Compare_Spectral_Vs_DEC_AMI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DT5hNAsu0X-u"
   },
   "source": [
    "# **LOAD LIBRARIES**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 16374,
     "status": "ok",
     "timestamp": 1620721793755,
     "user": {
      "displayName": "Shashi Kant Gupta",
      "photoUrl": "",
      "userId": "03361959783973937060"
     },
     "user_tz": -330
    },
    "id": "ouVW09h_0Usm",
    "outputId": "8fb47379-82ab-42c1-eb00-72da8813b9f7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K     |████████████████████████████████| 1.9MB 8.5MB/s \n",
      "\u001b[K     |████████████████████████████████| 358kB 7.7MB/s \n",
      "\u001b[K     |████████████████████████████████| 1.2MB 15.0MB/s \n",
      "\u001b[K     |████████████████████████████████| 102kB 11.7MB/s \n",
      "\u001b[K     |████████████████████████████████| 645kB 26.8MB/s \n",
      "\u001b[K     |████████████████████████████████| 552kB 38.1MB/s \n",
      "\u001b[K     |████████████████████████████████| 51kB 4.1MB/s \n",
      "\u001b[K     |████████████████████████████████| 61kB 6.8MB/s \n",
      "\u001b[K     |████████████████████████████████| 51kB 6.6MB/s \n",
      "\u001b[K     |████████████████████████████████| 133kB 11.0MB/s \n",
      "\u001b[?25h"
     ]
    }
   ],
   "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": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 21415,
     "status": "ok",
     "timestamp": 1620721799586,
     "user": {
      "displayName": "Shashi Kant Gupta",
      "photoUrl": "",
      "userId": "03361959783973937060"
     },
     "user_tz": -330
    },
    "id": "Qe3sDhQYbwDi",
    "outputId": "13465f91-fe52-4a50-ea78-3f15d27e467a"
   },
   "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, 11.5MB/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, 20.7MB/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, 6.62MB/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, 6.93MB/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": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 20469,
     "status": "ok",
     "timestamp": 1620721799587,
     "user": {
      "displayName": "Shashi Kant Gupta",
      "photoUrl": "",
      "userId": "03361959783973937060"
     },
     "user_tz": -330
    },
    "id": "0iqaDi7jb5qQ",
    "outputId": "7a2d469d-1aa1-4cfc-9702-cba5412eef10"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "executionInfo": {
     "elapsed": 26991,
     "status": "ok",
     "timestamp": 1620721806540,
     "user": {
      "displayName": "Shashi Kant Gupta",
      "photoUrl": "",
      "userId": "03361959783973937060"
     },
     "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": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 61005,
     "status": "ok",
     "timestamp": 1620721843329,
     "user": {
      "displayName": "Shashi Kant Gupta",
      "photoUrl": "",
      "userId": "03361959783973937060"
     },
     "user_tz": -330
    },
