, -0.0039,  0.0016,  0.0028, -0.0034])\n",
      "d tensor([0.0003, 0.0004, 0.0002, 0.0005, 0.0004])\n",
      "h# tensor([ 0.0000e+00,  9.1553e-05,  1.2207e-04,  3.0518e-05, -6.1035e-05])\n",
      "em tensor([-0.0006,  0.0014,  0.0038,  0.0054,  0.0060])\n",
      "v tensor([-0.0017, -0.0013, -0.0023, -0.0019, -0.0019])\n",
      "h# tensor([3.0518e-05, 6.1035e-05, 3.0518e-05, 0.0000e+00, 9.1553e-05])\n",
      "el tensor([0.0081, 0.0100, 0.0122, 0.0150, 0.0180])\n"
     ]
    }
   ],
   "source": [
    "for ad in adsAndLbl[:10]:\n",
    "    print(ad[1],ad[0].sig[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create transformations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Augmentetion\n",
    "tm = AudioTfmsManager.get_audio_tfms_manager(\n",
    "                            spectro=True, #We're going to replace it...\n",
    "                            mx_to_pad=max_duration, #1 sec window\n",
    "                            white_noise=True, noise_scl=1e-4, # Small noise\n",
    "                            modulate_volume=True, lower_gain=.90, upper_gain=1.05, # Not big volume variation\n",
    "                            random_cutout=False,\n",
    "                            pad_with_silence=False,\n",
    "                            pitch_warp=False,\n",
    "                            down_and_up=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "efb64d1e3dc84fa5a147b83c60e31bed",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(Output(), VBox(children=(Label(value='0:\\tfunctools.partial(<function tfm_add_white_noise at 0x7…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<exp.nb_AudioCommon.AudioData at 0x7fdb2a4046d8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sl=adsAndLbl[1]\n",
    "tm.try_data_augmentation(ad=sl[0],lbl=sl[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[functools.partial(<function tfm_add_white_noise at 0x7fdb2a8bc9d8>, noise_scl=0.0001, lower_gain=0.9, upper_gain=1.05),\n",
       "  functools.partial(<function tfm_modulate_volume at 0x7fdb2a8bca60>, noise_scl=0.0001, lower_gain=0.9, upper_gain=1.05),\n",
       "  functools.partial(<function tfm_pad_to_max at 0x7fdb2a8bcd90>, mx=10000),\n",
       "  functools.partial(<function tfm_spectro at 0x7fdb2a822158>, noise_scl=0.0001, lower_gain=0.9, upper_gain=1.05)],\n",
       " [functools.partial(<function tfm_pad_to_max at 0x7fdb2a8bcd90>, mx=10000),\n",
       "  functools.partial(<function tfm_spectro at 0x7fdb2a822158>, noise_scl=0.0001, lower_gain=0.9, upper_gain=1.05)]]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm.get_tfms()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([[functools.partial(<function tfm_spectro at 0x7fdb2a822158>, noise_scl=0.0001, lower_gain=0.9, upper_gain=1.05)]],\n",
       " [[functools.partial(<function tfm_spectro at 0x7fdb2a822158>, noise_scl=0.0001, lower_gain=0.9, upper_gain=1.05)]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This is the \"POST\" transformation group that we're going to replace, both on train and validation... \n",
    "(tm.train_tfmsg[-1:], tm.valid_tfmsg[-1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create multi spectrogram transformation:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c3d8ec3669941d6a19540353f49efc1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Tab(children=(Output(), VBox(children=(Label(value='0:\\tfunctools.partial(<function tfm_add_white_noise at 0x7…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s = sl[0] #sample without label\n",
    "sAug = tm.try_data_augmentation(s) #apply data augmentation and show results..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create multi channel transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tfm_trim(ad:AudioData, xBegin, xEnd):\n",
    "    '''Extract part of original signal'''\n",
    "    x = ad.sig[xBegin:xEnd]\n",
    "    return AudioData(x,ad.sr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([100])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfm_trim(s,0,100).sig.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "Image (1, 224, 101)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 224, 101])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scale 100%\n",
    "tfm_spectro_s100 = partial(tfm_spectro,n_mels=224, ws=200, n_fft=3200, to_db_scale=True)\n",
    "img = tfm_spectro_s100(sAug)\n",
    "display(Image(img),img.shape) #improved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sAugLen = sAug.sig.shape[0]\n",
    "sAugLen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQEAZABkAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQoKCgoKBggLDAsKDAkKCgr/2wBDAQICAgICAgUDAwUKBwYHCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgr/wAARCADgAGUDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDzy/8AhbDrekzXTIgvbW1m+zO6Z2SC0vgp/ApXS2nge9hvJrCQBl+1TLHtH8JubpE/TyRT4jJqV95NldHD6iHG3gNHLLfxgH2C3Q/75FdD4F8RxagtjPq0YjuWWzkmjJztdn0aVhnvzdMPxr1MzzbM6NFzbv2j1Wl/zP4rk8XjsKqMJc8Y6t9Y3u9OtuuitfzMTTfglomrX9tfJBGXlnh+bHO15bH/ANlnH50uifCnWUNpI8ytE6WxZAnPzJpJPP1kuD/wL2roNNvofC1jFqVtK7KlnFPguTzFYWsnHp/yDh+LE969V+G/hi7ume1vLQBLd54VJ5z5L6lCD+IsLc/jXxnEHGWZ5Nh5Vq1S8NUr79UdeBw1fHp0qN6kdFzSW3pdu3Xb9T5/1j4OR3+lNJqOl7gtnuctHn/l23H/ANBeuM8bfBqy0zUDGiSowunj8iJOJWLapEEPHTfEPx2+vP6OWHwQ8P6mXs5LNCrsUwVH3S8if+gvVjTP2S/BWs6vFq+sWELiOaKVVZRjcZ4JgfqHaQ/8CPrX5Zh/pW5Fl1STxPPaKvZa36pK/fQ/R8o8M+Nqcl9Vne7t8TSXd21/rr1X5EXX7PGu6FdR6JBoEdnbW88EgcKAZBLaYc4A442ivinxFoV94Z1mfQtTUC4tyFlUdiVBx+tft1+2Vo/hHwt4ieLToolxaw42gdQ7R/yWvxk+Ot/b6j8YvEdxajEa6tLEvH9w7P8A2Wv604A4ylxzw7SzT2TpqcVJJ+dz9m8GOIs8zDiDM8tx/LL2KjeUb6y5mm7tve9/y0OTooor7Y/ogKKKKACiiigD9G/gR8XLW98DaLeXmrederoen3Fy8m0Ox2SNvYDgZMLn0yD6V0+keLNOXxXaXk9+wNtAsRiEmFYiTR+SO5BsQAe2T618n+ErrxNb+HtPurgPBfz3Vzb3nltwgjTW1WPjqqsePoK6HGs6Z4gXxFbeIrt/tGswoI3l3Kqz6raFhj2V5FHovHavDxXDmCxLqTl1ufxXmHhzQoZli62FxPs1P2lkldXbleKtolro+y06H1ha+PNPvPDcUMVwp3aeIV57vapF/wC1j+dd14H/AGpYdLvbhLy5jYPc3Ei7T0Ekt9Ko/wC+b6D8/eviDQdV8Z+FzpmiWGpT3ULPYeZJMxYgINAiOCTxlTOx9SxNa8fxDtItF+1SBYjNaZNwoy0f+hROGPv+7t//AB2vmsx8O8lzmlKjiocybfy1PmYeH2ZYGrJYPEOSlLRxSutWrtPXa+i3t6H6f6L+2B4et4BqS3alQysQD23wsf0LflXKy/8ABSW3soDYz6dPBIht1JkGASrqHwe+Nrf98mvijwB8S7qbSZbnVGMXl6jfxLE2RmOO4ukQ4PrHbg1R+M/jO41H+zbqygeRm1MQokYOCf8AiYFyxHbYGPPUqB1NfkmE+jV4c/XmsVhPaKT0bbXLbbTrcrBZv4gTzVZfLFShHVc0bJXs3106d7LU0P2lP2ivFXiwHVJtQVLm1nFzqMMvJELpLKY8g8FfNU9/u475r889Z1OfW9XutZusebd3LzSY/vOxY/qa9/8A2g/Ekp0fXNQ0hismoPG104535t7ZD/5CZa+d6/pLAZbg8pwNPC4aChCK0SVkvJeh/T/hJwzh8hySdWK9+o1zN/E38Uk3u7SbtfWyCiiiuo/WQooooAKKKKAPv7Q/h9pniHwYdW0+FTFLfX43KOjrdX8b/juL1x0Pwj1201EWlvqskkTapBPiU52ganDPge2yQp9FWvWP2ZdQsNY+BVlbeYrSz393Mp9ptQ1GT+TV2ejfBa51nW7PUYb91RGhVoQBtcG40liTxnIUuBz/ABn2x8lV4kjlrrRxT5VFyS9EfwE+Lc0yTO8dl0p6RnNLmV/hul6a3Vzxex0KCyXTILpMEraqHbuxbTEH5sy07w54c8NW9qt3aabHKjQIZzGuSv8AokBy3/bOOD8CK+l9E/Z3sr6O0tJ7dS8Nrbv8w6Mgs3z+dux/4DXU6P8Asp/ZbG6kt7WNkS1nQjb6WmpoP/Hobb8q+XzDxU4ay+/tqtnfvbd23OOPEuPzBOFCnN35m3eysm301vb8bHzrffCibVrO/aztWimaxkKEDo3laif5iSovE/wq8YrdQWVtaJDaNr+3fJGS0n768hKr2BLyxEHnI3DHQ19car4B8N6Rry2jRoBJdXUZ47A6iv8A7WNTfEC28CXA8P6XGId//CR2lw44/wCfqCb+bV8ZS8XatTEUlRw8pwmnK9nolGUr/gl8zyqWOzPDu1SrFcjSalrq/d016c11fax+WPxT+Hvi2Dwx/Y+oaB9njisbKwsZDLu82WVba3YtxnKNG31GDnrj5sr9SP2vtJ8I+H4ra4gCCODXLJMAZ+aWZVX9ZRX5l+NrSDT/ABnq9haoFig1S4jjUdAokYAfkK/dcmzaGd5ZTxcYuKktmf1p4McXYjifK6yrU1Bwb2u02pcrs3r1u/Uy6KKK9M/agooooAKKKKAPrn9jvxnqnh3wvZWd5cuLe202N3ic/cKya+x/WNB/wGvrD4T/ABWtzqFxBcXKMLe5ESbT/dksozn3D2sg/Cvmj4D+FLPWPAEkduAslw17Arr1ANxrCL/6MNereG9EvLzxAL2wnAjeaJ3RR94NeWDZ/Kd/++6+R4hy3LsfQqxr/efwZ4h08ux/EeMq0/cq80ummjctdOuq+Wp9Dav8RJbMz6hocqtKLSQQx54ZjDKqD/vu7h/KrHhr9smwisNUj1C0aNHgkkt5Dja6s92Rg/8AXNlb6MK8R8O6n4kt4dNvLiJphJDp5njz90u2i7jz2ASVj9KwLvwf480nwg8Ijhu57fS8BAnloXjsSuP4sAvbyeuA/fHP5q/DzhfMIPD5hBSs42d7PR/5r8T4jDQr4Kr7OOKSlKXLo7bvll3Vl/esrO6uejfGH9pdbGO/8Vl3+z2C31y7R8kBU1SRsDuf3S1wHxJ+MfiHSdXtdTtmeQy+ILeD/WYMCo18of3BNpCuB657Vk+IvhD4j1XwT4h8NahfMz3P9sRxylPuCVtaVBjvtWRV99tZPxG8LXztBq17HciSDxUEWGD5t6te6jbozD+6Fulc+m0HtX2uR5JkGC5KWHjHlSS6PS2x7WW5dw/VxUHXmpy5mpXb1vBtO11Z819ell038v8A2g/2pLzVYNSia28+NYtOubd2k6y4hk2kdtoMbZ/2vavlLVNQm1bU7nVbgfvLmd5Xx6sxJ/nXdftC6yR48n0O2hCQxafZg4/iLW1u2fyVR+FefV+gwpUaFNUqSSiuiP7a4D4cyrh/IqawVLkU0m++3Xzdk35hRRRVH2wUUUUAFFFFAH1r+x14ylg8PWVtf3OUjsYJWLdS7XmrlyfwAr6D+GiaNpHix7lL2QrMi/unlJUENpKDAPT/AI9c4HdmPUmvh34a6/eaV4AvIbWd43l0SVI5I2IKlYdYkBBHQ5Feu6R4r8ZaZraXz+IHIXWkhTCjBjk1q3XaeP8AnkAmfqfevNzHKYY6jNc1ro/lHj7gKvjM3xmLw9ZQ5+ZNNb7v89D6wsriwuNFhFtIodrNAh9xbRBf/HttU/F3j660Xwxr/iBbQXMI0u/aCCLAK+Xa61OCSTj5la3UehPp08e8HfEnVZYNBto7jcs2kJI7E8kj+y/5iRqdonxBsdS8Knw9c6h9rhk01ba6lds7t+nxFs9uY5t3/Aq+ZnwzKc3d396/43/RH4xheEMwyzMXUqQVSCmuZWd+Xnd9ejfLp1f3n1B4xj0eya/2lMPNctx6GTVG/lXnPj8abYa2iTOux5t/P/PT7Sqj9Sa87+IPxi8Q3utan4atr9YHGlmSGdjnMklvrw6ezRq351zer/FGXxz4pn0XXY4xHaa7vicPkOU1TUhGPqPskbfX6V5+QcNYvBOPtJ301+5f5GGC4LzLFuGKk7RceaSW6TUrNd+x8n/GPTbjUNc0XUiM3Wq6Jppc9mY2VsAfzLD8K4Wu78eeLoU1/RI57YMunaDowQg9cWcLk/mcfhXCV+k6WR/oJkSrRyulGataP/t07fhYKKKKR7AUUUUAFFFFAHqvw+0i4vPh9O9tCXc6PIVAHUtDrKAD8cfnXd20OtWM4ivCGjm1y3niOScZ1qGRf/HJo+O2Kx/2eNb0x/CcWmTOvmQmJJAfRpL3H6Oa6WW/k1rxLZ6fZDMUd1aHAH3gLvQ3B/ASNW8moUm31PxbPsVXhmGJpVIe6nJ3faztb7zd8I36JceE2gfCN4cYDnqDLowH6VR8G634fvvh3aeG7rDRnRYLedyCu9jpUJPP/XEg/h7V0Vp4JhtrnQolZo1sdKAXaccLNYyY/K2A/GuW8XWNn4Z8Iaj4dtLMqINHlisWH3nkSwlQAHqWEcLe/FZU4xc2z4bCSwGYYl04NuTkpK2lv3k0n525r29WW/HF5eL44na4nBk8ttrjsvkeJQv5Aiudm1O0bxlI92zZTX5JoCGxtdb7XpMn6jI/GrWo2Or6vrkktxOS9xqF9axvjkbD4iUfkCB+Fcx4rsrm516W0tJSsh11iHHUbb7WmI/HaV+jGooQipJ+Z72RYHDtxpOST5EtOmj/AA1/M8i8X3ButWhkPbS7FP8Avm1iX+lZdaHiWNo9RjVu+n2h/O3jNZ9VLc/fsMoxoRS2t/mFFFFI2CiiigAooooA2/CHie58PCdLaZo3cmVGB43LBOq5/wCBSA/hXq/wu8e2lz4pS4ifesdzDCqnjAN3o0WfzjY/hXhtdX8JTJBrz3K8Dda4PuL+0P8AhVpuS5GfN8RZRhcdl9ZyWrT/AMj6kPxD0aTUNMtnuFC3GnbA/bLmyRR+JuV/Os3XfGnhkWNzJfRhmt9Le+iYgHeXttRbC+p2Rtx6GvJ/DN/J9n0x2kJ2CxC5PTDaB/hUsXiB4PCthe3IaUw2IOCeuNGlJH/jz/8AfVVGmrO2h+Q0+C8NQrrkb0dt7bSb/Vr5np9he6TDc311eOq/2Xq2pXjMx4VTc6+mfyDVla34Pe+8dXI06RURdUsbjI7j+0b15R+IlYfjWDq+qLLFq1m0m37ZLqUVyf8ApmX8QsP1Jqre/Fa98PeIZJbgh/O1eK33H+FP7Sv8k/REA+tRQhyrUjLcmzCMpTwz99wtZ7bJ/nzfeeS+M9N2IuosuP8AQNKEZ9Q1kM/qlc7W14n1x71I7A