Compare_Spectral_Vs_DEC_AMI_OracleVAD.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DT5hNAsu0X-u"
   },
   "source": [
    "# **LOAD LIBRARIES**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "executionInfo": {
     "elapsed": 11418,
     "status": "ok",
     "timestamp": 1620723204705,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "ouVW09h_0Usm"
   },
   "outputs": [],
   "source": [
    "!pip install torchaudio -q --no-deps\n",
    "!pip install speechbrain -q\n",
    "!pip install spectralcluster -q\n",
    "!pip install pyannote.metrics -q\n",
    "!pip install gdown -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 19201,
     "status": "ok",
     "timestamp": 1620723213531,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "Qe3sDhQYbwDi",
    "outputId": "bee9438f-ac10-4b0e-b1d5-a6bd16aa4e6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1XltkNas1LTOck62GIDYT0WcPDPGZEo7P\n",
      "To: /content/utils.py\n",
      "100% 16.8k/16.8k [00:00<00:00, 28.7MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1FWIRF2Kq6JB0fFHwjkZmVfEuGq0-SSC5\n",
      "To: /content/DEC.py\n",
      "100% 10.1k/10.1k [00:00<00:00, 8.78MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1hbepzjAxHTQS5QoAlKNP9zK0nMxdjauJ\n",
      "To: /content/optimumSpeaker.py\n",
      "100% 7.08k/7.08k [00:00<00:00, 4.29MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1RQkezkiXa5DVFHE7g4UKE2v8vcmgJFpm\n",
      "To: /content/baselineMethods.py\n",
      "100% 4.39k/4.39k [00:00<00:00, 3.47MB/s]\n"
     ]
    }
   ],
   "source": [
    "!git clone https://github.com/shashikg/speaker_diarization_ee698.git\n",
    "!cp -r speaker_diarization_ee698/core/. core"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 17259,
     "status": "ok",
     "timestamp": 1620723213531,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "0iqaDi7jb5qQ",
    "outputId": "bb7e0838-3099-411f-cb12-51f6bd276a64"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 16953,
     "status": "ok",
     "timestamp": 1620723213532,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "FGTt1sJS0bLP"
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function, division\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import torchaudio\n",
    "import os\n",
    "import torch\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from speechbrain.pretrained import SpeakerRecognition\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from sklearn.cluster import KMeans, SpectralClustering\n",
    "from sklearn import decomposition\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "from core.utils import DiarizationDataSet, get_metrics, plot_annot\n",
    "import core.baselineMethods as baselineMethods\n",
    "from core.DEC import diarizationDEC\n",
    "\n",
    "import shutil\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4Dv0fC5mhhao"
   },
   "source": [
    "# Download dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 4996,
     "status": "ok",
     "timestamp": 1620723244174,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "OwMmmaqYhnn0",
    "outputId": "ee595543-99bf-499c-f0e6-53303eb6dc59"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset already downloaded!\n",
      "Downloading precomputed VADs...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1Hzhks79Mq9py0yPfxI_e73Nx5M-XbJp6\n",
      "To: /content/tmp.zip\n",
      "100%|██████████| 44.0k/44.0k [00:00<00:00, 26.2MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download and Extraction Complete\n",
      "Precomputed X-vectors exists!\n",
      "Will use precomputed features...\n",
      "\n",
      "Downloading precomputed features...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1HjW9caW9f3Bqp2hvz97tvLPEz_fFv01X\n",
      "To: /content/tmp.zip\n",
      "31.0MB [00:00, 201MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download and Extraction Complete\n",
      "\n",
      "Data size: 16\n"
     ]
    }
   ],
   "source": [
    "audio_dataset = DiarizationDataSet(dataset_name='ami',\n",