    "id": "OwMmmaqYhnn0",
    "outputId": "94f8c7ba-9ecb-4725-b43c-40e4494df248"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading audio dataset...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1c0l9amE_0eVD1soSXvxUvJzuxzFxkn2u\n",
      "To: /content/tmp.zip\n",
      "668MB [00:05, 122MB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download and Extraction Complete\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, 16.7MB/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, 69.6MB/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=False)\n",
    "\n",
    "print(\"\\nData size:\", len(audio_dataset))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OiFZ_66hh6Nv"
   },
   "source": [
    "# Spectral - EigenGap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 115,
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      "f18f4a5d013c49d1a9847e7d6d9fcebc",
      "a3298af1dd3e46fca1358604cb54dc92",
      "7217395fa3e54afcaa979b07418ec3ed",
      "7cba642dae2d458fb85d3a9103eb963b",
      "e9755d6afb1f4eb2ab21a9eb114e03e5",
      "92420e6c6c72482ca139a7af886d3d43",
      "610c868524bf4b40899b160c9b753f0e",
      "458a2c615fe844fe934014e878188c7b"
     ]
    },
    "executionInfo": {
     "elapsed": 196365,
     "status": "ok",
     "timestamp": 1620722008975,
     "user": {
      "displayName": "Shashi Kant Gupta",
      "photoUrl": "",
      "userId": "03361959783973937060"
     },
     "user_tz": -330
    },
    "id": "x5JJ_CkFiEcI",
    "outputId": "4d71ea52-cf62-43d3-df81-50b456e42cd3"
   },
   "outputs": [
    {
     "data": {
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       "model_id": "55c352b97a6947a1b2dda2c704e70c05",
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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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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f18f4a5d013c49d1a9847e7d6d9fcebc",
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       "HBox(children=(FloatProgress(value=0.0, max=16.0), HTML(value='')))"
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     },
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    },
    {
     "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": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 166,
     "referenced_widgets": [
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      "9ed6cf8e6a034cd1b88b6640b22c84ba"
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    },
    "executionInfo": {
     "elapsed": 338473,
     "status": "ok",
     "timestamp": 1620722152408,
     "user": {
      "displayName": "Shashi Kant Gupta",
      "photoUrl": "",
      "userId": "03361959783973937060"
     },
     "user_tz": -330
    },
    "id": "D3myQoh-lv-U",
    "outputId": "b8b4e83a-60be-4b3f-854b-2aee3e7774cb"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "29050bdebb6f41a0900aa6d523a50844",
       "version_major": 2,
