diff --git a/data/speaker.csv b/data/speaker.csv
new file mode 100644
index 0000000..b6f40fc
--- /dev/null
+++ b/data/speaker.csv
@@ -0,0 +1,1032 @@
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diff --git a/data/speech_sample_output.csv b/data/speech_sample_output.csv
new file mode 100644
index 0000000..2d65eac
--- /dev/null
+++ b/data/speech_sample_output.csv
@@ -0,0 +1,299 @@
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diff --git a/examples/get_output_from_google_speech_model.ipynb b/examples/get_output_from_google_speech_model.ipynb
new file mode 100644
index 0000000..99d17dc
--- /dev/null
+++ b/examples/get_output_from_google_speech_model.ipynb
@@ -0,0 +1,690 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Input .wav files from gs into google's speech to text model "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import sys \n",
+    "import re\n",
+    "\n",
+    "sys.path.insert(0, '..')\n",
+    "\n",
+    "# Add google app crendtials\n",
+    "#os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"]=\"\"\n",
+    "\n",
+    "pd.set_option('max_colwidth', 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read participants df\n",
+    "df=pd.read_csv(\"../data/speaker.csv\")\n",
+    "\n",
+    "# Sample 100 IDs each from each race\n",
+    "id_lst=list(pd.concat(\n",
+    "    [\n",
+    "        df[df.race=='CHINESE'].sample(100, random_state=99),\n",
+    "        df[df.race=='MALAY'].sample(100, random_state=99),\n",
+    "        df[df.race=='INDIAN'].sample(100, random_state=99)\n",
+    "    ]\n",
+    ").id)\n",
+    "\n",
+    "# Remove 2 errors id\n",
+    "id_lst.remove(2888)\n",
+    "id_lst.remove(3151)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "scrolled": true
+   },
+   "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>id</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>race</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2852</td>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2670</td>\n",
+       "      <td>F</td>\n",
+       "      <td>MALAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3064</td>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2862</td>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2861</td>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1026</th>\n",
+       "      <td>2026</td>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1027</th>\n",
+       "      <td>2068</td>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1028</th>\n",
+       "      <td>2173</td>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1029</th>\n",
+       "      <td>2216</td>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1030</th>\n",
+       "      <td>2650</td>\n",
+       "      <td>F</td>\n",
+       "      <td>INDIAN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1031 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id sex     race\n",
+       "0     2852   F  CHINESE\n",
+       "1     2670   F    MALAY\n",
+       "2     3064   F  CHINESE\n",
+       "3     2862   F  CHINESE\n",
+       "4     2861   F  CHINESE\n",
+       "...    ...  ..      ...\n",
+       "1026  2026   F  CHINESE\n",
+       "1027  2068   M  CHINESE\n",
+       "1028  2173   F  CHINESE\n",
+       "1029  2216   M  CHINESE\n",
+       "1030  2650   F   INDIAN\n",
+       "\n",
+       "[1031 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "# Imports the Google Cloud libraries\n",
+    "from google.cloud import speech\n",
+    "from google.cloud import storage\n",
+    "\n",
+    "\n",
+    "# Instantiates a client\n",
+    "client = speech.SpeechClient()\n",
+    "\n",
+    "# Get the first 10 text outputs of each sampled IDs using google speech model\n",
+    "for p in id_lst:\n",
+    "    lst=[]\n",
+    "    i=0\n",
+    "    o=1\n",
+    "    # Speech file numbers can be missing inbetween\n",
+    "    while i!=10:\n",
+    "        if o>300:\n",
+    "            break\n",
+    "        name = f\"out/0{p}{o:04d}.WAV\"   \n",
+    "        storage_client = storage.Client()\n",
+    "        bucket_name = 'national_speech_corpus'\n",
+    "        bucket = storage_client.bucket(bucket_name)\n",
+    "        \n",
+    "        # Check if file exists\n",
+    "        if storage.Blob(bucket=bucket, name=name).exists(storage_client):\n",
+    "            gcs_uri = f\"gs://national_speech_corpus/out/0{p}00{o:02d}.WAV\"\n",
+    "            audio = speech.RecognitionAudio(uri=gcs_uri)\n",
+    "            config = speech.RecognitionConfig(\n",
+    "                language_code=\"en-SG\",\n",
+    "                enable_automatic_punctuation=False,\n",
+    "            )\n",
+    "            # Detects speech in the audio file\n",
+    "            response = client.recognize(config=config, audio=audio)\n",
+    "            \n",
+    "            # Check if there's output from the model\n",
+    "            if len(response.results) > 0:\n",
+    "                sentence = response.results[0].alternatives[0].transcript\n",
+    "\n",
+    "                # Replace digits into words\n",
+    "                my_dict = {'0': 'zero ', '1': 'one ', '2': 'two ', '3': 'three ', '4': 'four ', '5': 'five ', '6': 'six ', '7': 'seven ', '8': 'eight ', '9': 'nine '}\n",
+    "                for item in sentence:\n",
+    "                    if item in my_dict.keys():\n",
+    "                        sentence=sentence.replace(item, my_dict[item])\n",
+    "                lst.append(sentence)\n",
+    "            else:\n",
+    "                lst.append('')\n",
+    "            i+=1\n",
+    "        o+=1\n",
+    "    \n",
+    "    # Read transcript (truth) file for a particular participant\n",
+    "    # Drop even index, first column and keep only first 10 sentences \n",
+    "    output_df = pd.read_csv(f\"script/0{p}0.txt\", sep=\"\\t\", header=None).iloc[1:20:2].drop(0,axis=1).reset_index(drop=True)\n",
+    "    output_df.columns=['truth']\n",
+    "    output_df['output']=lst\n",
+    "    output_df.to_csv(f\"output/{p}.csv\",index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Clare\\AppData\\Local\\Temp/ipykernel_6324/1510333972.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_speaker['truth_count']=0\n",
+      "C:\\Users\\Clare\\AppData\\Local\\Temp/ipykernel_6324/1510333972.py:4: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_speaker['match_count']=0\n",
+      "C:\\Users\\Clare\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\indexing.py:1817: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  self._setitem_single_column(loc, value, pi)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Add accuracy metrics to the output df and sampled speaker df\n",
+    "df_speaker=df[df.id.isin(id_lst)]\n",
+    "df_speaker['truth_count']=0\n",
+    "df_speaker['match_count']=0\n",
+    "for p in id_lst:\n",
+    "    output_df = pd.read_csv(f'output/{p}.csv')\n",
+    "    output_df['truth_split'] = output_df.truth.apply(lambda x: str(x).lower().split())\n",
+    "    output_df['output_split'] = output_df.output.apply(lambda x: str(x).lower().split())\n",
+    "    output_df['truth_count']=output_df.truth_split.apply(lambda x: len(set(x)))\n",
+    "    output_df['match_count']=output_df.apply(lambda x:len(set(x.truth_split).intersection(set(x.output_split))),axis=1) \n",
+    "    output_df.to_csv(f\"output/{p}.csv\",index=False)\n",
+    "    \n",
+    "    df_speaker.loc[df_speaker.id==p,'truth_count']=output_df.truth_count.sum()\n",
+    "    df_speaker.loc[df_speaker.id==p,'match_count']=output_df.match_count.sum()\n",
+    "\n",
+    "df_speaker.reset_index().to_csv('speech_sample_output.csv',index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "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>truth</th>\n",
+       "      <th>output</th>\n",
+       "      <th>truth_split</th>\n",
+       "      <th>output_split</th>\n",
+       "      <th>truth_count</th>\n",
+       "      <th>match_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nine</td>\n",
+       "      <td>nine</td>\n",
+       "      <td>['nine']</td>\n",
+       "      <td>['nine']</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>De Fabio Factory and Apple</td>\n",
+       "      <td>disable Factory in apple</td>\n",
+       "      <td>['de', 'fabio', 'factory', 'and', 'apple']</td>\n",
+       "      <td>['disable', 'factory', 'in', 'apple']</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Oyakodon Yakizakana and Carrot Halwa</td>\n",
+       "      <td>oil Condon and carrot halwa</td>\n",
+       "      <td>['oyakodon', 'yakizakana', 'and', 'carrot', 'halwa']</td>\n",
+       "      <td>['oil', 'condon', 'and', 'carrot', 'halwa']</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>where can I find the best Red ruby</td>\n",
+       "      <td>where can I find the best way to be</td>\n",
+       "      <td>['where', 'can', 'i', 'find', 'the', 'best', 'red', 'ruby']</td>\n",
+       "      <td>['where', 'can', 'i', 'find', 'the', 'best', 'way', 'to', 'be']</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Kuih Kaswi waffle and Coconut Kuih</td>\n",
+       "      <td>Quaker sweet waffle in Coconut Creek</td>\n",
+       "      <td>['kuih', 'kaswi', 'waffle', 'and', 'coconut', 'kuih']</td>\n",
+       "      <td>['quaker', 'sweet', 'waffle', 'in', 'coconut', 'creek']</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>please tell me how to cook Satay Babat</td>\n",
+       "      <td>please tell me how to cook saute Bobbitt</td>\n",
+       "      <td>['please', 'tell', 'me', 'how', 'to', 'cook', 'satay', 'babat']</td>\n",
+       "      <td>['please', 'tell', 'me', 'how', 'to', 'cook', 'saute', 'bobbitt']</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>six five two three nine seven nine three</td>\n",
+       "      <td>six five  to three nine seven nine three</td>\n",
+       "      <td>['six', 'five', 'two', 'three', 'nine', 'seven', 'nine', 'three']</td>\n",
+       "      <td>['six', 'five', 'to', 'three', 'nine', 'seven', 'nine', 'three']</td>\n",
+       "      <td>6</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Denis D Cotta Paine Eric and Chong Tze Chien</td>\n",
+       "      <td>Dennis Dakota and Chong searching</td>\n",
+       "      <td>['denis', 'd', 'cotta', 'paine', 'eric', 'and', 'chong', 'tze', 'chien']</td>\n",
+       "      <td>['dennis', 'dakota', 'and', 'chong', 'searching']</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>I can't eat all of this Satay Babat</td>\n",
+       "      <td>I can't eat all of this Saturday Bobbitt</td>\n",
+       "      <td>['i', \"can't\", 'eat', 'all', 'of', 'this', 'satay', 'babat']</td>\n",
+       "      <td>['i', \"can't\", 'eat', 'all', 'of', 'this', 'saturday', 'bobbitt']</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Maxi Cash Cadbury and Ajinomoto</td>\n",
+       "      <td>Mexican Inn Camp Bowie and Angie nomoto</td>\n",
+       "      <td>['maxi', 'cash', 'cadbury', 'and', 'ajinomoto']</td>\n",
+       "      <td>['mexican', 'inn', 'camp', 'bowie', 'and', 'angie', 'nomoto']</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          truth  \\\n",
+       "0                                          nine   \n",
+       "1                    De Fabio Factory and Apple   \n",
+       "2          Oyakodon Yakizakana and Carrot Halwa   \n",
+       "3            where can I find the best Red ruby   \n",
+       "4            Kuih Kaswi waffle and Coconut Kuih   \n",
+       "5        please tell me how to cook Satay Babat   \n",
+       "6      six five two three nine seven nine three   \n",
+       "7  Denis D Cotta Paine Eric and Chong Tze Chien   \n",
+       "8           I can't eat all of this Satay Babat   \n",
+       "9               Maxi Cash Cadbury and Ajinomoto   \n",
+       "\n",
+       "                                      output  \\\n",
+       "0                                      nine    \n",
+       "1                   disable Factory in apple   \n",
+       "2                oil Condon and carrot halwa   \n",
+       "3        where can I find the best way to be   \n",
+       "4       Quaker sweet waffle in Coconut Creek   \n",
+       "5   please tell me how to cook saute Bobbitt   \n",
+       "6  six five  to three nine seven nine three    \n",
+       "7          Dennis Dakota and Chong searching   \n",
+       "8   I can't eat all of this Saturday Bobbitt   \n",
+       "9    Mexican Inn Camp Bowie and Angie nomoto   \n",
+       "\n",
+       "                                                                truth_split  \\\n",
+       "0                                                                  ['nine']   \n",
+       "1                                ['de', 'fabio', 'factory', 'and', 'apple']   \n",
+       "2                      ['oyakodon', 'yakizakana', 'and', 'carrot', 'halwa']   \n",
+       "3               ['where', 'can', 'i', 'find', 'the', 'best', 'red', 'ruby']   \n",
+       "4                     ['kuih', 'kaswi', 'waffle', 'and', 'coconut', 'kuih']   \n",
+       "5           ['please', 'tell', 'me', 'how', 'to', 'cook', 'satay', 'babat']   \n",
+       "6         ['six', 'five', 'two', 'three', 'nine', 'seven', 'nine', 'three']   \n",
+       "7  ['denis', 'd', 'cotta', 'paine', 'eric', 'and', 'chong', 'tze', 'chien']   \n",
+       "8              ['i', \"can't\", 'eat', 'all', 'of', 'this', 'satay', 'babat']   \n",
+       "9                           ['maxi', 'cash', 'cadbury', 'and', 'ajinomoto']   \n",
+       "\n",
+       "                                                        output_split  \\\n",
+       "0                                                           ['nine']   \n",
+       "1                              ['disable', 'factory', 'in', 'apple']   \n",
+       "2                        ['oil', 'condon', 'and', 'carrot', 'halwa']   \n",
+       "3    ['where', 'can', 'i', 'find', 'the', 'best', 'way', 'to', 'be']   \n",
+       "4            ['quaker', 'sweet', 'waffle', 'in', 'coconut', 'creek']   \n",
+       "5  ['please', 'tell', 'me', 'how', 'to', 'cook', 'saute', 'bobbitt']   \n",
+       "6   ['six', 'five', 'to', 'three', 'nine', 'seven', 'nine', 'three']   \n",
+       "7                  ['dennis', 'dakota', 'and', 'chong', 'searching']   \n",
+       "8  ['i', \"can't\", 'eat', 'all', 'of', 'this', 'saturday', 'bobbitt']   \n",
+       "9      ['mexican', 'inn', 'camp', 'bowie', 'and', 'angie', 'nomoto']   \n",
+       "\n",
+       "   truth_count  match_count  \n",
+       "0            1            1  \n",
+       "1            5            2  \n",
+       "2            5            3  \n",
+       "3            8            6  \n",
+       "4            5            2  \n",
+       "5            8            6  \n",
+       "6            6            5  \n",
+       "7            9            2  \n",
+       "8            8            6  \n",
+       "9            5            1  "
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sample transcribed text vs truth\n",
+    "speaker = pd.read_csv(f\"output/2002.csv\")\n",
+    "speaker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "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>sex</th>\n",
+       "      <th>race</th>\n",
+       "      <th>truth_count</th>\n",
+       "      <th>match_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>77</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>84</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>80</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>F</td>\n",
+       "      <td>INDIAN</td>\n",
+       "      <td>80</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>70</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>74</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>M</td>\n",
+       "      <td>MALAY</td>\n",
+       "      <td>68</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>82</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>79</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>82</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>298 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    sex     race  truth_count  match_count\n",
+       "0     F  CHINESE           77           60\n",
+       "1     F  CHINESE           84           64\n",
+       "2     M  CHINESE           80           53\n",
+       "3     F   INDIAN           80           53\n",
+       "4     M  CHINESE           70           45\n",
+       "..   ..      ...          ...          ...\n",
+       "293   M  CHINESE           74           50\n",
+       "294   M    MALAY           68           52\n",
+       "295   F  CHINESE           82           58\n",
+       "296   F  CHINESE           79           52\n",
+       "297   M  CHINESE           82           57\n",
+       "\n",
+       "[298 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# output to be passed into feat tests (subgroup disparity and min/max threshold)\n",
+    "df_speaker=pd.read_csv('../data/speech_sample_output.csv')\n",
+    "df_speaker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "race\n",
+       "CHINESE    0.671469\n",
+       "INDIAN     0.690566\n",
+       "MALAY      0.683185\n",
+       "Name: rate, dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sample accuracy stats across race\n",
+    "\n",
+    "tmp=df_speaker.copy()\n",
+    "tmp['rate']=tmp.match_count/tmp.truth_count\n",
+    "tmp.groupby('race').rate.mean()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "veritas",
+   "language": "python",
+   "name": "veritas"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/examples/model_card_output/data/speech_to_text_example.proto b/examples/model_card_output/data/speech_to_text_example.proto
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diff --git a/examples/model_card_output/model_cards/speech_to_text_example.html b/examples/model_card_output/model_cards/speech_to_text_example.html
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@@ -0,0 +1,2097 @@
+<!DOCTYPE html>
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+<html lang="en">
+
+<head>
+
+<style>
+
+  h1 {text-align: center;}
+
+  .row {
+
+    display: flex;
+
+  }
+
+  .col {
+
+    flex: 1;
+
+    align-items: center; 
+
+    justify-content: center;
+
+  }
+
+  .card {
+
+    padding: 1em;
+
+    border: 1px solid #DADCE0;
+
+    margin: 10px;
+
+  }
+
+  .card .card {
+
+    padding: 0.5em;
+
+    padding-bottom: 0em;
+
+    border: none;
+
+  }
+
+  .img-container {
+
+    display: flex;
+
+    flex-wrap: wrap;
+
+    justify-content: space-around;
+
+    text-align: center;
+
+  }
+
+  .img-item {
+
+    flex: 1;
+
+  }
+
+  .center {
+
+    margin-left: auto;
+
+    margin-right: auto;
+
+    text-align: center;
+
+  }
+
+  table, th, td { border: 1px solid black; }
+
+  th, td { border: 1px solid #CCC; height: 30px; }
+
+  caption { font-weight: bold; }
+
+</style>
+
+<title>
+
+  Model Card for Speech to Text Model
+
+</title>
+
+</head>
+
+<body>
+
+  <h1>
+
+    Model Card for Speech to Text Model
+
+  </h1>
+
+    <div class="row">
+
+      <div class="col card">
+
+        <h2>Model Details</h2>
+
+          <h3>Overview</h3>
+
+              Using an automated transcription tool, the government want to transcribe audio files recorded in trials for evidential purposes. The tool should be able to capture the various spoken accents and transcribe into text accurately so as not to bias against the defendant. The tool chosen here will be Google&#39;s Speech to Text Model evaluated on a set of audio files by National Speech Corpus.