    "                                   window_len=1500, \n",
    "                                   window_step=750,\n",
    "                                   use_oracle_vad=True)\n",
    "\n",
    "print(\"\\nData size:\", len(audio_dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OiFZ_66hh6Nv"
   },
   "source": [
    "# Spectral - EigenGap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 115,
     "referenced_widgets": [
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      "5cc164a8efba4de8a1baa0ad1f0e62af",
      "26ebf079319d49038996c5dc5e647c92",
      "b0c770f6b262416eaf1fa8bf8008f24b",
      "39c762b45eab4e268e61b87d0957cf17",
      "880eda544dd64a798fb9c4398b96a34a",
      "dda0d291f4bd4fcb925e10441944b7ed",
      "d6e92c37f6054a0d97b0c7aa7970957b"
     ]
    },
    "executionInfo": {
     "elapsed": 129002,
     "status": "ok",
     "timestamp": 1620723387157,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "x5JJ_CkFiEcI",
    "outputId": "7e97613f-b7fd-484a-d56a-2ba7011e67d0"
   },
   "outputs": [
    {
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       "model_id": "a1f9d8e2c36745d2b82ad08ddb266983",
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       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dff0f2b4f22a47ed9b03c9e1ba86d272",
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       "version_minor": 0
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
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     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "hypothesis_dir_spectral = baselineMethods.diarizationEigenGapNumSpkrs(audio_dataset, hypothesis_dir=\"./rttm_output_spectral/\")\n",
    "metric_spectral = get_metrics(audio_dataset.label_dir, hypothesis_dir_spectral)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WVWdrk5kv4vp"
   },
   "source": [
    "# DEC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 166,
     "referenced_widgets": [
      "e1101321d8cd45aaba228c9e5d10452d",
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      "dd18b8a1522340e8ba64d7fe10de3af1"
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    },
    "executionInfo": {
     "elapsed": 239104,
     "status": "ok",
     "timestamp": 1620723497928,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "D3myQoh-lv-U",
    "outputId": "01ba1de2-a218-48cc-9ec3-5dddcd263ab4"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1101321d8cd45aaba228c9e5d10452d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
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     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading pre-trained weights for auto encoder...\n",
      "Downloading Complete!\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "58ffb5bf792c44f6b23fc846372311c4",
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       "version_minor": 0
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      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "hypothesis_dir_dec = diarizationDEC(audio_dataset, hypothesis_dir=\"./rttm_output_dec/\")\n",
    "metric_dec = get_metrics(audio_dataset.label_dir, hypothesis_dir_dec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2TgnLCbohjn6"
   },
   "source": [
    "# Compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 237861,
     "status": "ok",
     "timestamp": 1620723497929,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "PTlgV9PWmJ-k"
   },
   "outputs": [],
   "source": [
    "df_dec = metric_dec.report()\n",
    "df_spectral = metric_spectral.report()\n",
    "\n",
    "df_spectral = df_spectral[[df_dec.keys()[0]]]\n",
    "df_dec = df_dec[[df_dec.keys()[0]]]\n",
    "\n",
    "df_both = df_dec\n",
    "df_both[\"DER (Spectral)\"] = df_spectral[[df_dec.keys()[0]]]\n",
    "df_both.columns = [\"DER (DEC)\", \"DER (Spectral)\"]\n",
    "\n",
    "df_both[\"DER Difference\"] = df_both[\"DER (Spectral)\"]-df_both[\"DER (DEC)\"]\n",
    "\n",
    "df_both = df_both[:-1]\n",
    "df_both = df_both.sort_values(by=['DER Difference'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111