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       "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": {
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    },
    {
     "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": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 337806,
     "status": "ok",
     "timestamp": 1620722152837,
     "user": {
      "displayName": "Shashi Kant Gupta",
      "photoUrl": "",
      "userId": "03361959783973937060"
     },
     "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": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 111
    },
    "executionInfo": {
     "elapsed": 337247,
     "status": "ok",
     "timestamp": 1620722152838,
     "user": {
      "displayName": "Shashi Kant Gupta",
      "photoUrl": "",
      "userId": "03361959783973937060"
     },
     "user_tz": -330
    },
    "id": "zEIp5MM9uR_A",
    "outputId": "ca4d5080-255c-414f-bf1d-b6bc2152da76"
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>DER (DEC)</th>\n",
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       "  <tbody>\n",
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       "      <th>TOTAL</th>\n",
       "      <td>23.933117</td>\n",
       "      <td>21.99261</td>\n",
       "      <td>-1.940507</td>\n",
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       "       DER (DEC)  DER (Spectral)  DER Difference\n",
       "item                                            \n",
       "TOTAL  23.933117        21.99261       -1.940507"
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     "status": "ok",
     "timestamp": 1620722152839,
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       "  <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",
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       "      <th>ES2004d</th>\n",
       "      <td>43.723054</td>\n",
       "      <td>21.358290</td>\n",
       "      <td>-22.364764</td>\n",
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       "    <tr>\n",
       "      <th>ES2004c</th>\n",
       "      <td>34.416146</td>\n",
       "      <td>15.847897</td>\n",
       "      <td>-18.568249</td>\n",
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       "    <tr>\n",
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       "      <td>46.961556</td>\n",
       "      <td>33.316673</td>\n",
       "      <td>-13.644883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2004a</th>\n",
       "      <td>45.792694</td>\n",
       "      <td>32.376355</td>\n",
       "      <td>-13.416339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EN2002a</th>\n",
       "      <td>34.793612</td>\n",
       "      <td>22.432196</td>\n",
       "      <td>-12.361416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TS3003c</th>\n",
       "      <td>17.558089</td>\n",
       "      <td>17.501432</td>\n",
       "      <td>-0.056657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS1009b</th>\n",
       "      <td>8.719037</td>\n",
