CleanedGroupProjectV2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4a852031",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\pydub\\utils.py:170: 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",
      "C:\\Users\\User\\anaconda3\\lib\\site-packages\\paramiko\\transport.py:219: CryptographyDeprecationWarning: Blowfish has been deprecated\n",
      "  \"class\": algorithms.Blowfish,\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "import scipy.io.wavfile as wavfile\n",
    "from scipy import signal\n",
    "from scipy.signal import spectrogram\n",
    "from scipy.fftpack import fft\n",
    "from pydub import AudioSegment\n",
    "import os\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import ParameterGrid\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "import librosa\n",
    "from scipy.stats import zscore\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e1bf3495",
   "metadata": {},
   "outputs": [],
   "source": [
    "dev_path = 'dsl_data/development.csv'\n",
    "eval_path = 'dsl_data/evaluation.csv'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a0d7879",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_dataframe(path):\n",
    "    df = pd.read_csv(path)\n",
    "    # Dataframe exploration\n",
    "    print('***** DATAFRAME INFO *****\\n')\n",
    "    print(df.info())\n",
    "    print('\\n\\n***** HEAD *****\\n')\n",
    "    print(df.head())\n",
    "    print('\\n\\n ***** POSSIBLE CLASSIFICATION OUTCOMES *****\\n')\n",
    "    #print(df.groupby(['action', 'object']).size())\n",
    "    sampleRates = list()\n",
    "    audios = list()\n",
    "    for i in range(0, df.shape[0]):\n",
    "        audio_path = df['path'].loc[i]\n",
    "        sampleRates.append(get_sample_rate(audio_path))\n",
    "        audios.append(get_audio_data(audio_path))\n",
    "    df['SampleRates'] = sampleRates\n",
    "    df['AudioData'] = audios\n",
    "    print('\\n\\n ***** SAMPLE RATES *****')\n",
    "    print(df.groupby(['SampleRates']).size())\n",
    "    \n",
    "    print('\\n\\n ***** Self-reported fluency level *****')\n",
    "    print(df.groupby(['Self-reported fluency level ']).size())\n",
    "    \n",
    "    print('\\n\\n ***** First Language spoken *****')\n",
    "    print(df.groupby(['First Language spoken']).size())\n",
    "    \n",
    "    print('\\n\\n ***** Current language used for work/school *****')\n",
    "    print(df.groupby(['Current language used for work/school']).size())\n",
    "    \n",
    "    print('\\n\\n ***** Gender *****')\n",
    "    print(df.groupby(['gender']).size())\n",
    "    print('\\n\\n ***** Age range *****')\n",
    "    print(df.groupby(['ageRange']).size())\n",
    "    return df\n",
    "\n",
    "def get_sample_rate(filepath):\n",
    "    sr, audio = wavfile.read(filepath)\n",
    "    return sr\n",
    "\n",
    "def get_audio_data(filepath):\n",
    "    sr, audio = wavfile.read(filepath)\n",
    "    return audio\n",
    "\n",
    "\n",
    "def edit_sample_rates(df, goal_sr):\n",
    "    for index, row in df.iterrows():\n",
    "    # Load the audio file\n",
    "        audio = AudioSegment.from_file(row['path'])\n",
    "    # Check if the sample rate is different from the target sample rate\n",
    "        if audio.frame_rate != goal_sr:\n",
    "            # Convert the sample rate to the target sample rate\n",
    "            audio = audio.set_frame_rate(goal_sr)\n",
    "            # get file format\n",
    "            file_format = os.path.splitext(row['path'])[1][1:]\n",
    "            # Save the converted audio\n",
    "            audio.export(row['path'], format=file_format)\n",
    "    sampleRates = list()\n",
    "    for i in range(0, df.shape[0]):\n",
    "        audio_path = df['path'].loc[i]\n",
    "        sampleRates.append(get_sample_rate(audio_path))\n",
    "    df['SampleRates'] = sampleRates\n",
    "    print(df.groupby(['SampleRates']).size())\n",
    "\n",
    "# definition of functions to represent each audio \n",
    "\n",
    "def plot_spectrogram(audio_data, sr):\n",
    "    frequencies, times, spec = spectrogram(audio_data, fs=sr)\n",
    "    plt.pcolormesh(times, frequencies, 10*np.log10(spec)) #changed the scale to dB\n",
    "    plt.colorbar()\n",
    "    plt.xlabel(\"Time (s)\")\n",
    "    plt.ylabel(\"Frequency (Hz)\")\n",
    "    plt.show()\n",
    "    return spec, len(audio)\n",
    "\n",
    "\n",
    "def plot_time_dimension(audio_data, sr):\n",
    "    audioArray = np.array(audio_data)\n",
    "    duration = len(audio) / sr\n",
    "    # Create the time axis in seconds\n",
    "    time = np.linspace(0, duration, len(audio))\n",
    "    plt.plot(time, audioArray)\n",
    "    plt.xlabel(\"Time (s)\")\n",
    "    plt.ylabel(\"Amplitude\")\n",
    "    plt.show()\n",
    "    \n",
    "def plot_freq_dimension(audio_data, sr):\n",
    "    audioArray = np.array(audio_data)\n",
    "    freqArray = np.abs(fft(audioArray))\n",
    "    decibels = 20 * np.log10(freqArray)\n",
    "    plt.figure(figsize=(10,4))\n",
    "    plt.plot(decibels)\n",
    "    plt.xlabel(\"Frequency (Hz)\")\n",
    "    plt.ylabel(\"Magnitude (dB)\")\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "def encode_categorical(df):\n",
    "    #ordinal encoding of categorical features\n",
    "    catFeatures = ['Self-reported fluency level ', 'First Language spoken', 'Current language used for work/school', 'gender', 'ageRange']\n",