+
+          <h3>Version</h3>
+
+              
+
+  
+
+    <div>name: v1</div>
+
+
+
+              
+
+  
+
+
+
+              
+
+  
+
+
+
+          
+
+          
+
+          <h3>Owners</h3>
+
+            
+
+              
+
+                <li>Timothy, Product Owner(s)</li>
+
+              
+
+                <li>Swan, Model Developer(s)</li>
+
+              
+
+                <li>Jason, Reviewer(s)</li>
+
+              
+
+            
+
+          
+
+          
+
+          
+
+          
+
+          <h3>Regulatory requirements</h3>
+
+              MAS Fairness, Ethics, Accountability and Transparency (FEAT) principles
+
+        </div>
+
+      
+
+      
+
+      <div class="col card">
+
+        <h2>Considerations</h2>
+
+          
+
+            <h3>Intended Users</h3>
+
+              
+
+  
+
+  <ul>
+
+  
+
+    <li>Judical System and Defendant</li>
+
+  
+
+  </ul>
+
+
+
+          
+
+          
+
+            <h3>Use Cases</h3>
+
+              
+
+  
+
+  <ul>
+
+  
+
+    <li>Automate transcriptions and reduce human error and manpower in making court decisions</li>
+
+  
+
+  </ul>
+
+
+
+          
+
+          
+
+          
+
+          
+
+          
+
+            <h3>Fairness Considerations</h3>
+
+              <ul>
+
+                <li>
+
+                  <div><b>Group at risk:</b> gender and race</div>
+
+                  <div><b>Benefits:</b> Reduce manpower and human error on transcription services. A more accurate model than current system will aid the judicial system in making better court decisions</div>
+
+                  <div><b>Harms:</b> Incorrectly transcribed/interpreted texts can impact on the defendant/ruling negatively.</div>
+
+                  <div><b>Mitigation Strategy:</b> Improve upon the existing model by further training and enhancements</div>
+
+                </li> </ul>
+
+          
+
+      </div>
+
+      
+
+    </div>
+
+    
+
+      
+
+  <div class="col card">
+
+    <h2>Datasets</h2>
+
+    
+
+      <div class="row">
+
+        <div class="col card">
+
+          <h3>National Speech Corpus Dataset</h3>
+
+            <p>Dataset taken from https://www.imda.gov.sg/programme-listing/digital-services-lab/national-speech-corpus</p>
+
+            
+
+              <h4>Sensitive data</h4>
+
+              <ul>
+
+              
+
+                <li>gender</li>
+
+              
+
+                <li> race</li>
+
+              
+
+              </ul>
+
+              
+
+                <h4>Sensitive data used in model</h4>
+
+                <ul>
+
+                
+
+                  <li>N.A. (Protected attributes are not trained in the speech to text model)</li>
+
+                
+
+                </ul>
+
+              
+
+              
+
+                <h4>Justification</h4>
+
+                <p> N.A.</p>
+
+              
+
+            
+
+            
+
+            
+
+        </div>
+
+      </div>
+
+    
+
+  </div>
+
+
+
+    
+
+    
+
+      
+
+<div class="col card">
+
+  <h2>Quantitative Analysis</h2>
+
+  
+
+    
+
+      
+
+<div class="col card">
+
+  <h3>MSE - 712.513 (Test Set)</h3>
+
+  
+
+  
+
+  
+
+  
+
+</div>
+
+
+
+    
+
+      
+
+<div class="col card">
+
+  <h3>MAE - 26.003 (Test Set)</h3>
+
+  
+
+  
+
+  
+
+    
+
+  <div class="row">
+
+    <div class="col">
+
+      
+
+        <div class="center"><i>About 65% of the words are correctly transcribed</i></div>
+
+      
+
+      
+
+  <div class="img-container">
+
+  
+
+  
+
+    <div class="img-item">
+
+      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90
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+' alt='None' />
+
+    </div>
+
+  
+
+  </div>
+
+    
+
+    </div>
+
+  </div>
+
+
+
+  
+
+  
+
+</div>
+
+
+
+    
+
+      
+
+<div class="col card">
+
+  <h3>MSE across sensitive groups  </h3>
+
+  
+
+  
+
+  
+
+  
+
+    
+
+      
+
+<div class="col card">
+
+  
+
+  <h4> Min Max Threshold Test </h4>
+
+  
+
+  
+
+  <div>
+
+    <b> Description: </b>
+
+    Test if the mae of the subgroups within sex 
+is lower than the threshold of 27.
+  
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Threshold: </b>
+
+    27 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Result: </b>
+
+    
+
+    
+
+      
+
+<table>
+
+
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td></td>
+
+      
+
+      <td>mae</td>
+
+      
+
+      <td>passed
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>sex_F</td>
+
+      
+
+      <td>26.143</td>
+
+      
+
+      <td>True
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>sex_M</td>
+
+      
+
+      <td>25.891</td>
+
+      
+
+      <td>True
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+  
+
+</table>
+
+
+
+    
+
+  </div>
+
+  
+
+   
+
+    <div><b style="color:green;"> Passed</b></div> 
+
+    
+
+  
+
+    
+
+  <div class="row">
+
+    <div class="col">
+
+      
+
+      
+
+  <div class="img-container">
+
+  
+
+  
+
+    <div class="img-item">
+
+      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAr8AAAF2CAYAAABnFvAPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90
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+TkSuQmCC
+' alt='Mean Absolute Error across sex subgroups' />
+
+    </div>
+
+  
+
+  </div>
+
+    
+
+    </div>
+
+  </div>
+
+
+
+  
+
+</div>
+
+
+
+    
+
+      
+
+<div class="col card">
+
+  
+
+  <h4> Min Max Threshold Test </h4>
+
+  
+
+  
+
+  <div>
+
+    <b> Description: </b>
+
+    Test if the mae of the subgroups within race 
+is lower than the threshold of 27.
+  
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Threshold: </b>
+
+    27 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Result: </b>
+
+    
+
+    
+
+      
+
+<table>
+
+
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td></td>
+
+      
+
+      <td>mae</td>
+
+      
+
+      <td>passed
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>race_CHINESE</td>
+
+      
+
+      <td>27.141</td>
+
+      
+
+      <td>False
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>race_INDIAN</td>
+
+      
+
+      <td>25.404</td>
+
+      
+
+      <td>True
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>race_MALAY</td>
+
+      
+
+      <td>25.47</td>
+
+      
+
+      <td>True
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+  
+
+</table>
+
+
+
+    
+
+  </div>
+
+  
+
+  
+
+    <div><b style="color:red;"> Failed</b></div>
+
+  
+
+  
+
+    
+
+  <div class="row">
+
+    <div class="col">
+
+      
+
+      
+
+  <div class="img-container">
+
+  
+
+  
+
+    <div class="img-item">
+
+      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAr8AAAF2CAYAAABnFvAPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90
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++JUkSVIzDL+SJElqxv8HBJeBc1T/ZxYAAAAASUVORK5CYII=
+' alt='Mean Absolute Error across race subgroups' />
+
+    </div>
+
+  
+
+  </div>
+
+    
+
+    </div>
+
+  </div>
+
+
+
+  
+
+</div>
+
+
+
+    
+
+  
+
+</div>
+
+
+
+    
+
+  
+
+  
+
+</div>
+
+
+
+    
+
+    
+
+    
+
+      
+
+<div class="col card">
+
+  <h2>Fairness Analysis</h2>
+
+  
+
+    
+
+      
+
+<div class="col card">
+
+  <h3>Equal mean squared error  </h3>
+
+  
+
+  <div>
+
+    <b> Segment: </b>
+
+    Gender and race 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Description: </b>
+
+    Disparity ratio of MSE of any 2 bins in the respective attribute should not be more than a factor of 1.1 
+
+  </div>
+
+  
+
+  
+
+  
+
+    
+
+      
+
+<div class="col card">
+
+  
+
+  <h4> Subgroup Disparity Test </h4>
+
+  
+
+  
+
+  <div>
+
+    <b> Description: </b>
+
+    Test if the maximum ratio of the mean squared error of any 2
+groups within race attribute exceeds 1.1. To
+pass, this value cannot exceed the threshold. 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Threshold: </b>
+
+    1.1 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Result: </b>
+
+    
+
+    
+
+      
+
+<table>
+
+
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td></td>
+
+      
+
+      <td>race_mse_max_ratio
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>0</td>
+
+      
+
+      <td>1.136
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+  
+
+</table>
+
+
+
+    
+
+  </div>
+
+  
+
+  
+
+    <div><b style="color:red;"> Failed</b></div>
+
+  
+
+  
+
+    
+
+  <div class="row">
+
+    <div class="col">
+
+      
+
+      
+
+  <div class="img-container">
+
+  
+
+  
+
+    <div class="img-item">
+
+      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAswAAAF1CAYAAAD8/Lw6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90
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+' alt='Mean Squared Error across race subgroups' />
+
+    </div>
+
+  
+
+  </div>
+
+    
+
+    </div>
+
+  </div>
+
+
+
+  
+
+</div>
+
+
+
+    
+
+      
+
+<div class="col card">
+
+  
+
+  <h4> Subgroup Disparity Test </h4>
+
+  
+
+  
+
+  <div>
+
+    <b> Description: </b>
+
+    Test if the maximum ratio of the mean squared error of any 2
+groups within sex attribute exceeds 1.1. To
+pass, this value cannot exceed the threshold. 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Threshold: </b>
+
+    1.1 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Result: </b>
+
+    
+
+    
+
+      
+
+<table>
+
+
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td></td>
+
+      
+
+      <td>sex_mse_max_ratio
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>0</td>
+
+      
+
+      <td>1.023
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+  
+
+</table>
+
+
+
+    
+
+  </div>
+
+  
+
+   
+
+    <div><b style="color:green;"> Passed</b></div> 
+
+    
+
+  
+
+    
+
+  <div class="row">
+
+    <div class="col">
+
+      
+
+      
+
+  <div class="img-container">
+
+  
+
+  
+
+    <div class="img-item">
+
+      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAsYAAAF1CAYAAADr3izzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90
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+' alt='Mean Squared Error across sex subgroups' />
+
+    </div>
+
+  
+
+  </div>
+
+    
+
+    </div>
+
+  </div>
+
+
+
+  
+
+</div>
+
+
+
+    
+
+  
+
+</div>
+
+
+
+    
+
+  
+
+</div>
+
+
+
+    
+
+</body>
+
+</html>
diff --git a/examples/sample-form-response-speech-to-text.json b/examples/sample-form-response-speech-to-text.json
new file mode 100644
index 0000000..9ef78a9
--- /dev/null
+++ b/examples/sample-form-response-speech-to-text.json
@@ -0,0 +1,301 @@
+{
+  "eventId": "4ceb5c3b-2878-4546-ad3e-9dab1147ba06",
+  "eventType": "FORM_RESPONSE",
+  "createdAt": "2021-09-26T07:14:49.410Z",
+  "data": {
+    "responseId": "100e0116-86e9-4625-8f80-dd539392104c",
+    "respondentId": "mZoZ90",
+    "formId": "mR4Nlw",
+    "formName": "Model Ops Card Builder",
+    "createdAt": "2021-09-26T07:14:49.000Z",
+    "fields": [
+      {
+        "key": "question_3yXOqx",
+        "label": "Name",
+        "type": "INPUT_TEXT",
+        "value": "Speech to Text Model"
+      },
+      {
+        "key": "question_waO1Wy",
+        "label": "Overview",
+        "type": "TEXTAREA",
+        "value": "Using an automated transcription tool, the government want to transcribe audio files recorded in trials for evidential purposes. The tool should be able to capture the various spoken accents and transcribe into text accurately so as not to bias against the defendant. The tool chosen here will be Google's Speech to Text Model evaluated on a set of audio files by National Speech Corpus."
+      },
+      {
+        "key": "question_3X5jBY",
+        "label": "Intended Users",
+        "type": "INPUT_TEXT",
+        "value": "Judical System and Defendant"
+      },
+      {
+        "key": "question_w8N9pP",
+        "label": "Intended Use Cases",
+        "type": "TEXTAREA",
+        "value": "Automate transcriptions and reduce human error and manpower in making court decisions"
+      },
+      {
+        "key": "question_m6DYq5",
+        "label": "Version",
+        "type": "INPUT_TEXT",
+        "value": "v1"
+      },
+      {
+        "key": "question_wbZMaE",
+        "label": "Product Owner(s)",
+        "type": "INPUT_TEXT",
+        "value": "Timothy"
+      },
+      {
+        "key": "question_wArYkN",
+        "label": "Model Developer(s)",
+        "type": "INPUT_TEXT",
+        "value": "Swan"
+      },
+      {
+        "key": "question_mBEYvY",
+        "label": "Reviewer(s)",
+        "type": "INPUT_TEXT",
+        "value": "Jason"
+      },
+      {
+        "key": "question_wzEBxk",
+        "label": "Please select any regulatory guidelines which the model should be in compliance with",
+        "type": "CHECKBOXES",
+        "value": [
+          "1f033a0c-1fa7-44a8-9309-0fe858283844",
+          "0a574b19-1a88-42e6-b4ba-b288b3fec5a4"
+        ],
+        "options": [
+          {
+            "id": "0a574b19-1a88-42e6-b4ba-b288b3fec5a4",
+            "text": "MAS Fairness, Ethics, Accountability and Transparency (FEAT) principles"
+          },
+          {
+            "id": "0e0e9512-6ea8-496e-9ee4-83dc1a23a5a7",
+            "text": "HKMA AI principles"
+          },
+          {
+            "id": "5fcd3e36-042c-4c71-b76a-e9ec325f6205",
+            "text": "Bank of England and FCA's guideline"
+          }
+        ]
+      },
+      {
+        "key": "question_wzEBxk_0a574b19-1a88-42e6-b4ba-b288b3fec5a4",
+        "label": "Please select any regulatory guidelines which the model should be in compliance with (MAS Fairness, Ethics, Accountability and Transparency (FEAT) principles)",
+        "type": "CHECKBOXES",
+        "value": true
+      },
+      {
+        "key": "question_wzEBxk_0e0e9512-6ea8-496e-9ee4-83dc1a23a5a7",
+        "label": "Please select any regulatory guidelines which the model should be in compliance with (HKMA AI principles)",
+        "type": "CHECKBOXES",
+        "value": false
+      },
+      {
+        "key": "question_wzEBxk_5fcd3e36-042c-4c71-b76a-e9ec325f6205",
+        "label": "Please select any regulatory guidelines which the model should be in compliance with (Bank of England and FCA's guideline)",
+        "type": "CHECKBOXES",
+        "value": false
+      },
+      {
+        "key": "question_wzEBxk_1f033a0c-1fa7-44a8-9309-0fe858283844",
+        "label": "Please select any regulatory guidelines which the model should be in compliance with (Not applicable)",
+        "type": "CHECKBOXES",
+        "value": true
+      },
+      {
+        "key": "question_n0V4jj",
+        "label": "Name of dataset",
+        "type": "INPUT_TEXT",
+        "value": "National Speech Corpus Dataset"
+      },
+      {
+        "key": "question_wM10jE",
+        "label": "Description of dataset",
+        "type": "TEXTAREA",
+        "value": "Dataset taken from https://www.imda.gov.sg/programme-listing/digital-services-lab/national-speech-corpus"
+      },
+      {
+        "key": "question_wzEVQ0",
+        "label": "Protected attributes in dataset",
+        "type": "TEXTAREA",
+        "value": "gender, race"
+      },
+      {
+        "key": "question_w5XNqE",
+        "label": "Protected attributes in production",
+        "type": "TEXTAREA",
+        "value": "N.A. (Protected attributes are not trained in the speech to text model)" 
+      },
+      {
+        "key": "question_3Eqpo2",
+        "label": "Justification of use of protected attributes",
+        "type": "TEXTAREA",
+        "value": "N.A."