       "      <td>8.684103</td>\n",
       "      <td>-0.034934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TS3003a</th>\n",
       "      <td>29.806248</td>\n",
       "      <td>29.806248</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EN2002c</th>\n",
       "      <td>16.629255</td>\n",
       "      <td>16.739044</td>\n",
       "      <td>0.109789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ES2004b</th>\n",
       "      <td>14.375617</td>\n",
       "      <td>14.623488</td>\n",
       "      <td>0.247871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS1009c</th>\n",
       "      <td>10.351180</td>\n",
       "      <td>10.726946</td>\n",
       "      <td>0.375766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EN2002d</th>\n",
       "      <td>20.305680</td>\n",
       "      <td>21.535155</td>\n",
       "      <td>1.229474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EN2002b</th>\n",
       "      <td>22.268479</td>\n",
       "      <td>24.200475</td>\n",
       "      <td>1.931996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TS3003b</th>\n",
       "      <td>17.177895</td>\n",
       "      <td>25.654118</td>\n",
       "      <td>8.476223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TS3003d</th>\n",
       "      <td>36.253148</td>\n",
       "      <td>47.080851</td>\n",
       "      <td>10.827703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IS1009d</th>\n",
       "      <td>18.431806</td>\n",
       "      <td>31.471310</td>\n",
       "      <td>13.039504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         DER (DEC)  DER (Spectral)  DER Difference\n",
       "item                                              \n",
       "ES2004d  43.723054       21.358290      -22.364764\n",
       "ES2004c  34.416146       15.847897      -18.568249\n",
       "IS1009a  46.961556       33.316673      -13.644883\n",
       "ES2004a  45.792694       32.376355      -13.416339\n",
       "EN2002a  34.793612       22.432196      -12.361416\n",
       "TS3003c  17.558089       17.501432       -0.056657\n",
       "IS1009b   8.719037        8.684103       -0.034934\n",
       "TS3003a  29.806248       29.806248        0.000000\n",
       "EN2002c  16.629255       16.739044        0.109789\n",
       "ES2004b  14.375617       14.623488        0.247871\n",
       "IS1009c  10.351180       10.726946        0.375766\n",
       "EN2002d  20.305680       21.535155        1.229474\n",
       "EN2002b  22.268479       24.200475        1.931996\n",
       "TS3003b  17.177895       25.654118        8.476223\n",
       "TS3003d  36.253148       47.080851       10.827703\n",
       "IS1009d  18.431806       31.471310       13.039504"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_both"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 335942,
     "status": "ok",
     "timestamp": 1620722152847,
     "user": {
      "displayName": "Shashi Kant Gupta",
      "photoUrl": "",
      "userId": "03361959783973937060"