4KLZWAUjt5dvtx+bmsWiVr6H73go1IYdKe4UUUVJ1BRRRQAUUUUAFdp8LbKRpjkY3SwAfje2H/AMUK4uus+Hfi7TNElCaqcbZEYP8ASe1cD/yDn8KunbmPOzWFapgZxpq7Ow8KtbLDZi8bCR2aTE5xjy4NIkz/AOOVteKLLTYdBvdOs4gBZ6VIygDhf9Cv4gB9BARXCaP4n0q+sp9OkuQn/EmkgBb+81vZwj9YjXTzX9tf+HZZ7e8Be+lvIjlsgIY9VKH/AMmU/St03yWR8bjcHiaeIjOzXvL01d7/AHJmx4g0bUbifUbaK2YGaXUEjYjjJPiDH0++v/fQrz/4o3LCW+aNuU1V+fQ/a9QOf1r2+91zSozdTmRMiaU8ns0l3/QtXgnjuO6uIL268pmD6g0hIHAAuLwH9WH5ipcHCBycISrVa96keW3L831ORJJOSc/Wiiiuc/TgooooAKKKKACiiigAoopVRnO1FJOCePQDJoASpUvbyIBY7qQBTkAOcA1EASCQOnWigTSe50Gm+M9cuI7i1vLuSTzIXJk3cj93OB/49OT+VeweCvCmm+JfC8l8LdZ1nu2iZCMgpNf24f8AIM/614hpNqf7PvbtxgLbDH4TQ5/Rq9A+BvxMufDOnSWF3GWtYbiPlf4F3NcOx9gsDfnW8JO1j5HibBYmpl85YDScXF6aX/4Ox5tq2nTaTqM2nz/eicjPqOxqvWl4q1VdY1Nb0JtcwIJR6OBzWbWMrJ6H1dJzlSTnvbUKKKKRoFFFFABRRRQAVJaz/ZpTJtzmN1/76Ur/AFqOigTSasXbSe1XT9QDgBpEQQg/9dAT+gqooGw4AJIPHpjBzS2xRbmNpPuhxu+ma1X0a3uYHv7I4TyUKL7eTKWz75iP51STaMpSjSevX/hjbOihvD8saIVY6NLJNx0eOSJSPzgameErODTfG7aBcOBa3cd6jKTjkRXMK/8AoR/Ouk8QW32UX8cXAa3voyPYSauf5xr+VcxfWEkvxPgtS4XbqczKT3C3Mpx+O0itmldWPnsPiPrlGrFuycW/TT9NDl76dbm9muUGBJKzAfU5qKgkk5PpRXOfTJWVgooooGFFFFABRRRQAYOM4oqaGWFbWSGRcszqV/BWH8yKWGzZrqS1m+Vo0kLD3VScfpTsTzWvcgq7Y6zNaRNbkZQxOgHplJFH/ow1BPZzQxiYodpOMn1AUn/0IVDRqhNQqRs9T02/8Tabqdnqc0UysGt7to2B6731Mj9JR+dYEuvafdeLjcyRhmXWEaN/7q/apZGP5MK5OOaWIERyFQwwwB6jp/WnLHutXuCTlZFXP1DH+laOo2eZQyqhhlJRejVv0I6KKKyPWCiiigAooooAKKKKAAY71IJriS4MocmSQkMfXdwfzzUdavhvSmu7i8SRMPaxRsR6E3MMf/s9NK7sZ1ZxpwcpD9VkWTTZNo6X1z+WYAKzLS1e7lMSHBETv+CqWP8AKrAu0bSDblsyGSViPqYj/wCymuztfDelnSIL+2jQXEujkt682F2f1MY/KrUed3OKtiY4Kn73Vu34HLSeEL+KaSF2yUSVuF67BNn/ANENXSeBPhtL4hiNjJDmWHUbH7TF6qbmeFx+AGfwrtvE2h21lPrE0KgND4fmljIHRi2pf4V0/wAO9It9P8RzTRFWkGpgTxL12TaowjJ+gV/1rTkjG7Pjsz4pqRyydWno0k162f8AXrY+cNWshp92tuARm3hkIPYvGrn/ANCqtWx48B/4SmclcAxQlR6KYkI/TFY9YSVpNH3tCbnRjJ9UFFFFI1CiiigAooooAK6PwfrECXeqTXSqpukhIOemLyByPyB/KucoyR0NOLszGvRjXpuD6/5p/oSO6SF3C4ZnyoHYHOR/Ku40i5Efg6DWGkG+S1ntioPQJaXag/8Aj361wYJUgjqKs2uq3trD9mWZjFh8Rk8AshQn8mNVCXKzHF4b6zBRT2f4dvyPYYPEQuLu5u7xgUNlcpKD/cV9XwPyxVz4dePodD+LN9o19uKXV/IqSt0Xyry7dRn6tgfSvNdK8RPfWF2WbaTDMrD/AHob5v8A2pUOu+IUXXriQtiVNYE2702zzsf/AENa1ck0fJ1OHaWJhWw1RaSjy+m9n8in8Qb601HxXcXVi4aLyoEUr0+WFFP6isWiisG7u59pRpKjSjTWyVgooopGgUUUUAFFFFABRRRQAUVNa24nguJT/wAsYQ4/77Vf/ZqhoEmm2i1azzW9hNJEcZkVT/wJJB/Imn+IgRr16W6m8lP/AI+adBD/AMUxdTkf8v1sB9Ck/wDhU/jWEQeJLtR3up+PpNIP6VTXunPGcXiGvX8OX/MyaKKKk6QooooAKKKKACiiigD/2Q==\n",
      "image/png": "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\n",
      "text/plain": [
       "Image (1, 224, 101)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 224, 101])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scale 50%\n",
    "tfm_spectro_s50 = partial(tfm_spectro,n_mels=224, ws=200//2, n_fft=3200*2, to_db_scale=True)\n",
    "img = tfm_spectro_s50(tfm_trim(sAug,0,sAugLen//2))\n",
    "display(Image(img),img.shape) #improved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([10000])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sAug.sig.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "Image (1, 224, 257)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([1, 224, 257])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scale 25%\n",
    "tfm_spectro_s25 = partial(tfm_spectro,n_mels=224, ws=200//4, n_fft=3200*4, to_db_scale=True)\n",
    "minLenForSpec = 6401\n",
    "img = tfm_spectro_s25(tfm_trim(sAug,0,max(sAugLen//4,minLenForSpec)))\n",
    "#img = img[:,:,:161] # Clamp to the size of first...\n",
    "display(Image(img),img.shape) #improved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tfm_multiSpectrumZoom(ad:AudioData)->torch.Tensor:\n",
    "    '''This transformation is going to produce a 3-channels spectrum image from single audio data'''\n",
    "    ret = None\n",
    "    ret = tfm_spectro_s100(ad) # 100%\n",
    "    wMax = ret.shape[2]\n",
    "    \n",
    "    img = tfm_spectro_s50(tfm_trim(ad,0,sAugLen//2)); img = img[:,:,:wMax] #TODO: runs in parallel...\n",
    "    ret = torch.cat([ret,img],dim=0) # Stack...\n",
    "\n",
    "    minLenForSpec = 6401 # Should be function of spectrogram parameters\n",
    "\n",
    "    img = tfm_spectro_s25(tfm_trim(ad,0,max(sAugLen//4,minLenForSpec))); img = img[:,:,:wMax] #TODO: runs in parallel...\n",
    "    ret = torch.cat([ret,img],dim=0) # Stack...\n",
    "\n",
    "    return ret"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24.6 ms ± 254 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "imgMc = tfm_multiSpectrumZoom(sAug)\n",
    "imgMc.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT**: multi spectrum transformation seems to be **VERY SLOW**..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Thnx Tom @ThomM\n",
    "def tfm_to_cuda(x):\n",
    "    if isinstance(x,AudioData): return AudioData(x.sig.cuda(), x.sr)\n",
    "    if isinstance(x,torch.Tensor): return x.cuda()\n",
    "    return x    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "sAugCuda = tfm_to_cuda(sAug)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.2 ms ± 22.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "imgMc = tfm_multiSpectrumZoom(sAugCuda)\n",
    "imgMc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQEAZABkAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQoKCgoKBggLDAsKDAkKCgr/2wBDAQICAgICAgUDAwUKBwYHCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgr/wAARCADgAGUDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDz3wl8FNH1rw1ZavJ4bgiae3RxbPGoKZA4+o6VYuvgfLKfKtPhxb3K5++WUCtq4vr66tYRpVrNd2M8jCKS0nClAWJ9emK9L+D/AMIT4iKwafqOoRyhA7pLdFsZPTk+lennfFeKyPCzxuKrqME27PmfKr9bTTVttUfxPRx+dY3G8tOpNyk3aPPLu9PiT0211drngvi/9mK0m0iSfUvCdtbIFyyiMH+lfEf7UHwu/wCEITT9ZitbZVur25VpraHYHTcDCPfCAgn/ABr9uLP9kbx14tsv+EdRIoYbkbZtSuJN3kL3YL3OBx9a/Nz/AILF+CPAnwntrD4VeCtrQaVr0awztJueYfZpDI2e/wAzKPrXJ4a+MOTcaZtUyehXVasoub5LuMYJpXlLWzcmko3cmruyS1/oLwrxHHHD/EeEo5hCp7DE1PZrnfVU6k20m3JpWjdpcuqu72Pg2iiiv3A/sIKKKKACiiigAooooAKKKKAP0Y8H/E/w1d+DdLk1JrnTZBEgazhmIMTgcodpwcYPTjivQfh5+1l/whMv2fw9Neyz4USl4HJQY4JOMelfK3hlNeXwvYNrFrLNNc2KzXMUTbXjdo2LAc8HLEda3PA+s3ukyGa/h1OzkclriK9lWQcZ6EEjGCOh7V4macKZPmuHnSxUPaRld8r2d/lc/iHM/DvLKNWticPUk5xlJxUZ2s3KWzXv2Wqu73011Ps7xP8Atr/tDXfg67m8PeIjHbXcYheNovLkjV2CsyP2IXOOOtfm/wDt3eOtU8feL7DVNVu7iZoJ7uBJZ1IMpVoy8mDzgscA9wueQRX0PP8AETxbc+GNWhtokaS4tWbR4HYhXKqMbiAcAnnp0r45+P15rd34qgXXr0yTxwSKIuyIJ5QjccZZAhP0ry+E+C8g4TjWeX4OlQc2runCMXLTTmaSbtt9/XU+18F8mzSfEcsVmVX2k6Kkoc05TklON3a7asvhbVtU1q9uEooor68/qkKKKKACiiigAooooAKKKKAPt/Xfh7rMdxb+IdEumlinsS4tN+0MrruUg9uCB+NQeFfhtPJqP2/xrHfiZG3NAb5nh5GCQBgHjHBHHp3r1b4d6NH8QfhH4SvdE0iS+lTw9aK8sTbTIfKAz+Q/UV6D8GP2Q/Eni3xAk+v6TeQmF922a6Z0IxzgdDxgV8nmfGmV5LgKtbMK0aap3vdrm0bSsr3106dT+DP9dcyjCtl6v7WMp01yx/ePlnOKV1totW1dd9DyubwpPJFFrGm2n7h4GitlI6FkYKfzIFfDXxutNO0/X4tMsXaT7IHgjnMrOJI1IB5Yn7s3npjqAgHTFfsPrmifA3wpo1/4e1q5FvexK1rbLjmOYhTvHuuc/hX55f8ABTTwn8OPDniGJPAhDot5EI5VX5WQrMjYPQnMK5+o9a87g7jOnxHWqUVh6kFa8ZSi1GUVZ3Utn8S9Xc+v8EeJK2G4q/s/ELmdZO3K78vJzfHpfZtXXXV6tW+T6KKK+9P7ECiiigAooooAKKKKACiiigD9C/2V/Fl7ZfCPwvDa6vNa+RpFucxfxARKcc+4FfRdr+1l4j8NFLLwqjPNE7tO1xEQCpAGc/ma+Z/hN4aNj8KtBtbGOVx/Y8Yi8hsPnya7DQbfxNqen/Y7C8vGdGO8XlsA2wj5eQBwf/rV8BxHwxw/ntVyx9KFSKb0klbXv3+Z/nNxJClDiLGYzC1uT97U6uNr1J63Wu67dtbHR/E74kS68954guCJGNtJMMDlpNpJ/wAK/O39oDxvrniLxHF4a1W5E0Wjl0t5sEGRHClCeevliPPT5i/Y192eJdGvbfS7lzbOUhtnKgD72FPH44x+Nfnj8StLi0XxxqGlRSyv9nkVHaeUu28Iu7LHr82ea+lyDC4TB4F0sOkoqyVuiS0R+1/R0wOXyxOMraSnHlab1aupJu/RtPXvfyMKiiivaP6uCiiigAooooAKKKKACiiigD7w/Z08U6Fe/DHwxBq2sTW8ltpdsGljk5YeWAQfxAP4V7r4E8feFUtYYtI1mG88zKh9wJYr1/8AQjXxP8Ab+803wZZTXF85RoyYiFz5YCnPHtXq/gHxnYwzjV9E1a0vGucRp8uyRGVQxB/76J6DjFeLnPD1HMaTvJ6620tf7r/ifw/4g+HaeaYuvQqSfvyaVly3lKUmm+XTdvV9HY+ivi7Y6HZaRcXAvk+xRKHkWM/Nhmw3PoOT+Ffl58b51uPirrMq6e1qPtCjyWxxiNRkY6g4yD3BFfdXijxfKlibCe8ZlBIlcnI2kHP86+E/i/qUd/40uUFhbwvF5Ss1s+5CBBEihTgfKAnH1qOHMtrZZl7p1J82u/8AXU+3+jplWIy/E4ypUXxJW1dkr9vP70rHLUUUV7p/VIUUUUAFFFFABRRRQAUUUUAe+/CmLVPD/gCxvXZ9j2UkgZYyxRSC27A68V2PgbWorjUT4qsdV0+9jvYYre3jSDy5oHCB3D5PX5s4wCAAOa5b4e+Lre6+FOmC01BbK7t9PaHz5IDIpVCwxtB55VTjrjNdf4V0XW7LTo/Emr21nLLdxQgCK2MTxs0YaTIJPft1AGDXY2nBJn4NxDKMpYn6zFRlKcoJbcyu9NVtondS6NNanQ+L9biswukzyn542DOD2xj+tfFbO7nLsScAZJ7DgV9X+OrG51LTp7GSd45vJeNJF+8Nylcj3HBr5PrCcFCEUj6fwuwdHCZfV5Hdvlv/AOTa/O4UUUVkfqQUUUUAFFFFABRRRQAUUUUAd18HfiHq3hzUYdItzbuPNaWAXZwiERuSCe2SAM/7Rr6E+GvxXg8eWH2/WdDiti7t88E4kib5zFw2Bk/IT06EV8kadO1teJcIUymSBIMqeDwfr0/GvWPhH4mj1KRml0KW2kdPMkmtmIt2ChYwQueGwmMfj3raCjVXLI/OuN+FsBmlCWIcLTS+JOzWuvVXvp0e3me4fEOCPU9DlsdKiIvJo3ETqcFSUcA/mF/OvkDxPpE2i6xJZy2bW4IDxxO2SqkZAJ7kdD7ivoX4hfEDUdF0+TU7SUrOLeXyhjPJU4OPrH/49XzjqOp32qzLPf3LyskYjRpDkhR0Ge/1onH2cFFnN4a5XjMtwM1Nrkdu976/KyIKKKKxP04KKKKACiiigAooooAKKKKAJLVS82xTGCVYZlOF6H9fT3xXsPwY03QH0Vb57dobooRKIpCY3AxhgMkZwQD75rybw/qFjpesQX2paet1AjESwN0ZSCD/ADz+Feh/Dj4heCdI860y9jFIJWSKYkqrFQow3pxnHrmt6Dinds+e4ip4mvgpQpRk9np+K7+e3zNr9oA3cGiwX+nq20u0LkDoCuc/hsP514tXq37QHime5is7PRbqOSxYPLIynOWcFAPoAD+LV5TSru8xcK0qlLJoKas9fW1+vnuFFFFYn0QUUUUAFFFFABRRRQAUUUUAFFFFAHcfB6zfxfqreFbxFe3is5JZRtJd41ILIuP4iCwGPWuP1jT20rVrrS2Ysba4eIsRjO1iM/pWh4E1rVdA8UWmqaFkXcLO0Q3HEh2khCO4JHP1qp4i1aXXdcutYmRVe5mZ2CdMn0q27wR51KliKeZ1JXXs3GNl1Uru+nmra+VilRRRUHohRRRQAUUUUAFFFFABRRRQAqsVYMp5ByKVnVgq7ANq4JHfknJ/P9KbVixtPtqSxomXRPMBz2HGMd8kr+VNCk1FXZsaDpf9geItK1C/nCKupW/ng8YRhG5/JWwa5+uz8aeDdXTUYGvJj5BuYoAe6mQBs/gCB+ArjKqa5XY48HUhXXtVK7a6bbt/qFFFFQdoUUUUAFFFFABRRRQAUUUUAFOhmkt5RNE5DKcgikWN3GVXjIGfc9KHR42KOpBBwQaBaPQ7DxV45j8Q/ZYY7grBIDvHQxygFEY/TCtXHUUVUpObuzDD4alhafJT2CiiipOgKKKKACiiigAooooAKKKKAFDsFKBjgkEj3H/66dOzvLhoypAClTnOe/5nJ/GmrG752ITjrge2f6H8q3/AOhr4p1prO5mbfFbmS35HzSK6nafY7jVRTk7GNerChTdSWy3KWi+GbzV9WfR40cyiCZoljXcZGSMuFH1wPzrOlTypGj3q21iNynIPuK938F+DbTwZcPBFPufT8Xy3Tx7iVMOHUAck4ToP7wrxbxWNLHiW+/sTZ9k+0v8AZ/LPy7c8Y9vSrqU+SKPJy3N1mOMqQgvdiotP1v8AddapeTM+iiisj3AooooAKKKKACiiigAooooAuaRfw2crR3SkwyKwcoPmBMboCPpvz+FaXgG8urTXptSs9qtBayz7P72z5go+rBR9M1g1Ja3M1ncJcwNh0bINVGVmjCtRjVpyj/MrHrmh+OZ/DMejX+pXHmzw6jbi9DtwIJbYoxPsDEzGvK/EOpW2sa7d6rZ2S20VxcPJHAvRATnFTJq872yNNOXctIkpduNrR7F/LLn8fes2qqT5jgwGWUcHXnWS96WnlbmctvJvTyYUUUVmesFFFFABRRRQAUUUUAFFFFABRRRQAUVM1uEs/PbqxXb9PmH/ALLUNAk0wooooGFFFFABRRRQAUUUUAFFFFAH/9k=\n",