+      },
+      {
+        "key": "question_wdb72K",
+        "label": "Name of dataset 2",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_mJ1yEz",
+        "label": "Description of dataset 2",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_mYj2zJ",
+        "label": "Protected attributes in dataset 2",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_mDqLvZ",
+        "label": "Protected attributes in production (dataset 2)",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_3jeK8Y",
+        "label": "Justification of use of protected attributes (dataset 2)",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_3laplv",
+        "label": "Name of dataset 3",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_wgbB8M",
+        "label": "Description of dataset 3",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_mRWOBj",
+        "label": "Protected attributes in dataset 3",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_wo9JVx",
+        "label": "Protected attributes in production (dataset 3)",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_3yXO86",
+        "label": "Justification of use of protected attributes (dataset 3)",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_3je4aQ",
+        "label": "What is the key metric used in evaluating the model's performance?",
+        "type": "INPUT_TEXT",
+        "value": "Mean Squared Error"
+      },
+      {
+        "key": "question_mOQRBg",
+        "label": "2nd metric (if applicable)",
+        "type": "INPUT_TEXT",
+        "value": "Mean Absolute Error"
+      },
+      {
+        "key": "question_mVpr1y",
+        "label": "3rd metric (if applicable)",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_mKp4dz",
+        "label": "Who are the individuals and groups that are considered to be at-risk of being systematically disadvantaged by the system?",
+        "type": "TEXTAREA",
+        "value": "gender and race"
+      },
+      {
+        "key": "question_31WYq4",
+        "label": "Expected Benefits",
+        "type": "TEXTAREA",
+        "value": "Reduce manpower and human error on transcription services. A more accurate model than current system will aid the judicial system in making better court decisions"
+      },
+      {
+        "key": "question_nGeMBk",
+        "label": "Expected Harms",
+        "type": "TEXTAREA",
+        "value": "Incorrectly transcribed/interpreted texts can impact on the defendant/ruling negatively."
+      },
+      {
+        "key": "question_mJ1r5z",
+        "label": "Mitigation strategies",
+        "type": "TEXTAREA",
+        "value": "Improve upon the existing model by further training and enhancements"
+      },
+      {
+        "key": "question_nPRoBe",
+        "label": "Type of fairness analysis conducted",
+        "type": "INPUT_TEXT",
+        "value": "Equal mean squared error"
+      },
+      {
+        "key": "question_3Eqpbr",
+        "label": "Segment of analysis",
+        "type": "INPUT_TEXT",
+        "value": "Gender and race"
+      },
+      {
+        "key": "question_nrDXrR",
+        "label": "Description of fairness analysis",
+        "type": "TEXTAREA",
+        "value": "Disparity ratio of MSE of any 2 bins in the respective attribute should not be more than a factor of 1.1"
+      },
+      {
+        "key": "question_w4aZNY",
+        "label": "Type of fairness analysis conducted 2",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_3jeK94",
+        "label": "Segment of analysis 2",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_w2j2x9",
+        "label": "Description of fairness analysis 2",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_3xVGN5",
+        "label": "Segment of analysis 3",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_mZ9xRB",
+        "label": "Type of fairness analysis conducted 3",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_3NqzBj",
+        "label": "Description of fairness analysis 3",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_n0V0Q9",
+        "label": "Please enter your email",
+        "type": "INPUT_EMAIL",
+        "value": "timothy.lin@cylynx.io"
+      }
+    ]
+  }
+}
\ No newline at end of file
diff --git a/examples/speech_to_text_example.ipynb b/examples/speech_to_text_example.ipynb
new file mode 100644
index 0000000..dbcc0d5
--- /dev/null
+++ b/examples/speech_to_text_example.ipynb
@@ -0,0 +1,2605 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import json\n",
+    "from IPython import display\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
+    "\n",
+    "# parent directory to work with dev\n",
+    "sys.path.insert(0, '..')\n",
+    "import verifyml.model_card_toolkit as mctlib\n",
+    "from verifyml.model_card_toolkit import model_card_pb2, ModelCard\n",
+    "from verifyml.model_card_toolkit.utils.tally_form import tally_form_to_mc\n",
+    "from verifyml.model_tests.utils import plot_to_str\n",
+    "from verifyml.model_tests.FEAT import (\n",
+    "    SubgroupDisparity,\n",
+    "    MinMaxMetricThreshold,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read output with truth and prediction columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read output\n",
+    "# truth column -> total no. of words spoken by the individual\n",
+    "# prediction column -> total no. of words correctly transcribed by google's speech to text model model\n",
+    "\n",
+    "output = pd.read_csv(\"../data/speech_sample_output.csv\")\n",
+    "output.rename(columns={'truth_count':'truth', 'match_count':'prediction'},inplace=True)\n",
+    "mse_test = round(mean_squared_error(output.truth, output.prediction),3)\n",
+    "mae_test = round(mean_absolute_error(output.truth, output.prediction), 3)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>sex</th>\n",
+       "      <th>race</th>\n",
+       "      <th>truth</th>\n",
+       "      <th>prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>77</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>84</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>80</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>F</td>\n",
+       "      <td>INDIAN</td>\n",
+       "      <td>80</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>70</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>74</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>M</td>\n",
+       "      <td>MALAY</td>\n",
+       "      <td>68</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>82</td>\n",
+       "      <td>58</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>79</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>82</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>298 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    sex     race  truth  prediction\n",
+       "0     F  CHINESE     77          60\n",
+       "1     F  CHINESE     84          64\n",
+       "2     M  CHINESE     80          53\n",
+       "3     F   INDIAN     80          53\n",
+       "4     M  CHINESE     70          45\n",
+       "..   ..      ...    ...         ...\n",
+       "293   M  CHINESE     74          50\n",
+       "294   M    MALAY     68          52\n",
+       "295   F  CHINESE     82          58\n",
+       "296   F  CHINESE     79          52\n",
+       "297   M  CHINESE     82          57\n",
+       "\n",
+       "[298 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Word count plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qBAUwcmhnXdp3akuWJ4U9gWPNbIaZXWpmmwHMbCvwjppK5zjODjR6Tn/WjMl09u/YrqyzfwezZkyuS/tObQkqBUnDJQ0HfgQ8V9ouK8fMHqyTnI7jxDR6Tn/mQaM449gDGDW0EwGjhnZyxrEH+OqjFiFt+mgxYEDSaiMDJtREIsdxUmmGOf2ZB41yJdCiBJWCmY2vpyCO42TD5/SdWlLRpiDphixljuPUB5/Td2pJ8ElB0kBgMLCnpGFsm0baHRhZB9kcx0mgNG3jHsVOLUizKXwM+CyRAljMNqXwLPDftRXLcZw0fE7fqRVpNoUfAz+W9Gkz+0kdZXIcx3EaRBY/ha2ShpY2JA2T9IlKJ0maLOmestezkj4bL2m9XtLD8fuwnemA47QjC+5ezdR5NzJ+9tVMnXejxx1yCiOLUjjFzDaUNuJoqadUOsnMlprZgWZ2IHAIsBG4ApgN3GBmk4Ab4m3HcTLSaOc1p7XJohT6lUdGldQB7JqznenAI2a2AjgGuCAuvwCYmbMux2lrGu285rQ2WZTCdcAlkqZLOgqYD1ybs533x+cB7G1mTwDE73slnSDpVEmLJC1au3ZtzuYcp3VpBuc1p3XJohS+DNwI/AfwSaIpn8xB8CTtCrwLuDSPYGZ2jpl1m1l3V1dXnlMdp6UJOam585pTBBWjpJrZVknnAzeaWTXPp0cDd5nZk/H2k5L2MbMnJO0DrKmiTsdpC5Kioc6aMZlZl97L5q3Wc1z/fnLnNacQsng0vwu4h3jKSNKBkq7K0cbxbJs6AriKKBQ38fuVOepynLYhZFBetOLpHSOSeT5EpyCyTB99HXgdsAHAzO4BxmWpXNIg4M3A5WXF84A3S3o43jcvs7SO00aEDMrzF65i8xbbrnzzFnNDs1MIFaePgFfM7JlqUjOb2UZgRK+ydUSrkRzHSSFkON5illjuhmanCLIohfslfQDokDQJOA34c23Fcpz2IZRFLRQNtUNKVAzVGpobmcXNaT6yTB99GngN8BJwEfAMUUwkx3F2kjRHtFA01OMPHV1YlFR3hHN6k/qkEDuqXWVmbwK+Wh+RHKd9SHNEu232UT3H9P4X3z12eCH/7tPa96eF9iRVKZjZFkkbJe1hZs/USyjHaRcqOaKFoqEWFSXVHeGc3mSxKbwILJF0PfBCqdDMTquZVI7TJjQ6i1qj23eajyxK4er45ThOBvIYbmfNmMycy5dsN4VTbh+otRF41ozJzLrs3u2WuPbvKN4Rrl2M2a3Qzyw2hQ/GNgXHcSpQMtyWbvIlwy0QnAaCZLtB3rqqpvdCpuQVr1VTt340mFbpZ+rqIzPbAmyUtEed5HGcPk01EUxnHjSK22YfxaPz3s5ts4/aTlHUOhrqmdct3S5cBsDmrcU6wrVLVNdW6afbFBynQIo03NbDCNwqbTQDrdJPtyk4Tgp554jTDLdF1lUPeYuiXYzZrdLPis5rZnYBUUC7xfHrorjMcVqaahy7pk1JDvM+bkRn7rpCzmshI3A18uZtoxrq0UYz0Cr9zBIl9UjgYeC/gZ8Cf5P0htqK5TiNp5o54pseSk4Idfuy9VXZGs449gBGDe1EwKihnZxx7AHBf/7V2jPytFEN9WijGWiVfmaZPvpP4C2lXAqSXk305HBILQVznEZTzRxx0UHs8jipVTunXZQjXKPbaAZaoZ9ZYh/1L0+uY2Z/A/rXTiTHaQ6qyXAW2tcRiDJc9Nx9rdtwWp8sSmGRpPMkHRm//ofItuA4LcOCu1czdd6NjJ99NVPn3ZgakC5tjrgeQezS5O3fb3vl4xnZIpLGy0kmi1L4D+CvRCGzPwM8AHy8lkI5Tj0JGWiB3HPEoXnl02ceUNh8s2dky4dHgs2HLDDX2XOANBh4MXZkK3k5D4gT6NSF7u5uW7RoUb2ac9qMqfNuTFxKOGpoZ0+k0mYiJG8oz0Kz9qNe9LXrWySSFptZd55zsjwp3ACUT0p2An/I04jjNDN9zenIM7Llo69d30aTZfXRQDN7vrRhZs/HuZcdpyWoxuGsmsBnRZ1Tr4xsRdLIQHGt4lRWL7I8Kbwg6eDShqRDAFexTsuQ1+Fs7oIlueeoq5nXDp0zbUpXXYzZRdHoOf1WcSqrF1mUwmeBSyXdKulW4GLgUzWVynHqSF6Hs/kLV+V2EqvGsSx0zk0Pra25MbtIGh0orlWcyupFxekjM7tT0hRgMtFahofMbHPNJXOcOpF3jr6aufsiHeEe37Cp5hnZiqQZ5vSbcVyalSxPCpjZZjO738yWuEJwWo28DmfVOKIV6QjX1+bCW6Uf7UImpeA4zUhRDkkhp6/QHH2lufugY1lHrzYqZDhLO6eavjfKgavSnL47ljUXWVYfOU7TUXiWqwSnr+6xw+keOzxx1UyoPCTXew4ZVV2Gs4RzFq14mt8uXp2r743MCtYU2eWczASd18pXHCVhZnfVRKIE3HnN6U2RDkn1qKsax7J61NVoB65mlatVqMZ5Le1J4T/j94FAN3Av0f+pfwEWAkdUI6TjFEGzZjgr0rGsHnU12oGrWeVqZ4JKwcymAUj6DXCqmS2Jt/cHvlgf8ZxWoygnpiIdkupRV5pjWWhMinRSK9qBqx7XsZEOb+1MFkPzlJJCADCz+4EDayaR07IU6cRUpENSPeqa0JUcBGDQrv2CYzJuRPINe0LXoMKit1bTx3pcx2lTujyIXYPIohQelHRuHDb7jXHo7AdrLZjTehTpxFSkQ1I96lq2Njl+5MNrXgiOye3L1iees2ztxsKit1bTx3pcx5seWttQh7d2JkuU1IFE4bNLKThvAX5mZi/WWLYe3NDcGoyffXXighsBj857e73FqSvjZl+d63iRvjhpeQPHqx7XsZ2/K0VStKEZADN7UdLZwDXlGdgcJy/tHJgsZAcIMXJoJ/945sXEc0LOc/WiHtexnb8rjaaiUpD0LuBMYFdgvKQDgW+Z2btqLJvTYsyaMZlZl97L5q3bbnT1zAxWZGTTuQuWMH/hKraY0aHI0e30mQcAJO47/tDRXHj7yh3qnzpxOHc8uj5xTBateDrxnOMPHV2oETZvJNhZMyZv51sAxQeYq0cbzU6jDO1ZnNe+DrwOuBnAzO6RNK6GMjmtTIMyg1XjJBU659JFK7ntkad7jttitt3Nu/xzad+Jh43hxMPG7KAsuscO547lvWwH8ZiUlEzSOUU5fIX6mMVBrpY3rHq00cw00qkvi01hoZkdKuluMzsoLrvPzP6lppKV4TaF1qCRjkrVtB06J0RpWic05fPIGW+rqVyNdrZziqOoa1wTmwJwv6QPAB2SJhHlav5znkYcBxrrqFRklNIQaTaDvA5n9XJE8yxuzUkjfytZlMKnga8CLwHzgeuAb2epXNJQ4Fxgf6LFFB8GlhLlZBgHLAfea2bJa++clqLoDGehef2k8moMl6FzQlR6UqhGrjyZ1+rlbOfUnkYa2iv6KZjZRjP7KjAdmGZmX82xHPXHwLVmNgV4LZF/w2zgBjObRJT/eXZ1ojt9jbyOSmkZzuYuWMKFt6/suXGV5u7f/IObE8sH7Zr8VQ9lXUuTd+/ddk08fkLXII4/dHRwX5Jcr2zZknj8uBGduTOvVWOEDfV/zyH9g3I5taeR2eIqKgVJ/yppCXAfsETSvXFKzkrn7U7k23AegJm9bGYbgGOAC+LDLgBmVie609fI66iUluFs/sJViW08vOaFXOWhrGtp8j71fHJKkWVrN3L6zAM48bAxPU8NHRInHjYm6Lz25HMvJ5bfvmx97sxr1RggQ/1Pk8upPY3MFpfF0Hwf8EkzuzXePgL4aSVDc7x09RzgAaKnhMXAZ4DVZja07Lj1ZjYs4fxTgVMBxowZc8iKFSuy98rpU4QclUJUcuzKQzXOUGmOaCGnsrzOaxDuZz2cxNJopOOck49qDM1Zwlw8V1IIAGb2J+C5DOftAhxM5P18EPACOaaKzOwcM+s2s+6urvAjvtP3yZv5bOTQzsIcuKqZo82bka3SvtDx9chYlreuRjvOObUnqBQkHRznVLhD0s/LYh/9lNhnoQKPAY+Z2cJ4+zIiJfGkpH3iNvYB1uxUD5w+RTArWSDzWe8vaD+i+dbQ3P2kvQYnlk+dODxYV0iuUHmo7VB52r6QvMcfOjp1Xjkk79wFS5g45xrGzb6aiXOuYe6CJUnV9xBqY+rE4bn64bQOWfIplPh62eeKT5xm9g9JqyRNjsNjTCeaSnoAOAmYF79fmU9kp6+SmpUswant0bXPs7VX8VaizGOPrn0+2E6/+LjybXqVldcF5HLgOuPYZKeykrNZEt1jh3PR7St3kOvQCSNY9tRGtpR5NHf0E91jhwcduELypjnVhWRLcxJL89p2WpeKNoWdqjyyK5xLFCJjGfAhot/CJcAYYCVwnJk9HaoD3HmtVcjrKBUi7/GV6vqnPQbW3IGrHlnUQoQc55zWp1DnNUknmtmFkj6ftN/MflCpcjO7hyhrW2+mZ5bQaRnyOkqFKEohlOqqhwNXPbKohShyvJzWJ236qDTZuVs9BHFan7yOUiEa/aRQyTibx0mtyCxqIdrJOOzZ2naeoKHZzH4uqQN41sy+2ftVRxmdFiFk1Dxswg4rkgHYfUBHYvmErkFBQ+jAjnw3wMG79gs6ZIUcuNIc3kJOdSEntWqyqIXaTzNatwNFZoRrZ1KXpJrZFsBDZDuFEHLIWb4u+V/vsy8l30iXrd3Ir085fAfFMHXicF7cku8J4tmXtgQdskIOXGkObyGnulBd1WRRC7W/8eWtiY5z7WIcLjIjXDuTJfbRnyWdRRSvqMct1MzuqplUTssy86BRO9zwPnfxPbnqKP0L//Uph++wrxonsbxTUWlz+tXYR5LGpJr2H9+widNnHtA2SqA3jQwi10pkUQqvj9+/VVZmgMfPdQqh0XPk1WRFK6quavriWcmS8XEphizpOKfVQxCnsRSZlSzvObNmTObzl9xD2VJ9+gkOnzB8u3X3JUpz5El1TZ2YfM7ADiVOLU3aazCHThiROytaqO+hDGuT9hqcGH+pmvn+ojPYVROhthnxbG3FkCUd53eB78XB7JA0DPiCmc2tsWxOnSgyK1k15xw8Zo/tFALAVoPxXUMY3zUk0YEqVNe+wwYmtr3HoP68mDCvf+iEEXSPHc78O1bt4EA2vmtIYla0tKxkadnSkhRf99hkg3lFCspgtzOZ15qNds/WVhRZAuL1ZFwrK7vLzA6uqWRluPNabalX9q8ina7y1pXWRt4lqUU6nBXpCNdopzqn+ahVQLwOSQPKGukEBqQc7/Qx6pX9q0inq6KMh9U4rxXpcFakI1yjneqc1iCLoflC4AZJv2Bb9rQL0k9xqqFRc7hFZiUbObQzGDOnGoNyUXWltVHUk0K12dLyXvdmyLzW1+wNTnayZF77HnA68M/Aa4Bvx2VOgTTS8SbkvJWWZSvkQDVo136JzltzFyzJnclscEpdofZDzmu7BObcD5swLJiVbXCgPC0rWd5saaGsc2nXPdT3NKe6EKFrcvyho1MjtLqTWOuSJfPaYOD3ZvZFoqQ5AyQl/yqcqmmk403IeSsty1bIgSqU4Wz+wlW5M5mFnNfmL1wVbD/kvPZKYCZq+bpNQZlD7ReZLS2UdS7tuof6nuZUFyJ0TU6feUDQqc6dxFqbLNNHtwD/Fq86+gOwCHgfcEItBWs3Gul4k3fuHKq3DyQ5an22Cue1osalyPFNk+vxDZtyOe7Vyz4BydckrdydxFqbLIZmmdlG4FjgJ2b2bmC/2orVftQjy1aIajKJFZmxq8isZHkpcnyryZZWzXVv5HelGdp3aksmpSDpcKIng1IMgSxPGE4O0rJs1ZpKmcSC2dJ6zd/371BVGbuqzUqWlK0t1H6ovOTwFmo/b1ay0LiEsqVVc90b+V1phvYbTSjrXauQRSl8BpgDXGFmf5U0AbiptmK1H6G53Xqs6Ogem5yqsnvs8KBRcdGKp3fMv2dwXPeY3EHZuscOp6PXDb6jnzh0wgh6FW/v8JXgwDW+a0jiOYt7O6HFfG3BkmBwves/f2TiNRnfNSTYFyBxXBateDpxHIHc172R35VmaL+RtIORvaaZ14rCnddqS5ozFNCUWcnyypXG8nlvz3X8xDnXBJeq1iOLm9M4inQcrAeFZl4rq/TVwBeBceXHm1nzjYBTFdUYDps1K1k9soylGebdGay1aQcjexbbwKXA2US5lpPX6Dl9hryOVZDvH3klY2Oe9iu1UdSTQl5505zais7i5k5izUU7RGLNYlN4xcx+ZmZ3mNni0qvmkjmFk9exqhTBNK9zU972Q05yIeexQbv2Czqc7Tmkf6JcIee1tExtIXlDmeKOP3R00IHssAnDChuvVpq/7msU6TjYrGRRCr+T9AlJ+0gaXnrVXDKncPI6VpXWqed1bsrbfshJLuQ89vCaF4IOZ08+93KiXHvvkax4RuyWHFU1Td7l6zYFjekhB7Ll6zYVNl7uJNY4inQcbFayTB+dFL/PKiszYELx4ji1JK9jVYm8zk152y/aDlAPJ7FQhrNqxzhv+05jaIdrkiX20fiElyuEPkijnY5C7RSdSS1P20U7iRU5xo2+Xs6OtMM1yeSEJml/Ii/mnmdtM/tlrYRyakPRmanyZuwKZQx73+tGc9HClTskoJnYlZytrORTkJRhberE4YmRVStlKwudExqvvH2sZDsoqi6ntrRDdrcsS1K/DhxJpBSuAY4G/gS4UuhjFJmZqpqMXUCiw9nCZesSM6/ttdsA9tptwHY3/6kTh/PrUw4H4IT/+csO+8Z3DdkuHWYpsuqja58PZiubu2BJ4jkQOZb1Hi8gmHUu1McQaRns8tbl1J52yO6WJfPaEuC1wN1m9lpJewPnmtk76yEguPNaM1Kkw1kaRTmWhRg1tJN/PPNicIlpUua3apz9Qs5NRdblOL2pifMasMnMtkp6RdLuwBrcyNz2NKuTVl6j9ePxcs88ddUjU12RGewcJw9ZlMIiSUOB/wEWA88Dd9RSKGd76uHAlNZGrR3O0ghlXgvty+u8NrLCk0JRzn5pBsqi6nKcIkhVCpIEnGFmG4CzJV0L7G5m99VDOCd9zrkoxVBpXjtp33sOGbWd7QAig9u+wwYmGodLDmpJN7mBHUpMjrP7gI7gXD+QuG/3AR2J/g2h8mlTunh07fOJRusJXYMS+37wmD0S+1GqK2lfyEFv2pSu7fpRXt49dnjLGzWd5iOLTWGxmR1SJ3kSaWebQj0CcFU7rz1rxuQd/kV/4ZJ7g/+6oRifhCLrqia4XprdpEj7xG2zj/IwF85OUSubwu2S/tXM7qxSLmcnqIezTLXz2nmyqBXpoFZkXdUE10uzNRRtn8jr8OY4O0uWMBfTgL9IekTSfZKWSPLpozpRD2eZtDbytp+Wxa0oJ7Ui60rrY96MdCOHdlZ1Tp5yx6k1WZTC0cBE4CjgncA74nenYIrKzJWXtDbS9iXJm5bFLbRv6sTkJDtpGc7yZmubOnF4Yqa2Uh+TsqUdf+joxORDacEA0/rfqOvrOHnIohRON7MV5S/g9FoL1m6EImJC/sxceUnLpBXaBwTlTc2WlsCa515iSy/vtS1bjTXPvRQ8p3vs8MR2QtnaxncNSXcES8iW9uja59naq7i0HRqv02cekBgsr2Q0bsT1dZw8ZDE032VmB5dtdwBLzGy/WgtXoh0MzX0to1M1zmshI2xeqslbUKRTXchonEZfu75Oa1CooVnSHOArQKekZ0vFwMvAOVVL6STS16IvVuO8VpR5uJoMZ0U61VWj2Pra9XXal6BSMLMzgDMknWFmc6qpXNJy4DmijG2vmFl3nIvhYqL0nsuB95pZckD9NqJeGZ2KWuJYjfNaMz4pVONUV8nIneRU1xczdvly2PYkS+jsqhRCGdPM7MCyR5jZwA1mNgm4Id5ue+phcCwyk1co81koW9q0KV1M6BqUuG/3AR25yid0DQo6g03oGpQ4jqFsadOmdAXHPs3QHaIUXK+kgEpOdeNGdPYpg7JnfWtfshiai+YY4IL48wXAzAbI0HSkGXuLoshMXqHMZ6FsaTc9tJZlazcm7nvh5a2JxtkXXu5t5o1YtnZjMFvbsrUbE8dx+brkJ4GbHlobHPtfn3J4MMNaiPkLVyWW375sfZ8yKHvWt/aloqF5pyqXHgXWE63t+LmZnSNpg5kNLTtmvZnt8DdO0qnAqQBjxow5ZMWKFTWTs10YP/vqxHl9AY/mjEY6bvbVuY4XOy7wKScpGmreNtLqKrLvaaTJnDfiayOp13g5taUaQ3PFJwVJEyUNiD8fKem0OEBeFqbGK5eOBj4p6Q1ZBTOzc8ys28y6u7paJyl2I2mko1Q1jl3VOMI12kksr1zNijvVtS9Zpo9+C2yR9CrgPGA8cFGWys3s8fh9DXAF8DrgSUn7AMTva6qQu+lJclRqdPuzZkwOOnDNXbCEiXOuYdzsq5k45xrmLtgWEC9pX2i+fdJeg4NtpDl25S3Pe069nMTyygWN/64k4U517UsWpbDVzF4B3g38yMw+B+xT6SRJgyXtVvoMvAW4H7gKOCk+7CTgymoEb2YabaQLtb9oxdOJDlyXLlqZaBydu2BJ0HA6vmvIDoph6sThfHLapKCTWMixKzRHv3DZumB599jhid7GISe5ethsiNtP8s4OydXo70qIeo2X03xkcV5bCPwI+CrwTjN7VNL9ZrZ/hfMmED0dQLT09SIz+46kEcAlwBhgJXCcme0Yt7iMvua81mhHpbyOZSHSopFWG/UzD2nz86MCSzwb7QyWt/+N/q44rU2toqR+CPg48J1YIYwHLqx0kpktI0rj2bt8HTA9j5B9jUY7KuV17AqRdnyRWcmqodFjHKKoDGuN7ofTvlRUCmb2AHBa2fajwLxaCtXXabSjUl7HshCVnhTytF3qe8ghKqk8jbR2Gul0lSZXqzi1Oa1N0KZQCpEdetVTyL5Go41006Ykr9Y6bMKw3E5aRRp0Q/PncxcsSSzfe7ddE9uYtNfgYB/Hjehs6Bx9SK5Bu/ZLtM2EnABD9ThOrUkzNJdCZF8bv06IX9cAl9VetL5Lo410Nz20NrF8+bpNuZ208hqH0/oecoiav3BVYvkuHR07hMKetNdgrv/8kcE+3r5sfUOdrkJyhZz9QuWhehyn1mQxNN9mZlMrldWSvmZobjTN6ngUkitEmrxF1lUkeeUK0ehr5bQGNXFeAwZLOqKskdcDyZlMnKagWR2P8mY4S5O3yLqKpKh2Gn2tnPYli1L4MPDfkpbHYSt+Gpc5KTTSIalom0bevoSOD8l1/KGjczvVpdVVTd+Lul55g+tNnTjcncScpiJ19VGcUOeNZvZaSbsTTTc9Ux/R+i4lg2ppbrs8y1Y97AqlNopYgZO3L1mOT1pldPGdvQLJxU51tz2yzYWlZJwFemwaSX3sHjs8V9+LvF5pY5+0+uj0mQd4iGqnqchiU7jZzI6sjzjJ9DWbQis5JNXDGSt0TohqMp+l0UrXy3HKqZXz2m2SziJKjNOzVMLM7sopX9vQSg5J9XDGyjsuRSTqydJ+X7xejrOzZFEKr4/fv1VWZoD/hQrQ17KopZHXSayavofOCVF0xFF3IHOcbWTJvDYt4eUKIYW+lkUtjVBfpk3pSmx/2pSu3H0POWr19lEokRZxtBpC7bsDmdOOZMmnsIekH0haFL/+U9Ie9RCur9LXsqilEerLTQ+tTWz/pofW5u57yFFrYyAjW1rms2oIte8OZE47kmX66H+JQl6/N97+IPAL4NhaCdUKzDxoVE1XkNRzHjypL5+7+J5g+3n7ntaXkld1LXGbguNsI4tSmGhm7ynb/qake2okj5ORSvPgoeWP9Wo/RF47RJrdpCibitsUHGcbWZzXNvXyaJ4K+F+oBpNmtwglxinPplbL9kOE7CDjRiTffNOC2xVpU2l0AEPHaSay+CkcCFwA7EEUkuVp4CQzq1uk1L7mp1AvQv+UJ865JldinKLbD5E3+U+ofFT8D75I3wJ3IHNakZr4KZjZPUDJoxkze7Y68ZyiCc3dh9bxF72+vyjbQV55i/R5KFFrG5Dj9BWyrD56RNKvgQ8A+9ZeJGdnCa3jL3p9f17yBrFLC27XrEH/HKevk8WmsB/wc2AE8H1JyyRdUeEcp4GkJcZpxkB9oSB2acHt3A7gOLUhy+qjLcDm+H0r8CSwppZCOTtHaZVR79VH3WOHN22gvlAQu0rB7dwO4DjFksXQvBFYAvwA+IOZrauHYOW4obkYPPCb47QXtUqyczxwC/AJ4DeSvilpejUCOo3FnbQcx6lEltVHVwJXSpoCHA18FvgS4Ba9PkYzO2mFloTWa6moL0l1nIiKSkHSb4EDgb8DtwL/B1hYW7GcWjBrxuTtbArQHMbZUJKbRSue5reLV9fcBtLopEiO00xkMTTPA+4ysy0Vj3SamiIzshVJKLhfyVDeu/zM65bWLbhgo8fGcepNlumjO+shiFMfmtFJK69TW9E2ELe1OM42shiaHaem5HVqK9oG4o5wjrONoFKIA98haUD9xHFanSTnubxObdXaQEKOe+4I5zjbSHtS+K/4/S/1EMRpfUKRTYHExDynzzygsGRFaVFV65EUyXH6CkHnNUm3Aw8CbwMu7r3fzE6rrWjbcOe11qCRznPuuOe0I0VHSX0H8CbgKGDxzgjmONBYg64bkx0nG0GlYGZPEXkwP2hm99ZRJqdFaaTzXDM77jlOM5Fl9dE6SVdIWiPpSUm/leQhtJ3cNNKg68Zkx8lGFqXwC+AqYCQwCvhdXOY4uWikQdeNyY6TjSxRUu81s9f2KrvHzA6spWDluKHZcRwnP7WKkrpW0omSOuLXiUDdw2c7juM4tSdL7KMPA2cBPwQM+HNclglJHcAiYLWZvUPScKIlruOA5cB7zWx9PrGbH4+66ThOX6Tik4KZrTSzd5lZl5ntZWYzzWxFjjY+Q+TvUGI2cIOZTQJuiLdbijRHKcdxnGamprGP4lVKbwfOLSs+Brgg/nwBMLOWMjSCtKibjuM4zUytA+L9iCghz9aysr3N7AmA+H2vpBMlnSppkaRFa9eurbGYxeKOUo7j9FVqphQkvQNYY2ZVeUOb2Tlm1m1m3V1dXQVLV1s86qbjOH2VikpB0tyyz3kipk4F3iVpOfAb4ChJFwJPStonrm8fYE0uifsA7ijlOE5fJS109pckHQ78e1lx5oipZjbHzPY1s3HA+4EbzexEIke4k+LDTgKuzC11k+OOUo7j9FXSlqQuBY4DJki6lWgF0QhJk81sZyym84BLJH0EWBm30XI0Y4Yzx3GcSqQphfXAV4Aj49c/AzOA2bFieH3WRszsZuDm+PM6YHpV0jqO4zg1JU0pvBX4OjAR+AFwL/CCmX2oHoI5juM49SdoUzCzr5jZdCKv4wuJFEiXpD9J+l2d5HMcx3HqSJYwF9eZ2Z3AnZL+w8yOkLRnrQVzHMdx6k+WMBdfKts8OS57qlYCOY7jOI0jl/OaZ2BzHMdpbWod5sJxHMfpQ7hScBzHcXpwpeA4juP04ErBcRzH6cGVguM4jtODKwXHcRynB1cKjuM4Tg+uFBzHcZweXCk4juM4PbhScBzHcXpwpeA4juP04ErBcRzH6cGVguM4jtNDlnwKfZIFd6/mzOuW8viGTYwc2smsGZM9Z7LjOE4FWlIpLLh7NXMuX8KmzVsAWL1hE3MuXwLgisFxHCeFlpw+OvO6pT0KocSmzVs487qlDZLIcRynb9CSSuHxDZtylTuO4zgRLakURg7tzFXuOI7jRLSkUpg1YzKd/Tu2K+vs38GsGZMbJJHjOE7foCUNzSVjsq8+chzHyUdLKgWIFIMrAcdxnHy05PSR4ziOUx2uFBzHcZweXCk4juM4PbhScBzHcXpwpeA4juP0IDNrtAwVkbQWWFHl6XsCTxUoTl+jnfvvfW9f2rn/5X0fa2ZdeU7uE0phZ5C0yMy6Gy1Ho2jn/nvf27Pv0N7939m++/SR4ziO04MrBcdxHKeHdlAK5zRagAbTzv33vrcv7dz/nep7y9sUHMdxnOy0w5OC4ziOkxFXCo7jOE4PLacUJA2VdJmkhyQ9KOlwScMlXS/p4fh9WKPlLBpJkyXdU/Z6VtJn26HvAJI+J+mvku6XNF/SwHbpO4Ckz8R9/6ukz8ZlLdl/Sf8raY2k+8vKgn2VNEfS3yUtlTSjMVIXR6D/x8XXfquk7l7H5+p/yykF4MfAtWY2BXgt8CAwG7jBzCYBN8TbLYWZLTWzA83sQOAQYCNwBW3Qd0mjgNOAbjPbH+gA3k8b9B1A0v7AKcDriL7z75A0idbt//nAW3uVJfZV0n5E34XXxOf8VFIHfZvz2bH/9wPHAreUF1bT/5ZSCpJ2B94AnAdgZi+b2QbgGOCC+LALgJmNkK+OTAceMbMVtE/fdwE6Je0CDAIep336/s/A7Wa20cxeAf4IvJsW7b+Z3QI83as41NdjgN+Y2Utm9ijwdyLl2WdJ6r+ZPWhmSxMOz93/llIKwARgLfALSXdLOlfSYGBvM3sCIH7fq5FC1oH3A/Pjzy3fdzNbDXwfWAk8ATxjZr+nDfoecz/wBkkjJA0C3gaMpn36D+G+jgJWlR33WFzWLuTuf6sphV2Ag4GfmdlBwAu0ziNzJiTtCrwLuLTRstSLeP74GGA8MBIYLOnExkpVP8zsQeD/AtcD1wL3Aq80VKjmQQll7bQOP3f/W00pPAY8ZmYL4+3LiJTEk5L2AYjf1zRIvnpwNHCXmT0Zb7dD398EPGpma81sM3A58Hrao+8AmNl5Znawmb2BaGrhYdqo/4T7+hjRU1OJfYmmFtuF3P1vKaVgZv8AVkmaHBdNBx4ArgJOistOAq5sgHj14ni2TR1Be/R9JXCYpEGSRHTdH6Q9+g6ApL3i9zFEBsf5tFH/Cff1KuD9kgZIGg9MAu5ogHyNIn//zaylXsCBwCLgPmABMAwYQbQi4eH4fXij5axR3wcB64A9ysrape/fBB4iml//FTCgXfoe9/9Woj9A9wLTW/naEym8J4DNRP+EP5LWV+CrwCPAUuDoRstfo/6/O/78EvAkcF21/fcwF47jOE4PLTV95DiO4+wcrhQcx3GcHlwpOI7jOD24UnAcx3F6cKXgOI7j9OBKwWk6JJ0h6UhJMyXl8kiX1CVpYRzm5N9qINvJks4qut5aIOl8Sf/eaDmcvoUrBacZORRYCLyRaP19HqYDD5nZQWaW99w+SwtE/nSaBFcKTtMg6UxJ9wH/CvwF+CjwM0lfSzh2rKQbJN0Xv4+RdCDwPeBtcU6Jzl7nzJP0QHzO9+Oy8yWdLelWSX+T9I64fKCkX0haEj91TEuQ4e2S/iJpT0lviT/fJelSSUPiY5ZL+mZcvkTSlIR6rpH0L/Hnu0v9lfRtSR9VxJmK8iUskfS+eP+Rkm6SdBGwJD7urLiPV1MWAC+p746TSKO98/zlr/IXUVjfnwD9gdtSjvsdcFL8+cPAgvjzycBZCccPJ/LoLDlsDo3fzycKItePKATAY8BA4AvAL+JjphCF0hhYqp/Ig/RWIo/5PYni2A+Oj/8y8LX483Lg0/HnTwDnJsg2G/gksDtwJ7E3KnATMBl4D1Gwuw5g71iWfYAjiYI+jo+PP7bsuJHABuDfQ333l7+SXv6k4DQbBwH3EN2IH0g57nDgovjzr4AjKtT7LPAicK6kY4mSEJW4xMy2mtnDwLK47SPiejGzh4AVwKvj46cR3fjfbmbrgcOA/YDbJN1DFHtnbFn9l8fvi4FxCbLdSpQH5AjgamBIHAJ7nEUx8o8A5pvZFosCHf6R6GkK4A6L4uQT11E67nHgxgx9d5zt2KXRAjgOQDz1cz5RFMeniOI4Kb7JHm5mmypUkRqvxcxekfQ6IpvD+4FPAUcFzjWSQw6XWEaUu+PVRHG2BFxvZscHjn8pft9C8m/uTqA7rvd6oiePU4iUCBVkeSFB9u0L0vvuONvhTwpOU2Bm91iUSvRvRP+6bwRmWJRiNEkh/JnoBgdwAvCntPrjOf49zOwa4LNEgRNLHCepn6SJRDf7pUTTQSfE574aGBOXQ/TUcCzwS0mvAW4Hpkp6VXz8oPicrH1/mSgRynvjum4Fvsg2I/stwPskdUjqInoiSIp0eQtRRMyOOHz0tAx9d5zt8CcFp2mIb3jrzWyrpClmljZ9dBrwv5JmEWXb+1CF6ncDrpQ0kOif9+fK9i0lmpLZG/i4mb0o6afA2ZKWECWsOdnMXooic0c5sSWdQJTM6J1Etob5kgbEdc4lUnBZuZUouulGSbcSPTGVlMIVRNNl9xI9CXzJzP6RYLS+gugJYEnc9h8z9N1xtsOjpDptjaTzgf9nZpc1WhbHaQZ8+shxHMfpwZ8UHMdxnB78ScFxHMfpwZWC4ziO04MrBcdxHKcHVwqO4zhOD64UHMdxnB7+fw3bq5A6Kp1AAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(output.truth, output.prediction)\n",
