data_collecting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#train an CNN model for the accent detector\n",
    "#for low tech demo: onlhy destringuish us accents with others \n",
    "\n",
    "#import models to be used\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import librosa\n",
    "import multiprocessing\n",
    "import sys\n",
    "\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "from keras import utils\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Flatten\n",
    "from keras.layers.convolutional import MaxPooling2D, Conv2D\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.callbacks import EarlyStopping, TensorBoard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEBUG = True\n",
    "SILENCE_THRESHOLD = .01\n",
    "RATE = 24000\n",
    "N_MFCC = 13\n",
    "COL_SIZE = 30\n",
    "EPOCHS = 10 #35#250"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yidanyuan/anaconda3/lib/python3.7/site-packages/pydub/utils.py:165: RuntimeWarning: Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work\n",
      "  warn(\"Couldn't find ffmpeg or avconv - defaulting to ffmpeg, but may not work\", RuntimeWarning)\n"
     ]
    }
   ],
   "source": [
    "#get audio data and information from speech accent archive using Webscraping\n",
    "import os # To interact with operating system and format file name\n",
    "import shutil # To copy file object from python to disk\n",
    "import requests\n",
    "import bs4 as bs \n",
    "from collections import Counter\n",
    "import re\n",
    "import urllib.request\n",
    "import sys\n",
    "from pydub import AudioSegment\n",
    "#source = requests.get(\"http://accent.gmu.edu/browse_language.php\")\n",
    "#soup = bs.BeautifulSoup(source.content, features='html.parser') \n",
    "#print(soup.prettify())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"specify desired languages; \n",
    "for now (check point #1): only english and mandarin are used;\n",
    "eventually/ideally, all languages will be used\"\"\"\n",
    "languages = [\"mandarin\",\"english\"]\n",
    "URL = 'http://accent.gmu.edu/'\n",
    "mp3_url = 'http://chnm.gmu.edu/accent/soundtracks/{}.mp3'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#get data and bio information from the url\n",
    "def create_df(languages = languages, url = URL):\n",
    "    import bs4 as bs \n",
    "    search_url = [url+'browse_language.php?function=find&language={}'.format(l) for l in languages]\n",
    "    htmls = []\n",
    "    for url in search_url:\n",
    "        #print('downloading from {}'.format(url))\n",
    "        htmls.append(requests.get(url).text)\n",
    "    #print(htmls)\n",
    "    \n",
    "    bss = [bs.BeautifulSoup(html,'html.parser') for html in htmls]\n",
    "    #print(bss)\n",
    "    people = []\n",
    "    counter = 0\n",
    "    for b in bss:\n",
    "        \n",
    "        for p in b.find_all('p'):\n",
    "            counter += 1\n",
    "            if p.a:\n",
    "                text = p.text.replace(' ','').split(',')\n",
    "                address = 'http://accent.gmu.edu/'+p.a['href']\n",
    "                speakerid = address.split('speakerid=')[1]\n",
    "                #print(speakerid)\n",
    "                s = bs.BeautifulSoup(requests.get(address).text,'html.parser')\n",
    "                each_bio = [li.text for li in s.find('ul','bio').find_all('li')]\n",
    "                native_language = each_bio[1].split(': ')[1]\n",
    "                eng_residence = each_bio[6].split(': ')[1]\n",
    "                if native_language.find(\"english\") != -1 and eng_residence.find('usa') != -1:\n",
    "                    if_us_native = 1\n",
    "                else:\n",
    "                    if_us_native = 0\n",
    "                x = [speakerid,address, text[0], text[1],native_language, if_us_native]\n",
    "                #print(counter)\n",
    "                #print(x)\n",
    "                \n",
    "                people.append(x)\n",
    "                                \n",
    "    df = pd.DataFrame(people, columns=['speakerid','href','language_num','gender',\\\n",
    "                                       'native_language','if_us_native'])\n",
    "    \n",
    "    return df"
   ]
  },
  {
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   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "df = create_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current working directory is /Users/yidanyuan/Documents/Academic/spring2019/ind135\n"
     ]
    }
   ],
   "source": [
    "#some helper code scratch\n",
    "#df.groupby('if_us_native').count()\n",
    "\"\"\"source = bs.BeautifulSoup(requests.get(df['href'][len(df['href'])-1]).text,'html.parser')\n",
    "each_bio = [li.text for li in source.find('ul','bio').find_all('li')]\n",
    "each_bio\"\"\"\n",
    "path = os.getcwd()  \n",
    "print (\"The current working directory is %s\" % path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>speakerid</th>\n",
       "      <th>href</th>\n",
       "      <th>language_num</th>\n",
       "      <th>sex</th>\n",
       "      <th>native_language</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>if_us_native</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>301</td>\n",
       "      <td>301</td>\n",
       "      <td>301</td>\n",
       "      <td>301</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>457</td>\n",
       "      <td>457</td>\n",
       "      <td>457</td>\n",
       "      <td>457</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              speakerid  href  language_num  sex  native_language\n",
       "if_us_native                                                     \n",
       "0                   301   301           301  301              301\n",
       "1                   457   457           457  457              457"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('if_us_native').count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_audio(df,destination_folder,url = mp3_url):\n",
    "    \n",
    "    if not os.path.exists(destination_folder):\n",
    "        print('creating audio destination folder'.format(destination_folder))\n",
    "        os.makedirs(os.getcwd() + \"/\" + destination_folder)\n",
    "    else:\n",
    "        print('audio destination folder already exists.')\n",
    "            \n",
    "    counter = 0\n",
    "    for lang_num in df['language_num']:\n",
    "        if not os.path.exists(destination_folder +'{}.wav'.format(lang_num)):\n",
    "            print('downloading {}'.format(lang_num))\n",
    "            (filename, headers) = urllib.request.urlretrieve(url.format(lang_num))\n",
    "            sound = AudioSegment.from_mp3(filename)\n",
    "            sound.export(os.getcwd() +\"/\"+ destination_folder + \"{}.wav\".format(lang_num),\\\n",
    "                         format=\"wav\")\n",
    "            counter += 1\n",
    "\n",
    "    return counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "creating audio destination folder\n",
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