data_cleaner.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, sys\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_path = './data'\n",
    "manifest_path = '{}/manifests'.format(base_path)\n",
    "dataset_path = base_path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Turn absolute path into relative path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for manifest_file in os.listdir(manifest_path):\n",
    "    if 'csv' not in manifest_file:\n",
    "        continue\n",
    "    df = pd.read_csv('{}/{}'.format(manifest_path, manifest_file), header=None)\n",
    "    df[0] = df[0].apply(lambda x: './' + x[x.index('data/'):])\n",
    "    df[1] = df[1].apply(lambda x: './' + x[x.index('data/'):])\n",
    "    df.to_csv('{}/{}'.format(manifest_path, manifest_file), index=False, header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>./data/LibriSpeech_dataset/test_other/wav/2414...</td>\n",
       "      <td>./data/LibriSpeech_dataset/test_other/txt/2414...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>./data/LibriSpeech_dataset/test_other/wav/7902...</td>\n",
       "      <td>./data/LibriSpeech_dataset/test_other/txt/7902...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>./data/LibriSpeech_dataset/test_other/wav/8188...</td>\n",
       "      <td>./data/LibriSpeech_dataset/test_other/txt/8188...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>./data/LibriSpeech_dataset/test_other/wav/3538...</td>\n",
       "      <td>./data/LibriSpeech_dataset/test_other/txt/3538...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>./data/LibriSpeech_dataset/test_other/wav/3528...</td>\n",
       "      <td>./data/LibriSpeech_dataset/test_other/txt/3528...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                                   0  \\\n",
       "0  ./data/LibriSpeech_dataset/test_other/wav/2414...   \n",
       "1  ./data/LibriSpeech_dataset/test_other/wav/7902...   \n",
       "2  ./data/LibriSpeech_dataset/test_other/wav/8188...   \n",
       "3  ./data/LibriSpeech_dataset/test_other/wav/3538...   \n",
       "4  ./data/LibriSpeech_dataset/test_other/wav/3528...   \n",
       "\n",
       "                                                   1  \n",
       "0  ./data/LibriSpeech_dataset/test_other/txt/2414...  \n",
       "1  ./data/LibriSpeech_dataset/test_other/txt/7902...  \n",
       "2  ./data/LibriSpeech_dataset/test_other/txt/8188...  \n",
       "3  ./data/LibriSpeech_dataset/test_other/txt/3538...  \n",
       "4  ./data/LibriSpeech_dataset/test_other/txt/3528...  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reduce manifest size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_reduce = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if is_reduce:\n",
    "    for manifest_file in os.listdir(manifest_path):\n",
    "        if 'csv' not in manifest_file:\n",
    "            continue\n",
    "        df = pd.read_csv('{}/{}'.format(manifest_path, manifest_file), header=None).head(100)\n",
    "        df.to_csv(manifest_file, index=False, header=False)\n",
    "\n",
    "        for col in df.columns:\n",
    "            split_path = df.loc[:,col].str.split('/')\n",
    "            split_file = list(split_path.apply(lambda x: x[-1]))\n",
    "            split_dir = split_path.apply(lambda x: '/'.join(x[:-1]))\n",
    "\n",
    "            for file in os.listdir(split_dir[0]):\n",
    "                if file not in split_file:\n",
    "                    os.remove('{}/{}'.format(split_dir[0], file))"
   ]
  }
 ],
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