+    "plt.xlabel('# of spoken words')\n",
+    "plt.ylabel('# of words transcribed correctly')\n",
+    "plt.title('No. of spoken words vs No. of words transcribed correctly')\n",
+    "word_match_plot = plot_to_str()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysing model perfomance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ROC/Min Max Threshold Test\n",
+    "\n",
+    "threshold_test_gender = MinMaxMetricThreshold(\n",
+    "    #test_name=\"\",        # Default test name and description will be used accordingly if not specified\n",
+    "    #test_desc=\"\",\n",
+    "    attr=\"sex\",\n",
+    "    metric=\"mae\",\n",
+    "    threshold=27,\n",
+    ")\n",
+    "threshold_test_gender.run(df_test_with_output=output)\n",
+    "threshold_test_gender.plot(alpha=0.05)\n",
+    "\n",
+    "threshold_test_race = MinMaxMetricThreshold(\n",
+    "    attr=\"race\",\n",
+    "    metric=\"mae\",\n",
+    "    threshold=27,\n",
+    ")\n",
+    "threshold_test_race.run(df_test_with_output=output)\n",
+    "threshold_test_race.plot(alpha=0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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ac2CuqlVJXphkXWvtsUl2SXJ8ktOTXNJaOyjJJePjVNUh4/pDkxyV5Jyq2mV+3QcAgIU13ykZK5I8sKpWJNk9yY1Jjkly3rj+vCTHjsvHJLmgtXZXa21jkmuSHDbP9gEAYEHNOTC31r6c5I+TXJ/kpiTfaK29P8l+rbWbxjo3Jdl33GRVkhsmdrFpLNtKVZ1SVRuqasPmzZvn2kUAAJi3+UzJ2CvDqPEBSR6e5EFV9ZzeJtOUtekqttbOba2ta62tW7ly5Vy7CAAA8zafKRlHJNnYWtvcWvtOkncl+Y9Jbqmq/ZNkvL91rL8pyZqJ7VdnmMIBAAA7rfkE5uuTPKWqdq+qSnJ4kiuTXJzkxLHOiUneMy5fnOT4qtq1qg5IclCSy+fRPgAALLgVc92wtXZZVb0zySeT3J3kU0nOTbJHkouq6uQMofq4sf4VVXVRks+N9U9trd0zz/4DAMCCmnNgTpLW2iuSvGJK8V0ZRpunq39WkrPm0yYAACwmv/QHAAAdAjMAAHTMa0rGcrb29L9f6i4sSzd/8atJvL4L5bqzj17qLgDAsmOEGQAAOgRmAADoEJgBAKBDYAYAgA6BGQAAOgRmAADoEJgBAKBDYAYAgA6BGQAAOgRmAADoEJgBAKBDYAYAgA6BGQAAOgRmAADoEJgBAKBDYAYAgA6BGQAAOgRmAADoEJgBAKBDYAYAgA6BGQAAOgRmAADoEJgBAKBDYAYAgA6BGQAAOgRmAADoWLHUHQDuu9ae/vdL3YVl5+YvfjWJ13ahXHf20UvdBWAJCMwAsEz5w2lh+MN0Ye2Mf5iakgEAAB0CMwAAdAjMAADQITADAECHwAwAAB0CMwAAdAjMAADQITADAECHwAwAAB0CMwAAdAjMAADQITADAECHwAwAAB0CMwAAdAjMAADQITADAECHwAwAAB0CMwAAdAjMAADQMa/AXFV7VtU7q+rzVXVlVT21qvauqg9U1dXj/V4T9c+oqmuq6qqqOnL+3QcAgIU13xHmP0nyvtbaY5I8LsmVSU5Pcklr7aAkl4yPU1WHJDk+yaFJjkpyTlXtMs/2AQBgQc05MFfVQ5L8RJK/SpLW2rdba7clOSbJeWO185IcOy4fk+SC1tpdrbWNSa5Jcthc2wcAgMUwnxHmRyXZnORNVfWpqnpDVT0oyX6ttZuSZLzfd6y/KskNE9tvGssAAGCnNZ/AvCLJE5O8rrX2hCR3Zpx+MYOapqxNW7HqlKraUFUbNm/ePI8uAgDA/KyYx7abkmxqrV02Pn5nhsB8S1Xt31q7qar2T3LrRP01E9uvTnLjdDturZ2b5NwkWbdu3bShmh9MD3v22UvdBQCA7TLnEebW2s1Jbqiqg8eiw5N8LsnFSU4cy05M8p5x+eIkx1fVrlV1QJKDklw+1/YBAGAxzGeEOUl+M8nbquoBSb6Y5JczhPCLqurkJNcnOS5JWmtXVNVFGUL13UlOba3dM8/2AQBgQc0rMLfWPp1k3TSrDp+h/llJzppPmwAAsJj80h8AAHQIzAAA0CEwAwBAh8AMAAAdAjMAAHQIzAAA0CEwAwBAh8AMAAAdAjMAAHQIzAAA0CEwAwBAh8AMAAAdAjMAAHQIzAAA0CEwAwBAh8AMAAAdAjMAAHSsWOoOALDjPOzZZy91F2DZ8z677zHCDAAAHQIzAAB0CMwAANAhMAMAQIfADAAAHQIzAAB0CMwAANAhMAMAQIfADAAAHQIzAAB0CMwAANAhMAMAQIfADAAAHQIzAAB0CMwAANAhMAMAQIfADAAAHQIzAAB0CMwAANAhMAMAQIfADAAAHQIzAAB0CMwAANAhMAMAQIfADAAAHQIzAAB0CMwAANAhMAMAQIfADAAAHQIzAAB0CMwAANAhMAMAQIfADAAAHfMOzFW1S1V9qqr+bny8d1V9oKquHu/3mqh7RlVdU1VXVdWR820bAAAW2o4YYX5RkisnHp+e5JLW2kFJLhkfp6oOSXJ8kkOTHJXknKraZQe0DwAAC2ZegbmqVic5OskbJoqPSXLeuHxekmMnyi9ord3VWtuY5Jokh82nfQAAWGjzHWF+bZLfSfLdibL9Wms3Jcl4v+9YvirJDRP1No1lW6mqU6pqQ1Vt2Lx58zy7CAAAczfnwFxVP5fk1tbaJ7Z1k2nK2nQVW2vnttbWtdbWrVy5cq5dBACAeVsxj21/PMl/qaqfTbJbkodU1VuT3FJV+7fWbqqq/ZPcOtbflGTNxPark9w4j/YBAGDBzXmEubV2RmttdWttbYaL+T7UWntOkouTnDhWOzHJe8bli5McX1W7VtUBSQ5Kcvmcew4AAItgPiPMMzk7yUVVdXKS65MclySttSuq6qIkn0tyd5JTW2v3LED7AACww+yQwNxauzTJpePyV5McPkO9s5KctSPaBACAxeCX/gAAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCAjjkH5qpaU1Ufrqorq+qKqnrRWL53VX2gqq4e7/ea2OaMqrqmqq6qqiN3xBMAAICFNJ8R5ruTnNZa+5EkT0lyalUdkuT0JJe01g5Kcsn4OOO645McmuSoJOdU1S7z6TwAACy0OQfm1tpNrbVPjsvfTHJlklVJjkly3ljtvCTHjsvHJLmgtXZXa21jkmuSHDbX9gEAYDHskDnMVbU2yROSXJZkv9baTckQqpPsO1ZbleSGic02jWXT7e+UqtpQVRs2b968I7oIAABzMu/AXFV7JPmbJC9urd3eqzpNWZuuYmvt3NbautbaupUrV863iwAAMGfzCsxVdf8MYfltrbV3jcW3VNX+4/r9k9w6lm9KsmZi89VJbpxP+wAAsNDm8y0ZleSvklzZWvufE6suTnLiuHxikvdMlB9fVbtW1QFJDkpy+VzbBwCAxbBiHtv+eJLnJvlsVX16LHtZkrOTXFRVJye5PslxSdJau6KqLkryuQzfsHFqa+2eebQPAAALbs6BubX20Uw/LzlJDp9hm7OSnDXXNgEAYLH5pT8AAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoENgBgCADoEZAAA6BGYAAOgQmAEAoGPRA3NVHVVVV1XVNVV1+mK3DwAA22NRA3NV7ZLkz5P8TJJDkpxQVYcsZh8AAGB7LPYI82FJrmmtfbG19u0kFyQ5ZpH7AAAA22yxA/OqJDdMPN40lgEAwE5pxSK3V9OUta0qVZ2S5JTx4R1VddWC9orFtk+Sryx1J5aj+sOl7gE7Ce+xBeI9xgTvswWyhO+zR860YrED86YkayYer05y49RKrbVzk5y7WJ1icVXVhtbauqXuByxX3mOw8LzP7lsWe0rG/0tyUFUdUFUPSHJ8kosXuQ8AALDNFnWEubV2d1X9RpJ/TLJLkje21q5YzD4AAMD2WOwpGWmtvTfJexe7XXYqptvAwvIeg4XnfXYfUq1tdc0dAAAw8tPYAADQITAzq6p6WFVdUFXXVtXnquq9VfXDVfWvU+qdWVW/PS6/uaqeOS5fWlUbJuqtq6pLx+X1VfWNqvr0xO2Icd3vVtUVVfWZsfzJE/u7aqL+OxfppYBFUVV3jPdrq6pV1W9OrPuzqjppXH5zVW2sqn+pqi9U1flVtWqi7nVVtc/E458f9/eYibJuG7Bcjef9WyYer6iqzVX1d1PqvaeqPjal7Hv/302z35VV9Z2q+rXx8SlVdeHE+oeM/58esGOfEQtJYKarqirJ3ya5tLX26NbaIUlelmS/7dzVvlX1MzOs+6fW2uMnbh+sqqcm+bkkT2yt/WiSI3LvH735xYn6z9zOvsAPkluTvGj8ZqHpvKS19rgkByf5VJIPd+qekOSjGb6haHvagOXoziSPraoHjo9/OsmXJytU1Z5Jnphkz+0IuMcl+XiG91uS/GWS1VsGg5K8MsOXHmycR99ZZAIzs3l6ku+01l6/paC19uncO7xuiz9K8vLtqL9/kq+01u4a2/xKa22r7+yG+4DNSS5JcmKvUhu8JsnNSbb647Sq9kjy40lOztaBeZvagGXoH5IcPS6fkOQdU9b/QpL/neSCbP2+mckJSU7LEJJXteFisecneW1VrUtyeIb/E/kBIjAzm8cm+cQM6x49OZUiya939vOxJHdV1dOnWfefpkzJeHSS9ydZM37MfE5V/eSUbd42Ud8/PCx3Zyc5rap22Ya6n0zymGnKj03yvtbaF5J8raqeOI82YLm4IMnxVbVbkh9NctmU9VtC9Dvy/RHjGVXVmiQPa61dnuSiJM9KktbaZzJ8pe4lSV7YWvv2DnsGLAqBmfm4dnIqRZLXz1L/1Zl+lHnqlIxrW2t3JPmxDD+RvjnJhVPmVE5OyXjJDngusNMaP7q9PMmzt6F6zVB+QoZwkPH+Xv/5b2cbsCyMQXZthvfDvb7ytqr2S3Jgko+Of2jeXVWPnWWXx2cIysnW77M/T/Ll1tqHd0DXWWQCM7O5IkNwnbfW2oeS7JbkKdtY/57W2qWttVck+Y0MH43BfdXvJ3lpZv93+wlJrpwsqKqHJvmpJG+oquuSvCTJs8ZrFObSBiwnFyf542w9HeNZSfZKsnF836zN7NMyTkhy0lj/4iSPq6qDxnXfHW/8APKPIrP5UJJdq+p5Wwqq6klJHjnH/Z2V5Hdmq1RVB0/8I5Mkj0/ypTm2CT/wWmufT/K5DBfDbqUGL8ww//99U1Y/M8n5rbVHttbWttbWJNmY5Gnb0wYsU29M8srW2menlJ+Q5KjxPbM2w+DRjIG5qg5O8qDW2qqJbf6gtw0/OARmusaLFX4+yU+PX4NzRZIzk8zpArzxlx43TymeOof5mUn2SHJeDV9j95kkh4ztbjE5h/mDc+kL/AA6K8nqKWV/VFX/kuQLSZ6U5OnTzI88IcO33Uz6m0w//WK6NmDZaq1taq39yWRZVa1N8ogM33axpd7GJLdv+YrTJC+vqk1bbpn5fTbr3Gd2fn7pDwAAOowwAwBAh8AMAAAdAjMAAHQIzAAA0CEwAwBAh8AMAAAdAjMAAHQIzAAA0PH/AdC9P09wBgiuAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Subgroup Disparity Test\n",
+    "\n",
+    "sgd_test_gender = SubgroupDisparity(\n",
+    "    attr='sex',\n",
+    "    metric='mse',\n",
+    "    method='ratio',\n",
+    "    threshold=1.1,\n",
+    ")\n",
+    "sgd_test_gender.run(output)\n",
+    "sgd_test_gender.plot(alpha=0.05)     # default alpha argument shows 95% C.I bands\n",
+    "\n",
+    "sgd_test_race = SubgroupDisparity(\n",
+    "    attr='race',\n",
+    "    metric='mse',\n",
+    "    method='ratio',\n",
+    "    threshold=1.1,\n",
+    ")\n",
+    "sgd_test_race.run(output)\n",
+    "sgd_test_race.plot(alpha=0.05)    # default alpha argument shows 95% C.I bands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bootstrap model card from tally form and scaffold assets\n",
+    "We can add the quantitative analysis, explainability analysis and fairness analysis sections to a bootstrap model card for convenience. In this example, we use an existing model card which we created from the tally form response.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert form response to model card protobuf\n",
+    "pb = tally_form_to_mc(\"sample-form-response-speech-to-text.json\")\n",
+    "\n",
+    "# Initialize the mct and scaffold using the existing protobuf\n",
+    "mct = mctlib.ModelCardToolkit(output_dir = \"model_card_output\", file_name=\"speech_to_text_example\")\n",
+    "mc = mct.scaffold_assets(proto=pb)\n",
+    "mc.model_details.name = \"Speech to Text Model\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convert test objects to a model-card-compatible format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# init model card test objects\n",
+    "mc_threshold_test_gender = mctlib.Test()\n",
+    "mc_threshold_test_race = mctlib.Test()\n",
+    "mc_sgd_test_race = mctlib.Test()\n",
+    "mc_sgd_test_gender = mctlib.Test()\n",
+    "\n",
+    "\n",
+    "# assign tests to them\n",
+    "mc_threshold_test_gender.read_model_test(threshold_test_gender)\n",
+    "mc_threshold_test_race.read_model_test(threshold_test_race)\n",
+    "mc_sgd_test_race.read_model_test(sgd_test_race)\n",
+    "mc_sgd_test_gender.read_model_test(sgd_test_gender)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Add quantitative analysis\n",
+    "\n",
+    "# Create 3 PerformanceMetric to store our results\n",
+    "mc.quantitative_analysis.performance_metrics = [mctlib.PerformanceMetric() for i in range(0, 3)]\n",
+    "mc.quantitative_analysis.performance_metrics[0].type = \"MSE\"\n",