    },
    "executionInfo": {
     "elapsed": 236289,
     "status": "ok",
     "timestamp": 1620723497930,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "zEIp5MM9uR_A",
    "outputId": "de369114-20ef-4166-f072-c1336c595423"
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DER (DEC)</th>\n",
       "      <th>DER (Spectral)</th>\n",
       "      <th>DER Difference</th>\n",
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       "    <tr>\n",
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       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TOTAL</th>\n",
       "      <td>13.136877</td>\n",
       "      <td>14.961229</td>\n",
       "      <td>1.824352</td>\n",
       "    </tr>\n",
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       "       DER (DEC)  DER (Spectral)  DER Difference\n",
       "item                                            \n",
       "TOTAL  13.136877       14.961229        1.824352"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
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     "output_type": "execute_result"
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   "source": [
    "df_dec[-1:]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 576
    },
    "executionInfo": {
     "elapsed": 235324,
     "status": "ok",
     "timestamp": 1620723497931,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
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     "user_tz": -330
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    "id": "vjQP9QqUvuOZ",
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   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DER (DEC)</th>\n",
       "      <th>DER (Spectral)</th>\n",
       "      <th>DER Difference</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
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       "      <th>IS1009a</th>\n",
       "      <td>40.505644</td>\n",
       "      <td>27.827065</td>\n",
       "      <td>-12.678579</td>\n",
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       "      <th>TS3003c</th>\n",
       "      <td>9.313898</td>\n",
       "      <td>8.689440</td>\n",
       "      <td>-0.624457</td>\n",
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       "    <tr>\n",
       "      <th>TS3003a</th>\n",
       "      <td>19.346016</td>\n",
       "      <td>19.346016</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>ES2004a</th>\n",
       "      <td>13.617823</td>\n",
       "      <td>13.627691</td>\n",
       "      <td>0.009869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EN2002c</th>\n",
       "      <td>10.733794</td>\n",
       "      <td>10.837061</td>\n",
       "      <td>0.103267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS1009b</th>\n",
       "      <td>6.512912</td>\n",
       "      <td>6.879391</td>\n",
       "      <td>0.366479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TS3003d</th>\n",
       "      <td>26.203707</td>\n",
       "      <td>26.602833</td>\n",
       "      <td>0.399126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2004b</th>\n",
       "      <td>8.212175</td>\n",
       "      <td>8.714347</td>\n",
       "      <td>0.502172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2004c</th>\n",
       "      <td>8.296036</td>\n",
       "      <td>8.873851</td>\n",
       "      <td>0.577816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EN2002b</th>\n",
       "      <td>14.385894</td>\n",
       "      <td>15.062901</td>\n",
       "      <td>0.677007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS1009c</th>\n",
       "      <td>6.533338</td>\n",
       "      <td>7.383215</td>\n",
       "      <td>0.849877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EN2002d</th>\n",
       "      <td>13.851969</td>\n",
       "      <td>16.060576</td>\n",
       "      <td>2.208607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EN2002a</th>\n",
       "      <td>15.867623</td>\n",