    "    dicts = list()\n",
    "    dic_fluency = {'native' : 0, 'advanced' : 1, 'intermediate':2, 'basic': 3}\n",
    "    dicts.append(dic_fluency)\n",
    "    dic_first_lan = {'English (United States)': 0, 'English (Canada)': 1, 'French (Canada)' : 2, 'Spanish (Venezuela)':3, 'Telugu':4 }\n",
    "    dicts.append(dic_first_lan)\n",
    "    dic_current = {'English (United States)': 0, 'English (Canada)': 1, 'English (Australia)' : 2, 'Spanish (Venezuela)':3 }\n",
    "    dicts.append(dic_current)\n",
    "    dic_gender = {'female': 0, 'male': 1 }\n",
    "    dicts.append(dic_gender)\n",
    "    dic_age = {'22-40':0, '41-65':1, '65+': 3}\n",
    "    dicts.append(dic_age)\n",
    "    for i in range(0, len(catFeatures)):\n",
    "        dic = dicts[i]\n",
    "        feat = catFeatures[i]\n",
    "        df[feat] = df[feat].replace(dic)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cec3dce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c5ac19ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "***** DATAFRAME INFO *****\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9854 entries, 0 to 9853\n",
      "Data columns (total 10 columns):\n",
      " #   Column                                 Non-Null Count  Dtype \n",
      "---  ------                                 --------------  ----- \n",
      " 0   Id                                     9854 non-null   int64 \n",
      " 1   path                                   9854 non-null   object\n",
      " 2   speakerId                              9854 non-null   object\n",
      " 3   action                                 9854 non-null   object\n",
      " 4   object                                 9854 non-null   object\n",
      " 5   Self-reported fluency level            9854 non-null   object\n",
      " 6   First Language spoken                  9854 non-null   object\n",
      " 7   Current language used for work/school  9854 non-null   object\n",
      " 8   gender                                 9854 non-null   object\n",
      " 9   ageRange                               9854 non-null   object\n",
      "dtypes: int64(1), object(9)\n",
      "memory usage: 770.0+ KB\n",
      "None\n",
      "\n",
      "\n",
      "***** HEAD *****\n",
      "\n",
      "   Id                                               path         speakerId  \\\n",
      "0   0  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0a312...  2BqVo8kVB2Skwgyb   \n",
      "1   1  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0ee42...  2BqVo8kVB2Skwgyb   \n",
      "2   2  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/1d9f3...  2BqVo8kVB2Skwgyb   \n",
      "3   3  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/269fc...  2BqVo8kVB2Skwgyb   \n",
      "4   4  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/5bbda...  2BqVo8kVB2Skwgyb   \n",
      "\n",
      "            action  object Self-reported fluency level   \\\n",
      "0  change language    none                     advanced   \n",
      "1         activate   music                     advanced   \n",
      "2       deactivate  lights                     advanced   \n",
      "3         increase  volume                     advanced   \n",
      "4         increase  volume                     advanced   \n",
      "\n",
      "     First Language spoken Current language used for work/school  gender  \\\n",
      "0  English (United States)               English (United States)  female   \n",
      "1  English (United States)               English (United States)  female   \n",
      "2  English (United States)               English (United States)  female   \n",
      "3  English (United States)               English (United States)  female   \n",
      "4  English (United States)               English (United States)  female   \n",
      "\n",
      "  ageRange  \n",
      "0    22-40  \n",
      "1    22-40  \n",
      "2    22-40  \n",
      "3    22-40  \n",
      "4    22-40  \n",
      "\n",
      "\n",
      " ***** POSSIBLE CLASSIFICATION OUTCOMES *****\n",
      "\n",
      "\n",
      "\n",
      " ***** SAMPLE RATES *****\n",
      "SampleRates\n",
      "16000    9854\n",
      "dtype: int64\n",
      "\n",
      "\n",
      " ***** Self-reported fluency level *****\n",
      "Self-reported fluency level \n",
      "advanced         346\n",
      "basic             56\n",
      "intermediate       8\n",
      "native          9444\n",
      "dtype: int64\n",
      "\n",
      "\n",
      " ***** First Language spoken *****\n",
      "First Language spoken\n",
      "English (Canada)              8\n",
      "English (United States)    9642\n",
      "French (Canada)             148\n",
      "Spanish (Venezuela)          35\n",
      "Telugu                       21\n",
      "dtype: int64\n",
      "\n",
      "\n",
      " ***** Current language used for work/school *****\n",
      "Current language used for work/school\n",
      "English (Australia)          65\n",
      "English (Canada)            156\n",
      "English (United States)    9598\n",
      "Spanish (Venezuela)          35\n",
      "dtype: int64\n",
      "\n",
      "\n",
      " ***** Gender *****\n",
      "gender\n",
      "female    4589\n",
      "male      5265\n",
      "dtype: int64\n",
      "\n",
      "\n",
      " ***** Age range *****\n",
      "ageRange\n",
      "22-40    7496\n",