+    "mc.quantitative_analysis.performance_metrics[0].value = str(mse_test)\n",
+    "mc.quantitative_analysis.performance_metrics[0].slice = \"Test Set\"\n",
+    "\n",
+    "mc.quantitative_analysis.performance_metrics[1].type = \"MAE\"\n",
+    "mc.quantitative_analysis.performance_metrics[1].value = str(mae_test)\n",
+    "mc.quantitative_analysis.performance_metrics[1].slice = \"Test Set\"\n",
+    "mc.quantitative_analysis.performance_metrics[1].graphics.description = (\n",
+    "  'About 65% of the words are correctly transcribed')\n",
+    "mc.quantitative_analysis.performance_metrics[1].graphics.collection = [\n",
+    "    mctlib.Graphic(image=word_match_plot)\n",
+    "]\n",
+    "\n",
+    "mc.quantitative_analysis.performance_metrics[2].type = \"MSE across sensitive groups\"\n",
+    "mc.quantitative_analysis.performance_metrics[2].tests = [mc_threshold_test_gender, mc_threshold_test_race]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add the fairness requirement for subgroup disparity\n",
+    "mc.fairness_analysis.fairness_reports[0].tests = [mc_sgd_test_race, mc_sgd_test_gender]\n",
+    "\n",
+    "\n",
+    "mct.update_model_card(mc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Card Display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<!DOCTYPE html>\n",
+       "\n",
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+       "\n",
+       "<head>\n",
+       "\n",
+       "<style>\n",
+       "\n",
+       "  h1 {text-align: center;}\n",
+       "\n",
+       "  .row {\n",
+       "\n",
+       "    display: flex;\n",
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+       "\n",
+       "  .col {\n",
+       "\n",
+       "    flex: 1;\n",
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+       "    align-items: center; \n",
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+       "\n",
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+       "  .card {\n",
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+       "  .card .card {\n",
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+       "\n",
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+       "\n",
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+       "\n",
+       "    text-align: center;\n",
+       "\n",
+       "  }\n",
+       "\n",
+       "  .img-item {\n",
+       "\n",
+       "    flex: 1;\n",
+       "\n",
+       "  }\n",
+       "\n",
+       "  .center {\n",
+       "\n",
+       "    margin-left: auto;\n",
+       "\n",
+       "    margin-right: auto;\n",
+       "\n",
+       "    text-align: center;\n",
+       "\n",
+       "  }\n",
+       "\n",
+       "  table, th, td { border: 1px solid black; }\n",
+       "\n",
+       "  th, td { border: 1px solid #CCC; height: 30px; }\n",
+       "\n",
+       "  caption { font-weight: bold; }\n",
+       "\n",
+       "</style>\n",
+       "\n",
+       "<title>\n",
+       "\n",
+       "  Model Card for Speech to Text Model\n",
+       "\n",
+       "</title>\n",
+       "\n",
+       "</head>\n",
+       "\n",
+       "<body>\n",
+       "\n",
+       "  <h1>\n",
+       "\n",
+       "    Model Card for Speech to Text Model\n",
+       "\n",
+       "  </h1>\n",
+       "\n",
+       "    <div class=\"row\">\n",
+       "\n",
+       "      <div class=\"col card\">\n",
+       "\n",
+       "        <h2>Model Details</h2>\n",
+       "\n",
+       "          <h3>Overview</h3>\n",
+       "\n",
+       "              Using an automated transcription tool, the government want to transcribe audio files recorded in trials for evidential purposes. The tool should be able to capture the various spoken accents and transcribe into text accurately so as not to bias against the defendant. The tool chosen here will be Google&#39;s Speech to Text Model evaluated on a set of audio files by National Speech Corpus.\n",
+       "\n",
+       "          <h3>Version</h3>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div>name: v1</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "              \n",
+       "\n",
+       "  \n",
+       "\n",
+       "\n",
+       "\n",
+       "              \n",
+       "\n",
+       "  \n",
+       "\n",
+       "\n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          <h3>Owners</h3>\n",
+       "\n",
+       "            \n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <li>Timothy, Product Owner(s)</li>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <li>Swan, Model Developer(s)</li>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <li>Jason, Reviewer(s)</li>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "            \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          <h3>Regulatory requirements</h3>\n",
+       "\n",
+       "              MAS Fairness, Ethics, Accountability and Transparency (FEAT) principles\n",
+       "\n",
+       "        </div>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <div class=\"col card\">\n",
+       "\n",
+       "        <h2>Considerations</h2>\n",
+       "\n",
+       "          \n",
+       "\n",
+       "            <h3>Intended Users</h3>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <ul>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <li>Judical System and Defendant</li>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </ul>\n",
+       "\n",
+       "\n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "            <h3>Use Cases</h3>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <ul>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <li>Automate transcriptions and reduce human error and manpower in making court decisions</li>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </ul>\n",
+       "\n",
+       "\n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "            <h3>Fairness Considerations</h3>\n",
+       "\n",
+       "              <ul>\n",
+       "\n",
+       "                <li>\n",
+       "\n",
+       "                  <div><b>Group at risk:</b> gender and race</div>\n",
+       "\n",
+       "                  <div><b>Benefits:</b> Reduce manpower and human error on transcription services. A more accurate model than current system will aid the judicial system in making better court decisions</div>\n",
+       "\n",
+       "                  <div><b>Harms:</b> Incorrectly transcribed/interpreted texts can impact on the defendant/ruling negatively.</div>\n",
+       "\n",
+       "                  <div><b>Mitigation Strategy:</b> Improve upon the existing model by further training and enhancements</div>\n",
+       "\n",
+       "                </li> </ul>\n",
+       "\n",
+       "          \n",
+       "\n",
+       "      </div>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "  <div class=\"col card\">\n",
+       "\n",
+       "    <h2>Datasets</h2>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <div class=\"row\">\n",
+       "\n",
+       "        <div class=\"col card\">\n",
+       "\n",
+       "          <h3>National Speech Corpus Dataset</h3>\n",
+       "\n",
+       "            <p>Dataset taken from https://www.imda.gov.sg/programme-listing/digital-services-lab/national-speech-corpus</p>\n",
+       "\n",
+       "            \n",
+       "\n",
+       "              <h4>Sensitive data</h4>\n",
+       "\n",
+       "              <ul>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <li>gender</li>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <li> race</li>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "              </ul>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <h4>Sensitive data used in model</h4>\n",
+       "\n",
+       "                <ul>\n",
+       "\n",
+       "                \n",
+       "\n",
+       "                  <li>N.A. (Protected attributes are not trained in the speech to text model)</li>\n",
+       "\n",
+       "                \n",
+       "\n",
+       "                </ul>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <h4>Justification</h4>\n",
+       "\n",
+       "                <p> N.A.</p>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "            \n",
+       "\n",
+       "            \n",
+       "\n",
+       "            \n",
+       "\n",
+       "        </div>\n",
+       "\n",
+       "      </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  <h2>Quantitative Analysis</h2>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  <h3>MSE - 712.513 (Test Set)</h3>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  <h3>MAE - 26.003 (Test Set)</h3>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  <div class=\"row\">\n",
+       "\n",
+       "    <div class=\"col\">\n",
+       "\n",
+       "      \n",
+       "\n",
+       "        <div class=\"center\"><i>About 65% of the words are correctly transcribed</i></div>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "  <div class=\"img-container\">\n",
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+       "V+CrwCPAUuDoRstfo/6/O/78EvAkcF21/fcwF47jOE4PLTV95DiO4+wcrhQcx3GcHlwpOI7jOD24\n",
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+       "eSFB9u0L0vvuONvhTwpOU2Bm91iUSvRvRP+6bwRmWJRiNEkh/JnoBgdwAvCntPrjOf49zOwa4LNE\n",
+       "gRNLHCepn6SJRDf7pUTTQSfE574aGBOXQ/TUcCzwS0mvAW4Hpkp6VXz8oPicrH1/mSgRynvjum4F\n",
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+       "EWXb+1CF6ncDrpQ0kOif9+fK9i0lmpLZG/i4mb0o6afA2ZKWECWsOdnMXooic0c5sSWdQJTM6J1E\n",
+       "tob5kgbEdc4lUnBZuZUouulGSbcSPTGVlMIVRNNl9xI9CXzJzP6RYLS+gugJYEnc9h8z9N1xtsOj\n",
+       "pDptjaTzgf9nZpc1WhbHaQZ8+shxHMfpwZ8UHMdxnB78ScFxHMfpwZWC4ziO04MrBcdxHKcHVwqO\n",
+       "4zhOD64UHMdxnB7+fw3bq5A6Kp1AAAAAAElFTkSuQmCC\n",
+       "' alt='None' />\n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  <h3>MSE across sensitive groups  </h3>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <h4> Min Max Threshold Test </h4>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Description: </b>\n",
+       "\n",
+       "    Test if the mae of the subgroups within sex \n",
+       "is lower than the threshold of 27.\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Threshold: </b>\n",
+       "\n",