       "      <td>18.795199</td>\n",
       "      <td>2.927576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2004d</th>\n",
       "      <td>13.956947</td>\n",
       "      <td>20.575643</td>\n",
       "      <td>6.618696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TS3003b</th>\n",
       "      <td>8.725804</td>\n",
       "      <td>17.179800</td>\n",
       "      <td>8.453996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS1009d</th>\n",
       "      <td>19.513548</td>\n",
       "      <td>28.636278</td>\n",
       "      <td>9.122730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DER (DEC)  DER (Spectral)  DER Difference\n",
       "item                                              \n",
       "IS1009a  40.505644       27.827065      -12.678579\n",
       "TS3003c   9.313898        8.689440       -0.624457\n",
       "TS3003a  19.346016       19.346016        0.000000\n",
       "ES2004a  13.617823       13.627691        0.009869\n",
       "EN2002c  10.733794       10.837061        0.103267\n",
       "IS1009b   6.512912        6.879391        0.366479\n",
       "TS3003d  26.203707       26.602833        0.399126\n",
       "ES2004b   8.212175        8.714347        0.502172\n",
       "ES2004c   8.296036        8.873851        0.577816\n",
       "EN2002b  14.385894       15.062901        0.677007\n",
       "IS1009c   6.533338        7.383215        0.849877\n",
       "EN2002d  13.851969       16.060576        2.208607\n",
       "EN2002a  15.867623       18.795199        2.927576\n",
       "ES2004d  13.956947       20.575643        6.618696\n",
       "TS3003b   8.725804       17.179800        8.453996\n",
       "IS1009d  19.513548       28.636278        9.122730"
      ]
     },
     "execution_count": 15,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 231936,
     "status": "ok",
     "timestamp": 1620723497931,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "cNkpz4-XpHDs",
    "outputId": "ab66c13c-6851-460c-e576-2055f2627999"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better DER for 13 examples.\n",
      "Poor DER for 2 examples.\n",
      "Same DER for 1 examples.\n"
     ]
    }
   ],
   "source": [
    "print(\"Better DER for\", np.sum(df_both['DER Difference'].values>0), \"examples.\")\n",
    "print(\"Poor DER for\", np.sum(df_both['DER Difference'].values<0), \"examples.\")\n",
    "print(\"Same DER for\", np.sum(df_both['DER Difference'].values==0), \"examples.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "executionInfo": {
     "elapsed": 230765,
     "status": "ok",
     "timestamp": 1620723497932,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "vnXwVIAwqoSC"
   },
   "outputs": [],
   "source": [
    "filenames = [\"EN2002c\"] + list(df_both.index[[-1, -2, -3, 2, 1, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 279405,
     "status": "ok",
     "timestamp": 1620723547245,
     "user": {
      "displayName": "Shashi Kant",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiHv2woo7B1czb0XKsLBEOsgMWsuYITOryCdNNv7A=s64",
      "userId": "09783338171816410510"
     },
     "user_tz": -330
    },
    "id": "9hAGvSouLxTe",
    "outputId": "64e052ab-7aaa-4ca5-945d-06d11c67b232"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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ytPfn0284YVqHti5D22adY+w6jr2v9+vEi07I+uc9Y/Qctx/7kmkZbj/2JdP70ddvR+39+s47Ts/73vKCadurdW6vSX195ZHPX7ae/XqccfH103td80mSK498/qK0teynXXpqzrnm7EV51nK06X/7r160pBwnXnTC6HW9/diXLLq39Rx/cdZJ0+31GvSvVc2jpqv/9+s6dJ/atvjnr3tWks39pU1f72W/7G25z7nm7Gm697z1g4v6f7IwzrVpj3v/e6dtur5v+12yuK8NjTknn39BXnj6J0fHtqPf++4lfXjWWPjqsy7L6z/67mn+Y8655uxpW2i31e31Xp18/gVzjb2zXHLSGwfbbv/9PNrytuP50Pv2mFnXYp5zDTnj4uvz+o++e8kx/XbXjhv1mrftqL22bX79ftrOM+0cOTSG/8VZJ43OG/0ytW2xX5+6v9a3rftQm+j3mTH12Bf/1e9O+1s/39qWq5PPv2C6vc7H9bgtUcfOJDn3+NdN1z39dUfrpX97/PR1fzzrq32p3rc61tY6JJn2/baPtOnq/0PnaM//6TecML2nY327GppDLjnpjbn92JfkPW/94PSan3bpqfn0G06YtrV51bza+TPJdNss9Zg6Lg9d1zqv9OtY8+6vHcbm7Xr9PvTqZ4yWZ9baZDlja5t2f798tU5t3er/F5z8vME8+ucYa4v1/