     },
     "user_tz": -330
    },
    "id": "cNkpz4-XpHDs",
    "outputId": "d9954d14-413c-4646-bb43-9d92e383ee0a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Better DER for 8 examples.\n",
      "Poor DER for 7 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": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 748,
     "status": "ok",
     "timestamp": 1620722256339,
     "user": {
      "displayName": "Shashi Kant Gupta",
      "photoUrl": "",
      "userId": "03361959783973937060"
     },
     "user_tz": -330
    },
    "id": "vnXwVIAwqoSC"
   },
   "outputs": [],
   "source": [
    "filenames = [\"EN2002c\"] + list(df_both.index[[-1, -2, -3, 2, 1, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "executionInfo": {
     "elapsed": 65178,
     "status": "ok",
     "timestamp": 1620722322394,
     "user": {
      "displayName": "Shashi Kant Gupta",
      "photoUrl": "",
      "userId": "03361959783973937060"
     },
     "user_tz": -330
    },
    "id": "9hAGvSouLxTe",
    "outputId": "e5716879-ea2c-4c3b-da13-ce1164caf45f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------\n",
      "DEC\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",
      "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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      "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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\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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N3LD1G0N5j74//bjPJJs+k9lt52Ttgacl+bPMXvOzWXto73U2ndFL+OT1SZKD9/lAkvcn12wa5LHyoL/tHeT3PpiVByV3/Ot/zn53vzArDxp/jP1tpjF78aG9Y8uf7ThxW94brjtnsKg+5rreZs+by9rjzhgc26LLOVI3O5XHzppy37uU9zWbBm04e/GhSXr1ePt3jh0kPeKbVbtsOiM3fPOc3HDe3KCf3v6dY3N7W/8PPOCvkrxlaFflzs9lywWfS5KsefYrpy5iufNzizumKWy54MwkyY2XfmG+PNdsSq7ZlC2XHpM1kzZut19z7GXz6V/bNQDOypYLbsuaZ78yy268MNl0YW9xNZ7q91tmt2ZNkmMOunp+3aYzevm39dUv76j9n3hAVbaLs+bZC9OM23bZjSPlqvvbpjOy5Wvz9XHt3AGdbTu/7zPn27atz0G/reu3av9+W6x59ivzgNuvGKSt99u1n/4xjeZ1yx2vz13ffmuStwzaaculvYu4ZasOSnL/Kqez2u1u66yzvqH26x/PxVcmJ2a+viaZZgxfsyk5aiZ572zy5GpZhl9vmd2aZJ/M7HivUxv0gUq/P+6suq9MM4bH9c/5vG4au77fH3a0fpp5Z9mqr+Szb74tycUTtxnta9O4du6APOMtG5O8MckXO1J8fXgM7ais/XYbGbNDRpc9eWZ+m35/bMd5Mr4NFmPSPLFsVe8/2r7/rdva8bhwzurqC/0+VO78XJbdeNnQMYy2/42XfmF4bs/IGO5rj3/L7NaceuxlCw+kzf/73/p4kj9q2+ag9Nru9JHEX+z92zQ7PM4H9XzWyDZfzJYLuvtZ3V/XPPAj85uMnjsm6UrbL8tQf1jfXne+OA884E9Tt1m/Htcce1myfiazb98/a09NjljT78N9/f+4Hq2TSeb7/8qnXLKI7fr7+vpg3u73qdF8F1OGQVnuN1/fB265PMnlSc5bOK66zpf9c17/fbtu9ktzWfuR0Tq7Z83X4VmZPe+gKfZXt9lZmT3v1XngAW/Nz7+541pm08d656RDbs5oP549b/92X0m/Pme3vTrZNv6ae+g6eUG9nrFw/LRGrx1G1ddlnefaMdd3/e2OXj/TO752DC+YF6rrsWU3XpjPXnpMb/3otUGSXHxl7tpnRfa7+5Jk0yXJk9fnhut+ZD6PNtm/XPjtJPPXKf3ly268MB/+0q9l7XEbsumGY3Jje17YcsGZWfPBg/KDzz4nN2y9NoN23DRbze1te1XX9V110fm663pmmmuc0bT1dd9i8xodR6PrdpTX0D5vTb+P95w+dL7ZcsGZgzZubnnQ8HlxgtmLD83a1/3lyNJ2bGw6Y74tFsyN8+Pnhq3fSPY/I7P9z/RHzSTvvXzhGJh0rNU5fOj96Hbt8qMP+NfudQvyb8fyNef0YgTfnN9m5f2SXFMlPeryzL76GYNjOGyfbd