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wav2vec-awb

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jimregan commited on
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1 Parent(s): c6b1f01

add plot

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  1. kaggle-notebooks/wer/AWB_WER_plots.ipynb +335 -0
kaggle-notebooks/wer/AWB_WER_plots.ipynb ADDED
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+ {
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+ "nbformat": 4,
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+ "nbformat_minor": 0,
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+ "metadata": {
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+ "colab": {
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+ "name": "AWB WER plots.ipynb",
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+ "provenance": [],
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+ "collapsed_sections": []
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+ },
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+ "kernelspec": {
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+ "name": "python3",
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+ "display_name": "Python 3"
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+ },
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+ "language_info": {
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+ "name": "python"
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+ }
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+ },
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "metadata": {
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+ "id": "vK9KbVT8ClG0"
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+ },
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+ "outputs": [],
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+ "source": [
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+ "import matplotlib.pyplot as plt\n",
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+ "import pandas as pd"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "MINS_DATA = \"\"\"\n",
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+ "5\t0.057692307692307696\n",
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+ "10\t0.057692307692307696\n",
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+ "15\t0.05576923076923077\n",
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+ "20\t0.046153846153846156\n",
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+ "25\t0.038461538461538464\n",
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+ "30\t0.046153846153846156\n",
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+ "35\t0.03653846153846154\n",
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+ "40\t0.03653846153846154\n",
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+ "45\t0.025\n",
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+ "50\t0.03653846153846154\n",
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+ "55\t0.026923076923076925\n",
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+ "60\t0.032692307692307694\n",
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+ "\"\"\""
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+ ],
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+ "metadata": {
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+ "id": "xTj5ZZWmEDoB"
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+ },
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+ "execution_count": 2,
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+ "outputs": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "mins = []\n",
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+ "wer = []\n",
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+ "for line in MINS_DATA.split(\"\\n\"):\n",
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+ " if \"\\t\" in line:\n",
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+ " parts = line.split(\"\\t\")\n",
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+ " mins.append(int(parts[0]))\n",
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+ " wer.append(float(parts[1]) * 100)"
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+ ],
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+ "metadata": {
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+ "id": "cbSr-TOTNy6m"
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+ },
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+ "execution_count": 3,
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+ "outputs": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "pd.options.display.float_format = '{:,.2f}'.format\n",
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+ "df = pd.DataFrame(data={\"Minutes\": mins, \"WER\": wer})"
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+ ],
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+ "metadata": {
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+ "id": "F6pLJCGFN5e6"
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+ },
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+ "execution_count": 4,
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+ "outputs": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "df"
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+ ],
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 425
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+ },
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+ "id": "saY52xevV9zo",
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+ "outputId": "f3e8eb1c-b664-4949-c3df-b8dc9d5df5ac"
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+ },
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+ "execution_count": 5,
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+ "outputs": [
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+ {
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+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": [
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+ " Minutes WER\n",
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+ "0 5 5.77\n",
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+ "1 10 5.77\n",
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+ "2 15 5.58\n",
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+ "3 20 4.62\n",
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+ "4 25 3.85\n",
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+ "5 30 4.62\n",
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+ "6 35 3.65\n",
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+ "7 40 3.65\n",
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+ "8 45 2.50\n",
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+ "9 50 3.65\n",
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+ "10 55 2.69\n",
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+ "11 60 3.27"
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+ ],
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+ "text/html": [
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+ "\n",
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+ " <div id=\"df-9390132b-c2ff-4e3b-9528-b03b74f0357a\">\n",
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+ " <div class=\"colab-df-container\">\n",
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+ " <div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe thead th {\n",
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+ " text-align: right;\n",
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+ " }\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " <th>Minutes</th>\n",
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+ " <th>WER</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tbody>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>5</td>\n",
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+ " <td>5.77</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>1</th>\n",
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+ " <td>10</td>\n",
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+ " <td>5.77</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2</th>\n",
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+ " <td>15</td>\n",
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+ " <td>5.58</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>3</th>\n",
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+ " <td>20</td>\n",
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+ " <td>4.62</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>4</th>\n",
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+ " <td>25</td>\n",
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+ " <td>3.85</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>5</th>\n",
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+ " <td>30</td>\n",
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+ " <td>4.62</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>6</th>\n",
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+ " <td>35</td>\n",
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+ " <td>3.65</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>7</th>\n",
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+ " <td>40</td>\n",
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+ " <td>3.65</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>8</th>\n",
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+ " <td>45</td>\n",
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+ " <td>2.50</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>9</th>\n",