9qPqraNtWq6dh3Ztt+hdeXYmD7Wp2o/aNfX/TYy6zrefuxL8sev/j9L6lP7c388quduj2/fD60R2j7ZH3f69frNc982eO9a/XZd07d5/ea5b5umbcepseew2mbacyTJce9/76K0Q+Pz2HNTf25qj+2PT3V/u45s135Dzrnm7LzoPX+/JM+xcg4ZKlO9B+15+vnX9+2/E/7u5Fz27EOTjD8/vO8tL1hy//pzfpLcetia0TFiaPwYavN1LOyPX+36sf/+tEtPna7Z+n20zjH9+tVzD635+9r1+qyxarnX86736jUYmgvGznnONWdPx8qxOaGmre2v/wzUrrXGxu9bD1szWK6xMeT3zv3NRdvrdTv5/AuWPFv0+/Npl56aZ//RPy65PmdcfP00XV231zVHuzbqP/PXvnnapacuGdOH4gb12rR59tcm7dq2/xxctW25bUtD2ufHedfv/bmtrVf/2W0edS00lO9Quln6fTZZOu/0r8c7X/6G0fsxNqe2cbn2nLUd9ufL9rihZ4n+2rWfftaarh9LbPtVW99Xvf790zF4qJ8Pta+hebRV3/fXocuNPSsKmt43CZp+aNPjcuYlNyzad+YlNwxuS5KNP7oh6++8Oned/s4kyV2nvzOb1q1byamn7jr9nbnqvl0Xna++7p+/78xLbsh5154zun/9nVdPX5937TlL/vXz/8LN35q+3v+CDywpZ/t6pfW96/R3LspjaPsFe9yYJFn1wU/MnUd9/4WbvzV63Wq9zrzkhjzj4vNXVO4kc9W1X65ajvb+rPrgJ6Z1aOsytG3WOcau49j7er9u/97t+akrbxw9x6Z166Zl2LRu3fR+9PXbUXu/7jr9nbnwKT+ctr1a5/aa1Nd7fuVLy9azX48zL7lheq9rPkmy51e+tChtLfv6O6/OedeesyjPWo42/Z2PvHdJOW7/3u2j13XTunWL7m09xwV73DjdXq9B/1rVPGq6+n+/rkP3qW2Lf/8ruyTZ3F/a9PVe9svelvu8a8+ZpvvQpsct6v/JwjjXpr3pxv2mbbq+b/tdsrivDY05l63fJd/49++Pjm133vrEJX141lj4hZu/la/e+7Fp/mPOu/acaVtot9Xt9V5dtn6XucbeWfa/4AODbbf/fh5tedvxfOh9e8ysazHPuYaceckN+eq9H1tyTL/dteNGveZtO2qvbZtfv5+280w7Rw6N4RfscePovNEvU9sW+/Wp+2t927oPtYl+nxlTj930yJun/a2fb23L1WXrd5lur/NxPW5L1LEzSZ5x8fnTdU9/3dH6fre5fv3xrK/2pXrf6lhb65Bk2vfbPtKmq/8PnaM9/6oPfmJ6T8f6djU0h+x/wQeyad26fGjT46bXfP2dV2fVBz8xbWvzqnm182eS6bZZ6jF1XB66rnVe6dex5t1fO4zN2/X6PevC4f1Dec2zLhk6dmitMlS+Wqe2bvX/wz589WAe/XOMtcX6f+1HVdvGWjVdu45s2+/QunJsTB/rU7UftOvrfhuZdR03rVuXcx936JL61P7cH4/qudvj2/dDa4S2T/bHnX697nrUJwfvXavfrmv6Nq+7HvXJadp2nBp7Dqttpj1Hktx0436L0g6Nz2PPTf25qT22Pz7V/e06sl37DTnv2nPyb7c/ckmeY+UcMlSmeg/a8/Tzr+/bfxt/dEP2vub2hTxG1rkXPuWHS+5ff85Pkvs2bBgdI4bGj6E2X8fC/vjVrh/779ffefV0zdbvo3WO6devnntozd/XrtdnjVXLvZ53vVevwdBcMHbO8649ZzpWjs0JNW1tf/1noHatNTZ+37dhw2C5xsaQrzzq24u21+t22fpdljxb9Pvz+juvzt33PHzJ9Tnzkhum6eq6va452rVR/5m/9s31d169ZEwfihvUa9Pm2V+btGvb/nNw1bblti0NaZ8f512/9+e2tl79Z7d51LXQUL5D6Wbp99lk6bzTvx7/d/9njd6PsTm1jcu156ztsD9ftscNPUv016799LPWdP1YYtuv2vpevesTpmPwUD8fal9D82irvu+vQ5cbe3w8HwAAAACgIWgKAAAAANAQNAUAAAAAaAiaAgAAAAA0BE0BAAAAABqPWOkBG/beJ3nV++ZOv+ZNF+Vxh/SO34rWvOmiZdMc85Gjt2jfUP71/YezfN22pO5jx2zYe5/kT56cYz5ydP5smbznOe9YveY9fkvOOabegz+bM995znV/08xbn+Xaz6z86r3cEm1+y5V1zZsuyof7aSdtabkyzko/z/nnvT7zHt8/1zxlntVfxraPlbvtJ3Wcm7eO9dh5xqx50s3qw2PmKetYmoXtJ6/ofEM+3Lye534upy1vv+z3Z+zfUkP59us1lKbdVq/t4w6Zsw/05ugl1/FPnjy8fZkyzdrelnPs/di2ebTHta83l+fkpfPWq963xefbkrL078+strhc3ivt7/OYtz8s1++Gjpu3PB8e2LaS+euB1K6jhsyzvpqZ9/1MP0/5tuScK13nDh938hb177EyjvWpul6Zt40M7ZtVprF9NZ+VzkkrHXdWMs4Oj3tL9813/4bTruTYec47tK2265Wap47znD+5f2NYsvIxcqXPLitpd/dn7dI+w46db7lxqJrn+eWBHsu25Fxt/g/EOL2S/Vtyzmq5+Xula4J5+9PYvvu7vhtal856/p9lvj4wPGcN5TFPPTav92bnO7Run6cMyTLjwApica1+XG5o/5hZbXe5uXSJkfJvyfrzweIvTQEAAAAAGoKmAAAAAACNFX88f+8NX09W8OfX697y3BzzkXctOn5rfvxq3VueO7qv/unwhS/8h8H9x3zk6Om+sT//bfNf86aLpu83vG9ybSaG6tjuX049vn/Mou2T8m54zT6DeQ/lMXbth+pVr9dKyl3Psdwxs9pAvQcbXjN8DfvHjp2rTTd2HYfymffeDaUbaltDbWnoXPVebon2usyqR7Jwrze8b3E5altqyzrzWjd9parHzmpvy/Wv5QyVsT3P0H3ul7n2l+Xuc//eDJW57Td1nBtLO3Rsv68tl76vPa7ur9uWS1/LOqQt/9g9u/CF/5A1X1h6/pXa0HxCY+x+rmTOaMvb1q//vt3eHjevedtwvwzJ0noNXeO2HdVre8xH3rUkbb8tD83RS8avgb5azSpTvz79/cu1wXbOnGWoP7T9pH1dy7PmCwPz1pznm7cMY2Wp+venvYezrttQ3suNC/OOG63+2DR2j5frd0Nj3LzXecPAJ7KG5q/BYx/gtWO7juo7Zpn11XLlWekaaHS+nbEunWfOHrJcW+yvB4eO6/e39thZ7XKsbQ31qdoP2vV1f305z3Ucq8+sfTWfwfF15NzT/SuY18fuxdA97497Y/uWu39DY3ZNOzRmzypn/171zzm0rbbrlRo61yxjZUrmuxazLNcOhubpfvqVPLfNs65uzzev9hm2taS/jYyTi8o+x/PLcuuKoes1er5lxrpZa/Gx/r2c5dLPs2ad55l8yHLz91jfHdPvT2Ppx/bN29/HDK1LZz3/zzLP+r3OWYu29cbHsWs5pK73hvIdSjc7r83j/qw5fdE128KvnurH5Yb2943Fg9qyzZpLB42Uf571Z/Vgf1TfX5oCAAAAADQETQEAAAAAGoKmAAAAAAANQVMAAAAAgMaKfgjqYY9/fJLk+J1uy85PP3zRvhOPWL0kfd22x8NX5wk/9ajsdsqRSZLdTnlt7v3MZ7eowLud8tocfPd3c9gznjzz3ENOPGJ1dv3pl4/uP3DPg6avjztgabrvPmbxeQ7Zd/fp6+vWvjJ798rZvl5pfdvjx7av3bhfkuSmVxy56Nyz8qjbDtl39xy63x6D56j1OvGI1fnUDS/Ny1ZS8CQ7rVmzbJp+2eo9bO/PTa84Mgfv9eT0jV2bsXSzrsPQ+3q/9nrUrvn2k/fLPs9+4WD+O61ZM+0H937ms1m78fGD6fpt6aZX7DW9X7ud8toc86VP5oZffdJk3+Y6t+36xCNW587/95SZ93moTiceujqfv3Hj9F7XPO980lOy6td/dZq2tqUD9zwoBz/2yYv66