      "41-65    2115\n",
      "65+       243\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = load_dataframe(dev_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d6da9ca5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SampleRates\n",
      "16000    9854\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "target_rate = 16000\n",
    "edit_sample_rates(df, target_rate) #bring all sr's to 16kHz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "28325fb7",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>path</th>\n",
       "      <th>speakerId</th>\n",
       "      <th>action</th>\n",
       "      <th>object</th>\n",
       "      <th>Self-reported fluency level</th>\n",
       "      <th>First Language spoken</th>\n",
       "      <th>Current language used for work/school</th>\n",
       "      <th>gender</th>\n",
       "      <th>ageRange</th>\n",
       "      <th>SampleRates</th>\n",
       "      <th>AudioData</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0a312...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>change language</td>\n",
       "      <td>none</td>\n",
       "      <td>1</td>\n",
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       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0ee42...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>activate</td>\n",
       "      <td>music</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16000</td>\n",
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       "    </tr>\n",
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       "      <td>2</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/1d9f3...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>deactivate</td>\n",
       "      <td>lights</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16000</td>\n",
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       "    </tr>\n",
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       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/269fc...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>increase</td>\n",
       "      <td>volume</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16000</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/5bbda...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>increase</td>\n",
       "      <td>volume</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>16000</td>\n",
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       "      <td>9849</td>\n",
       "      <td>dsl_data/audio/speakers/vnljypgejkINbBAY/4fb3d...</td>\n",
       "      <td>vnljypgejkINbBAY</td>\n",
       "      <td>decrease</td>\n",
       "      <td>volume</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>9850</th>\n",
       "      <td>9850</td>\n",
       "      <td>dsl_data/audio/speakers/vnljypgejkINbBAY/59e6a...</td>\n",
       "      <td>vnljypgejkINbBAY</td>\n",
       "      <td>deactivate</td>\n",
       "      <td>lights</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>dsl_data/audio/speakers/vnljypgejkINbBAY/5c81c...</td>\n",
       "      <td>vnljypgejkINbBAY</td>\n",
       "      <td>deactivate</td>\n",
       "      <td>lights</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16000</td>\n",
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       "      <td>9852</td>\n",
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       "      <td>vnljypgejkINbBAY</td>\n",
       "      <td>deactivate</td>\n",
       "      <td>lights</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16000</td>\n",
       "      <td>[333, 339, 342, 344, 346, 349, 353, 355, 357, ...</td>\n",
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       "      <th>9853</th>\n",
       "      <td>9853</td>\n",
       "      <td>dsl_data/audio/speakers/vnljypgejkINbBAY/61b7a...</td>\n",
       "      <td>vnljypgejkINbBAY</td>\n",
       "      <td>increase</td>\n",
       "      <td>volume</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16000</td>\n",
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      ],
      "text/plain": [
       "        Id                                               path  \\\n",
       "0        0  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0a312...   \n",
       "1        1  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0ee42...   \n",
       "2        2  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/1d9f3...   \n",
       "3        3  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/269fc...   \n",
       "4        4  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/5bbda...   \n",
       "...    ...                                                ...   \n",
       "9849  9849  dsl_data/audio/speakers/vnljypgejkINbBAY/4fb3d...   \n",
       "9850  9850  dsl_data/audio/speakers/vnljypgejkINbBAY/59e6a...   \n",
       "9851  9851  dsl_data/audio/speakers/vnljypgejkINbBAY/5c81c...   \n",
       "9852  9852  dsl_data/audio/speakers/vnljypgejkINbBAY/5ef42...   \n",
       "9853  9853  dsl_data/audio/speakers/vnljypgejkINbBAY/61b7a...   \n",
       "\n",
       "             speakerId           action  object  Self-reported fluency level   \\\n",
       "0     2BqVo8kVB2Skwgyb  change language    none                             1   \n",
       "1     2BqVo8kVB2Skwgyb         activate   music                             1   \n",
       "2     2BqVo8kVB2Skwgyb       deactivate  lights                             1   \n",