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+ " <td>50</td>\n",
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+ " <td>3.65</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>10</th>\n",
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+ " <td>55</td>\n",
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+ " <td>2.69</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>11</th>\n",
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+ " <td>60</td>\n",
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+ " <td>3.27</td>\n",
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+ " </tr>\n",
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+ " </tbody>\n",
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+ "</table>\n",
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+ "</div>\n",
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+ " <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9390132b-c2ff-4e3b-9528-b03b74f0357a')\"\n",
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+ " title=\"Convert this dataframe to an interactive table.\"\n",
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+ " style=\"display:none;\">\n",
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+ " \n",
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+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+ " width=\"24px\">\n",
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+ " <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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+ " <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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+ " </svg>\n",
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+ " </button>\n",
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+ " \n",
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+ " <style>\n",
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+ " .colab-df-container {\n",
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+ " display:flex;\n",
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+ " flex-wrap:wrap;\n",
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+ " gap: 12px;\n",
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+ " }\n",
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+ "\n",
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+ " .colab-df-convert {\n",
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+ " background-color: #E8F0FE;\n",
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+ " border: none;\n",
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+ " border-radius: 50%;\n",
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+ " cursor: pointer;\n",
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+ " padding: 0 0 0 0;\n",
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+ " width: 32px;\n",
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+ " }\n",
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+ "\n",
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+ " .colab-df-convert:hover {\n",
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+ " background-color: #E2EBFA;\n",
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+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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+ " fill: #174EA6;\n",
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+ " }\n",
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+ "\n",
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+ " [theme=dark] .colab-df-convert {\n",
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+ " background-color: #3B4455;\n",
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+ " fill: #D2E3FC;\n",
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+ " }\n",
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+ "\n",
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+ " [theme=dark] .colab-df-convert:hover {\n",
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+ " background-color: #434B5C;\n",
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+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+ " fill: #FFFFFF;\n",
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+ " }\n",
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+ " </style>\n",
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+ "\n",
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+ " <script>\n",
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+ " const buttonEl =\n",
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+ " document.querySelector('#df-9390132b-c2ff-4e3b-9528-b03b74f0357a button.colab-df-convert');\n",
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+ " buttonEl.style.display =\n",
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+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+ "\n",
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+ " async function convertToInteractive(key) {\n",
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+ " const element = document.querySelector('#df-9390132b-c2ff-4e3b-9528-b03b74f0357a');\n",
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+ " const dataTable =\n",
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+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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+ " [key], {});\n",
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+ " if (!dataTable) return;\n",
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+ "\n",
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+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
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+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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+ " + ' to learn more about interactive tables.';\n",
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+ " element.innerHTML = '';\n",
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+ " dataTable['output_type'] = 'display_data';\n",
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+ " await google.colab.output.renderOutput(dataTable, element);\n",
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+ " const docLink = document.createElement('div');\n",
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+ " docLink.innerHTML = docLinkHtml;\n",
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+ " element.appendChild(docLink);\n",
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+ " }\n",
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+ " </script>\n",
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+ " </div>\n",
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+ " </div>\n",
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+ " "
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+ ]
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+ },
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+ "metadata": {},
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+ "execution_count": 5
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+ }
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+ ]
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+ },
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+ {
291
+ "cell_type": "code",
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+ "source": [
293
+ "import numpy as np\n",
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+ "ax = plt.gca()\n",
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+ "ax.set_xticks(np.arange(5, 125, 5))\n",
296
+ "ax.set_xticklabels(labels=mins, minor=True)\n",
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+ "\n",
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+ "df.plot(kind='line', x='Minutes', y='WER', ax=ax)"
299
+ ],
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+ "metadata": {
301
+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 297
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+ },
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+ "id": "yxMFzpL4N9q3",
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+ "outputId": "c4b04593-2e33-458f-8752-44fc1ba23830"
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+ },
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+ "execution_count": 7,
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+ "outputs": [
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+ {
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+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": [
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+ "<matplotlib.axes._subplots.AxesSubplot at 0x7f271c39dcd0>"
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+ ]
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+ },
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+ "metadata": {},
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+ "execution_count": 7
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+ },
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+ {
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+ "output_type": "display_data",
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+ "data": {
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+ "text/plain": [
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+ "<Figure size 432x288 with 1 Axes>"
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+ ],
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+ "image/png": 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\n"
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+ }