omHLhyz26M357/nD3ZeUo69HrXX6HXdac2aHHfA5ut43AEvz26nHJm1G7+WnQ8/fHruuq+9VvV61O31//49G9K2xedfek/yws39pT2mvZdtnvd+5rPTch93wMuz05pdsvPTD8/x378t659wwKL0ezx8dZ7zH542TXvht2/Mkfs+e9qmL/z2jdn9vkMX9bu2rw2NTes23ZN/vWWPHH3I0J1P9nzCV/PCA563aNus8fCQfXfPrjsflUNXDff96rgDXp6r7rgyBz/2yYu2JclVd1w53XbYgffk4L0OnpnXcq5b+8olY9Gs/jlL23ba8Xzo/dAxK7HccScesTrXfv+oJcfUvpUstLt23Kj9r03fzjtP3PmoRfvafppkOs8c/LDNc+TQGL52436LxqFWv0y1nus23ZOddj5qSfq6vz9eDWnnzFnq8Tv9YN/sdsqROf77ty3Jt7bl6rAD75lur/Px8TvdNtf5xsrw+Rs3Jkk+9ayXLln3DNXxJ8rm+g2NZ63ax3Y75cjc+5nPZuenHz4da+t1+unH/ESOPmTvXHfn4j5S09UyDJ2jPf9Nr9grazf+5KLzDvXtZOm4euKhq3Pdna/Mgbdem+N3ui03Ta75gXsetDA+P2zxemw5161dyCvJous5z7qhjtPtuNy32ymvzdqNX8tX9991UR1r/v2xpM59ffX6XXzMfjl+pDxbOk6NpR2a09ry1fGhvX/13l127EFZO5JHa6ittHPruk33LNo31sZqfxvqB0Pry0P23X20bDWPfl61H7Tr6347mXUdd1qzJi+77fNJNv9ARV3XHr/T0jm8nr89vn0/637XftKOO/16XfKNZ+bnfvbOJfeu1d9W73mdB048YnUu+cYzp2m/+5jN49TYc9iuP/3yRfN2Pceq/W5clHZofB5bm/TnpvbYoftZ20RdR67duN907TfkuANenr++5QdL8hwr55ChMu32vYV70J6nn//Q+0/fvTobfv7WJWvHNO+P+dIjsstLX7qkHu2cnyQP23vv7Pobi9P182oNtfk6Z6zd+LUl5xt7f9UdV2bVZM3WPhcki9dh/bVxMrzm72ufX2aNVcu97l+vMXU+OO6Apc9e/eeF9vVlx96XtRmfE2ra2v6mbefRm8eButYaG9Metvfeg/17rO086XtXLNper9+6TffkO71ni2TxOvzAPQ/KPbv8aMm5Tjxidb72iIOmaTafc/OaKln6zL92473Z+fDDc9UdV+Y/HbF5TTIWMzjxiNXZ7dGvXZRnf97orzmG1p/tXDf2/L+5Dq9d9Pw4jzpntWq96hhXn93mUdfjQ/kOpZtlqJ32551+2/mN6y7OT/7Oq5bkNWst3sblaptq837Z9Yvny/a4oWeJ2gfrmrSf/tnf+9cleVX9NXU/VlbLdNB3b83Tjv7lfPruxWVOxttX39jzYP/Zf7dTXpu8/nWj+ZSu60Z39j31qU/tLr/88rnTAwAAAABsj0opV3Rd99ShfT6eDwAAAADQEDQFAAAAAGgImgIAAAAANARNAQAAAAAagqYAAAAAAA1BUwAAAACAhqApAAAAAEBD0BQAAAAAoCFoCgAAAADQEDQFAAAAAGgImgIAAAAANARNAQAAAAAagqYAAAAAAA1BUwAAAACAhqApAAAAAEBD0BQAAAAAoCFoCgAAAADQEDQFAAAAAGgImgIAAAAANARNAQAAAAAagqYAAAAAAA1BUwAAAACAhqApAAAAAEBD0BQAAAAAoCFoCgAAAADQEDQFAAAAAGgImgIAAAAANARNAQAAAAAagqYAAAAAAA1BUwAAAACAhqApAAAAAEBD0BQAAAAAoCFoCgAAAADQEDQFAAAAAGgImgIAAAAANARNAQAAAAAagqYAAAAAAA1BUwAAAACAhqApAAAAAEBD0BQAAAAAoCFoCgAAAADQEDQFAAAAAGgImgIAAAAANARNAQAAAAAagqYAAAAAAA1BUwAAAACAhqApAAAAAEBD0BQAAAAAoCFoCgAAAADQEDQFAAAAAGgImgIAAAAANARNAQAAAAAagqYAAAAAAA1BUwAAAACAhqApAAAAAEBD0BQAAAAAoCFoCgAAAADQEDQFAAAAAGgImgIAAAAANARNAQAAAAAagqYAAAAAAA1BUwAAAACAhqApAAAAAEBD0BQAAAAAoFG6rps/cSm3J7l56xUHGPHYJHds60IAM+mnsH3TR2H7p5/C9k0fZUe0b9d1ew3tWFHQFNg2SimXd1331G1dDmCcfgrbN30Utn/6KWzf9FEeanw8HwAAAACgIWgKAAAAANAQNIUfD3++rQsALEs/he2bPgrbP/0Utm/6KA8pvtMUAAAAAKDhL00BAAAAABqCprCdKKXcVEq5qpTyxVLK5ZNte5RSPl5KuW7y/+6T7aWU8q5SyvWllCtLKYdu29LDjqeUclYp5bZSytXNthX3yVLKKyfpryulvHJb1AV2VCP99M2llA2T+fSLpZRfb/adNumn15ZSnttsP2qy7fpSyhse7HrAjqqUsk8p5eJSypdLKetLKf9tst18CtuBGX3UXArx8XzYbpRSbkry1K7r7mi2vS3Jxq7r/nAy8ezedd2pk0nrvyb59SRPS/LHXdc9bVuUG3ZUpZRnJLk7yV92XXfQZNuK+mQpZY8klyd5apIuyRVJfqnrum9tgyrBDmekn745yd1d1729l/YXkpyb5LAkP5Pkn5M8cbL7q0l+LcktST6X5GVd1335wagD7MhKKU9I8oSu6z5fStktC/PgC5OcEPMpbHMz+uhLYy4Ff2kK27kXJPnA5PUHsjCB1e1/2S1Yl+SnJhMe8ADpuu5TSTb2Nq+0Tz43yce7rts4ebD7eJKjtn7p4aFhpJ+OeUGS87quu7fruhuTXJ+Fh77Dklzfdd3Xuq7blOS8SVrgfuq67tau6z4/eX1XkmuS7B3zKWwXZvTRMeZSHlIETWH70SX5p1LKFaWU/zzZ9viu626dvP5GksdPXu+d5OvNsbdk9uQGPDBW2if1Vdg2Tpp8tPes+rHf6KewTZVSViU5JMm/xHwK251eH03MpSBoCtuRX+667tAkz0vymslHDqe6he/S8H0asJ3QJ2G79WdJVif5xSS3JnnHti0OUEr5ySQfTvLfu677TrvPfArb3kAfNZdCBE1hu9F13YbJ/7cluSALH3H4Zv3Y/eT/2ybJNyTZpzn8ZyfbgK1rpX1SX4UHWdd13+y67kdd192X5IwszKeJfgrbRCnlkVkIxpzddd3fTDabT2FjvIKpAAAC9klEQVQ7MdRHzaWwQNAUtgOllF0nX7ydUsquSZ6T5OokFyapvw76yiR/O3l9YZLfmvzC6Jok/958xAnYelbaJy9K8pxSyu6TjzU9Z7IN2Ep63/G9NgvzabLQT48rpexcStkvyf5JLsvCj1XsX0rZr5SyU5LjJmmB+6mUUpKcmeSarutOb3aZT2E7MNZHzaWw4BHbugBAkoXvcbpgYc7KI5Kc03Xdx0opn0tyfinlxCQ3Z+FXDJPkH7Pwq6LXJ7knyW8/+EWGHVsp5dwkRyR5bCnlliRvSvKHWUGf7LpuYynlf2VhIZkk/7Prunl/tAZYxkg/PaKU8otZ+LjvTUn+S5J0Xbe+lHJ+ki8n+WGS13Rd96NJPidlIQDz8CRndV23/kGuCuyo/mOSVyS5qpTyxcm234/5FLYXY330ZeZSSMrCV8gAAAAAAJD4eD4AAAAAwCKCpgAAAAAADUFTAAAAAICGoCkAAAAAQEPQFAAAAACgIWgKAMBWVUrZs5Tyxcm/b5RSNkxe311K+dNtXT4AAOgrXddt6zIAAPAQUUp5c5K7u657+7YuCwAAjPGXpgAAbBOllCNKKX8/ef3mUsoHSimXllJuLqW8qJTytlLKVaWUj5VSHjlJ90ullE+WUq4opVxUSnnCtq0FAAA7IkFTAAC2F6uT/GqSY5J8KMnFXdcdnOR7SY6eBE7fneTFXdf9UpKzkrx1WxUWAIAd1yO2dQEAAGDio13X/aCUclWShyf52GT7VUlWJTkgyUFJPl5KySTNrdugnAAA7OAETQEA2F7cmyRd191XSvlBt/nL9+/Lwrq1JFnfdd3h26qAAAA8NPh4PgAAPy6uTbJXKeXwJCmlPLKUcuA2LhMAADsgQVMAAH4sdF23KcmLk/xRKeVLSb6Y5OnbtlQAAOyIyuZPPQEAAAAA4C9NAQAAAAAagqYAAAAAAA1BUwAAAACAhqApAAAAAEBD0BQAAAAAoCFoCgAAAADQEDQFAAAAAGgImgIAAAAANP4/AV3qszZ+mI4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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fW9+atibJmZ97TE6/7Olj19yh853MnZdy1VD7pt0XT9NeJ7dve+3QfVGSufWjPX/dGG37YvRansybN23bujLLZ97Ym/NtXslw/m2+o2Np77bcf957B0/b10irNp+3qHvGdp4OzlH//HzXF67Ow37163Pnux2z7bZR7WvfSe0YHVP9sTOuzp/c8pLcd+D2PGHz5UPXmEtnLs0pv/YzSebmwcq6NTMve9v88tpymuvMoC4j6/bBN27Nw5+2d+74Lu1bZrPzwBnZ/7An53u3vD+3/9YfDq7Lo69/J71WBjjZLT1ouv11c4/3be/9G/XcLXPpjiZouv11c8c35R5acW1WJalfuqf3/Ev3ZNXm83LoU/sGzwdpuzT9fasOvz3J2/L4U5472N4eO6rNc/RmYZBns702ZS+kLXNc+dNumOqX7hm0baEbqzbvLm1b9y6vSfkspU2jeY+rc1dmm35iG75xcOr+aXVr69HmMa6vB3U7/Lrh8ZsMj/FJ47lJf+COdTnw4XceUXBl14ffmWRygHKh/YtNkyQH9tww7+/ottZiymyP3dXvg7Y+bbo2zbQ6HthzQ87fsDHJ3Fjpztfnb/lIcstc2i6vceW0bRjdP9rmrtzLN7wrn9r92qFjunIWymO0L7ryR7d123d98b7ki8N5duW0fxdqU/f8wJ4b8qwVPzlvPk4b/+O279n9+/O2j9a/a9PouVx74OO9B90cGrcutNu2vy67vrgu+eI7c/6GjUPX5j27fz+5cXq/tHU6f8PGoWPa/b0x9a7ha2M39/dtz4E71qWePn992bX7yOZ1q6vDuPM1bvvotkf9yy3zM93eXLf6bRhsT6avw9PSdPtGr4Ht2txp0zR5HvrSbJLhNWZ0zRkdf6Njsb2Gt+Nh0/rPJ9s/nrpi87xjVx3+9Fy99m3PoRXXDuoxzop7rs+uL67L5avPzMzht+f+FdcmX9rXGxdNuw6t2Dw0lz5/y0dyYeaCprs+/M5csP7z8/ur/3jX7auS/vmsK+bG2HLp+vWuO3blrg/vmhtj21+XXXvW5YIMrw1JBtfjJKnfODgxz5X3/E6S3x5aH3ft2T94fPmGdw3SDx0/ck/X5tvtT+bu5w59aTarkszse3sv4b7tk8fpyDkc1Pfwp3Ngz7/Mu7631+xJuv4aOtftPUT/eTe+kpF7jk43h/Ztz4E964bq0v29fMO7suLw9cn2/n/WtfO9y2P0vvyefYNth1Zsyar+5tF52PXv6sO/M1TveUbvkcZde7o271k3rz873Ro1aui++/A/9h/PDuq9VCsPvifJSNB0tI/afnvull6f9dvQjaFdN+0ezIekWd/66XbtWZekN64n3XMOne+mHu156Y45kqBpe53s5m3b54P1Y9x5HanTaLrRedOVl8xdo3tjpwmajnuN2OY7OpaSHPry/HuUrl2LuWdsDc5RNxbX/9NcvbrrRDPvBtsm1XdaO0bb2h874+o8ep/cPS8b5uZD17cz/evcvPLG1a2dhyPrdv3WbLL9ZXPHd2nv2ZddXzwlSa8Oj7nrzKHyx10jAB5qfDwfAAAAAKAhaAoAAAAA0BA0BQAAAABoCJoCAAAAADQETQEAAAAAGoKmAAAAAACNlccl1y1l/ONj6Juv33bkB28pyYprx+YxLd9J+0a3H1XdjiCfB6K8IyljKccslPZo6rbY8zZwjMbsB354/XE7djF5LyXNscpv2jGTjl9qvtPO97i8FlOH42Fc3y6lv8elW6h90/I+VvNxWt1H9125PnPzaaF1Ycy2SfVYbJuXmmbS3G/rcazH0KTxOelcXzmp+OO01k60mHPYX2snWeq6cqzWunH5rl4oUdO20XpMHRPzxv26pVbvuBsd3+0YW8x4n3peFhiXR31Om/u5sedwEXN61FLmZJK5/prU1pHtS2nz8VizjkmfT9s+sn+hPlywPlPu2Rdj9Zg6ZdK2cdsHz9cNreFD69sYy3V9m3Z9WvT6scR5M7T9aPp6EWUu5f547DlaqMxp+5c49sfVaSn7JpZ7JHVcRNoP/PD6XL7hXcftdS3Ag9VxCprWZgGpR5HP5Av/6qs3H/lFfEtNXr9tbB6rr9489LzdP2lfu/2b/XwXY6H6T8tnWr2WWs5C+SylTW150/pyKWUfad3aY8eVPXreBtrxexSu/OCeJR/T3TxNOnah/YtN06W78oN7hv5Os5gyxx3T1mdcuqXmO23ud3kN3TSP1GFafY+lcX27lP5u07fPO5PaNKmN3XhfzPVgWtppfTjvXG4pc/Np0rrQbhuZd5PO9aR+mdani+rzCXO/rceRzOvWvMByU/dxY7U95soP7pnyou3YXLcWbXRt785x97hLs4jr/lLWqOPx4m311ZuTLQskavp3tN7jztW447KlJg/gf9ws1uqrNyc/3Hs8OsYWM2+mrvULjMujPqfN/dzYczhunGbK+p/J69bEuT/lGjZu+1LavNi1YimWOu/mWaidI9eBha5rC94rtuf4iOqb6derpq5D7WifJ8nIPdPYa17jSK5vnaOZE6Pja2jcTls/2jQT2jXtddCgzEnr+1B5C8yZkTIntmfE4Ny05Yw7n1ODjmNety62HRPG1GidJ92jLlinafdQbdrF3guMpL3yg3sGr6vGvc4UPAUeqnw8HwAAAACgIWgKAAAAANAQNAUAAAAAaAiaAgAAAAA0BE0BAAAAABorl3zEZa9NdmztPT73smR28+R0R6vNo3k8s29vkqQ8/tQkyYrZ03rbn3NuDu+9e/C8e3xox1czs+msJMmh2342M0m+9K/X5knZnJnnnDt07KjRPEf3jerqtBhtmePymqY8/tSxdZpUzrRtC+W1lDZNKm+0nDbPqW3/tw8fnLul1q3Nd/Tx6Lke1K32x1k33vZumzzGW834XHP3X2ftpS9e+JgxNl7xiqPav9g0SbJm/cXz/q7dcEn2774+azdcsqg82jL3775+KL+uHm192nSLqWtXp3Jf7zx357E7X+sOvjArN509r85dvl1Zo+WMPu+Ob9uwdsMl+dqBO7P+oh/Nvr3b8oTNlw+VM1pGksG+W7d9dGxfTNqWJBuf+Mjkwqvm9cG4flzM8zXrL87XzrgzZz754qHt7Vzo5kE7Nw/t+Opgf3n8qVl/yo/mrvtunlivaeNl/5rnZW3SWyuS8evCyHV+47c+llx4VWbWzF2bk2T9KT+amdlHzyt/dHwN2rnm3MEx3fnoxt/aDZfk/pu/ldXPPH+u7G7un3tZ1tx9eOz6snH94ubWNBuveEVu3fbRPGHz5UPbJ42L0W33PuI786jRBJeNXLem9fekYyftG3cNHHdMV+bI/pnH783hfPfQGjO65nTPu/E4bb1tx8P+R1yXtU++JGXf3DowWP+ve0rvf4X79ykz++bX4/Deu1Pv/mbv8amXZOMTD+VrZ9yZs0752cyUvcnsZb1xsXfboF0zZXgtWXfwhUP13HjFK5Iz7prYXxvPuT95xquyf/f188bYuHuQ463rzzPO3pizLu2dw3sf8Z151MU/lo2P2JVk7no4OKY/Nw/t+GrKaasn5vntU3+6d5N52VwfbtxwxSDd1w7cmXOa9KPnv71Wdfdx7f1Ydz838/i9vefn/mxm8tm58TrhetOew66cw9c9JWvWb5h3fe/aP03XX4Myd2xNNl3Ve96MnY3f+li+dkavzfPuObp51v+7JrfOq8vGK16Rrx24M2cfvCQrNj1vrgLt3Bu9L9+xNTltdpD3zP4/TtLbPzoPB+ftMz+dlfnEXHvGGd0+eu3p2rzhjKE2tLo16pyR7e19d73usSmZO8dH4tsP/8n5L3baenbP23479dxBGzobn7phUO+kWd/66bq23vO5O3L6ZU8fas9Ae76TufNS9g5VZ6mvCUaPm3nOuVlz+OJ5a/Ng/WjPX1eXzqRreZlfp9F7697YmZBXa8Lru+zdlpnz5t+jdOUs5p6xNThH/fPzrS9cnYd19erOd1fHhV7Tdq99J7VjtK39fZPG/n0Hbh/cB3T1ruUL6eZnNw8O1d51bl557bbmOjOpLeVhe5Onjen3U8/Nxic+Mvsf9uQkGczJca9/J71WBjjZlVrrohNfdNFF9cYbbzyO1QEAAAAAOP5KKTfVWi8at8/H8wEAAAAAGoKmAAAAAAANQVMAAAAAgIagKQAAAABAQ9AUAAAAAKAhaAoAAAAA0BA0BQAAAABoCJoCAAAAADQETQEAAAAAGoKmAAAAAAANQVMAAAAAgIagKQAAAABAQ9AUAAAAAKAhaAoAAAAA0BA0BQAAAABoCJoCAAAAADQETQEAAAAAGoKmAAAAAAANQVMAAAAAgIagKQAAAABAQ9AUAAAAAKAhaAoAAAAA0BA0BQAAAABoCJoCAAAAADQETQEAAAAAGoKmAAAAAAANQVMAAAAAgIagKQAAAABAQ9AUAAAAAKAhaAoAAAAA0BA0BQAAAABoCJoCAAAAADQETQEAAAAAGoKmAAAAAAANQVMAAAAAgIagKQAAAABAQ9AUAAAAAKAhaAoAAAAA0BA0BQAAAABoCJoCAAAAADQETQEAAAAAGoKmAAAAAAANQVMAAAAAgIagKQAAAABAQ9AUAAAAAKAhaAoAAAAA0BA0BQAAAABoCJoCAAAAADQETQEAAAAAGoKmAAAAAAANQVMAAAAAgIagKQAAAABAQ9AUAAAAAKAhaAoAAAAA0BA0BQAAAABoCJoCAAAAADQETQEAAAAAGoKmAAAAAAANQVMAAAAAgIagKQAAAABAQ9AUAAAAAKAhaAoAAAAA0BA0BQAAAABoCJoCAAAAADRKrXXxiUs5kGTf8asOMMEZSe5a7koAU5mncGIzR+HEZ57Cic0c5WR0bq11zbgdSwqaAsujlHJjrfWi5a4HMJl5Cic2cxROfOYpnNjMUR5qfDwfAAAAAKAhaAoAAAAA0BA0hQeH31nuCgALMk/hxGaOwonPPIUTmznKQ4rvNAUAAAAAaHinKQAAAABAQ9AUThCllL2llJ2llB2llBv7204vpVxTSrm5//fR/e2llPL2UsotpZS/L6U8ZXlrDyefUsp7Sil3llJ2NduWPCdLKS/tp7+5lPLS5WgLnKwmzNMtpZTb++vpjlLKDzb7XtOfp18opXx/s/0H+ttuKaX88gPdDjhZlVK+o5RybSnlc6WU3aWUn+tvt57CCWDKHLWWQnw8H04YpZS9SS6qtd7VbHtTkq/XWt/YX3geXWv9pf6i9V+T/GCSpyV5W631actRbzhZlVKek+TeJO+vtW7sb1vSnCylnJ7kxiQXJalJbkry1FrrPy1Dk+CkM2Gebklyb631zSNpn5zkA0kuSfLYJJ9M8qT+7n9I8u+SfCXJDUmurLV+7oFoA5zMSilnJzm71vrpUsq/SW8d/A9Jror1FJbdlDn64lhLwTtN4QT3giTv6z9+X3oLWLf9/bXn/yU5rb/gAcdIrfVTSb4+snmpc/L7k1xTa/16/4XdNUl+4PjXHh4aJszTSV6Q5A9qrQdrrbcluSW9F32XJLml1nprrfVbSf6gnxY4SrXWO2qtn+4//ucke5KcE+spnBCmzNFJrKU8pAiawomjJvnLUspNpZSf7m9bW2u9o//4q0nW9h+fk+TLzbFfyfTFDTg2ljonzVVYHq/sf7T3Pd3HfmOewrIqpcwmuTDJ38V6CieckTmaWEtB0BROIM+qtT4lyfOSvKL/kcOB2vsuDd+nAScIcxJOWO9K8sQkm5LckeQ3l7c6QCnlUUn+KMmraq3faPdZT2H5jZmj1lKIoCmcMGqtt/f/3pnko+l9xGF/97H7/t87+8lvT/IdzeGP628Djq+lzklzFR5gtdb9tdZDtdbDSf5PeutpYp7CsiilrEovGPN/a60f6W+2nsIJYtwctZZCj6ApnABKKY/sf/F2SimPTPJ9SXYl+dMk3a+DvjTJn/Qf/2mSl/R/YfTpSe5pPuIEHD9LnZOfSPJ9pZRH9z/W9H39bcBxMvId35ent54mvXn6I6WUh5dSzktyfpLr0/uxivNLKeeVUh6W5Ef6aYGjVEopSd6dZE+t9X81u6yncAKYNEetpdCzcrkrACTpfY/TR3trVlYm+f1a61+UUm5I8oellJ9Ksi+9XzFMkj9P71dFb0nyL0le9sBXGU5upZQPJNmc5IxSyleSvDbJG7OEOVlr/Xop5b+ndyOZJK+vtS72R2uABUyYp5tLKZvS+7jv3iQ/kyS11t2llD9M8rkk307yilrroX4+r0wvADOT5D211t0PcFPgZHVpkp9IsrOUsqO/7VdiPYUTxaQ5eqW1FJLS+woZAAAAAAASH88HAAAAABgiaAoAAAAA0BA0BQAAAABoCJoCAAAAADQETQEAAAAAGoKmAAAcV6WUx5RSdvT/fbWUcnv/8b2llN9a7voBAMCoUmtd7joAAPAQUUrZkuTeWuubl7suAAAwiXeaAgCwLEopm0spH+s/3lJKeV8p5W9KKftKKS8spbyplLKzlPIXpZRV/XRPLaVsL6XcVEr5RCnl7OVtBQAAJyNBUwAAThRPTPI9Sf59kt9Lcm2t9YIk/5rk+f3A6f9OckWt9alJ3pPkDctVWQAATl4rl7sCAADQ9/Fa6/2llJ1JZpL8RX/7ziSzSb4rycYk15RS0k9zxzLUEwCAk5ygKQAAJ4qDSVJrPVxKub/Offn+4fTuW0uS3bXWZyxXBQEAeGjw8XwAAB4svpBkTSnlGUlSSllVStmwzHUCAOAkJGgKAMCDQq31W0muSPI/SymfTbIjyTOXt1YAAJyMytynngAAAAAA8E5TAAAAAICGoCkAAAAAQEPQFAAAAACgIWgKAAAAANAQNAUAAAAAaAiaAgAAAAA0BE0BAAAAABqCpgAAAAAAjf8PFsdvxCGWXxIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "Spectral\n",
      "---------------------------\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name in filenames:\n",
    "    print(\"\\n---------------------------\\nDEC\\n---------------------------\\n\")\n",
    "    plot_annot(name=name, collar=0.25, skip_overlap=True, groundtruth_path=audio_dataset.label_dir, hypothesis_path=hypothesis_dir_dec)\n",
    "    plt.show()\n",
    "\n",
    "    print(\"\\n---------------------------\\nSpectral\\n---------------------------\\n\")\n",
    "    plot_annot(name=name, collar=0.25, skip_overlap=True, groundtruth_path=audio_dataset.label_dir, hypothesis_path=hypothesis_dir_spectral)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "M6diVRDO1wt8"
   },
   "outputs": [],
   "source": []
  }
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