      "image/png": "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\n",
      "text/plain": [
       "Image (1, 224, 101)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": "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\n",
      "image/png": "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\n",
      "text/plain": [
       "Image (1, 224, 101)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQEAZABkAAD/2wBDAAIBAQEBAQIBAQECAgICAgQDAgICAgUEBAMEBgUGBgYFBgYGBwkIBgcJBwYGCAsICQoKCgoKBggLDAsKDAkKCgr/2wBDAQICAgICAgUDAwUKBwYHCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgoKCgr/wAARCADgAGUDASIAAhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEAAwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSExBhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElKU1RVVldYWVpjZGVmZ2hpanN0dXZ3eHl6goOEhYaHiImKkpOUlZaXmJmaoqOkpaanqKmqsrO0tba3uLm6wsPExcbHyMnK0tPU1dbX2Nna4uPk5ebn6Onq8vP09fb3+Pn6/9oADAMBAAIRAxEAPwDnNR8NWkd/p99NApOnfFCO8jYjlGaO4BYfhIw/GotY8I2GraV8QdHRAx0zWJo5k9POa1lwfqJgfxrT8T6jCmm65cBuLbXre5U+n7hzmt3Up7BfEfjrT49oa/0K21Mj+8f+JepP8q6njcXh3CpHf3fwq4Zv8Js/i2ji61XDpNtuHvRt0ak6jfysn6u5S+JGkwi9+JNxsGZtG0HUW9xGHOfyj/Srtp4ChPifVYIowBf6Ld3gwOrPuyfzU1L4mEOpatr1s5yNX+FVqfqUgu2/rW74E1q11bWvDV8HDDUfCuoxE56mKa6U/wDoNfMzzDHYPKOaHSK/8loxa/GhI3xsHinTXM3blT7apfq1b0Of8O+E7byvDOhQQqDqHwWawjUDtA9sAv4eb+tWfDnw7tda1bRNQMQP9s2EKSnHUxQ4H/oVbng68tEuvhhfuB+9tbnSyfZprYY/8cq38KtYtW07wbK7DNr4ol01j7mODA/8erysbm2Z4LD1p0r399fc8bFfjTia41wzTEQlKT1tzf8Ab0oSfytU/AT4X+ARpf8AwhepJCB9h1XWYX46GfzW/UtWZ/wqa/n0Pwhp0cKiDT5tb0+9Vl6QlL0rj/gUafhXqGny2mn+GLyZVH/Es8c28A9vNNuP/aldJqdlYado/jEhFB0eR5l/2fMinY/+hGvzrF8dZjhsxlUitZTsv+3p8q/9TInvYbKZ1sOueV/dbd/KnJv/ANMy+djxfw18A4PC0Ftqb24V9FvjducYwiNcgfpAv5V2H/ClorC1k054edJ8Ty62Bt6B2uAD/wCOGvSPi3o72egfE+DTol82z+HpubUf9NGXVSP1Ars9V8JR3HirxUqoNk3gq1aMf7Xmajk/qK+AzPxjzXGQhiqs/i5X8pKjP8sTM+wwvhtyRmlNt3kt9W17VX+bpL5tny9rH7INrqWt3sz2IPmay12cp1DlAf8A0H9K17z9lpNbvEuZLU/6boxu+nRylr/8U/519ZQ+FbFp/OMQ/eacknTvuX/Gr1t4R0+CTTovLUY8P7eR/wBM4/8A4mvksT9JTihU4csneCa/8lj+sD6Sj4K4avJ+0qNq6tdvS8mtNezX3I+BvFP7NH9kpbW1jZ7VVrkYVf8Ap4kP/s1FfU/ijRdOnuAGjX5JZB09Wz/Wiv2PK/HTO54CEql3J7/ff9T8xx/Ak6eLnGFZ2v3Pzs1PxpBqnhvXoopwTPYWkwwf76yKD+lbt94jCfEHxPqs8vyw/DS3tkO7+PzYS/8A6IWvn+z+2eEdO1byZ5ZbTTtF0mwuGkkz5aW8N5IXJPUnKA1v+OPiLqFl478ZaA9nMCdLmt45FXI2C3kkVzj7o3Iy89x71/ZtfIKVafJHs/uvQ/SCPQp8IV6OJnRwUlKDi9dE+VvDxel9+WV7dme7HxlDc6zp0vnDn4Yxq/PrbyL/AO1Kj+EXiqHSofhokR2q/hvWJ5/m6PNJJMf1mJrw2f4qxab4hutKec+fZfDa3IjB5IMltGDj0zJirvgf4nwvpnhC6WUr5GiWy8/wiewhk/8AZxXkVuE4VcI6CW6f406y/wDcpyYnhrNsNg5zhC8Z2f3Uqso/c1G3qe8eFfGgm034YeXL/qPFN08hz0RHkb+cVHw+8XnTdI0IyT/NJ8W5ZsZ6RjYg/D91XgHwu+MEaT6XoGoXDebpNrr+oO5BCpGt5qiJz0zthb/vmtbSvirZNb2yx3Y/0HUpL9zn7u28u4yfzhb8q5K/B8MR7SmlpJyf/gU8TL/3IYYnh3N8BXnThSvFW17rmm4/fyQPrJfHsN34a8Y2aT/N/wALGsGiGeymwaus8XfEG3OmfFdhcDbLp1q0fP8A07sD/wChV8deBvjTLqHj2+8LzXP/ACEvG8ctsoP+sjW00pi3vhpMZ9xXY2PxXfxX4Yu54LosniCJoS2fvBUtlH/oz9a/N8f4VQeJjNrTmhL7qmEl/wC6rOHG1s7y3lVSk1DlWvlKnKL/APTtT/wFn2j478b6feXHxDiMykXPgGBOvX5NRP8AWu9i8b2M3iS8zOMz+GYojz1xLcDH6mvz78RftKagNJ1HVgJZX1Hw08IiQfM5jt7shR7kvxXosP7Rd3BfnU2nbbHqcemynnj9/eNz+CivxXOvo74+OChSj0il8406cF+NI+rw/iDmeFl7epRdpSf/AKVUnL7ueS+TPsOw+KumvFpo+1LmfR2PX+79lP8A7UFWtV+LemWmr2ls12oK6SBgt6xn/Cvg2X48eItO8QR23mzqNH1K40yRCMh0lj04q/rgGFx9c1n+K/2jPFd94z1G9t3YWtjo0ZjKuSzFd6OMe3mRn33e3PmUvosPFYtSc0oOM383L3fwmj0P+Is57Gj7OlTvO0Jb9Fypr1u4/wDgXk7fQ+v/AB3003s+btcC7YD5u3lxH+tFfD/i34ua5o0Vkt4knmzxM8gyTgg7P5KKK/onCeAmURw8VHZfpp+h8jSy3jrMYfWKdmpX69m1373Od8L+GofFfhH4r210gaM3VvAB/staupH866b4m/DxI/G/jy9WEbz4Xt/1kmH/ALVrB+AGv2+o+BPidOXGZPEYi5P9wTL/AEr2T4gWsFz408eRhQceFLYfiJov8TX6njcwr4TOJx6f8HCr/wBuZ6OeY3F5ZxFWpJtKPs1b/uFgm/8A0hHh1t8L7i7+Mi6w0O6G9+DVuJBjgOtxZyA/iG/8dqPSvhBr8uleGtDt2MJufhn891GPmiuobO3iR1BGCR855/uivefCnhq2ufFGnt5QO/4RW5H/AHzZmuk8G+DbK7XwQ3lgmfw1f254/upKP/ZK8fEcYLApzfZv/wAkqS/9xnPj+Ls0w9aNOl9mKiv+3fbQX48v3angWl/AmeXVvD+pISsc+m6jHeRbciVHh1kFT9XuA3/AafovwG1SKfwyLMDZd+INQjvw6Z8yF7vUZFT8HmT8AfWvp3TPAtpBZeHCYRkX1xbnjsXul/8AZq1vCngWwe20VzCubTxw0DcdN7g/+1B+dfN4zxKjhYzqr+8vu9ul+MUeSuKuIsXL2alurfJ834pVtP8ACuyPmjwn8CbyTWtF8Q6fEIbq2+IsFms2zOIBdaX5if8AAo4Aue1dp4l+D+oeH/AGo31gDAdG8SWrMQn/ACzMli7r+KoR+Ne7+DvAlpFaxqIRmP4pxsOOxktDXW/EX4eWVz4A8YWSxD/SdctgvH+zAP6V8Tmvi7yZ9Rov4ZTjF+jrU4v/AMlqSOmcc3zPC/WakrqmpSt3Xs+ez7+9CVu3M+58x+LPg4bU+ILRIiDpN9pqjjojq24fjzU/i34aXsOh/E20tmbzbDXLG+tP+mZ+c4HsTk/ia9n8c6dbnxt8ULA2+I4oNFmR8cFi14pA9x5Q/MVFrenWcur/ABViZRh7XT2/Hy5K0wfiBj8VRo1Ja6Yef/gUsHJ/+nZfeebVwmJy6u4OV7JtX1+H2jX3+xV/V9zkT8DxdeOvEt40BIm8mZB2zlTn/wAfrjdI+GAn+IF34eaHJl8LPOcjqR9kJ/VTX1JbR2CaxdMduZtLhY++GhFeXeHY7Jv2gJiQuE8FzBvrviH9K8XIePs4xWHxfNf93QjJesY01+aMa2VRw9aNp/Hzxfovh+5WPAfHnwtju7yMzW33Cyrkeyn+tFeuePbOwimgJA+ZpT+TAf0or9hyzi3G1MDCVn/TPBhn2b4CKoU6rSjtr8z4E+GfxGt/AXw4+IN9qExWCTxqFd8cJ5kb4J9BuPX3r6a8V+Loo/GHj2SWXppsUBJPbeT/AOyV8eafa2urfCf4v6bcci21Cyu1GejbZWU/mlfRHiJbrxB8SfijoKOVWLRtNuUYHnLyzg/oBX6BmWGw9TGXmu93/wBxKP8Akj+gvEDJsBVzSpU+GXNeb/u2y1J/KM2ew+B9fFpqOgXF0cM3wgslcHsSLAH9Wrr/AAFr8EWofD7Tyw3LBqYbnoGtr2QfyFeUXNy63dlLAceT8MrYLj0U2Df+yVseCNcmTxB4B1UMTFe+HLmeI/7X9m3pP8q/Ms1ySnXwE5Pdxnb1VKuvym/uPyPGUZ1czWIivd9+67NPE1F+CX3Ht91rdra6fpTtIAINVnnPsq3Nzn9FNTeFfEsYgQq4Pn/EyxeHHdC1kp/XdXmus6vdatoOl2lrOUk1DTdSWJh2fzdQwf5Vk/CrxxqGpXHgNZYZVS88U3EdzvUjE1vdxp39SvB7jBr4mtwhGtlFWTeqc9P8KxE/zi0eXgamLjiPaxScU4prqtIO/wB0WvVH0H4Y1232R4YYk+Jdvj8rI/1ro/GPjSx/sW7tmlH+l+IYuM9cCH/EV4l4X8TSpa6aS/LfE+EHn0Ww/wAKzfHXje+isLW+M52Q+K0tpOf43+yBf618VU8Po5hnsJSduWUvvUotfjFHqRzzE0MB7GmruaS++Gv4SZ0vi74jWF7ceK9WhmU/2rf20CHP3hGbo/8As1YuufEm1S/+KNwtwPku7C0fno2ybj9K8yvdNv8ASdC1DTLrU5TL4a1OOS5y3MivFexqX+pQn6rS/FPwpc6bb/FldMYxTXevafeyuvV2X7UvP/AY0X6Cv1XA8JZLhasKEJXTdJJ+UamGS/8AJIwfzOTB0p4yrNYmrZy0Ts7e9OzbvrZwqSkvu6M9r1j4uppvi6SBrjAhtjbMM/xeZGR+iGuB074mw2Xxr1yZrgZtvDrxdemZYf8A4oVx3xKfV9O8V+OdbuZ8WthcQXVuB1UbZw+fbla5HxDp8+k/FDxJ4otZ5B9s8M/aZELkgs5sgCPT5Yeg9Se9enkfBuURwj5Uv3lGMX6v2MvymwwWCq4qpV9pW+xJwa2537P3fVRn96+Z6L4++JEMzWT+eOUmPX/ps4/pRXx98U/2ita0jSdDuTbtI10dSI2Z+4mo3EaH8VUUV+kYHhXBUMLGnppf82fp+V+BGcZjgY4iTV25Lf8Alk4/oeeeFvE8bfCT4p6mDxfJoyge7iZf/Zq+odA1aGT47fEpHYfvfCmlDn1Ekp/rXxJoGtvYfCnxLppBI1DUdNiznpsFw/8A7Livpe48eWnhz4s+O9avbtY0k0uztwWbGShuWI/KJj+FezicJ9Zb8/8A5KD/APbT9Y8ROGq2KxGIjRjd1FO1v8WXRX/pD+49w0vVYp7izDsDn4cRA/8Afu2FdP4LFr/wj/gJxjdaeEroD2zbXKD9Hr598O/F21nvLVRMAYvhXFcTDd9zd9hwD9Q9ei+B/iHbPpPh6EXAzF4UjbGege3jP/tT9a+ZzDJ6lWklDpf8YSX/ALcj8A4h4dzbAUnJQerb/wDJMR+kl956T4S1qO5j8AF34aTUQ/03Xpp+kXDabaeF7jT4Fd4PiNEoXdjEUl5bCQ/gpc/hXkfgH4q2dxpfgC6hugyypqTKQe32fVJM/wDkI11eieP7YW+hETqd2vtOoz3WaX/4z+leRjMirOVSMdnzfi8S/wApnjZhlGPyzMVKVJtRnK61+zUqrX5U2ekeGtVjeLTgH4/4WWjj6bbGqHi29gvPDF8rHO34oWwX6KsR/mtcV4J8f211b6DNHOCJ/G8cgOeuXslqhoXxEt/EvhWWWOQ4k+J8yENxlop44Sfz/nXF/YFalj+e20r/APk8n+hlDK8bRjKUoP3LRfk7cn5wl9x6t8X9F0+10z4p6lbxIs9zPZxCQLyQpv2UZ9jIx/E1N8WntW1T4mJxjztPH45uv8a474kfEa3vfDniuVphi91G1J568XA/rWT8X/ilp9lN8Sb66vERf7e023JZsfM7zhR+JYD8a8zKshzCM6CqXuuRfc8Gv/bGLA4bHZlRjCEG25JL5uEUvlzo6L9oCNJfDHxIEZwz+HMqR/eIH+Jrhb7XLLX9ETxLatmPUPhtZXCMVwSrQWrg89Pv1pfGDxrDNp/juAzD5rVIDz9eP0rznxL4s0/w7p82gW7JHHp/w1McaLwESGLTkA9gA4r6zIstr0cHS5/7v/pugvzR63DuWVsVlkaai+d1br/C6dC/4yifPHiGL+2/hx4K1O7Qb5tJumbPqb64P9aKg8Ya3a6L8JPh95kgXzNFuR+Vy5/9mor9EpSioK++v5s/tbJaWJngm4LT2lZL5VqpwOh6V9u+Fev3oJzaanYSYHcbbhf/AGfP4V7N8VtHjurv4jTPGGktLK2uoWI5Us96pI/4DKw/GvJ/Ad0kfwv8X20jcOLMqD65mr2Xx/fwN4h+JNsxz5ng63lx/us+f/Q6zWln5fqeVxFWxEM6jy/Zk38vaYO//pTOe0XR0sdb1CeFyrah8GtNuJTn+6bFP/QYhV7w74j13w14heRb55IB8I7bU7eAscB0sbRCB7f6OT9WNQTT7ZZ2jbDL8A7fBHYgQn+lXbXSVvtc8GxMCV134UvprEdgtjcsT/46BXPHTX+tl/kfMVqkatOXt0mnF3uv5afN/wCk02jW+Gnh7VvD2g+GYmu3fydIuoowSfkb7P4hOfx81R+Aqz4C1zxTqV74Lupr5/st3qN0ka5/5aibXCwPttMP/fNdz4f0yB9L0xlUfI1xEPwOoJ/7Oa534R6fbTeEPBl5IBmy8Z30an3a4u4//a1RJRjzNd3+Uj4jF5rSxVHF1pwTk5yWy05oYxr8UvvZzXwh8Y+IdO0PwF4fvL1naLxoYppiTubydV0lB/467A1oeEfiRr1lceGdK1AIgv8AxlFLcRKOVlYaA7AH0zcTH8ap+B7K1bRdIvkUGSx+K8tvnuA2q6cxH/jq1JZeHFvvEmmasWIGmePY0Cjp80GjNn8PJqKtJTnK3n/7f/me1mUMtxGMxCnBJP2ren2m6zX4zjY0Pjj468W6Z8Er+7064IvZ/EMMW9R0UmVl/HDKPxrnvib481TxZP8AGPTIzI9u3jC2JYOQYWtrTUXDD28y1j/OtH4wtLefCLxPHbEGTSvEFu3PYxpvI/8AHTVDVNCFprXxxsreAkm9jnQAdWktdQJx9S5q50oKqml1/WL/AMi8joZfhMKp+zipxqprTviME19ylGxP8d/jPq+kS/EqwWUGRfFdlbW45/1ckUznP/ft/wBKr+OvEdzfat4582csieCLiOEf3VaDRTj88n8a5f8AaA0z7brnxQuCSDaeINHufrmGeP8A9q1Z8QCaW58XKwIaXwP5n1H2bR2P/oJq6cEqUV2t+Cj/AJHs5XlOW08pwk6UEm4wv/4Blz/Jfizzz4h6wL/4c+BrBj81tpV2SPY3kqj/ANAoqj4xtNngnwjff89NMuY/++b2c/8As9Fas/Zsopwp4Llhtz1PvdWo3+LMmy1iSz0C+0mNsfbJoS4/2U3n+ZFekeK/G63fjPxtemb93N4SS1TB6lpLYfzY15RVgajdk3TFyTdJtmJ7jerfzUVSlpYjG5VQxdVVGtf85U5P/wBNo9gn1ZPOu8uPl+C9vAfqUh4/UV2/gi9tpNa+G0MhBaPwPcsPbFs6/wAnNeK6lqVyDrDJMRjwVpsK47KRYgj8cn866zSPGNxpHifwzOVJWw+Hty8QB5Y/ZJmI/wDHKOVcv9eh+eZtkEquC5Yb8s/xoSj+dQ918E+IYp9F0su2C+raioB9EudQ/otYHwa1FL34b+F5Ipf9f45lnQg9V+3Bv5ZrgdF+Is1l4Z0K5tHy4sdX1FEJxuwNYYfrtpPhN4sutJ0b4W6HEP3N3rN2ZT6Fbjj+YrN04uLXr/7d/mfE1+FK1LB4lrdzcv8AwCnjH+h0Pw1ulk8Mu6nPmfGoMPp9r04/0rotFu4ooNRl4+X4iW+3/wABrD/CvOvhRrb2vhfw/b3OQ958XgGH+1mxY/8AoNP0r4ixSW+nWyFt+q/EzAGP4YorJD+pX86Siua/m/zkdmYZFXrY+vyLTnl9ylK/4QZ0/iJlvvD/AMVNFllOP+ErCr/sq9vIB/I1va+II/HnxPzjEraaH+pW+WuA8Qa35mgfFHV4JCDP4qESZ/vRQXGf5VpeOvFzwa78V7+IE/ZtW06IAHqFF4x/rTjH3o38vzj/AJGMMqr1KkYR6+yT9b5f+sSD4/2kULfFKdFAM8eiyt7nzin8hW3qui2r3GtvtUiXwIqnjqPstrn/ANBFct8cNahv0+JSxyA7f7KgPP8AEtxIT/Krq+PLK7m1W3WcFo/h2ksgz0zaWhH/AKFWkFyq3l/kd2FweNWS4flTulG//grA/wCSPJvGy5+FfglwOfJ1AH/wKP8AjRUHi+7SX4b+ELQN80UN8SPTdcn/AAopS3P2jKoyhhWn/PU/GpNnLVNbFRDcbuvkjb9d61DRkjoetSei1dHVwIt/Lq8bZwfCdsR/2zW1b/2SugvbZ31zw5FEMtL8OLn8zaXgrj9A1hVkvTMcGTRJLce+FGP5Cu8068tZrzw9eMwzH4Hngz7hJFx/4+a2S5lp/Wp8tmlOrSkrLS0v/TaX5oX4cW0dzq/w+SQZWbR9Vgceo33f/wAXU/gC6jXw38NLlhzB40kgB9mljP8A7NWf8PNVit9R8AszgfZ4NSDZ7AtL/jSeEr2O28H+AgW5j8a+a308yL/Ck4vl0/rb/M8TMMNVm6kbb834rHL8pI2/CyiG38PIvSL40t/K2/wqGy0OOy0zTrgqN1n8VphGe4TahI/OAflR4Vvo3tNLfcOPi4ZRz2KJ/hWtqktvI02nowGzx88igHu8c/8AjTjC8/67s82qq0MbKPRtt+nPJP8ACbF1TQ547DxLptwvy3HxEuZpF/6ZNb3qjPsStWfGPh64j/4WTcty2pa8zoPZbe+x+pqz4vvUTUfGM8b5CNFOi56N/wATIE/yrT+IF7b29n4kmJGPNkmb/v1e/wD16cYL+vkeNQr4hVKLt8Xs3874aX/tqPH/AIg6jdC48fRJIW+0+Mo0kzzlFe9YD8Cq/lUd5qsum6l4kkt14/4RG1sgT3UGzgb/ANBP5VparpB1LU/GUy4KjUbq8kyOu1dRUfqRWhqvw/l1iGwkgzG2t+DFn+UZ+dZ4Zj/6EKUYO1uv/Dn29HEYLD4elSq2Wkfwp4eT/CmeXalf/a7HT7UOSLW1ZNuehM0j/wAmFFU6KybufbxioKy/q7v+oUUUUigBI6GtNPEOoW9haLBNhrdJoVz/AHHwSP1NZlSJBJJbvcKPljIDfjn/AApptbEThCduZf1sWrPX7+yltZYXwbSKRIfbfuyf/Hv0qaHxTe2+n6dYxDA068a4iOerEqR/KsyRGido3GCpII96taxpM2j3CW8xyXiDjjsSR/SmnKxnKlQlJJpXf/B/+Sf3m54S8XParp+nXEh/c+JoL9mJ69mP6D860rTxu+oa5bTqzBbzxct1k9kGBj8RIa4kQT+ckIQ7327B65wR/MVagM0NnZ3MH3hevs/3gIj/AFFVGckcdfL8LObnbVpr77v8/wAj0KL4i2etSaxbNLmTUUmZT6orXTD9JK2fG/i77fa6s0bho7u3kUEHuF1QH/0AV5d4fsp4bmO82kLLpt6yN7rDLn+n51tz3ckfguxmlckyWB3E9yz6mP61optrU8XE5Rg4Ymm6a2aX4f8A2iO48EJBqGveLrOTkS2dyCD33T3X/wAVXfaPpASf4e2syAM3gmdJB7qlp/U15N4J8Qpo3jPWIbqTH2zV5LNM+rO5A/Mmva9OlguLjwxrCSfJYaHNbkf77WoH/oNE03yuPmfn3GFPE4avTlH4XGVvX6vyL8bHy5qeniPwjpN7HGS001yGIH90oB/M0V0Gkm1l8E6XBOASr3DDJ9XA/pRUunfU/WqeMcOaLV/el+bOIooorE9QKsQXKxabcW5+9K8ePoNxqvUkUXmDGezH8lzTRMkmtSfU4vN1m7SPtNKw+gJP9K1vG4Go65ZQW2Nz2scY/wB7ewrHtpmN29xN1kilOfUlGH86veHpzd+MtPaZiwbUI1APYGQf41Saenc5qkXC1T+SL/L/AIBp22mxSav4ebaP9I1G3ib/AL82h/8AZzVLQLZbn+w4MZEmuMh/H7P/APXrT8O3P2ubwpORjHiXYc/7KWS/0qLwjb+WbCN/vWfieDd9GKg/qlWtX/XkefKrOMZJ9E/zqL/I3f7Dh0/wTgAb7O/1G0JPX/j2uv8A4kVR8d2K2HhQwIuBbXkcIA9PtWqcfpWtrcjN4e8Ywh+LHxdMQP8AZkivEx+Zp3xX0sQ6JrsZyfs+qW8g/wCBXWpf/F1UneOh4mGxU5YunzPeaf3qD/8AchiSAx+PL4Y/1PjaLPtmWX/Cut+H/wAQtVX4a61d6qT5ulC3ljA6mJp4GH6Vg+JYY7Hxh4rlx/qNbtJx+MpJ/wDQqjeSS08Ia5ZQLgXHh6yd/wDgI00/ykai7ih4yjRx+CpRnG9/ZP8AGk396k0clrM11YWthZw3DBVtX4Hr58vP6Ciq2r3Juo7RyeRbsP8AyLIf60VjJu59fSglTV1rr+bKm0lS2OAcE1LdWclvdT2+D+5Yhs+mcf1FMVlFs6dy6kfk3+NbcKwajr17GwGJruOMDHrMoP8AKhK4VKjhr0/4YzJNOCef1/dWkU3/AH15f/xdR2hwf+AS/wDoFat4gH2//sB2Z/8ASaqFjCr6fNNj5kkwD7GOQn/0Gm1qRCo5Qbfl+KIbtNlvasB96An/AMiOP6VL4euFtPEFjdOcCK8icn2Dg1pzaQEvobErkQ6XeHn/AGGuP/iazNZs1sjBGo6xPn8JZF/pQ007ijUhWi4d0/ufMbHhm8SG30BCeYvEjSfhi3/wp2jXyW95dyFuH1mB0/4DKSf5isa2uJLW3s7n+GK9d1PuBGT/ACFSQK66fZXQJy+oSAn1wIj/AFpqRjPDxbk31uvxbO08V3cNraePIFfAudctJEGepb7Qx/nWn8Ub+K70rxUUYEG5sgv4XN5n+deb6xr13qTzh5AVuWgkm46ukZX/ANmatjVNYur3wdeXcvW9urdpOfWW8f8AnVcyadv63PJ/sl0Z0pt6qUfw9mv/AHGafj6+Vte8YurYMlxbhfqHH+BqaOZJ9H1ZM5A8KRfmItNH81rnPF0002p61NISC+rLnnqP3uP5CtvwtYXcmla9Bc8uvhV5R/uYsmX/AMdxTbvJ/wBdx1aFOhgKbb25P/cKOKmJMcQPaM4/76NFTahbmC2s2YYMlszEfSWRf6UVi9z6OLTjdf1qVav+Hrhl16zZ2PzX0TMfXD1Qq1pQCGa8xzbRrKv18xB/Whbk1UnTa76ffp+po62wtbu7t3PL6NZoPrstz/So9NspreFrW4XBubR7mMeqrDPzS+OwF8Rsqnj7Faf+k0ddHr2mC1vNBkAwJvBMr/j9mnP9RWlryfkebLEKlRpr+dN/+ApP9Rmoqq+JLgD+Gw1Rf/S4/wBBWLrunm9vbGNCf39zNAPY+ex/9nqe/wBXz4v1BG6KuoRqfUstxj9ZKntl83W9HjcdPFE0f4eZAf8A2Y1TtL+vQypKeHSm/wCVf+kyZlrprXfhjSliHz3OsXUK/XZa4/8AQq1YtE8nwhZPImGXxFHG2e2+CNiP0qx4NtBdad4PUjIfxrNGR65FjWz4nhS38H3dzGMLa+L4XP0WzB/pSgk4tnNWx8lXjRX87/8AS5ROa8W+GLbStO1K6SIBodXSFTjoDJeqR/5BX8q1PE3htYPCusRQR8WN/AqKB/0938YFa3xZ0xLfwfrF4eGbxSqAfS41Q/yatnxjZxR6X4pjCjA1Kx7eupXuael2jy4ZvUqU6Mm73mvzpL/25nPal4Xim8UeJ7F4siK400gEd5JYl/8AZzXVWXhoR6Vf6kqACT4Vea+B1ZrSBs/+Q6fqVmiePvGDY63+hoP+/wDa/wCNdZpmkm48C3EZXDv8LIYj7E6fSnLf0f5/8E+WzfOqqw9F30aj+McK/wBTwD4gaK2l2mmSuuDItymPZbh2H6OKK6D4626waboWByRcn9Yz/Wipqq02j9Pymu8RgI1H1cvwk0edVPZzrDb3UZP+tgCj/v4jf0qCisz0WlJWNHxTc/a9VWcnk2NqD9Rbxj+ldl4vvI/M8NxhhlPA8in/AIFaSivPXkeVt0jZIAGT6AYH6CtbXfEMl5eWksZBFtpEVopz28naf/QjWiktWebiMG6lSjbaCn+MUihf3f2nVJ75T/rJ2ce+STXQWN2h8Q6Xkj5fFEsh+heD/CuXq3p99JFqcd63JilMwH+0Pm/oKUZanVXoKdOy7Nfg1+p2fw4aN7XwhEx5XxuzgfjYD+tSeItYim+Gd/PnP2nxSwU/S0A/rWV8P9RWHUfC9sTynijefbL2n/xFU7e4abwXpelPyk/iGdnyfSO3UfoxqouyseBPB3zHnf8AMn/5UqS/Q7D4q6gLvwNq0e7JXxify8zUT/WtbxNqEbW/iiOZxl9biRf+AXmpuP0SvPda146j4e1mBnJ87xHFcJn0K3P9W/Wt3xvq2y38SujfMviZVHtltT/+Lp3tK556yyVJ0qXaSf40f/kTrda1eNfiT4qttwy2raT/AOOz23/xJrt/DGuwT+FLuRiMp4Atgwz0zYJ/8VXifijXpofiPrt9uwp8TxJJ/uxyuR/6LFalr44m0nS/EFihYh/BlgikH7pa3sof/Zifwptp/wBf12PEzHh2WLwOHUd+WH5YZP8ABFL46X0d1BosCMCY47gn8XUf+y0Vyvi7VH1We2naQsBFJt56Azyn+RFFZ1Jc02z9FyzD/VcFGl2v+LbMiiiioO4ACQSB0HNFWbSHNvdluqWysPxkT/Gq1Ak7toUqQASOoyKnmT7PFbygf623Y/8Ajzr/AEovEC21owH3rck/9/HH9K0dQ05ZND0KReGuFljJ+kxx/wChVSW5lOok436tr7k/8iHwtLLa69pV2T8ianGy/UOhP9KRL0Q6Jp65/wBTqU8hH1WD/wCJqS0Ty9J0u5A5OrTDP0W3P9ayi7FBGTwCSB7n/wDUKd7IzUI1anN20+5v/MUSyCMxBjtZgxHqRnH8z+ddJ4wuCLnxNbE8v4mVvyN0P/Zq597Xbp8d6P45nQ/gFP8A7NWz46JTxN4hiB4/t+T9Hm/xpLRGdVRnioryv900P8et/wAVL4kHr4ikP/j89F9cmP8AtNGPMvh2xQfh9kP/ALLUvjmAP4h8VSH/AJZ+IHI/7+zCszxQ7x6kiI2A+l2QYDuPs0R/mBVPT+vU5sJGNTD0Y/3V+EaT/QpXWfIts/8APA4/77ein36hbSyP961J/wDIsgoqHuerB3j9/wCbP//Z\n",
      "image/png": "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\n",
      "text/plain": [
       "Image (1, 224, 101)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Display channels inepenently\n",
    "imgMc = tfm_multiSpectrumZoom(sAug)\n",
    "for i in range(imgMc.shape[0]):\n",
    "    display(Image(imgMc[i:i+1,:,:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b4813a3d000e42fd9ee7d5ba88a8adb6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<div style=\"background-color:black;color:white;text-align:center;\">None</div>', lay…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "tensor([[[2.9722e-02, 3.1939e-02, 3.2092e-02,  ..., 0.0000e+00,\n",
       "          0.0000e+00, 0.0000e+00],\n",
       "         [2.2265e-01, 2.7813e-01, 3.2486e-01,  ..., 0.0000e+00,\n",
       "          0.0000e+00, 0.0000e+00],\n",
       "         [1.1255e-01, 1.5073e-01, 1.7103e-01,  ..., 0.0000e+00,\n",
       "          0.0000e+00, 0.0000e+00],\n",
       "         ...,\n",
       "         [3.8001e-05, 6.6901e-05, 5.2621e-04,  ..., 0.0000e+00,\n",
       "          0.0000e+00, 0.0000e+00],\n",
       "         [1.8052e-04, 1.8975e-05, 5.8354e-04,  ..., 0.0000e+00,\n",
       "          0.0000e+00, 0.0000e+00],\n",
       "         [1.8291e-04, 1.9025e-05, 2.4738e-04,  ..., 0.0000e+00,\n",
       "          0.0000e+00, 0.0000e+00]]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm.try_train_tfms(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[2.5317e-02, 2.7676e-02, 2.7458e-02,  ..., 0.0000e+00,\n",
       "          0.0000e+00, 0.0000e+00],\n",
       "         [1.8978e-01, 2.4230e-01, 2.7938e-01,  ..., 0.0000e+00,\n",
       "          0.0000e+00, 0.0000e+00],\n",
       "         [9.5766e-02, 1.3217e-01, 1.4733e-01,  ..., 0.0000e+00,\n",
       "          0.0000e+00, 0.0000e+00],\n",
       "         ...,\n",
       "         [1.5457e-05, 5.0001e-05, 5.7724e-04,  ..., 0.0000e+00,\n",
       "          0.0000e+00, 0.0000e+00],\n",
       "         [8.2644e-05, 1.2659e-05, 5.7762e-04,  ..., 0.0000e+00,\n",
       "          0.0000e+00, 0.0000e+00],\n",
       "         [9.5344e-05, 2.7503e-05, 2.5761e-04,  ..., 0.0000e+00,\n",
       "          0.0000e+00, 0.0000e+00]]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tm.apply_valid_tfms(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create final transformations\n",
    "\n",
    "We now create the final transofrmation that outputs a \"multi-channel\" image where channels:\n",
    " - 0: 100% signal\n",
    " - 1: 50% gignal made doubling the X resolution of spectrogram \n",
    " - 2: 25% gignal made *4 the X resolution of spectrogram\n",
    " \n",
    "The expected result on the final training is to improve classification performance on \"short\" sounds that are actually the most common.\n",
    "\n",
    "**IMPORTANT**: now our output tensor is a 3-channel tensor so we can use the standard architecture\n",
    " \n",
    "**TODO** : possible improvements\n",
    " - Increaze the number of zooms\n",
    " - Sliding window to cope with long signals\n",
    " - Stack multiple different transformations at one"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "#Cuda\n",
    "tm.train_tfmsg[0]=[tfm_to_cuda]\n",
    "tm.valid_tfmsg[0]=[tfm_to_cuda]\n",
    "(tm.train_tfmsg[0],tm.valid_tfmsg[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<function __main__.tfm_multiSpectrumZoom(ad: exp.nb_AudioCommon.AudioData) -> torch.Tensor>],\n",
       " [<function __main__.tfm_multiSpectrumZoom(ad: exp.nb_AudioCommon.AudioData) -> torch.Tensor>])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace the spectrogram transform with tfm_spectro_stft\n",
    "tm.train_tfmsg[-1]=[tfm_multiSpectrumZoom]\n",
    "tm.valid_tfmsg[-1]=[tfm_multiSpectrumZoom]\n",
    "(tm.train_tfmsg[-1], tm.valid_tfmsg[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bfe9947c830844abaeedb9c9507869c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<div style=\"background-color:black;color:white;text-align:center;\">None</div>', lay…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "tensor([[[-66.0176, -58.9926, -59.0273,  ..., -80.0000, -80.0000, -80.0000],\n",
       "         [-50.2108, -47.4407, -47.5590,  ..., -80.0000, -80.0000, -80.0000],\n",
       "         [-44.5753, -41.8915, -41.0916,  ..., -80.0000, -80.0000, -80.0000],\n",
       "         ...,\n",
       "         [-80.0000, -80.0000, -80.0000,  ..., -80.0000, -80.0000, -80.0000],\n",
       "         [-80.0000, -80.0000, -80.0000,  ..., -80.0000, -80.0000, -80.0000],\n",
       "         [-80.0000, -80.0000, -80.0000,  ..., -80.0000, -80.0000, -80.0000]],\n",
       "\n",
       "        [[-46.3409, -39.6680, -42.0297,  ..., -80.0000, -80.0000, -80.0000],\n",
       "         [-45.8509, -40.2488, -40.0177,  ..., -80.0000, -80.0000, -80.0000],\n",
       "         [-44.7247, -40.6746, -37.6013,  ..., -80.0000, -80.0000, -80.0000],\n",
       "         ...,\n",
       "         [-80.0000, -80.0000, -80.0000,  ..., -80.0000, -80.0000, -80.0000],\n",
       "         [-80.0000, -80.0000, -80.0000,  ..., -80.0000, -80.0000, -80.0000],\n",
       "         [-80.0000, -80.0000, -80.0000,  ..., -80.0000, -80.0000, -80.0000]],\n",
       "\n",
       "        [[-27.1973, -56.2320, -42.7943,  ..., -30.7080, -42.6326, -42.1131],\n",
       "         [-26.9945, -51.8560, -42.1458,  ..., -27.9539, -37.5867, -39.5423],\n",
       "         [-26.6808, -47.4853, -41.3004,  ..., -24.7685, -32.6638, -36.1212],\n",
       "         ...,\n",
       "         [-80.0000, -80.0000, -80.0000,  ..., -74.7607, -80.0000, -80.0000],\n",
       "         [-80.0000, -80.0000, -80.0000,  ..., -74.5549, -80.0000, -80.0000],\n",
       "         [-80.0000, -80.0000, -80.0000,  ..., -74.4291, -80.0000, -80.0000]]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Try single one\n",