1lBRjh9nwAAAAAgIqgKQAAAABARdAUAAAAAKAiaAoAAAAAUBE0BQAAAACoCJoCAAAAAFT2vbd3uOFZ79u5DdeXJOdUrzP+9bj1C/KbtK9pdJRnFw3Vz47y3dX106bZGbsr3wX76LVBv976f08/rrsM4/rfuc85duxuJq27J9Lvqp3b39qxeZ3ald2CMXXMfNqReq7L01W2Uz940ND+x5W/c9t6f2PG+fx2r5+Yf6eOPtO1fVc9TbOfxRzrvIVtNSn9ULmmmQvHrZt2DE+cj49Jcs+Oia4+1++PO2+4r+xKWy6m3+3qvNMbS4vbZvp6en37d+3I353Lb0G7LbbPTTk2F298e43Wb78ORtOOmyMGFjGvJNnBHHzM2DzH593ddpPzGb/NxDlx2jln6nKMT7Oj9t/wrPfl9I8k3ccyoU7GmjwWduTc5xy74Py3uLw60o7W27j3k9qls+537hinN5r/NPubsh3Xl2T9TMf6V0/O/h7+vDD1/LSreVTn2q4xssNrus515+ywDJPmq+mOfe3CskzTBtOeO3b1Gmaa9PdUGQYWzg0L6nJ9Sfa5aBH5n1O9HjfOdnZOmrYMHel2pq13dV+DeQFgce71oOnpH3nuzgVO1zdJf7v1TTUBNu3fMr+8a1lXfp37KePXjarLs8PyTzfRD9XPpHwnlXO0bnYmj5017b53Je/B+/k+0a+30z/y3HbdacP9oNVfP9oHT/3gpUPv64uU0XWT9D4UTZ9+2jwn2dH+urffPPJ+7Xxek8ZLv8/0j3N0vFXlGV8Xa6v9d6cZu+3oOK/728j++208rn4666Wjz9TbDz4kdNTTNPvpyqu/fHw79+tq/kJ2YpsPXXBOMRfW6ertdzT/jNtH/bqur3tK17y1i+Ou7ivTjOFJaRbT78b1/UnbDxv+cLOz++wyP0duzvCYHd731PU+YczOp5mij44ZmztrcnuNfnjcnNE5q6sv9JcN6r3juOp5cnTZxDm4znNo/XC9DM8n3W23IJ+h/dbbjO9nC+bErvlgR7rSjquDHczNtd75/o/TfSyjdTJJvU3XWNjRdj298tbbLzav0bQLz78L+toOzped75NFlGtn9I+lrp9J+xut/9q4Oulq5x189ujqrx3XOGO3mfKaIBn9T5Ux++3IY+x1y+i8UG+/yHIPfd5K91w1VLaO/Cdf0/RtzqC9Jl13jJtXxl2HjC4bZ9znyNH1C7Ybk6ZrDpvUdzrVfXz4fDN0Pnnz33fvs8vQ552F15Pd++1KM8Y0c3edblxbT2qPSdcN4/bV2Ta7+z+DgL2V2/MBAAAAACqCpgAAAAAAFUFTAAAAAICKoCkAAAAAQEXQFAAAAACgsu9iEt/RJMse+sDs86ADeguOXJeZlWuG0sycMv/++ptOz8zxN+f6g0/PEasfMki7fNWDc8SaZb00c9uTZPC+79LPPyXH/tKTMnP7F3P9j52eI5LMHPnZweuse9NQ+u/c/Ws5uC1T37bbTsrKJD+4/3PytZtvy9Grrs5dzW/liq9emZVjjrG/zTQGxzZN4ra8y7/x3cGi5asePJ/XKWsGdTFzyprkIcPHt6hyrpu87VR57Kwp973TeV/998mKE7Lt72azMm0b3HpMkuTAg+d/ZXTQT9rtlv/rDTni5546WH/gwZfmR3+i11fuv++vLNhVs/8Ts+aZj1l0EZv9n7jobXZk9cmvyPatl2TZqsfNLzxyXbLihKw+cMtU2+fWCwbp7/zcZVlYzJdk9cnfT5JsP/zELHvU4wZ1nav/fni/SVbPrEqSXHbbihzTX7fuTVm96rBB0sOP/anO8tz5uTsG+1998vGdacZtu/3wE7OsKsdQf1v3pqy+828Hbx+25o7su8/Ctu1bffIr5t+09Tn6vj6e0W1uPfAn84A27eo7b524nwXt1y6/9NNfy48u+5X5vA+7KasP3JLttx+Y7Vv3y7I7L0mO62/xkjbd58fuK6nar3b80b2/o+Ozbtu+acbwkeuSq5Yn6x4yvGzk9eqZVbnz+rkd57cIgz5Q6ffHnVX3lWnG8Lj+2c/rtmsPG7u+34e2b71k4vppbN/66Kw++a7s6BdhR/vaNB625o5cP/fMHLEmSR7bkeIhWX3y06Yva7/dRsbsAkPLlif5aOc4377/o6be9yST5ontWx+dZavul/0OuTvbtzZZtmrhnNXVF/p9qNn/idl++FFDx92fD+rtR+eGzjHcWj2zKtsPOmrBGOjnv98hTxvsJ/nnJDMduTy2t3zdSZ159OeaOv3qk+9MVz+r++vcLc/K4Aqwng92pCttvyyjfeTIdZnZ/+Z8847hNhvU42E3JZ//UGZOuTNJqj7cN3ps05jv/9s+87isfMq0270kyUeTHNHWX79PLcx3MWUYlOWuZw2u4W5f/Yjsf/ljexf1/Trrz+9d58vRc157fTWzck1Hnd2z5uvwJe21747295Kh1zOnXJNvXvormdv4zax55kjSdc/o/f3Wockhw31k5pQ7231dm/64mFm5KTnulWOvuYeuk+v6u3pDsuL0kX3Prx+9dhhVX5d1OnJdVh+4MI/edcLZufLj++bop71wMIYXzAvV9dj2w0/M6gMP7K3vmleOPzr323517v7W47LPY05Mkix/6Hd7eVTXmI888UHZ/6BfH1yn9G0//MTB56HDl381y574q4Oy/uCftqb8xO1ZfseDM2jHdSclV21Ljsp8ex25Ltu+enD3Z5KOa4v+MSYZvo7pWjZOx9zSuX5SXqPjaFx+Xee6/jX2YB+ntivm+3t9vll98ity5dazcnSS8sBvJ4+e7vPWzPE3V/2+r98Wb5pviwVz4/z75asenKx7U2b2bz/3XvXvyZG3JCt+beEOd3QNOW79yPIr7/iJHN21bijdR5P8QpJk5uY2RvCAywbptn32a1l59L/NJ7/qEb154KbeMdx0962ZPFIBekrTNFMnXrt2bbN58+bdWBwAAAAAgN2vlDLbNE3nN1Hcng8AAAAAUBE