+       "    27 \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Result: </b>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td></td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>mae</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>passed\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>sex_F</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>26.143</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>True\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>sex_M</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>25.891</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>True\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "   \n",
+       "\n",
+       "    <div><b style=\"color:green;\"> Passed</b></div> \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  <div class=\"row\">\n",
+       "\n",
+       "    <div class=\"col\">\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "  <div class=\"img-container\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div class=\"img-item\">\n",
+       "\n",
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+       "zmH57CTfWvnyYfsIv1xldPdnqupOSQ5L8twkpyb5ZHffdfGyVXW1JLdN8u0kN0xy3pVZK8Du5kp4\n",
+       "DX1jprD86JWqGXaGYQ9cZVTVzZJ8q7tfl+QFSe6cZE1V3XVu37uqbj8v/nuZeh+OSHJ8Ve29K2oG\n",
+       "2F1cCa+hb0vyf5L804oXDztAzy9XJT+d5C+q6gdJLk/y+CTfT/LSqrp+pvP92Kq6PMlvJzmkuy+r\n",
+       "qvcneVqSp++iugF2B6v6GtrdlyV5fpLMwyhgl/DzxgAADMOwBwAAhmHYA8zmy5hdc9HsI7v747ui\n",
+       "HoA9iddQ9hSGPQAAMAzDHgAAGIbwCwDAMIRfAACGIfwCADAM4RcAgGH8fxUhV4DqMB3wAAAAAElF\n",
+       "TkSuQmCC\n",
+       "' alt='Mean Absolute Error across sex subgroups' />\n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <h4> Min Max Threshold Test </h4>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Description: </b>\n",
+       "\n",
+       "    Test if the mae of the subgroups within race \n",
+       "is lower than the threshold of 27.\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Threshold: </b>\n",
+       "\n",
+       "    27 \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Result: </b>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td></td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>mae</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>passed\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>race_CHINESE</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>27.141</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>False\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>race_INDIAN</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>25.404</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>True\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>race_MALAY</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>25.47</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>True\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div><b style=\"color:red;\"> Failed</b></div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  <div class=\"row\">\n",
+       "\n",
+       "    <div class=\"col\">\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "  <div class=\"img-container\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div class=\"img-item\">\n",
+       "\n",
+       "      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAr8AAAF2CAYAAABnFvAPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
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+       "Zhh+JUmS1AzDryRJkpph+JUkSVIzxg2/STZK8v0klyW5NMlb+/Y1k5yW5Bf9zzVmv1xJkiRp6iYy\n",
+       "8nsPsH9VPQX4a+AfkmwGHACcUVWbAmf01yVJkqTl1rjht6qur6rz++U7gMuADYDtgaP61Y4CXj1L\n",
+       "NUqSJEkzYlJzfpPMBZ4JnAOsW1XXQxeQgXVmvDpJkiRpBk04/CZ5NHAisF9V3T6J2+2dZGGShYsW\n",
+       "LZpKjZIkSdKMmFD4TbISXfD9SlV9vW++Icl6ff96wI2j3baqjqiq+VU1f86cOTNRsyRJkjQlE/m2\n",
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+       "kjRDxg2/VfUjIGN0bzWz5UiSJEmzx//wJkmSpGYYfiVJktQMw68kSZKaYfiVJElSMwy/kiRJaobh\n",
+       "V5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZhh+JUmS1AzDryRJkpph+JUkSVIzDL+SJElqhuFXkiRJ\n",
+       "zTD8SpIkqRmGX0mSJDXD8CtJkqRmGH4lSZLUDMOvJEmSmmH4lSRJUjMMv5IkSWqG4VeSJEnNMPxK\n",
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+       "JEmSpJk3bvitqh8AtyyFWiRJkqRZNZ05v/smuaifFrHGWCsl2TvJwiQLFy1aNI3dSZIkSdMz1fB7\n",
+       "OPB4YB5wPfDxsVasqiOqan5VzZ8zZ84UdydJkiRN35TCb1XdUFX3VtV9wBeALWa2LEmSJGnmTSn8\n",
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+       "nIVaJEmSpFnlf3iTJElSMwy/kiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZhh+JUmS1AzD\n",
+       "ryRJkpph+JUkSVIzDL+SJElqhuFXkiRJzTD8SpIkqRmGX0mSJDXD8CtJkqRmGH4lSZLUDMOvJEmS\n",
+       "mmH4lSRJUjMMv5IkSWqG4VeSJEnNMPxKkiSpGYZfSZIkNcPwK0mSpGYYfiVJktQMw68kSZKaYfiV\n",
+       "JElSMwy/kiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZhh+JUmS1AzDryRJkpph+JUkSVIz\n",
+       "DL+SJElqhuFXkiRJzTD8SpIkqRmGX0mSJDXD8CtJkqRmGH4lSZLUDMOvJEmSmjFu+E3ypSQ3Jrlk\n",
+       "oG3NJKcl+UX/c43ZLVOSJEmavomM/B4JvHxY2wHAGVW1KXBGf12SJElaro0bfqvqB8Atw5q3B47q\n",
+       "l48CXj2zZUmSJEkzb6pzftetqusB+p/rjLVikr2TLEyycNGiRVPcnSRJkjR9s/6Bt6o6oqrmV9X8\n",
+       "OXPmzPbuJEmSpDFNNfzekGQ9gP7njTNXkiRJkjQ7php+vwXs2S/vCXxzZsqRJEmSZs9EvursWOBs\n",
+       "4ElJrk3yBuAQ4CVJfgG8pL8uSZIkLddWHG+FqtptjK6tZrgWSZIkaVb5H94kSZLUDMOvJEmSmmH4\n",
+       "lSRJUjMMv5IkSWqG4VeSJEnNMPxKkiSpGYZfSZIkNcPwK0mSpGYYfiVJktQMw68kSZKaYfiVJElS\n",
+       "Mwy/kiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZhh+JUmS1AzDryRJkpph+JUkSVIzDL+S\n",
+       "JElqhuFXkiRJzTD8SpIkqRmGX0mSJDXD8CtJkqRmGH4lSZLUDMOvJEmSmmH4lSRJUjMMv5IkSWqG\n",
+       "4VeSJEnNMPxKkiSpGYZfSZIkNcPwK0mSpGYYfiVJktQMw68kSZKaYfiVJElSMwy/kiRJaobhV5Ik\n",
+       "Sc0w/EqSJKkZhl9JkiQ1w/ArSZKkZqw4nRsnuQq4A7gXuKeq5s9EUZIkSdJsmFb47b24qm6age1I\n",
+       "kiRJs8ppD5IkSWrGdMNvAacmOS/J3jNRkCRJkjRbpjvtYcuqui7JOsBpSS6vqh8MrtCH4r0BNt54\n",
+       "42nuTpIkSZq6aY38VtV1/c8bgW8AW4yyzhFVNb+q5s+ZM2c6u5MkSZKmZcrhN8mjkqw6tAy8FLhk\n",
+       "pgqTJEmSZtp0pj2sC3wjydB2vlpV352RqiRJkqRZMOXwW1VXAs+YwVokSZKkWeVXnUmSJKkZhl9J\n",
+       "kiQ1w/ArSZKkZhh+JUmS1AzDryRJkpph+JUkSVIzDL+SJElqhuFXkiRJzTD8SpIkqRmGX0mSJDXD\n",
+       "8CtJkqRmGH4lSZLUDMOvJEmSmmH4lSRJUjMMv5IkSWqG4VeSJEnNMPxKkiSpGYZfSZIkNcPwK0mS\n",
+       "pGYYfiVJktQMw68kSZKaYfiVJElSMwy/kiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZhh+\n",
+       "JUmS1AzDryRJkpph+JUkSVIzDL+SJElqhuFXkiRJzTD8SpIkqRmGX0mSJDXD8CtJkqRmGH4lSZLU\n",
+       "DMOvJEmSmmH4lSRJUjMMv5IkSWqG4VeSJEnNmFb4TfLyJFck+WWSA2aqKEmSJGk2TDn8JlkB+Byw\n",
+       "DbAZsFuSzWaqMEmSJGmmTWfkdwvgl1V1ZVX9BTgO2H5mypIkSZJm3nTC7wbANQPXr+3bJEmSpOXS\n",
+       "itO4bUZpqxErJXsDe/dX/5DkimnsU8uftYGblnURD0X5yLKuQMsRz7NZ4nmmnufYLFnG59gmozVO\n",
+       "J/xeC2w0cH1D4LrhK1XVEcAR09iPlmNJFlbV/GVdh/RQ5nkmzS7PsbZMZ9rDucCmSf4qycOBXYFv\n",
+       "zUxZkiRJ0syb8shvVd2TZF/ge8AKwJeq6tIZq0ySJEmaYdOZ9kBVnQKcMkO16MHJKS3S7PM8k2aX\n",
+       "51hDUjXiM2qSJEnSQ5L/3liSJEnNMPxKkiSpGYZfjSvJE5OckuSXSS5LckKSdZMsSHLSsHWPTLJj\n",
+       "v3xmkvn98lVJThxYb8ckR/bLeyVZlOSCgctmSR6W5NNJLklycZJzk/zVwPYuHlj/00vtAZFmWZK5\n",
+       "SS7plxckqSSvHOg/KcmCfvnMJFckuSjJ5Uk+m+QxA+v+Ydi2/ynJXUlWH2hb4j6kh6L+PKskBw20\n",
+       "rZ3k7iSfHbbuhUmOHdZ2/+vdKNt+Zr/tl/XXD04e+MbbJJskuXLwXNXSY/h9CEhnVo5lkpWBk4HD\n",
+       "q+oJVfUU4HBgzhQ2Nz/JU8foO76q5g1cfgbsAqwPbF5VTwd2AG4buM2LB9b/xynUI03ZbJ53o7gW\n",
+       "+Jcl9L+2qjYHNgf+DHxzCevuRvdVlTtMch/SUrcUzrMrge0Gru8ELPbNVUmeQpeXXpjkURPc7m7A\n",
+       "j/qfAAcB2/fbAvgU8O6qum2KdWsaDL8PUv1frJclOQw4H/hikoVJLk3y/oH1np3kx/1frT9JsmqS\n",
+       "FZJ8tB9JvSjJPkvY1e7A2VX17aGGqvp+VV0yhbI/Bhw4ifXXA66vqvv6/V5bVbdOYb/SjFiK591w\n",
+       "FwK/T/KSJa1UVX8B3g5snOQZo9T/eODRwLt44EV5UvuQZttSPs/uBC5L/y4l3aDLCcPW2R04GjgV\n",
+       "eNUE6g+wI7AX8NIkK1fVncA/A4cl2QZYtaq+Mu6DoVkxra860zL3JOBvq+rNSdasqluSrACckWRz\n",
+       "4HLgeGCXqjo3yWp0J/obgN9X1bOTPAI4K8mpVfXrUfbxNOC8JdTwgiQXDFzfGDhpjHVPAN6c5Amj\n",
+       "9O2S5PkD15/br/+jJC8AzgCOqaqfDqzz/ST39stHVdUnl1CnNFOWxnk3mg/2l9OWtFJV3ZvkQuDJ\n",
+       "dIF20G7AscAPgSclWaeqbpzsPqSlYGmeZ8cBuyb5HXAv3X+rXX+gfxfgJX1N+9KdQ0uyJfDrqvpV\n",
+       "kjOBbYGvV9UpSd4A/Afw/CVtQLPL8PvgdnVV/W+/vHOSvemO6XrAZkDRjZyeC1BVtwMkeSmw+cBc\n",
+       "pdWBTYGJvggP+mFV3f+WUfp5vGO4F/go8E7gO8P6jq+qfYe1XZvkScDf9JczkuxUVWf0/S+uKv8X\n",
+       "u5a2ZXLeVdUPk9D/MTiejNG+K7BDVd2X5Ot0b/F+bor7kGbT0jzPvks3LeEGukB9vyTPBhZV1dVJ\n",
+       "rgW+lGSNcd6F3I0uUNP/3AP4en/9c8AqVXXFEu+9ZpXh98HtjwDpPgT2NuDZVXVrH0BXpnsBHO2L\n",
+       "nAO8paq+N4F9XAq8aGbKBbq3jt7JsDlVY6mqP9MF5e8kuQF4Nd0osLSsLI3zbiwfopuXe89YK/Sj\n",
+       "Y08HLhvWvjldCDite1eWh9PNd/zcsE2Muw9pKVhq51lV/SXJecD+wFOBVw507wY8OclV/fXVgNcA\n",
+       "/z7atvrz7zXAq5L8S1/PWklWrao7gPv6i5Yh5/w+NKxG94vi90nWBbbp2y8H1u//cqWfD7Ui3b+k\n",
+       "flOSlfr2J2bsSfxfBZ6X5BVDDUlenuTpUym0qu4GPgnsN966SZ6VZP1++WF0H+a5eir7lWbBbJ53\n",
+       "o6qqU4E1gBHzefttrgR8GLimqi4a1r0b8L6qmttf1gc2SLLJZPYhLWVL6zz7OPCOqrp5qKF/3dmJ\n",
+       "7kPXc6tqLrA9I+fLD9oauLCqNupvswlwIt3AjZYTjvw+BFTVhUl+SjeaeiVwVt/+lyS7AJ9Jsgrd\n",
+       "fKit6f5inQuc30/MX8QYJ2ZV3ZlkO+DQJIcCdwMXAW8F1ppiyV+k+8DNoOFzft9M90vvC/28LYCf\n",
+       "AINfPzM45/eiqnr9FOuRJm02z7txfIiR3+bwlSR/Bh4BnE73Aj3crjwQHIZ8o28/ZwL7kJa6pXWe\n",
+       "VdWljHxH8oXAb6vqtwNtPwA2S7Jef/3z/WsjwDXAFXTn1aATgTfRvfOp5YD/3liSJEnNcNqDJEmS\n",
+       "muG0BwHQz+Ed/pbMn6vqOcuiHqkFnnfS7PM803BOe5AkSVIznPYgSZKkZhh+JUmS1AzDryRJkpph\n",
+       "+JUkSVIzDL+SJElqxv8HBJeBc1T/ZxYAAAAASUVORK5CYII=\n",
+       "' alt='Mean Absolute Error across race subgroups' />\n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  <h2>Fairness Analysis</h2>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  <h3>Equal mean squared error  </h3>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Segment: </b>\n",
+       "\n",
+       "    Gender and race \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Description: </b>\n",
+       "\n",
+       "    Disparity ratio of MSE of any 2 bins in the respective attribute should not be more than a factor of 1.1 \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <h4> Subgroup Disparity Test </h4>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Description: </b>\n",
+       "\n",
+       "    Test if the maximum ratio of the mean squared error of any 2\n",
+       "groups within race attribute exceeds 1.1. To\n",
+       "pass, this value cannot exceed the threshold. \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Threshold: </b>\n",