    "tm.try_train_tfms(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataBlock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61, ['aa', 'ae', 'ah'])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create classes for all data!\n",
    "classes = list(sorted(all_phonemes_df.PHN.unique()))\n",
    "len(classes), classes[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[functools.partial(<function tfm_add_white_noise at 0x7fae1a42b840>, noise_scl=0.0001, lower_gain=0.9, upper_gain=1.05),\n",
       "  functools.partial(<function tfm_modulate_volume at 0x7fae1a42b8c8>, noise_scl=0.0001, lower_gain=0.9, upper_gain=1.05),\n",
       "  functools.partial(<function tfm_pad_to_max at 0x7fae1a42bbf8>, mx=10000),\n",
       "  <function __main__.tfm_multiSpectrumZoom(ad: exp.nb_AudioCommon.AudioData) -> torch.Tensor>],\n",
       " [functools.partial(<function tfm_pad_to_max at 0x7fae1a42bbf8>, mx=10000),\n",
       "  <function __main__.tfm_multiSpectrumZoom(ad: exp.nb_AudioCommon.AudioData) -> torch.Tensor>]]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfms = tm.get_tfms()\n",
    "tfms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = (AudioList.from_df(src, path, cols=['SampleAndSr'])\n",
    "        .split_from_df('is_valid')\n",
    "        .label_from_df('PHN', classes=classes)\n",
    "        .transform(tfms)\n",
    "        .databunch(bs=64)) # NOTE: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = cnn_learner(data, models.resnet50, metrics=accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**No need to adapt the first layer: is actually a 3-channels image**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First layer shape: torch.Size([64, 3, 7, 7])\n"
     ]
    }
   ],
   "source": [
    "#adapt_first_layer(learn.model,nChannels)\n",
    "print(f'First layer shape: {learn.model[0][0].weight.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
      "Min numerical gradient: 9.12E-03\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEKCAYAAADTgGjXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xl8VNX9//HXJzsJSSAkrAGCbAKySUAEtaCoVRC1LsUW90pbW5eq9Vdbv9ra2m+rtS51xV0pauvyFVutWgUXFCTsyCKr7CQkQMhCJpmc3x8z0YhJSCaZzJL38/GYB3fuPXPnc5gknznn3HuOOecQEREJREyoAxARkcilJCIiIgFTEhERkYApiYiISMCUREREJGBKIiIiEjAlERERCZiSiIiIBExJREREAhYX6gCaKjMz0+Xk5IQ6DBGRiLJ48eK9zrmslj5vxCWRnJwc8vLyQh2GiEhEMbMvg3FedWeJiEjAlERERCRgSiIiIhIwJREREQmYkoiIiARMSURERAKmJCIiIgFTEhERCXPlHi93v72WZdv2hzqUb1ESEREJc/vLPTw0dyNrdhWHOpRvURIREQlzZR4vAMkJsSGO5NuUREREwlxZRU0SCb+ZqpRERETCXKmnCoAUtURERKSpyv3dWe2UREREpKm+aokkqjtLRESaqGZMpF28WiIiItJEZWqJiIhIoEp1ia+IiASq3OMlxiAxLvz+ZIdfRCIi8g2lnipSEuIws1CH8i1KIiIiYa6swktyYvh1ZYGSiIhI2Cur9Ibl3eqgJCIiEvbKKqrCclAdlERERMJeqaeNJhEz22JmK81smZnl1XHczOwBM9tgZivM7NhgxiMiEonKPeHbndUaUU10zu2t59gZQH//4zjgEf+/IiLiV+rx0qNjG2yJNMLZwHPOZwHQwcy6hTgmEZGwUu7x0i4+PFsiwU4iDnjHzBab2Yw6jvcAttV6vt2/T0RE/Eo9VaSE6SW+wU5t451zO82sM/Cuma11zn1Y63hdd864w3f4E9AMgF69egUnUhGRMFVWEb5jIkFtiTjndvr/zQdeA8YcVmQ70LPW82xgZx3nmemcy3XO5WZlZQUrXBGRsFPprcbjrW57V2eZWYqZpdZsA6cBqw4rNge4xH+V1ljggHNuV7BiEhGJNOG8vjoEtzurC/Caf66XOGC2c+4/ZvYTAOfco8CbwJnABqAMuDyI8YiIRJxwngYegphEnHObgOF17H+01rYDfhasGEREIl24t0RCfYmviIg0oGZVwzY5sC4iIs1T052lloiIiDSZurNERCRgpWE+sK4kIiISxmpaIu3i1RIREZEmKqtQS0RERAJUVqkxERERCVBZhZcYg8S48PxzHZ5RiYgI4J/BNyEO/+wfYUdJREQkjJV7vLQL064sUBIREQlrpR5v2A6qg5KIiEhYK6uoCttBdVASEREJa2Uer5KIiIgEpsxTFbaTL4KSiIhIWCvzeMN2fXVQEhERCWtlHi/t4tUSERGRAJR6qtQSERGRwPgG1tUSERGRJqr0VuOpqtbVWSIi0nThviAVKImIiIStck94r68OSiIiImHr61UN1RIREZEmUktEREQCVupf1VBjIiIi0mQaWBcRkYDVJBFNBS8iIk1WM7DeLr4Nt0TMLNbMlprZv+o41svM5vqPrzCzM4Mdj4hIpCirqLk6q223RK4D1tRz7FbgH865kcA04OFWiEdEJCKUVbbxMREzywYmA0/UU8QBaf7tdGBnMOMREYkkZRVeYgwS48J35CHYbaT7gJuB1HqO/xZ4x8yuAVKASUGOp0Ut3FTIiu0HKCrzsK/Uw74yD+0T4xnaI42h2R0Y3C2NdmH8DUJEwluZx0tKQhxmFupQ6hW0JGJmU4B859xiM5tQT7GLgGecc/eY2fHA82Z2jHOu+rBzzQBmAPTq1StYITdadbXj/vfWc/976wGIjzU6JifQMTmBwlIPryzZDkBsjDGiZwd+eFwvzhzajaQwHhwTkfBT5qkK+y+iwWyJjAem+gfLk4A0M5vlnJteq8yVwHcBnHOfmlkSkAnk1z6Rc24mMBMgNzfXBTHmIyrzVHHjP5bz1qrdnD8qm/+ZPJi0dl9/U3DOsae4ghXb97Ni+wHeXLmLG/6xnD/8ew0X5vbkh8f1omdGciirICIRotTjDetBdQhiEnHO3QLcAuBvidx0WAIB2AqcAjxjZoPwJZuCYMVUXe2IiQm8WbhjfzlXPZvH2t3F3Dp5EFee0OdbzUwzo2t6El3Tu3LakK7ceNoAPtlYyHOfbmHmhxt57MONnNg/ix+M6cUpgzoTH9uyfZ1f7DnI3pIK0pLiaZ8YR2pSHB2SE4gNsN7OOfaVVdIxOT6sm9Qi0ajcUxXWg+oQ/DGRbzGzO4A859wc4EbgcTP7Bb5B9succ0Fpaby/dg9/e38DD0wbGVBL4IMvCrjhpWV4qqp58rLRTBzYuVGvMzPG98tkfL9Mdh0o58XPtvHSom38ZNZiOqcm8sPjevPTCX1JaMbAmXOOeV8U8PiHm/hkY+G3jreLj+XobqkM7pbG4O5pDOySylFZ7RtMDM455m8o5P73vmDRln1kpCRwbK8OjOzVkeHZHUiIi/GtdeCtpsrrSEmMpUO7BDokx9MhOT6s5/oRiRSlFd6wTyIWpL/ZQZObm+vy8vKa/Lp3Pt/Njf9YDgZ3nTeMM4Z2a9TrKr3V/OWddTz2wSYGdknloR8eS7/O7Zv8/rVVeauZu66A2Qu/ZO66AkbndOSR6aPIbJ/YpPPs3F/O3HX5PPvJFr7YU0LXtCSuOCGHoT06cPBQJQcPVXHwUCVbi8r5fOcBVu8q5uChqq9en94unj6ZKfTJTKFXRjK9O/keB8orefD9DSzZup9u6Ul8f3RPduwrZ/HWfWwqKG1UbCf2z+TWyYMZ2LW+aypE5EjOfvBjOiQn8OwVY5p9LjNb7JzLbYGwvnnetpJEALYWlnHNC0tYvv0A08f24tbJgxsc7N5WVMY1Lyxl2bb9/OC4Xtw2peHygXh92Q5ufnkFnVISmHlJLsf0SK+3bE2LY+7afD5ev5dNe31/0Ad1S2PGSX2YPLR7gy0a5xzb95WzIb+ETXtL2by3hM17S9lcUMqu4kPU/lHonp7E1RP7cUFuNolxX9d5X6mHNbuKcUB8bAzxsUZsjFFa4eVAuYf9ZZVs31fOc59uoaSiimljevGLSQPISm1aghQROPWvH9C/S3se/uGoZp9LScSvOUkEwFNVzd1vr+XxjzZzdNdU/nDOMeTmZHyjTHW1Y/ZnW/nzW2sB+NN5w5g8rHEtl0Cs3H6AGc/nsa/Mw5/PG8bU4d2/1c20t6SCX72ykv+u2UNyQizH9cnghP5ZjO/XiYFdUps9XlFR5WVbUTlbi0rxVFVz8tFdmtXFtq/Uw/3vrWfWgi9Jio/l3JE9GN0ng9E5HemW3q5ZsYq0FeP/9D5jj+rEPRcOb/a5lET8mptEary/dg+3vLqSPcUVTB7WjV9992h6ZiSzdncxt7y6kqVb9zOubyf+fN6wVrmaquBgBVf/fTGLtuzjmB5pXDI2h6kjupMUH8u7q/fwq1dWcLCiiptPH8glx+c06w98a9pYUMI976xj3rqCryaT69GhHSf2z+R7x2YzOqejBuxF6jHyjnc4a3h37jj7mGafS0nEr6WSCPgu1330g03M/HAj1Q4mDszivTX5pLWL59bJgzh3ZI9W/QPnqarmpbxtPP+pb4wjvV08w3t24MMvChjcLY37po1gQJfIHGOo8lazZtdB8r4sYtGWoq+SSs+MdnxvZDbnj8rWpc8ihxlw61tcPj6HW84Y1OxzKYn4tWQSqbHrQDl3/2cdry/fyXnH9uCWMwbRMSWhRd+jKZxzLNxcxPOffslH6wuYPrY3108aEDGtj8Yo81Txn1W7eXXJDuZv3EusGdPH9uaak/vRqdYFBgUHK5i14EtW7TjA9LG9mTAwSy0XaROqvNX0+81b3HDqAK49pX+zz6ck4heMJCKhtWN/OQ/P3cCLi7bRLj6WH590FN8ZmMWsBV/yf0t34vFWk9k+gb0lHsbkZPD/zhjIqN4ZRz6xSAQrPlTJsN++w62TB/GjE49q9vmClUR0Mb+EXI8O7bjz3KFcPr4Pd/1nLfe8+wX3vPsFSfExXDg6m8vH96Fnx2ReytvGA++t57xHPmXSoC78z5RB9O6UEurwRYKirCL811cHJREJI/06t2fmJbnkbSli7e6DTB7a7RvdiheP7c15x/bg6flbeGTeRk6790Oum9Sfq048qsXv/BcJtZoFqVISw/tmQ/3mSdjJzclg+tjedY5LJSfE8bOJ/fjvDd9hwsAs7vrPOs7628cs2bovBJGKBE+5Ry0RkaDpmp7EYxfn8s7nu7nt9c/53sOfkNMpmWHZHRiWnc7wnh04pnt62M+AKlKfUv+qhuE+7YmSiES004Z0ZVy/TGYv/JLFX+5j0ZYi5iz3rW0WG2MM7JLKiF4dGNGzA5MGdSEjhFfdiTRFJKxqCEoiEgXaJ8Yx46S+Xz3PP3iIFdsOsGzbfpZt288by3Yye+FWkhNiufj43sw48ahvXEYsEo40sC4SIp1Tk5g0OIlJg7sAvmlsVu8q5vGPNvH4h5t47pMvmT62F1eecBRd05NCHK1I3WoG1tUSEQmxmBjjmB7p3D9tJNee0p+H3t/Akx9v5omPNzM6J4OzhnXjjKHdmjyLskgw1Qysh/uiVLo6S9qUvlnt+ev3RzD3pglcf8oAiko9/M/rnzPmzv9y1XN5rNlVHOoQRQC1RETCWu9OKVw3qT/XntKPdXsOMmfZTmYt+JIzH/iIs4Z15xenDqBPpm5klNApq/ASY5AY5tMdKYlIm2ZmHN01jaO/m8aPT+rLzI828tTHW/j3yl2cPbw7Z4/swbi+nXQzo7S6Mo+XlIS4sJ8rTklExC89OZ5fnn40l43rw0NzN/Dy4u28unQHHZPjOWNoNy4Ylc3IXh1DHaa0EWWeqoi4z0lfr0QOk5WayG+nDiHv1kk8dvEoTuifxWtLdnDeI5/w5spdoQ5P2ogyjzfsB9VBLRGReiXFx3L6kK6cPqQrJRVVXPrUZ1z34lJSk+I4sX9WqMOTKFfmqQr7QXVQS0SkUdonxvHUpaPpm9WeGc8tZvGXmqtLgqu0wqskIhJN0pPjee7KMXROS+SKZxaxdrcuB5bgKav0hv3d6qAkItIknVOTmHXlcSTFxzD9ic/4ZMPeUIckUaqsQt1ZIlGpZ0Yyf//RcaQlxfGDJxZyxxurOeSfLE+kpZR51BIRiVr9Oqfy72tP5NLje/PU/M1M+dvHrNpxINRhSRQp81SF/YJUoCQiErB2CbH87uxjeO6KMZQcquLsh+Zz7QtLWbF9f6hDkyhQ6vHqPhGRtuCkAVm8ff1JXD4uh/fX5jP1wflc+OinvP35bpxzoQ5PIlCVtxpPVTUp6s4SaRvSk+O5dcpgPr3lZG6dPIgd+8v58fOLuf6lZVRUabxEmiZSFqSCVkgiZhZrZkvN7F/1HL/QzFab2edmNjvY8YgEU2pSPD868Sg++OUEfnn6QF5ftpNLnvyMA2WVoQ5NIkikLEgFrdMSuQ5YU9cBM+sP3AKMd84NAa5vhXhEgi4uNoafTezHfd8fwZKt+zj/0U/Yvq8s1GFJhCjzTwMfNQPrZtbXzBL92xPM7Foz69CI12UDk4En6ilyFfCQc24fgHMuv3Fhi0SGc0b24NkrxrC7+BDfe/gT3aAojVLmX5CqXXyUJBHgFcBrZv2AJ4E+QGO6nu4Dbgaq6zk+ABhgZvPNbIGZfbeuQmY2w8zyzCyvoKCgkSGLhIdxfTN55afjiDFj+hOfsXlvaahDkjC3r8wDQIfkhBBHcmSNTSLVzrkq4FzgPufcL4BuDb3AzKYA+c65xQ0UiwP6AxOAi4An6mrhOOdmOudynXO5WVma+E4iz4Auqcz60XFUO8f0Jxayc395qEOSMFZU6ksiGSnRk0Qqzewi4FKgZoA8/givGQ9MNbMtwIvAyWY267Ay24HXnXOVzrnNwDp8SUUk6vTr3J7nrhhDcXkl059cyN6SilCHJGFqb4kviWS2j54kcjlwPHCnc26zmfUBDk8I3+Ccu8U5l+2cywGmAe8756YfVuz/gIkAZpaJr3trUxPiF4kox/RI58nLRrNzf7nvqq1yXbUl31ZUWkFsjJGWdKTv6qHXqCTinFvtnLvWOfeCmXUEUp1zfwrkDc3sDjOb6n/6NlBoZquBucAvnXOFgZxXJFKM6ZPBo9NHsT7/IN97eD5rdmmwXb6pqNRDx+QEYmLCe2lcaPzVWfPMLM3MMoDlwNNm9tfGvolzbp5zbop/+zbn3Bz/tnPO3eCcG+ycG+qcezGQSohEmgkDO/PsFWMoPlTFOQ/NZ/bCrbq7Xb5SWOKhUwSMh0Dju7PSnXPFwPeAp51zo4BJwQtLJPqN65vJm9eeyJg+Gfz6tZVc++IyDh5S95b4WiKdImA8BBqfROLMrBtwIV8PrItIM2WlJvLs5WP45ekDeXPlLs59+BO2FemmxLausNQTEVdmQeOTyB34xi82OucWmdlRwPrghSXSdsTEGD+b2I9ZVx5HfvEhzn14Pku3avndtqywpCK6urOcc/90zg1zzv3U/3yTc+684IYm0rYc37cTr149nuSEOKbNXMBbK3eFOiQJgUpvNcWHqshISQx1KI3S2IH1bDN7zczyzWyPmb3in9JERFpQv87tee3qcQzpnsbVs5fw9PzNoQ5JWtm+mhsNo2xM5GlgDtAd6AG84d8nIi2sU/tEZl81ltMGd+F3b6zmXyt2hjokaUWF/iSSGU3dWUCWc+5p51yV//EMoPlHRIIkKT6WBy4ayeicjtzwj+Us0RhJm1FYEjlTnkDjk8heM5vuXxsk1symA7opUCSIEuNieeziXLqlJzHjuTxdtdVGFJb6psOJtkt8r8B3ee9uYBdwPr6pUEQkiDJSEnjqstF4qqq54plFFOs+kqj39eSLUTSw7pzb6pyb6pzLcs51ds6dg+/GQxEJsr5Z7Xn04lFs3lvKz/6+hCpvfSsrSDQoKvUQY9ChXfjPmwXNW9nwhhaLQkQaNK5vJn88dygfrd/LnW/WuVCoRIm9Jb4bDSNh3izwrecRqMiooUiUuHB0T9buPshT8zdzdNdUvj+6V6hDkiAoKq2ImEF1aF5LRLPFibSyX595NCf2z+TW/1vFoi1FoQ5HgqAogqY8gSMkETM7aGbFdTwO4rtnRERaUVxsDA9edCzZHZP5yfOL2aEVEqNOYamHThEyqA5HSCLOuVTnXFodj1TnXHO6wkQkQOnJ8Tx+SS4ebzVXPZtHuccb6pCkBUXSDL7QvO4sEQmRfp3b88BFI1mzu5hfvbpCa5FEiUpvNfvLKqOnO0tEwtfEgZ258dQBvL5sJ0/P3xLqcKQF7Cvz3SMSKTP4gpKISES7ekI/Th3chTvfXMOCTZpEItJF2o2GoCQiEtFiYoy/Xjic3hnJ/Hz2EnYd0EB7JCvyz5ulMRERaTWpSfHMvGQU5R4vP5m1BE+V7miPVDUz+Ko7S0RaVb/OqfzlguEs37afh+ZuCHU4EqDCEt/kixpYF5FWd8bQbpw7sgcPzd3A5zsPhDocCUBRqQcz6JCsJCIiIXD7WYPpkJzATf9coW6tCFRY6qFjcgKxETJvFiiJiESVDskJ/PHcY1izq5iH56lbK9IUlXoiajwElEREos5pQ7pyzojuPPi+urUiTWFJZM2bBUoiIlHp9rOGqFsrAhWWVkTU5b2gJCISlTqmJHCnv1vr9jmfa1qUCBFpM/hCKyQR/5rsS83sXw2UOd/MnJnlBjsekbbi9CFduXpCX174bCtPaVqUsOetduwvr4you9WhdVoi1wH1LsVmZqnAtcDCVohFpE256bSBnD6kC3f+ezVz1+aHOhxpwL4yD85BprqzvmZm2cBk4IkGiv0euAs4FMxYRNqimBjj3u+PYFC3NK55YSnrdh8MdUhSj8KSmnmzlERquw+4GahzZM/MRgI9nXP1dnWJSPMkJ8TxxKW5JCfEcuWzi766K1rCS2Fp5N2tDkFMImY2Bch3zi2u53gMcC9wYyPONcPM8swsr6CgoIUjFYl+3dLb8fglueQfrOBXr67UQHsYKvpq3iyNidQYD0w1sy3Ai8DJZjar1vFU4Bhgnr/MWGBOXYPrzrmZzrlc51xuVlZWEEMWiV7De3bgl6cN5N3Ve3h58fZQhyOH+SqJaEzExzl3i3Mu2zmXA0wD3nfOTa91/IBzLtM5l+MvswCY6pzLC1ZMIm3dlSf04bg+GfzujdVsKyoLdThSS2GJb96sjhE0bxaE4D4RM7vDzKa29vuKiG+g/S8XDAfgpn8up7pa3VrhorC0gg7t4iNq3ixopSTinJvnnJvi377NOTenjjIT1AoRCb6eGcncdtZgFm4u4qn5m0MdjvhF4o2GoDvWRdqkC0ZlM2lQF+56ex1rdhWHOhzB150VaYPqoCQi0iaZGX86byjp7eK5+MmFfLFH94+EWlGpJ+IG1UFJRKTNymyfyAtXjSXGjGkzF6hFEmLqzhKRiNOvc3te+vHxJMTGcNHjC1i1Q1PHh4K32lFUFnlriYCSiEib1yczhZd+PJaUhDh++MRCJZIQ2O+fN0stERGJSL07pfDijLGkJMTys9lLKK2oCnVIbcrXNxpqYF1EIlTPjGTumzaSrUVl/PHNeifeliAo/GrKE7VERCSCjemTwY9O6MPfF27lgy80T11r2VPsm8Q8M1UtERGJcDeeNpD+ndtz88vLOVBWGepw2oSN+SXEGPTulBzqUJpMSUREviEpPpa/XjiCwhIPt81ZFepw2oQNBSXkdEohMS421KE0mZKIiHzL0Ox0fn5yP15ftpM3lu8MdThRb/2eEvp2bh/qMAKiJCIidfrZxH6M6NmBG/+xnLc/3x3qcKJWpbeazXtL6a8kIiLRJD42hmcuH83g7mlc/fclvL5sR6hDikpfFpZSVe3o30VJRESiTIfkBGb96DhG53Tk+peWMXvh1lCHFHU25JcA0L9zaogjCYySiIg0qH1iHM9cPoYJA7L49WsreUbTx7eo9Xt8SeSorJQQRxIYJREROaKk+FgeuziXSYO68Id/r2Htbk3W2FLW55eQ3bEdyQlxoQ4lIEoiItIoCXEx3HX+MNLaxfOrV1bi1aqILWJ9fknEDqqDkoiINEFGSgK3TRlM0Yo1rD//EkhLg5gY379XXw0bN4Y6xIjirXZsLCihn5KIiLQVZ+9azrvPXEPf11+AgwfBOd+/TzwBw4bBW2+FOsSIsX1fGZ6q6ogdVAclERFpio0bsQsuINFziPhq7zePVVZCWRmcf75aJI1UM6jeL0Iv7wUlERFpinvu8SWLhlRWwr33tk48EW69//JedWeJSNswa1bjksjzz7dOPBFuff5BuqQlkpYUH+pQAqYkIiKNV1LSsuXauI35JRE9HgJKIiLSFO0b2e3S2HJtmHOO9fmRfWUWKImISFNMnw7xR+h6iY+Hiy9unXgi2M4DhyjzeCN2zqwaSiIi0ng33njEJOLi4+EXv2ilgCLX+j0HAeiXpSQiIm1F377w8suQnPytZOLi4imLT+QvV/0Bb5+jQhRg5Phq4sUuGhNpkJnFmtlSM/tXHcduMLPVZrbCzN4zs97BjkdEmumMM2DFCpgx4xt3rNuPZ/Dei+/yUNIA7v/vF6GOMuxtyC+hU0oCGSkJoQ6lWVpjxq/rgDVAWh3HlgK5zrkyM/spcBfw/VaISUSao29fePBB36OWKc7xoXcFD7y/geE9O3DKoC4hCjD8RcOgOgS5JWJm2cBk4Im6jjvn5jrnyvxPFwDZwYxHRILLzPj9OccwpHsa17+0jC17S0MdUlhyzrF+z0ElkUa4D7gZqG5E2SsBTbojEuGS4mN5dPooYsz4yazFlHu8R35RG1NwsILiQ1URPXtvjaAlETObAuQ75xY3oux0IBe4u57jM8wsz8zyCgoKWjhSEWlpPTOSuW/aCNbtOcivX1uJc5o2vrZoGVSH4LZExgNTzWwL8CJwspnNOryQmU0CfgNMdc5V1HUi59xM51yucy43KysriCGLSEuZOLAz158ygNeW7uD/tD77N6zccQBALZGGOOducc5lO+dygGnA+8656bXLmNlI4DF8CSQ/WLGISGj8/OR+5PbuyG2vf86uA+WhDidsvL5sJ8Oy0+mclhTqUJqt1e8TMbM7zGyq/+ndQHvgn2a2zMzmtHY8IhI8sTHGXy4YTpXXcfPLK9StBazeWczqXcWcPyo6riNqlSTinJvnnJvi377NOTfHvz3JOdfFOTfC/5ja8JlEJNLkZKbw6zOP5qP1e5n92dZQhxNyryzZTnyscdaw7qEOpUXojnURCbrpY3tzYv9M7vz3GrYWlh35BVGq0lvN/y3dwaRBXegY4TcZ1lASEZGgMzP+fN4wYs246Z/L8Va3zW6teesKKCz1RE1XFiiJiEgr6d6hHbdPHcJnW4r4f6+saJOJ5JXF28lsn8BJA6LnKtPWmPZERASA847twY595dz73y/wVFVzz4XDiY9tG99li0o9vLd2D5cenxNVdVYSEZFWY2ZcN6k/ifEx/OmttVRUefnbRceSEBc9f1TrM2fZDiq9jvOiqCsL1J0lIiHwk+/05fazBvP253v4yazFHKqM/qlRXlmygyHd0xjUra65aCOXkoiIhMTl4/vwx3OH8v7afG78x3Kqo3iMZO3uYlbuOBBVA+o11J0lIiHzg+N6UVpRxZ1vriG7YztuOXNQqENqcRVVXv73zbXExxpTh0fHvSG1KYmISEj96MQ+bNtXxmMfbiI7I5mLx0bP2nSHKr38dNZiPviigD+ccwyd2ieGOqQWpyQiIiFlZtx+1hB27i/n9tdX0T09KSoWszpU6eXHz/sSyB/PHcoPjusV6pCCQmMiIhJysTHGAxeNZEj3dH4+eykrtx8IdUjNcqjSy1XP5fHBFwX86XvRm0BASUREwkRyQhxPXpZLRkoCP3puEbsPHAp1SAG77fVVfLxhL3edN4xpY6I3gYCSiIiEkc6pSTx12WhKK7xc+ewiSiuqQh1Sk+UXH+K1pTu4ZGxycuDVAAAM70lEQVRvLhzdM9ThBJ2SiIiElYFdU/nbD0ayZlcx17+0LOKmR5m14Euqqh2Xj+8T6lBahZKIiISdiQM7c9uUwby7eg93/WdtqMNptEOVXv6+cCunHN2ZnMyUUIfTKnR1loiEpcvG92HT3lIe+3ATqUlx/HRCP2JjLNRhNWjO8p0UlnraTCsE1BIRkTB225TBTB7Wjb+88wXnP/oJG/IPhjqkejnneHr+FgZ2SWVc306hDqfVKImISNiKi43hwYtGcv+0EWzeW8qZ93/MQ3M3UOWtDnVo37JgUxFrdhVz+fgczMK7xdSS1J0lImHNzDh7RA/G9c3kttdXcffb63ju0y1MGNCZCQOzGN8/k7Sk+FCHydPzN9MxOZ5zRvYIdSitSklERCJCVmoij0wfxbur9/Da0u28uWoXL+VtIy7GmHi0byC+Z0ZySGLbWljGu2v2cPWEviTFx4YkhlBREhGRiHLq4C6cOrgLVd5qlmzdz3tr9/D8p19y2r0fcsOpA7h8fA5xrbzo08yPNhJrxsVjc1r1fcOBxkREJCLFxcYwpk8Gt5wxiHdv+A7j+nbizjfXcPZD81m2bX+rxFDlrea3cz5n1oKtXDi6J13Tk1rlfcOJORdZN/Lk5ua6vLy8UIchImHGOcdbq3Zz+5zPKThYwZg+GVw+LodTB3cJSsukpKKKa2YvYe66Aq48oQ+/PnNQWF+CbGaLnXO5LX5eJRERiSbFhyp58bOtPPfpl2zfV0739CQuGZfDZeNyWmy8Ysf+cq58ZhHr80v43dQhTI+A6euVRPyURESkMbzVjvfW7OGZT7bwycZC+mSm8Puzj+GE/pnNOu9H6wv4xUvLqKis5qEfHstJA7JaKOLgClYS0ZiIiESl2BjjtCFdmX3VWJ6/cgzOOaY/uZDrXlxK/sGmzxBc5a3m7rfXcslTn9ExOYFXrx4XMQkkmNQSEZE24VCll0fmbeSReRuJj/UlmO8e05XvDMg6YjfXrgPlXPvCUhZt2ce00T25/awhtEuIrEt51Z3lpyQiIs2xqaCEh+dt5N3VezhQXklyQiwTBmZxbK+ODO6WxuDuaXRITmD7vjLmritg7tp8Ptm4l1gz/vi9oZw9IjJvJozYJGJmsUAesMM5N+WwY4nAc8AooBD4vnNuS0PnUxIRkZZQ6a1m4aYi3lq1i/fX5rOr1iJYnVISKCz1ANArI5mJA7O4bHwf+kTwzLzBSiKtcbPhdcAaIK2OY1cC+5xz/cxsGvBn4PutEJOItHHxsTGc0D/zq4H2vSUVrN5ZzOpdxWzIL+HorqlMPLozR2WmtKm5sJoqqEnEzLKBycCdwA11FDkb+K1/+2XgQTMzF2l9bCIS8TLbJ3LSgCwNljdRsK/Oug+4Gahvys0ewDYA51wVcABoO3Moi4hEuKAlETObAuQ75xY3VKyOfd9qhZjZDDPLM7O8goKCFotRRESaJ5gtkfHAVDPbArwInGxmsw4rsx3oCWBmcUA6UHT4iZxzM51zuc653KwsNTVFRMJF0JKIc+4W51y2cy4HmAa875ybflixOcCl/u3z/WU0HiIiEiFafSp4M7sDyHPOzQGeBJ43sw34WiDTWjseEREJXKskEefcPGCef/u2WvsPARe0RgwiItLyNHeWiIgETElEREQCFnFzZ5lZAfDlYbvT8d1j0tC+hp7XbNfelwnsDTDMuuJpSpmm1udI28GuS0PlwqkuDcXZmDL6OdNn01jh+NmkOOda/vJW51zEP4CZR9rX0POa7cP25bVkPE0p09T6HGk72HVpqFw41aW5n41+zvTZtNXPpqFHtHRnvdGIfQ09f6OeMi0ZT1PKNLU+jdkOVGPPUV+5cKpLY8/TUnU5/Ll+zhqmz6bhY6H8bOoVcd1ZrcXM8lwQZrwMBdUlfEVTfaKpLhBd9QlmXaKlJRIMM0MdQAtSXcJXNNUnmuoC0VWfoNVFLREREQmYWiIiIhKwqE8iZvaUmeWb2aoAXjvKzFaa2QYze8BqrUxjZteY2Toz+9zM7mrZqBuMqcXrY2a/NbMdZrbM/ziz5SOvM56gfDb+4zeZmTOzzJaL+IgxBeOz+b2ZrfB/Lu+YWfeWj7zOeIJRl7vNbK2/Pq+ZWYeWj7zOeIJRlwv8v/vVZhb0cZPm1KGe811qZuv9j0tr7W/w96pOwbrsK1wewEnAscCqAF77GXA8vinr3wLO8O+fCPwXSPQ/7xzh9fktcFM0fDb+Yz2Bt/HdT5QZyfUB0mqVuRZ4NILrchoQ59/+M/DnCK7LIGAgvumccsO1Dv74cg7blwFs8v/b0b/dsaH6NvSI+paIc+5DDpte3sz6mtl/zGyxmX1kZkcf/joz64bvF/hT5/vffQ44x3/4p8CfnHMV/vfID24tvhak+oREEOtyL77F0Fp1wC8Y9XHOFdcqmkIr1SlIdXnH+RafA1gAZAe3Fj5Bqssa59y61ojf/34B1aEepwPvOueKnHP7gHeB7wb6NyLqk0g9ZgLXOOdGATcBD9dRpge+9U5qbPfvAxgAnGhmC83sAzMbHdRoj6y59QH4ub+b4Skz6xi8UI+oWXUxs6nADufc8mAH2kjN/mzM7E4z2wb8ELiN0GmJn7MaV+D7phsqLVmXUGlMHery1YqyfjX1Cqi+rT4VfKiZWXtgHPDPWt19iXUVrWNfzbfAOHzNwLHAaOAfZnaUP3u3qhaqzyPA7/3Pfw/cg++XvFU1ty5mlgz8Bl+3Sci10GeDc+43wG/M7Bbg58DtLRzqEbVUXfzn+g1QBfy9JWNsrJasS6g0VAczuxy4zr+vH/CmmXmAzc65c6m/XgHVt80lEXytr/3OuRG1d5pZLFCzlO8cfH9Yaze3s4Gd/u3twKv+pPGZmVXjm5smFGv3Nrs+zrk9tV73OPCvYAbcgObWpS/QB1ju/8XKBpaY2Rjn3O4gx16XlvhZq2028G9CkERoobr4B3GnAKeE4kuXX0t/LqFQZx0AnHNPA08DmNk84DLn3JZaRbYDE2o9z8Y3drKdQOob7AGhcHgAOdQakAI+AS7wbxswvJ7XLcLX2qgZZDrTv/8nwB3+7QH4moYWwfXpVqvML4AXI7Uuh5XZQisOrAfps+lfq8w1wMsRXJfvAquBrNb8TIL5c0YrDawHWgfqH1jfjK83paN/O6Mx9a0zrtb+MEPww/MCsAuoxJdpr8T3bfU/wHL/D/Vt9bw2F1gFbAQe5OubMxOAWf5jS4CTI7w+zwMrgRX4voF1i9S6HFZmC617dVYwPptX/PtX4JsHqUcE12UDvi9cy/yP1rrSLBh1Odd/rgpgD/B2ONaBOpKIf/8V/s9jA3D5kerb0EN3rIuISMDa6tVZIiLSApREREQkYEoiIiISMCUREREJmJKIiIgETElEIp6ZlbTy+z1hZoNb6Fxe883Qu8rM3jjSzLZm1sHMrm6J9xZpCbrEVyKemZU459q34Pni3NcTBQZV7djN7FngC+fcnQ2UzwH+5Zw7pjXiEzkStUQkKplZlpm9YmaL/I/x/v1jzOwTM1vq/3egf/9lZvZPM3sDeMfMJpjZPDN72XxrYPy9Zm0F//5c/3aJf4LE5Wa2wMy6+Pf39T9fZGZ3NLK19ClfTyTZ3szeM7Ml5lvf4Wx/mT8Bff2tl7v9ZX/pf58VZva7FvxvFDkiJRGJVvcD9zrnRgPnAU/4968FTnLOjcQ3I+4fa73meOBS59zJ/ucjgeuBwcBRwPg63icFWOCcGw58CFxV6/3v97//Eecf8s/bdAq+GQMADgHnOueOxbd+zT3+JPYrYKNzboRz7pdmdhrQHxgDjABGmdlJR3o/kZbSFidglLZhEjC41gynaWaWCqQDz5pZf3wzlMbXes27zrnaazZ85pzbDmBmy/DNXfTxYe/j4esJKxcDp/q3j+frtRhmA3+pJ852tc69GN/aDuCbu+iP/oRQja+F0qWO15/mfyz1P2+PL6l8WM/7ibQoJRGJVjHA8c658to7zexvwFzn3Ln+8YV5tQ6XHnaOilrbXur+fal0Xw8s1lemIeXOuRFmlo4vGf0MeADf2iFZwCjnXKWZbQGS6ni9Af/rnHusie8r0iLUnSXR6h18a28AYGY1U2anAzv825cF8f0X4OtGA5h2pMLOuQP4lr+9yczi8cWZ708gE4He/qIHgdRaL30buMK/vgRm1sPMOrdQHUSOSElEokGymW2v9bgB3x/kXP9g82p80/cD3AX8r5nNB2KDGNP1wA1m9hnQDThwpBc455bim5F1Gr4Fm3LNLA9fq2Stv0whMN9/SfDdzrl38HWXfWpmK4GX+WaSEQkqXeIrEgT+VRbLnXPOzKYBFznnzj7S60QijcZERIJjFPCg/4qq/YRguWGR1qCWiIiIBExjIiIiEjAlERERCZiSiIiIBExJREREAqYkIiIiAVMSERGRgP1/L1HXTSA26o0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_find(learn)\n",
    "learn.recorder.plot(suggestion=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.fit_one_cycle(8, slice(1e-2))\n",
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 43:22 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>2.510447</td>\n",
       "      <td>2.228601</td>\n",
       "      <td>0.345918</td>\n",
       "      <td>05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2.030383</td>\n",
       "      <td>1.885063</td>\n",
       "      <td>0.426302</td>\n",
       "      <td>05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.742497</td>\n",
       "      <td>1.618058</td>\n",
       "      <td>0.484505</td>\n",
       "      <td>05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.541707</td>\n",
       "      <td>1.509322</td>\n",
       "      <td>0.523832</td>\n",
       "      <td>05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.372915</td>\n",
       "      <td>1.400216</td>\n",
       "      <td>0.553563</td>\n",
       "      <td>05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.179106</td>\n",
       "      <td>1.348506</td>\n",
       "      <td>0.581878</td>\n",
       "      <td>05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.009866</td>\n",
       "      <td>1.326380</td>\n",
       "      <td>0.588013</td>\n",
       "      <td>05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.916222</td>\n",
       "      <td>1.333756</td>\n",
       "      <td>0.585339</td>\n",