0BQAAAACoCJoCAAAAAFQETQEAAAAAKoKmAAAAAAAVQVMAAAAAgIqgKQAAAABARdAUAAAAAKAiaAoAAAAAUBE0BQAAAACoCJoCAAAAAFQETQEAAAAAKoKmAAAAAAAVQVMAAAAAgIqgKQAAAABARdAUAAAAAKAiaAoAAAAAUBE0BQAAAACoCJoCAAAAAFQETQEAAAAAKoKmAAAAAAAVQVMAAAAAgIqgKQAAAABARdAUAAAAAKAiaAoAAAAAUBE0BQAAAACoCJoCAAAAAFQETQEAAAAAKoKmAAAAAAAVQVMAAAAAgIqgKQAAAABARdAUAAAAAKAiaAoAAAAAUBE0BQAAAACoCJoCAAAAAFQETQEAAAAAKoKmAAAAAAAVQVMAAAAAgIqgKQAAAABARdAUAAAAAKAiaAoAAAAAUBE0BQAAAACoCJoCAAAAAFQETQEAAAAAKoKmAAAAAAAVQVMAAAAAgIqgKQAAAABARdAUAAAAAKAiaAoAAAAAUBE0BQAAAACoCJoCAAAAAFQETQEAAAAAKoKmAAAAAAAVQVMAAAAAgIqgKQAAAABARdAUAAAAAKAiaAoAAAAAUBE0BQAAAACoCJoCAAAAAFQETQEAAAAAKoKmAAAAAAAVQVMAAAAAgIqgKQAAAABARdAUAAAAAKBSmqaZPnEpNya5ZvcVBxjjsCQ3LXUhgImMU9izGaOw5zNOYc9mjLI3OrJpmsO7ViwqaAosjVLK5qZp1i51OYDxjFPYsxmjsOczTmHPZoxyX+P2fAAAAACAiqApAAAAAEBF0BR+OPzfpS4AsEPGKezZjFHY8xmnsGczRrlP8UxTAAAAAICKb5oCAAAAAFQETWEPUUq5upQyV0r5cillc7vs0FLKp0opl7d/D2mXl1LKO0spV5RS/rmU8tilLT3sfUop7y6lfKOUsqVatugxWUp5fpv+8lLK85fiWGBvNWacri+lXNeeT79cSnl6te6/tuN0Wynl56vlT2uXXVFK+b17+zhgb1VK+fFSykWllK+WUraWUl7VLnc+hT3AhDHqXApxez7sMUopVydZ2zTNTdWydyS5uWmat7cnnkOapvnd9qT1n5I8PcnxSf530zTHL0W5YW9VSvkPSW5N8pdN06xuly1qTJZSDk2yOcnaJE2S2SQzTdN8awkOCfY6Y8bp+iS3Nk3zhyNpH5Xk3CSPS3JEkk8neWS7+l+S/FySa5N8IcmpTdN89d44BtiblVKWJ1neNM0XSykHp3ce/MUkL4jzKSy5CWP0V+NcCr5pCnu4ZyV5b/v6vemdwPrL/7Lp+X9JHtSe8IB7SNM0/5Dk5pHFix2TP5/kU03T3Nx+sPtUkqft/tLDfcOYcTrOs5Kc1zTN95qmuSrJFel96HtckiuaprmyaZo7k5zXpgV2UdM0NzRN88X29XeSXJrkoXE+hT3ChDE6jnMp9ymCprDnaJJ8spQyW0p5abtsWdM0N7Svv55kWfv6oUn+rdr22kw+uQH3jMWOSWMVlsYr21t7392/7TfGKSypUsqKJMcluTjOp7DHGRmjiXMpCJrCHuRJTdM8NsmJSV7R3nI40PSepeF5GrCHMCZhj/WnSR6e5DFJbkjyP5e2OEAp5QFJ/irJq5umuaVe53wKS69jjDqXQgRNYY/RNM117d9vJPmb9G5x2N6/7b79+402+XVJfrza/GHtMmD3WuyYNFbhXtY0zfamae5qmubuJGeldz5NjFNYEqWU/dILxry/aZq/bhc7n8IeomuMOpdCj6Ap7AFKKQe1D95OKeWgJE9NsiXJxiT9Xwd9fpKPtK83Jnle+wujj0/y79UtTsDus9gx+YkkTy2lHNLe1vTUdhmwm4w84/uX0jufJr1xekop5f6llKOSPCLJJen9WMUjSilHlVL2T3JKmxbYRaWUkuQvklzaNM3/qlY5n8IeYNwYdS6Fnn2XugBAkt5znP6md87Kvkk+0DTNx0spX0jyoVLKi5Jck96vGCbJ36X3q6JXJLk9yQvv/SLD3q2Ucm6SE5IcVkq5Nsmbkrw9ixiTTdPcXEr5b+ldSCbJm5ummfZHa4AdGDNOTyilPCa9232vTnJ6kjRNs7WU8qEkX03ygySvaJrmrjafV6YXgLlfknc3TbP1Xj4U2Fs9MclpSeZKKV9ul70uzqewpxg3Rk91LoWk9B4hAwAAAABA4vZ8AAAAAIAhgqYAAAAAABVBUwAAAACAiqApAAAAAEBF0BQAAAAAoCJoCgDAblVK+bFSypfbf18vpVzXvr61lPJ/lrp8AAAwqjRNs9RlAADgPqKUsj7JrU3T/OFSlwUAAMbxTVMAAJZEKeWEUsrftq/Xl1LeW0r5x1LKNaWUXy6lvKOUMldK+XgpZb823UwpZVMpZbaU8olSyvKlPQoAAPZGgqYAAOwpHp7kZ5M8M8n7klzUNM2aJN9N8ow2cPonSU5ummYmybuTvHWpCgsAwN5r36UuAAAAtC5smub7pZS5JPdL8vF2+VySFUlWJlmd5FOllLRpbliCcgIAsJcTNAUAYE/xvSRpmubuUsr3m/mH79+d3nVrSbK1aZonLFUBAQC4b3B7PgAAPyy2JTm8lPKEJCml7FdKWbXEZQIAYC8kaAoAwA+FpmnuTHJykv9eSvlKki8n+emlLRUAAHujMn/XEwAAAAAAvmkKAAAAAFARNAUAAAAAqAiaAgAAAABUBE0BAAAAACqCpgAAAAAAFUFTAAAAAICKoCkAAAAAQEXQFAAAAACg8v8BXbLBvlvXjjsAAAAASUVORK5CYII=\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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      "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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