+       "\n",
+       "    1.1 \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Result: </b>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td></td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>race_mse_max_ratio\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>0</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>1.136\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div><b style=\"color:red;\"> Failed</b></div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  <div class=\"row\">\n",
+       "\n",
+       "    <div class=\"col\">\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "  <div class=\"img-container\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div class=\"img-item\">\n",
+       "\n",
+       "      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAswAAAF1CAYAAAD8/Lw6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
+       "bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsT\n",
+       "AAALEwEAmpwYAAAeiklEQVR4nO3df7xlZV0v8M9XRkFEA2RAnBkdFMLAmz8aUW/eGoNeUHQvZHIF\n",
+       "06BISim1SyaYr+SqFN16Xe1WaGQq+AvITLllpqJc86Zwxx9piAg4CCO/xh+IUKLgc/9Ya3Rz5pzn\n",
+       "zJwz55zx8H6/Xvu1137Ws9bz7L3WnvmcZz9r72qtBQAAmN79lroDAACwMxOYAQCgQ2AGAIAOgRkA\n",
+       "ADoEZgAA6BCYAQCgQ2AG6KiqN1fVq5e6Hyy+qjqpqj661P0Alp7ADMtQVV1XVd+uqn2mlH+6qlpV\n",
+       "rV2CPr2sqjZW1R1VtamqLlzsPuxoY6C6Z3xOk7eHL3XfANhxBGZYvjYmOWHLg6r6D0keuBQdqaoT\n",
+       "kzw3yRGttT2SrEtyyRL0Y8UC7PZjrbU9ptxu3Ja2t7c/C9T/Bd93p81dFrvNpbIUry+w4wjMsHy9\n",
+       "JckvTTw+Mcn5kxWqateq+uOqur6qbqmq11fVA8d1e1XV31XV5qr6+ri8emLbS6vqVVX1f6vqm1X1\n",
+       "/qkj2hOelOQfW2vXJklr7ebW2rkT+zqgqv7PuJ8PVNWfVdVbx3Xrq2rTlH5fV1VHjMuHVdXHquq2\n",
+       "qrpp3PYBE3VbVZ1aVVcnuXos+7lxtP22qvrnqvrRifpPqKpPjn25MMlu2/yKTzH286VV9Zkkd1bV\n",
+       "gWN/Tq6q65N8qKruV1Uvr6ovVdWtVXV+Vf3QuP3aqfWnaWO247R3Vb2pqm4c17978nUd+3dzkjeN\n",
+       "58Nrx7o3jsu7jvX3Gfd9W1V9rar+qaruN657aVV9eXzNrqqqw2d4Pd5cVa+rqvdW1Z1Jnl5VR1fV\n",
+       "p6rq9qq6oarOnLLN08ZjdNu4/qSxfMZzd5p2DxzPr29U1VfG4zr5+q6YqHtpVf3qvTevPx23/fzk\n",
+       "cxvP24+Mz/uDVfXnE+ftVsdulmM923l+ZlW9s6ouHNv7ZFU9bqLuNh0DYG4EZli+Pp7kIVX1IzWM\n",
+       "5D0ryVun1PnDJD+c5PFJDkyyKsnvjevul+RNSR6Z5BFJ/j3Jn03Z/tlJfjnJvkkekOS3O335pap6\n",
+       "SVWtq61HFt+e5BNJ9knyqgzhflvdk+S3xm2fmuTwJC+YUufYJE9OckhVPTHJG5P8WpKHJvmLJBeP\n",
+       "AewBSd6d4Y+NvZP8dZJf2I6+TOeEJEcn2TPJ3WPZTyb5kSRHJjlpvD09yaOS7JGtX+fJ+lPNdpze\n",
+       "kmT3JIdmOE6vmVj3sAzP85FJTknyu0mekuF8eFySw5K8fKx7WpJNSVYm2S/Jy5K0qjo4yW8keVJr\n",
+       "7cFjH6/rvB7PTnJWkgcn+WiSOzP8Ybdnhtfp+VV1bJJU1SOS/EOSPx3bfXyST4/76Z27U70qyfuT\n",
+       "7JVk9bi/bfXkJF/McH69Ism7qmrvcd3bk1ye4Tw6M8OnKFNt77HuOSbDObn32Pa7q+r+czgGwPZq\n",
+       "rbm5uS2zW4b/LI/IEHb+IMlRST6QZEWSlmRtksoQVh49sd1Tk2ycYZ+PT/L1iceXJnn5xOMXJHlf\n",
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+       "V1XVNVV1+mK3DwAA22NRA3NV7ZLkz5P8TJJDkpxQVYcsZh8AAGB7LPYI82FJrmmtfbG19u0kFyQ5\n",
+       "ZpH7AAAA22yxA/OqJDdMPN40lgEAwE5pxSK3V9OUta0qVZ2S5JTx4R1VddWC9orFtk+Sryx1J5aj\n",
+       "+sOl7gE7Ce+xBeI9xgTvswWyhO+zR860YrED86YkayYer05y49RKrbVzk5y7WJ1icVXVhtbauqXu\n",
+       "ByxX3mOw8LzP7lsWe0rG/0tyUFUdUFUPSHJ8kosXuQ8AALDNFnWEubV2d1X9RpJ/TLJLkje21q5Y\n",
+       "zD4AAMD2WOwpGWmtvTfJexe7XXYqptvAwvIeg4XnfXYfUq1tdc0dAAAw8tPYAADQITAzq6p6WFVd\n",
+       "UFXXVtXnquq9VfXDVfWvU+qdWVW/PS6/uaqeOS5fWlUbJuqtq6pLx+X1VfWNqvr0xO2Icd3vVtUV\n",
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+       "+oeM/58esGOfEQtJYKarqirJ3ya5tLX26NbaIUlelmS/7dzVvlX1MzOs+6fW2uMnbh+sqqcm+bkk\n",
+       "T2yt/WiSI3LvH735xYn6z9zOvsAPkluTvGj8ZqHpvKS19rgkByf5VJIPd+qekOSjGb6haHvagOXo\n",
+       "ziSPraoHjo9/OsmXJytU1Z5Jnphkz+0IuMcl+XiG91uS/GWS1VsGg5K8MsOXHmycR99ZZAIzs3l6\n",
+       "ku+01l6/paC19uncO7xuiz9K8vLtqL9/kq+01u4a2/xKa22r7+yG+4DNSS5JcmKvUhu8JsnNSbb6\n",
+       "47Sq9kjy40lOztaBeZvagGXoH5IcPS6fkOQdU9b/QpL/neSCbP2+mckJSU7LEJJXteFisecneW1V\n",
+       "rUtyeIb/E/kBIjAzm8cm+cQM6x49OZUiya939vOxJHdV1dOnWfefpkzJeHSS9ydZM37MfE5V/eSU\n",
+       "bd42Ud8/PCx3Zyc5rap22Ya6n0zymGnKj03yvtbaF5J8raqeOI82YLm4IMnxVbVbkh9NctmU9VtC\n",
+       "9Dvy/RHjGVXVmiQPa61dnuSiJM9KktbaZzJ8pe4lSV7YWvv2DnsGLAqBmfm4dnIqRZLXz1L/1Zl+\n",
+       "lHnqlIxrW2t3JPmxDD+RvjnJhVPmVE5OyXjJDngusNMaP7q9PMmzt6F6zVB+QoZwkPH+Xv/5b2cb\n",
+       "sCyMQXZthvfDvb7ytqr2S3Jgko+Of2jeXVWPnWWXx2cIysnW77M/T/Ll1tqHd0DXWWQCM7O5IkNw\n",
+       "nbfW2oeS7JbkKdtY/57W2qWttVck+Y0MH43BfdXvJ3lpZv93+wlJrpwsqKqHJvmpJG+oquuSvCTJ\n",
+       "s8ZrFObSBiwnFyf542w9HeNZSfZKsnF836zN7NMyTkhy0lj/4iSPq6qDxnXfHW/8APKPIrP5UJJd\n",
+       "q+p5Wwqq6klJHjnH/Z2V5Hdmq1RVB0/8I5Mkj0/ypTm2CT/wWmufT/K5DBfDbqUGL8ww//99U1Y/\n",
+       "M8n5rbVHttbWttbWJNmY5Gnb0wYsU29M8srW2menlJ+Q5KjxPbM2w+DRjIG5qg5O8qDW2qqJbf6g\n",
+       "tw0/OARmusaLFX4+yU+PX4NzRZIzk8zpArzxlx43TymeOof5mUn2SHJeDV9j95kkh4ztbjE5h/mD\n",
+       "c+kL/AA6K8nqKWV/VFX/kuQLSZ6U5OnTzI88IcO33Uz6m0w//WK6NmDZaq1taq39yWRZVa1N8ogM\n",
+       "33axpd7GJLdv+YrTJC+vqk1bbpn5fTbr3Gd2fn7pDwAAOowwAwBAh8AMAAAdAjMAAHQIzAAA0CEw\n",
+       "AwBAh8AMAAAdAjMAAHQIzAAA0PH/AdC9P09wBgiuAAAAAElFTkSuQmCC\n",
+       "' alt='Mean Squared Error across race subgroups' />\n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <h4> Subgroup Disparity Test </h4>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Description: </b>\n",
+       "\n",
+       "    Test if the maximum ratio of the mean squared error of any 2\n",
+       "groups within sex attribute exceeds 1.1. To\n",
+       "pass, this value cannot exceed the threshold. \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Threshold: </b>\n",
+       "\n",
+       "    1.1 \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Result: </b>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td></td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>sex_mse_max_ratio\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>0</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>1.023\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "   \n",
+       "\n",
+       "    <div><b style=\"color:green;\"> Passed</b></div> \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  <div class=\"row\">\n",
+       "\n",
+       "    <div class=\"col\">\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "  <div class=\"img-container\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div class=\"img-item\">\n",
+       "\n",
+       "      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAsYAAAF1CAYAAADr3izzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
+       "bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsT\n",
+       "AAALEwEAmpwYAAAZOElEQVR4nO3de7xvd13f+deHhDsiIAEhiQYkRcFa7SOi1I5zFFpQtPBox0eD\n",
+       "l4ZKh6K0VYdODYzWKzPUcVrb6eCYein1QoyMSsZaFdNib4oG5aEDyBC5hgQISkQZBcHv/PFbx/44\n",
+       "OZcdz945Sfp8Ph6/x16X71rr81tr7XPe+7u/67dnrRUAAPzX7h7nugAAALgzEIwBACDBGAAAKsEY\n",
+       "AAAqwRgAACrBGAAAKsEY4KPMzL+cmW8/13VwdGbmkplZM3P+ua4FuHMRjOFuYGbeOjMfmpmHnrD8\n",
+       "tVsAuOQc1PTCmXnLzPz+zNw4Mz96R9dw2GbmWTPzke097b8eea5rA+DsCcZw9/GW6pnHZ2bmz1b3\n",
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+       "2Ju/ovpX+w1m5t4z850z8/aZeffM/J8zc99t3YNn5qdm5paZed82fdHetq+amW+bmf80M783Mz93\n",
+       "Yg/1ns+sfnat9VtVa613rbWu2tvXo2bmF7b9vHJm/vnM/NC27tjM3HhC3W+dmSdv00+YmV+cmVtn\n",
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+       "s9Xz7Jl5e/VvZ+YeM/MNM/O2mXnPzPyrmfnYbftLTmx/kmOc6To9ZGZ+YGZu2tb/5P553ep7V/UD\n",
+       "2/3wXVvbm7bpe2/tH7rt+9aZ+Z2Z+Q8zc49t3dfPzDu3c/bGmXnSKc7HF87M67d275yZv7+37qTX\n",
+       "ZGb++sy8eWYeuM1/wcy8a2YuOMn+7zMzPzQzv73t51dm5uF71+LJe22/+fg9tucrt/d988w8f6/t\n",
+       "fWfmpdv5e8PM/IP9e/Ik1/n8mfkrM/O6rY5Xzcyn7LVfM/OYvfk/Ga6zd11eODPv3fb9ZQc5h8Dh\n",
+       "E4zh7uOXqgfOzKfMzHnVX69ODAL/qPoz1adXj6kurP7htu4e1Q9Un1h9QvUH1T8/Yfsvrf5m9bDq\n",
+       "XtWp/pP+pepvzMz/ODOXbfXs+5HqNdVDq29rF+IP6iPV123bPrF6UvXVJ7R5RvVZ1eNm5s9X31/9\n",
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+       "/YnOdJ1+sLpf9fh21+mf7K37+Hbv8xOr51T/U/XZ7e6HP1c9ofqGre3zqxurC6qHVy+s1sw8tvo7\n",
+       "1WeutT5mq/GtpzgX31f97a3dp7YF/dNdk7XWj1a/WP2zmfm4bR9/a611y0n2f0X1sdXF236eu52P\n",
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+       "UPexa/dwXO1+df7Y6rPWWg9s15tau57ms6npj9ZaP1b9erugc3P14Jm5/16zT9ib/kC7AL87+C7w\n",
+       "7Y8v/e7qN6tLtzpfeJIa94PuO6oXnRBm77fWetlWy4Uzs7/9J3R21hmW3dQuGO4f78PVu8+wj+NO\n",
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+       "pOMn2/WE1umvSTPz6e16g19W/bNTnYjt3vqWtdbjqr9QfVH/ZZz9R91H7YYrnOjik733dvfFRado\n",
+       "9yeH35v+qPO43U8Xt+s1rt0Pkaer5WTfD8evw6nOIXAEBGO4+3l2u6EE+z1Qx3ux/kX1T2bmYVUz\n",
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+       "bft7thsecu+99R9Tvb/6/Zn55OqrzlDOv6ieOzOfNTv3P15bu7GsH67+3vYA1V9tN872KL2s+rrZ\n",
+       "PYD4gHY93j+61vrwGbY77pTXaa11c/VvqpfM7iG9e87M555iP8dr+YaZuWB2D1L+w7Zx6bN7OO4x\n",
+       "W8h7f7ux3R+ZmcfOzOfP7iG9P9xq+ciJO96u7ZfNzMeutf5obx91mmsyM/fZanhhu/HsF87MiWPI\n",
+       "jx/j82bmz24/PL2/3ZCU48d4bXX5dg4uq/67k+ziG7ce+Mdvxzr+kYLXVC/YzuGF7cZUn8411dNm\n",
+       "5knbPfv86oPthnkcr+VLZ+a8mXlqu2E8Jzr+/fDftAv4P3aGcwgcAcEY7mbWWr+11rr+FKu/vt2v\n",
+       "x39p+zX8z7frfaz6rnYf7/bedg/P/cxZlPH+dsHm7e3G4X5H9VVrrf+4rf/Sdg/H/U67YPcnwz7W\n",
+       "Wr/bbvzy97brcftAu4fAjvv72/a/1y5gnfbzkbdz8d+3e0Dtfe3e/7O2dR+q/uo2/752Dyye2NN+\n",
+       "oifObT/H+DPPsM2+7283BODft+vd/8Pq796O7b+r01+nr2gXEH+z3Vjgrz3Nvr693Q8pv95uPO+v\n",
+       "bstq91Daz1e/3+4HiJestV7V7oeUF2/Hf1e7nswXnmL/X1G9dbvXntv2ENvprkm7MfI3rrW+e631\n",
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+       "uS4C4C7qA9Wnzsx911p/UP2l6p3nuCY4ED3GAMBh+zfV07bpZ1YvO4e1wIEJxnBb990bRvET57oY\n",
+       "gLugq6vLZ+Y+1adVrz7H9cCBGEoBt2UoBcBZWGv9+vZ8xjPz0a3chQjGAMBRuLb6zupY9XHnthQ4\n",
+       "GMEYADgK31/97lrrN2bm2DmuBQ5EMAYADt1a68bqn57rOuD28JfvAAAgn0oBAACVYAwAAJVgDAAA\n",
+       "lWAMAACVYAwAAJVgDAAAlWAMAACVYAwAAFX9/862TVD3XhCzAAAAAElFTkSuQmCC\n",
+       "' alt='Mean Squared Error across sex subgroups' />\n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "</body>\n",
+       "\n",
+       "</html>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Export to html\n",
+    "html = mct.export_format(output_file=\"speech_to_text_example.html\")\n",
+    "display.display(display.HTML(html))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "veritas",
+   "language": "python",
+   "name": "veritas"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}