       "      <td>05:31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3Xl8FdXZwPHfk31fCRAIEDZlJ4TIIqigVAW3qlix+lpX6tJXLbWtdlFrX1vs4lar1qqoVcEFcau7giAqGJB9DRIUEkjYskD2PO8fM1xCdiA3N8l9vp/P/dy5c8/MfWayPPecmXOOqCrGGGMMQICvAzDGGNN2WFIwxhjjYUnBGGOMhyUFY4wxHpYUjDHGeFhSMMYY42FJwRhjjIclBWOMMR6WFIwxxngE+TqAo9WpUydNTU31dRjGGNOuLFu2bLeqJjVVrt0lhdTUVDIzM30dhjHGtCsisq055az5yBhjjIclBWOMMR6WFIwxxni0u2sKxpiOo6Kigu3bt1NaWurrUDqMsLAwUlJSCA4OPqbtLSkYY3xm+/btREdHk5qaioj4Opx2T1XZs2cP27dvp3fv3se0D2s+Msb4TGlpKYmJiZYQWoiIkJiYeFw1L0sKxhifsoTQso73fPpVUnhzxQ72Hij3dRjGGNNm+U1SKCip4NY5K/jRv770dSjGmDZiz549pKWlkZaWRteuXenevbvndXl5875AXn311WzcuNHLkbYev7nQXFJeBUBWXrGPIzHGtBWJiYmsWLECgHvuuYeoqChuv/32I8qoKqpKQED936FnzZrl9Thbk9/UFEorqnwdgjGmncjKymLIkCHccMMNpKenk5uby/Tp08nIyGDw4MHce++9nrLjx49nxYoVVFZWEhcXxx133MHw4cMZO3YseXl5PjyKY+M/NQVLCsa0aX94ey3rcgpbdJ+DusVw93mDj2nbdevWMWvWLJ544gkAZs6cSUJCApWVlUycOJGpU6cyaNCgI7YpKCjgtNNOY+bMmcyYMYNnnnmGO+6447iPozVZTcEYY+rRt29fTjrpJM/r2bNnk56eTnp6OuvXr2fdunV1tgkPD2fy5MkAjBw5kuzs7NYKt8V4vaYgIoFAJrBDVc+t9d5VwF+BHe6qR1X1KW/EUVpR7Y3dGmNayLF+o/eWyMhIz/LmzZt5+OGHWbp0KXFxcVxxxRX19gUICQnxLAcGBlJZWdkqsbak1qgp3Aqsb+T9l1U1zX14JSGA1RSMMceusLCQ6OhoYmJiyM3N5YMPPvB1SF7j1ZqCiKQA5wD3ATO8+VlNsaRgjDlW6enpDBo0iCFDhtCnTx/GjRvn65C8RlTVezsXeQ34MxAN3N5A89GfgXxgE/BzVf2+nv1MB6YD9OzZc+S2bc2aK+IIa3YUcO4/Pgdg65+nWC9KY9qA9evXM3DgQF+H0eHUd15FZJmqZjS1rdeaj0TkXCBPVZc1UuxtIFVVhwEfA8/VV0hVn1TVDFXNSEpqcja5eg3pHssvzzoRgLJKu75gjDH18eY1hXHA+SKSDcwBTheRF2oWUNU9qlrmvvw3MNKL8RAeHAhYU5IxxjTEa0lBVe9U1RRVTQWmAZ+q6hU1y4hIco2X59P4BenjFhHiJAXrs2CMMfVr9c5rInIvkKmqbwG3iMj5QCWwF7jKm5+dGBUKQG5BKcmx4d78KGOMaZdaJSmo6gJggbt8V431dwJ3tkYMAL0SIwDYvq+E9J7xrfWxxhjTbvhNj2aALjFhAOwqsKn/jDGmPn6VFGLCgggPDmRnoSUFYwxMmDChTke0hx56iJtuuqnBbaKiogDIyclh6tSpDe43MzOz0c9+6KGHOHjwoOf1lClT2L9/f3ND9xq/SgoiQueYUPKLypoubIzp8C677DLmzJlzxLo5c+Zw2WWXNbltt27deO211475s2snhXfffZe4uLhj3l9L8aukANA5OpS8IqspGGNg6tSpvPPOO5SVOV8Us7OzycnJIS0tjTPOOIP09HSGDh3Km2++WWfb7OxshgwZAkBJSQnTpk1j2LBhXHrppZSUlHjK3XjjjZ4ht++++24AHnnkEXJycpg4cSITJ04EIDU1ld27dwPwwAMPMGTIEIYMGcJDDz3k+byBAwdy/fXXM3jwYM4888wjPqel+M3Q2Yd0jg5j/c6WHZ7XGNMC3rsDdq5u2X12HQqTZzb4dmJiIqNGjeL999/nggsuYM6cOVx66aWEh4czb948YmJi2L17N2PGjOH8889vcCSExx9/nIiICFatWsWqVatIT0/3vHffffeRkJBAVVUVZ5xxBqtWreKWW27hgQceYP78+XTq1OmIfS1btoxZs2axZMkSVJXRo0dz2mmnER8fz+bNm5k9ezb//ve/+dGPfsTcuXO54ooraodzXPyuppAUHUp+oTUfGWMcNZuQDjUdqSq/+c1vGDZsGJMmTWLHjh3s2rWrwX0sXLjQ88952LBhDBs2zPPeK6+8Qnp6OiNGjGDt2rX1Drld0+eff86FF15IZGQkUVFRXHTRRSxatAiA3r17k5aWBnhvaG6/qykkRYdSVFZJSXkV4W5nNmNMG9DIN3pv+uEPf8iMGTNYvnw5JSUlpKen8+yzz5Kfn8+yZcsIDg4mNTW13qGya6qvFrF161b+9re/8fXXXxMfH89VV13V5H4aG48uNDTUsxwYGOiV5iO/qyl0jnZOql1sNsaAczfRhAkTuOaaazwXmAsKCujcuTPBwcHMnz+fpgbhPPXUU3nxxRcBWLNmDatWrQKcIbcjIyOJjY1l165dvPfee55toqOjKSoqqndfb7zxBgcPHuTAgQPMmzePU045paUOt0l+V1Po7PZV2FlYSk+3M5sxxr9ddtllXHTRRZ5mpMsvv5zzzjuPjIwM0tLSGDBgQKPb33jjjVx99dUMGzaMtLQ0Ro0aBcDw4cMZMWIEgwcPrjPk9vTp05k8eTLJycnMnz/fsz49PZ2rrrrKs4/rrruOESNGtNosbl4dOtsbMjIytKn7fxtzaAjtlPhwPv/16S0YmTHmaNnQ2d7RJofObqv6dXY6nkSF+l0lyRhjmuR3/xnDggPJ6BVPUKBNsmOMMbX5XU0BIDkunJz91oHNmLagvTVht3XHez79Mil0iwsjt6CE6mr7ZTTGl8LCwtizZ48lhhaiquzZs4ewsLBj3offNR8BdI8Lp6JK2X2gjM7Rx37yjDHHJyUlhe3bt5Ofn+/rUDqMsLAwUlJSjnl7v0wK3dwJdnL2l1pSMMaHgoOD6d27t6/DMDX4afPRoaTQ8r0BjTGmPfN6UhCRQBH5RkTeqee9UBF5WUSyRGSJiKR6Ox5wmo/AkoIxxtTWGjWFW4H1Dbx3LbBPVfsBDwL3t0I8xIQHERkSyA5LCsYYcwSvJgURSQHOAZ5qoMgFwHPu8mvAGdLQ2LQtGxfd4sLJtdtSjTHmCN6uKTwE/AqobuD97sD3AKpaCRQAiV6OCXD7KhRYTcEYY2ryWlIQkXOBPFVd1lixetbVuWFZRKaLSKaIZLbUrWvd48LsmoIxxtTizZrCOOB8EckG5gCni8gLtcpsB3oAiEgQEAvsrb0jVX1SVTNUNSMpKalFguseF87u4nK+33uw6cLGGOMnvJYUVPVOVU1R1VRgGvCpqtaeN+4t4Cfu8lS3TKt0bRzb12mlWrjZOs0YY8whrd55TUTuBTJV9S3gaeA/IpKFU0OY1lpxjOgRT3hwIFl5xa31kcYY0+a1SlJQ1QXAAnf5rhrrS4FLWiOG2gIChD5JkXybf8AXH2+MMW2SX/ZoPqRPUhTf7raagjHGHOLfSaFTJNv3lVBaUeXrUIwxpk3w76SQFIkqbNtjdyAZYwz4eVLom+RMzfltvjUhGWMM+HlS6N0pEoBvd9vFZmOMAT9PCpGhQXSNCWOL1RSMMQbw86QAznWFLXZbqjHGAJYU6JsUxbf5xTZHrDHGYEmBPkmRFJVWsru43NehGGOMz1lSsDuQjDHGw5KC3YFkjDEefp8UuseFExoUYDUFY4zBkgIBAUK/zlGsyy30dSjGGONzfp8UAE5KTWDZtn1UVdsdSMYY/2ZJARjSPZbSimq22nUFY4yfs6QADEqOAWBtToGPIzHGGN+ypAD07xJFSFAA33y339ehGGOMT3ktKYhImIgsFZGVIrJWRP5QT5mrRCRfRFa4j+u8FU9jggMDGNkzni+27PbFxxtjTJvhzZpCGXC6qg4H0oCzRWRMPeVeVtU09/GUF+Np1LAesWTvPkhlVbWvQjDGGJ/zWlJQx6Gb/4PdR5u9vadvUhTlVdVs31fi61CMMcZnvHpNQUQCRWQFkAd8pKpL6il2sYisEpHXRKSHN+NpzKEJd9ZbfwVjjB/zalJQ1SpVTQNSgFEiMqRWkbeBVFUdBnwMPFfffkRkuohkikhmfn6+V2Id2j2WuIhg5m/M88r+jTGmPWiVu49UdT+wADi71vo9qlrmvvw3MLKB7Z9U1QxVzUhKSvJKjCFBAQzoGk1Wng13YYzxX968+yhJROLc5XBgErChVpnkGi/PB9Z7K57m6N85ms27iqm2ns3GGD/lzZpCMjBfRFYBX+NcU3hHRO4VkfPdMre4t6uuBG4BrvJiPE0amhJLUVklG3YW+TIMY4zxmSBv7VhVVwEj6ll/V43lO4E7vRXD0Tq5byIAzyzeyt8uGe7jaIwxpvVZj+YaUuIj6B4XzuZdVlMwxvgnSwq1nDs8mXW5hZRWVPk6FGOMaXWWFGo5qVcCFVXKsm37fB2KMca0OksKtYzr14nQoAA+WrfL16EYY0yrs6RQS3hIIKf078Q7q3IoLqv0dTjGGNOqLCnU48eje7K7uJwF1rvZGONnLCnU45T+SYQFB/D11r2+DsUYY1qVJYV6BAcGMLZPIgs2eWecJWOMaassKTRgdJ9Etu05yM6CUl+HYowxrcaSQgMO9W5+dP5mH0dijDGtx5JCA4Z2jyU8OJCXlnxHXqHVFowx/sGSQgNEhDduHgfA7a+tQtVGTjXGdHyWFBpxYtdoLh/di4Wb8lmctcfX4RhjjNdZUmjC7WedCMDSrZYUjDEdnyWFJsSGBzO4WwxLs63PgjGm47Ok0Ayn9E8iM3sfRaUVvg7FGGO8ypJCM0w8MYnKamVx1m5fh2KMMV5lSaEZRvaKJzosiJteXO7rUIwxxqu8lhREJExElorISnce5j/UUyZURF4WkSwRWSIiqd6K53gEBQYwomc81Qr3vLXW1+EYY4zXeLOmUAacrqrDgTTgbBEZU6vMtcA+Ve0HPAjc78V4jsu/rhgJwLNfZFNSbrOyGWM6Jq8lBXUUuy+D3UftHmAXAM+5y68BZ4iIeCum4xEeEshL140GYPbS73wcjTHGeIdXrymISKCIrADygI9UdUmtIt2B7wFUtRIoABLr2c90EckUkcz8fN+NXHpyv06M6p3Akwu/pbyy2mdxGGOMt3g1KahqlaqmASnAKBEZUqtIfbWCOuNJqOqTqpqhqhlJSUneCLXZbprQl52Fpfx3dY5P4zDGGG9olbuPVHU/sAA4u9Zb24EeACISBMQCbbqX2Kn9k+iVGMHsJd/7OhRjjGlxzUoKItJXRELd5QkicouIxDWxTdKhMiISDkwCNtQq9hbwE3d5KvCptvGR5wIChGkn9WRp9l4e+HCjr8MxxpgW1dyawlygSkT6AU8DvYGXmtgmGZgvIquAr3GuKbwjIveKyPlumaeBRBHJAmYAdxz1EfjAteN7Mywllkc+zeKVTKsxGGM6DmnOF3MRWa6q6SLyS6BUVf8hIt+o6gjvh3ikjIwMzczMbO2PrSNnfwknz/yU2PBglv1uEkGB1g/QGNN2icgyVc1oqlxz/5NViMhlOE0977jrgo81uI6gW1w4j/54BAUlFfx3da6vwzHGmBbR3KRwNTAWuE9Vt4pIb+AF74XVPpx6gnMn1K1zVvDasu0+jsYYY45fs5KCqq5T1VtUdbaIxAPRqjrTy7G1eTFhwdx6Rn8Abn91pc3OZoxp95p799ECEYkRkQRgJTBLRB7wbmjtw22T+nPPeYMAmLt8h4+jMcaY49Pc5qNYVS0ELgJmqepInFtM/Z6I8KOTepDWI47bX13JX96vfdetMca0H81NCkEikgz8iMMXmo0rIiSIp3/iXNR/bMEW1uYU+DgiY4w5Ns1NCvcCHwBbVPVrEekDbPZeWO1PYlQoK+76AbHhwZzzyOf8+b31lhyMMe1Os/optCVtpZ9CQ5Zt28vFj3/pef37cwdx7fjePozIGGNauJ+CiKSIyDwRyRORXSIyV0RSjj/MjmdkrwQW/Woi/TpHAfDHd9ZZjcEY0240t/loFs44Rd1whrt+211n6tEjIYKPZ5zG7OvHEBkSyDmPfE727gO+DssYY5rU3KSQpKqzVLXSfTwL+HYM63ZgbN9EXrzemWzuzAcXUl3dvprqjDH+p7lJYbeIXOFOmhMoIlcAe7wZWEeR1iOOi9NTKK+q5sN1O30djjHGNKq5SeEanNtRdwK5OMNcX+2toDqa+y8eygldorjhheUUl1X6OhxjjGlQc4e5+E5Vz1fVJFXtrKo/xOnIZpohKDCAX501AIAhd3/Apl1FPo7IGGPqdzzjPc9osSj8wKRBXbh6XCoAD328ybfBGGNMA44nKdQ3v7JpxN3nDebi9BTeXb2Tf3xiff+MMW3P8SQFu5XmGNwx2WlG+vtHm5i1eKuPozHGmCM1mhREpEhECut5FOH0WWhs2x4iMl9E1ovIWhG5tZ4yE0SkQERWuI+7jvN42ryk6FDPOEl//3ATB8vtwrMxpu1oNCmoarSqxtTziFbVoCb2XQn8QlUHAmOAm0VkUD3lFqlqmvu49xiPo105Y2AXXr1hLMVllQy66wN2FZb6OiRjjAGOr/moUaqaq6rL3eUiYD1Ob2gDZPSKp09SJAAXPfaFdWwzxrQJrTLbvIikAiOAJfW8PVZEVorIeyIyuDXiaQtEhE9/MYFpJ/Vgx/4SXliyzdchGWOM95OCiEQBc4Hb3Il6aloO9FLV4cA/gDca2Md0EckUkcz8/HzvBtzK/nCBkwef/3Kb1RaMMT7n1aQgIsE4CeFFVX299vuqWqiqxe7yu0CwiHSqp9yTqpqhqhlJSR1ryKXQoED+fslwsvKKufiJL3wdjjHGz3ktKYiIAE8D61W13vmcRaSrWw4RGeXG43djKl04ojvRYUF8891+3l+T6+twjDF+zJs1hXHA/wCn17jldIqI3CAiN7hlpgJrRGQl8AgwTdvbrD8tICBA+Pq3k+gWG8YNLyyn4GCFr0Myxvgpm3mtDVm2bR8XP/4FPxnbiz9cMMTX4RhjOpAWnXnNtI6RveLpmRDBJxvy7KKzMcYnLCm0Mb848wS27yvh13NXUVRqzUjGmNZlSaGNOX94N/p1juLVZdsZes+H1tvZGNOqLCm0MSLCqz8d63k9+k+f8OmGXT6MyBjjT+xCcxtVVa1M+Nt8vt9bcsT6yUO68vgVI3l3dS4frdvF7WedSNeYMAIDbCRzY0zDmnuh2ZJCG7dhZyGXPP4lRU1M43nXuYO4ZnzvVorKGNPeWFLogDbsLOTshxZ5Xt9yRn8eqTVZzxs3jyOtR1xrh2aMaeMsKbQnqiDNb/6prKomMEBwO4OzeVcRZz+8iKpqJTk2jP8Z24trx/cmNCjQWxEbY9oZ66fQXuRtgEczYNlzUFnWrE2CAgM8CQGgf5dotvxpCn+6cCi5BaX85f2NnPqX+ZRWVHkramNMB9XURDnG28oPQEgUvH0LLJgJJ/8MRl4FIZFHvatLMlJYl1tAZvY+NuwsYsDv3yciJJCD5VUs+tVEeiREtHz8xpgOxZqP2gJV2PIpfP4gZC+C8AQYcyOMuh7C449pl+c8soi1OUeOVD5n+hjG9ElsiYiNMe2MXVNor75bAp8/AJved2oQGdfA2JshuutR7aagpIIDZZX8d1UuW/cc4KUl3wHw1JUZTBrUxRuRG2PaMEsK7d3ONU7NYe3rEBAMI66AcbdAfOox7S4rr4hrn8tk256D3DF5AMmxYZwxsAtRodaCaIw/sKTQUezZAosfhpWzoboKhk6F8T+HzgOPeldvrtjBrXNWHLHu7vMGcfU4699gTEdnSaGjKcyBL/8Jmc9AxUEYcC6MnwEpI5u9i4qqau6Yu5qo0ECWf7ef1TsKPO/dcFpffn32iUfc1WSM6TgsKXRUB/fCkn/BkiegdD/0Pg1O+QX0PvWo+joAZO8+wIS/LThi3UvXj+bkvnVmRDXGtHOWFDq6siLInAVfPgrFu6B7BpwyA06YDAFH1/2ktKKKK59ZytKtewHoHhfOM1edxIldo70RuTHGB3zeeU1EeojIfBFZLyJrReTWesqIiDwiIlkiskpE0r0VT4cTGu1ceL51FZz7IBzIhzk/hsdPhlWvQFXjYyXVFBYcyCs/HcvrN51MWHAAO/aXcNZDC1lX65ZWY0zH57WagogkA8mqulxEooFlwA9VdV2NMlOA/wWmAKOBh1V1dGP7tZpCA6oqYe08WPR3yF8Pcb1g3K2QdjkEhzV7N8VllWzILWTqE18C8MQV6Zw9JNlbURtjWonPawqqmquqy93lImA90L1WsQuA59XxFRDnJhNztAKDYNglcOMXMG02RHaC/86Ah4fB4kec5qZmiAoNIiM1gbvOHQTADS8s5/u9B70ZuTGmDWmVsY9EJBUYASyp9VZ34Psar7dTN3GYoxEQAAOmwHWfwJVvObeufvR7eHAIzP+Tc6G6Ga4el8pL140mLDiAG19cRpXNGW2MX/B6UhCRKGAucJuq1m6kru92mTr/fURkuohkikhmfn6+N8LseESgz2lw5Ztw3aeQOh4+u99JDh/81rnFtdHNhZP7deKe8wazZkchTy36tpUCN8b4kleTgogE4ySEF1X19XqKbAd61HidAtT5b6WqT6pqhqpmJCUleSfYjixlJEx7EW76CgaeB189Dg8Ph7ducTrHNWLqyBTCgwP583sbyC9q3iiuxpj2y5t3HwnwNLBeVR9ooNhbwJXuXUhjgAJVzfVWTH6v80C46F9wy3JIvxJWznGG7X7tWmdYjXoEBQbw2BXOTWEn3fexJQZjOjhv3n00HlgErAaq3dW/AXoCqOoTbuJ4FDgbOAhcraqN3lpkdx+1oKJd8NU/4eunobwYTjjb6QjXY9QRxVSVaU9+xRK3H0OnqBBCgwKZfmofEiJDGN07gc4xzb/DyRjT+qzzmmm+kn2w9Cn46jEo2QuppzjjK/U9/Yhe0tc++zWfbMirdxcvTx/DaBuW25g2y5KCOXrlB5wZ4L74BxTlQHKaM+HP4AshPA5VZV1uId/mH6BzdCiXPvnVEZsP6R5D36Qorh7Xm+EpsTaOkjFtiCUFc+wqy2DVy84AfPkbIDAUBpwDwy9zag+BRw63vThrN9c/n8nB8vqn/3zz5nHc+fpqbp7Yj6y8Yh78eBOPXZ7OWYO7EhhgicOY1mBJwRw/Vcj5xhm2e/VrTtNSVBcYeomTILoOOaL4yu/386vXVlFaWcW2Pc3r8NY3KZL3bj2VkCCbLtwYb7KkYFpWZTls/tBJEJs+gOoK6DrUSQ5DL4GozkcULyqtoFrh8QVbeOKzLQQGCFXVyqjUBApLK9iw83AP60tGpvC7cwcRGx7c2kdljN+wpGC858AeWDMXVr7k1CQkEPpNgrTLnFFaGxhraXdxGYmRIZ5rDarKHXNX83Lm4U7tV52cyt3nDbLrEca0MEsKpnXkbXBqD6tecS5Oh8XC4Isg7ceQclKTczzsLi7jgkcXs2N/yRHrO0WFsLu4HID7LhzCj0f1tERhzHGwpGBaV3UVbP3M6RC37i2oLIGEvk7z0vBLIa5nk7uoqKqm/2/fa/D9F64dzfj+NgGQMcfCkoLxnbIiWPemkyCyFznrUk9xEsSg8525IBqws6CUxVm7mTI0mbDgAApKKpj88CJyC0oBOO2EJE47IYlrxtu80sYcDUsKpm3Yt825vXXlbNj7LQRHOOMvDb/MmUI0ILBZu1n+3T4ueuwLz+vfnTOQ607p462ojelwLCmYtkUVvl/qJIc1r0NZAcR0h2E/chJE0olN7mLvgXLS//iR5/Xlo3ty34VDvRm1MR2GJQXTdlWUwqb3YMVsyPoYtAq6pbu3t06FiIRGN9+ws5CzH3Kapdb+4SwiQ4MaLW+MsaRg2ouiXbDmNSdB7FoNAcFwwllOguh/JgSF1LvZos35/M/TS4kKDWL5739gnd+MaYLPp+M0plmiu8DYm+HGz+GGz2H0T51mppcvhwcGwLu/gh3LneanGsb368TQ7rEUl1Vy5oOfsX3fQVSVzOy9NPeLTlFpBe+v2UlhaYU3jsyYdslqCqbtqaqELZ86neM2vAtVZZA0AIZPg2GXQkw3T9E/vbueJxfWPyvcucOSmTSwCzNeWcH1p/QhLDiQ0soqLh/Vi4c/2czc5ds9ZTN/N4lOUaFePzRjfMWaj0zHULIP1r7hXKD+fgkgzl1L3UdCp/5oYj/e+C6cn7/9XYt83L0XDObi9BQ27CxkQNcYAkQIDQogwAbuM+2cJQXT8ezZ4naOexP2boHqysPvRSRCYj9I7A+JfdHEfty3pILnNwbyl0tP4tv8YjbuKiImLJjcglJ+MKgLZw3uStfYMO59ex3PLN7a6EeHBAYw/dQ+DO4Ww8QBnQkLbt6ttMa0FZYUTMdWVeH0gdizGfZkwe7NTtLYsxmKdx0uJwEQ2wM69XeTRr/Dy9HdIMC5rKaq5BeV8Zt5a1izo4CCkgpKKuofCvyQMX0SePLKDKJDg2wIDtPmWVIw/qu00EkUh5JEzaRRceBwueAIZyiOTv1q1DL6Oa/DYj3FqqqV215ewciecfx3dS5fZ++r85E2BIdp63yeFETkGeBcIE9Vh9Tz/gTgTeBQvf11Vb23qf1aUjDHTBWKcuvWLPZkObUOrVEziEzyNEUdrmX0h/hUCiuFwpIKbnxhORt2FlJR5fwNXTiiO//3wyHWb8K0Sc1NCt787X0WeBR4vpEyi1T1XC/GYMxhIs6dSzHdnIvVNVWWw77sujWLTe/DN/+psY9AYuJ7EZPYn7f796NiVF/e+C6chfvimPfNdgYmRzOtlL4YAAAR6klEQVT91L6teljGtCSvJQVVXSgiqd7avzEtKigEkk5wHrWV7HdrFVk1kkYWbF1IcGUJlwCXAH8JDyfr4y5krTmRfgNHuDWMvk4to5FBAI1pS3xdzx0rIiuBHOB2VV1bXyERmQ5MB+jZs+khmI1pUeFxkDLSedRUXe3MIeHWLIqz17Jv7XJidy5H8z5EqNE0G51c90J3Yj+I61VnzmtjfMmrF5rdmsI7DVxTiAGqVbVYRKYAD6tq/6b2adcUTFuWs7+Ek2d+SijlrLvtRAL3Zrk1jENNUlnOXNeHBARDQu9a1y/cpBHZqclJioxprrZwTaFRqlpYY/ldEXlMRDqp6m5fxWTM8eoWF86MH5zAAx9t4on1Idw88fy6hQ7u9SSIyvxNBOzJImDvFsj6CKrKD5cLi0UT+7OkMJ5Fe+M5ffzJjBxxkpM8gsNb76CMX/FlTaErsEtVVURGAa8BvbSJgKymYNq6qmrl5JmfsKuwjG6xYSz45URyC0roGhtGZZWyYWch2/eVsHDTbs9QG+/ecgqfbczlpQ8X00d2ck5yMYND8yjYvp5UyaGbHK5dKILEptRtiurUH2JS2FtSSWRoIKFB1sHOHNYWbkmdDUwAOgG7gLuBYABVfUJEfgbcCFQCJcAMVf2i/r0dZknBtAdrcwo455HPW2RfU0emMG14InfNepM+kktvyaVPQC5p4bvpxQ4Cyos9ZasDw9hUkcQWTaZ736H06DeMhF6DkKguEB7vXPC2Jim/5POk4C2WFEx7UVWtzF76HbMWbyUyNIhV2wsA6JUYQf/O0YzqHc/V43qTlVfMj//9FfsOVvDSdaM5uV8nnlr0Lf/33/X88qwTuWlCX0SE0ooqvtt7kFtmf8OGnUXupyhvX9WfgSF5PD73A0ILvqWP5NBbdtJLdhEk1UcGJYFOcohIcJ7D4yG8xnJEfP3rLZm0e5YUjOnAlny7h4Wb8/nn/C113pt741j6JUXzx7dXsnzFN6TKTv5ydjc6BR10rmeU7HMfh5b3O881ahx1BATVnywaSyQRCRASZcmkjbCkYIwfWJdTyB/fWceuwlKGpsTy4I/SjhjR9fu9B5n0wGeUVVaz8q4ziY0IbnhnlWWHE0RJjeRRbyLZBwfd55pDh9TmSSa1kkV4vHOrb3AEBIVCUJj7cJcDQ458fcSzu9zM+b2Nw5KCMQaAD9bu5Kf/Wcao1ARe/umYlh+8r7KsRtJoLInsPTLpVBw8vs8NCGpmIqm93l0ODG046QBoNVRXOcOfqLrL1c7rmsue96pqbHOoXHWNcs3cX2PbDDzPmVfkGLT5W1KNMa3jrMFdGdo9lqXZe+l957tMGtiZmRcPa7lJhYJCIbqr8zgalWVQUeI8V5Yefq4qd1/XWFdZXut1Y2XLDj9KC45cX1V2+HXNode9TpyajQQ413U8y+4jINBZ71mWWuXc55K6gzG2eKRWUzCm4ysuq2Tsnz6hqMz5R5gSH85jl6czpFus/04gVFVZI0nUSjZwlP/EA2ot137P9+fYmo+MMUcoq6zi6c+3sur7At5fu9OzfuEvJ9IzMcKHkZnWYEnBGFMvVeWNFTv4+csrj1jfv3MU7992KoH+WnPo4JqbFAJaIxhjTNshIlw4IoXsmecw98aTPes35xXT9zfvkr27kbuJTIdnNQVjDFXVyml/nc/2fSWedV1jwhjULYZPN+QBcM6wZN5dnYsqfP7riSRFh7JgYz4n900kOuzIW12/33uQkKAAusSEtepxmIZZ85Ex5qi98c0Obnt5xTFvf//FQ3n4483kFJR61t134RAys/dx26T+9EqMbIkwzTGwpGCMOWblldUEBgjffLePuIgQUhMjKCipICQogDdW5PD7N9Yc1/4HJsfQJymSX555IqmdLFG0BuunYIw5ZiFBzuXGjNQEz7pEt1/D/4zpRZ9OkUSGBjE8JdbTGe7LLXuYtXgrF47ozmknJhEREkRWXhG/e2MN5ZXV7Cwo9dQg1ucWsj63kB37Sph308kt36HOHDOrKRhjWo2qogpb8ouZ980OHluwhSeuSOfsIcm+Dq3Ds7uPjDFtjogQECD07xLNjB+cQK/ECG54YTnPLt6KqlJUWkFBSQXV1e3ry2pHYjUFY4zPvLUyh1tmf1Pveyvu+gFxESGtHFHHZTUFY0ybd/7wbnzyi9MY2Su+znu3v7qSyqrqerYy3mQ1BWNMm/P051v54zvrALhtUn/W5xYyeUgyU4Ymey6Cm6Pj81tSReQZ4Fwgr4E5mgV4GJgCHASuUtXlTe3XkoIx/uFfn23hz+9tOGJdVGgQo3snkL3nADn7S+kcE8qArtFUVcMlGSmcNbgre4rLWLJ1LyGBAQQHBfBF1m6uGd+bTbuK2FlQSkp8BCelxhMU6F/JpS0khVOBYuD5BpLCFOB/cZLCaOBhVR3d1H4tKRjjPwoOVvCLV1cQFBBAYICwYGMeB8qrPO+P7BXPsm2Hh5M+KTWer7ObP7z0HZMHcP0pfSguq6SwpIKusWEEd9Bk4fOk4AaRCrzTQFL4F7BAVWe7rzcCE1Q1t7F9WlIwxn+pKlXVSnlVNYuz9nD6gM58m1/Mh+t28dcPNnrKnTGgM5GhQby1MoeLRnRHRJi7fDvXjOtNYWkFry3b3uBnpCZGcPnoXlw6qgdRIUEdZmjx9pAU3gFmqurn7utPgF+raqP/8S0pGGPqsyW/mGXb9nHJyJQmO8PtPVBOzv4SfvvGGlZ+v7/Rsn+6cCg/Ht2zJUP1ifbQo7m+n1q9GUpEpgPTAXr2bP8/HGNMy+ubFEXfpKhmlU2IDCEhMoQ3bx5HSXkVgQFCSFAAH6/bxYDkaP7+4SZW7ygge/cBfjNvNV9n7+X/fjiEiJDADt/72pqPjDGmAZVV1Uz/zzLPSLGJkSH89ZJhnD6gC899kc3db62lc3QoM35wAtNGNe8La2lFFd/vPUhhaSVFpRVMOLGzNw/Boz00H50D/IzDF5ofUdVRTe3TkoIxpjUVlVbw+IItfLI+j427ihosl94zjjnTx7JhZyG9EiKJjTg8nLiq8tLS7/jtvLoDCY7oGcdjl6eTHBveaByqely1FJ8nBRGZDUwAOgG7gLuBYABVfcK9JfVR4GycW1Kvbup6AlhSMMb4zr4D5Ty2IIv1uUWM6ZPA9FP7svdAOWP+/EmdsmHBAYzqnUh+URnrcwvrvH/moC58uG6X53VggNA9LpyqamV0nwR+MLALZZXV/GbeagJE6JEQwcyLhjK8R9wxxe7zpOAtlhSMMW1NwcEKJj+80DMKbIBAaFAgFVXVVLrjOKXEhzPvpnF0igrxfOM/WF7Jf77cVqc/Rn1CgwJ4/Ip0Th/Q5ZhitKRgjDGtqLyymtLKKmLCjmw2KqusJiw4sMnt//XZFg6UV3HtuN68sGQbnaOdocpP6Z9EZXU1VdV6XJMUWVIwxhjjYQPiGWOMOWqWFIwxxnhYUjDGGONhScEYY4yHJQVjjDEelhSMMcZ4WFIwxhjjYUnBGGOMR7vrvCYi+cC2Y9y8E7C7BcPpKOy81GXnpC47J3W1p3PSS1WTmirU7pLC8RCRzOb06PM3dl7qsnNSl52TujriObHmI2OMMR6WFIwxxnj4W1J40tcBtFF2Xuqyc1KXnZO6Otw58atrCsYYYxrnbzUFY4wxjfCbpCAiZ4vIRhHJEpE7fB1PaxKRbBFZLSIrRCTTXZcgIh+JyGb3Od5dLyLyiHueVolIum+jbxki8oyI5InImhrrjvociMhP3PKbReQnvjiWltLAOblHRHa4vysrRGRKjffudM/JRhE5q8b6DvO3JSI9RGS+iKwXkbUicqu73n9+V1S1wz+AQGAL0AcIAVYCg3wdVysefzbQqda6vwB3uMt3APe7y1OA9wABxgBLfB1/C52DU4F0YM2xngMgAfjWfY53l+N9fWwtfE7uAW6vp+wg9+8mFOjt/j0FdrS/LSAZSHeXo4FN7rH7ze+Kv9QURgFZqvqtqpYDc4ALfByTr10APOcuPwf8sMb659XxFRAnIsm+CLAlqepCYG+t1Ud7Ds4CPlLVvaq6D/gIONv70XtHA+ekIRcAc1S1TFW3Alk4f1cd6m9LVXNVdbm7XASsB7rjR78r/pIUugPf13i93V3nLxT4UESWich0d10XVc0F5w8B6Oyu96dzdbTnwF/Ozc/cppBnDjWT4IfnRERSgRHAEvzod8VfkoLUs86fbrsap6rpwGTgZhE5tZGy/n6uoOFz4A/n5nGgL5AG5AJ/d9f71TkRkShgLnCbqhY2VrSede36vPhLUtgO9KjxOgXI8VEsrU5Vc9znPGAeTpV/16FmIfc5zy3uT+fqaM9Bhz83qrpLVatUtRr4N87vCvjRORGRYJyE8KKqvu6u9pvfFX9JCl8D/UWkt4iEANOAt3wcU6sQkUgRiT60DJwJrME5/kN3RPwEeNNdfgu40r2rYgxQcKja3AEd7Tn4ADhTROLdZpUz3XUdRq3rRxfi/K6Ac06miUioiPQG+gNL6WB/WyIiwNPAelV9oMZb/vO74usr3a31wLlLYBPOnRK/9XU8rXjcfXDuCFkJrD107EAi8Amw2X1OcNcL8E/3PK0GMnx9DC10HmbjNIdU4HyLu/ZYzgFwDc5F1izgal8flxfOyX/cY16F8w8vuUb537rnZCMwucb6DvO3BYzHaeZZBaxwH1P86XfFejQbY4zx8JfmI2OMMc1gScEYY4yHJQVjjDEelhSMMcZ4WFIwxhjjYUnBtDkiUuWO0LlSRJaLyMlNlI8TkZuasd8FItKh5tM9XiLyrIhM9XUcpu2wpGDaohJVTVPV4cCdwJ+bKB8HNJkUfEVEgnwdgzHNZUnBtHUxwD5wxqMRkU/c2sNqETk0GudMoK9bu/irW/ZXbpmVIjKzxv4uEZGlIrJJRE5xywaKyF9F5Gt3ILifuuuTRWShu981h8rXJM5cFfe7+1wqIv3c9c+KyAMiMh+43x2P/w13/1+JyLAaxzTLjXWViFzsrj9TRL50j/VVdyweRGSmiKxzy/7NXXeJG99KEVnYxDGJiDzq7uO/HB7YzRiHr3vP2cMetR9AFU5P0g1AATDSXR8ExLjLnXB6igqQypFzAkwGvgAi3NeHep8uAP7uLk8BPnaXpwO/c5dDgUycOQN+weEe4IFAdD2xZtcocyXwjrv8LPAOEOi+/gdwt7t8OrDCXb4feKjG/uLdY1sIRLrrfg3chTM2/0YOT6Mb5z6vBrrXWtfQMV2EM4xzINAN2A9M9fXP3B5t52HVWtMWlahqGoCIjAWeF5EhOAngT+4or9U4QxF3qWf7ScAsVT0IoKo15ww4NMDZMpxkAs64NMNqtK3H4ozt8zXwjDtA2huquqKBeGfXeH6wxvpXVbXKXR4PXOzG86mIJIpIrBvrtEMbqOo+ETkXZ2KXxc5QPIQAXwKFQCnwlPst/x13s8XAsyLySo3ja+iYTgVmu3HliMinDRyT8VOWFEybpqpfikgnIAnn230STs2hQkSygbB6NhMaHqa4zH2u4vDvvwD/q6p1BixzE9A5wH9E5K+q+nx9YTawfKBWTPVtV1+sgjNBy2X1xDMKOAMnkfwMOF1VbxCR0W6cK0QkraFjEmd6TRvbxjTIrimYNk1EBuA0dezB+bab5yaEiUAvt1gRztSJh3wIXCMiEe4+Epr4mA+AG90aASJygjijy/ZyP+/fOCNnNjRf9aU1nr9soMxC4HJ3/xOA3eqM0/8hzj/3Q8cbD3wFjKtxfSLCjSkKiFXVd4HbcOY8QET6quoSVb0L2I0zZHO9x+TGMc295pAMTGzi3Bg/YzUF0xaFi8ihphoBfqKqVSLyIvC2iGRy+JoDqrpHRBaLMwH9e6r6S/fbcqaIlAPvAr9p5POewmlKWi5Oe00+znSLE4BfikgFUIxzzaA+oSKyBOdLVp1v9657gFkisgo4yOFhmP8P+KcbexXwB1V9XUSuAmaLSKhb7nc4ye9NEQlzz8vP3ff+KiL93XWf4IyIu6qBY5qHc01jNc7Ipp81cl6MH7JRUo05Dm4TVoaq7vZ1LMa0BGs+MsYY42E1BWOMMR5WUzDGGONhScEYY4yHJQVjjDEelhSMMcZ4WFIwxhjjYUnBGGOMx/8D1LjvX4vlsgEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(8, slice(1e-2))\n",
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('pass1_phn_ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.freeze_to(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
      "Min numerical gradient: 7.59E-07\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_find(learn)\n",
    "learn.recorder.plot(suggestion=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 43:25 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.896854</td>\n",
       "      <td>1.322199</td>\n",
       "      <td>0.588800</td>\n",
       "      <td>05:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.920582</td>\n",
       "      <td>1.326068</td>\n",
       "      <td>0.593991</td>\n",
       "      <td>05:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.852924</td>\n",
       "      <td>1.326780</td>\n",
       "      <td>0.592575</td>\n",
       "      <td>05:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.752048</td>\n",
       "      <td>1.331454</td>\n",
       "      <td>0.597137</td>\n",
       "      <td>05:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.635256</td>\n",
       "      <td>1.332507</td>\n",
       "      <td>0.602171</td>\n",
       "      <td>05:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.532356</td>\n",
       "      <td>1.351067</td>\n",
       "      <td>0.596036</td>\n",
       "      <td>05:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.492863</td>\n",
       "      <td>1.360443</td>\n",
       "      <td>0.598710</td>\n",
       "      <td>05:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.490748</td>\n",
       "      <td>1.353495</td>\n",
       "      <td>0.598238</td>\n",
       "      <td>05:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(8, slice(1e-4))\n",
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('pass2_phn_ms')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.unfreeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
      "Min numerical gradient: 1.58E-06\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lr_find(learn)\n",
    "learn.recorder.plot(suggestion=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Total time: 10:43 <p><table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.315588</td>\n",
       "      <td>5.934622</td>\n",
       "      <td>0.310959</td>\n",
       "      <td>01:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.314943</td>\n",
       "      <td>5.922575</td>\n",
       "      <td>0.310959</td>\n",
       "      <td>01:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.325263</td>\n",
       "      <td>5.942605</td>\n",
       "      <td>0.308904</td>\n",
       "      <td>01:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.311082</td>\n",
       "      <td>5.910153</td>\n",
       "      <td>0.313014</td>\n",
       "      <td>01:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.301132</td>\n",
       "      <td>5.894815</td>\n",
       "      <td>0.313014</td>\n",
       "      <td>01:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0.286729</td>\n",
       "      <td>5.904769</td>\n",
       "      <td>0.313699</td>\n",
       "      <td>01:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.286385</td>\n",
       "      <td>5.898260</td>\n",
       "      <td>0.310274</td>\n",
       "      <td>01:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0.286107</td>\n",
       "      <td>5.893535</td>\n",
       "      <td>0.309589</td>\n",
       "      <td>01:20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.fit_one_cycle(8, slice(1e-6, 1.1e-5))\n",
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.save('pass3_phn_ms')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not so good result: should work on it ;-)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phoneme classification\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}