       "3     2BqVo8kVB2Skwgyb         increase  volume                             1   \n",
       "4     2BqVo8kVB2Skwgyb         increase  volume                             1   \n",
       "...                ...              ...     ...                           ...   \n",
       "9849  vnljypgejkINbBAY         decrease  volume                             0   \n",
       "9850  vnljypgejkINbBAY       deactivate  lights                             0   \n",
       "9851  vnljypgejkINbBAY       deactivate  lights                             0   \n",
       "9852  vnljypgejkINbBAY       deactivate  lights                             0   \n",
       "9853  vnljypgejkINbBAY         increase  volume                             0   \n",
       "\n",
       "      First Language spoken  Current language used for work/school  gender  \\\n",
       "0                         0                                      0       0   \n",
       "1                         0                                      0       0   \n",
       "2                         0                                      0       0   \n",
       "3                         0                                      0       0   \n",
       "4                         0                                      0       0   \n",
       "...                     ...                                    ...     ...   \n",
       "9849                      0                                      0       1   \n",
       "9850                      0                                      0       1   \n",
       "9851                      0                                      0       1   \n",
       "9852                      0                                      0       1   \n",
       "9853                      0                                      0       1   \n",
       "\n",
       "      ageRange  SampleRates                                          AudioData  \n",
       "0            0        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "1            0        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "2            0        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "3            0        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "4            0        16000  [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...  \n",
       "...        ...          ...                                                ...  \n",
       "9849         0        16000  [315, 321, 328, 335, 340, 342, 344, 351, 351, ...  \n",
       "9850         0        16000  [320, 322, 327, 332, 334, 334, 336, 338, 343, ...  \n",
       "9851         0        16000  [49, 52, 55, 59, 61, 65, 70, 73, 76, 81, 85, 8...  \n",
       "9852         0        16000  [333, 339, 342, 344, 346, 349, 353, 355, 357, ...  \n",
       "9853         0        16000  [325, 331, 333, 335, 340, 342, 346, 351, 353, ...  \n",
       "\n",
       "[9854 rows x 12 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encode_categorical(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64db8072",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d320c1a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_all_audios(df):\n",
    "    fig, ax = plt.subplots(1,1)\n",
    "    for ind in range(0, len(df)):\n",
    "        audio = df['AudioData'].loc[ind]\n",
    "        sr = df['SampleRates'].loc[ind]\n",
    "        audioArray = np.array(audio)\n",
    "        duration = len(audio) / sr\n",
    "        time = np.linspace(0, duration, len(audio))\n",
    "        ax.plot(time, audioArray)\n",
    "    ax.set_ylabel(\"Amplitude\")\n",
    "    ax.set_xlabel(\"Time [s]\")\n",
    "    plt.show()\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "095c6464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_audios(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "615d7b89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# By normalising the amplitude of the audios it is possible \n",
    "#to lower the impact of different recording conditions:\n",
    "\n",
    "def normalise_audios(df):\n",
    "    normalised = list()\n",
    "    for idx, rec in df.iterrows():\n",
    "        normalised.append(zscore(rec['AudioData']))\n",
    "    df['AudioData'] = normalised\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1822078e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = normalise_audios(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bd13a0c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_audios(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "299ad240",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_duration_histogram(df):\n",
    "    recList = []\n",
    "    lenList = []\n",
    "\n",
    "    for idx, row in df.iterrows():\n",
    "        rec = row['AudioData']\n",
    "        sr = row['SampleRates']\n",
    "        recList.append(rec)\n",
    "        lenList.append(len(rec)/sr)\n",
    "\n",
    "    plt.xlabel('Seconds')\n",
    "    plt.ylabel('Frequency')\n",
    "    hist_array = plt.hist(np.array(lenList), bins='auto', color='#0504aa', alpha=0.7, rwidth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "458bcfd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_duration_histogram(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0476cdf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardise_length(df, seconds):\n",
    "\n",
    "    recListAdj = []\n",
    "    lenListAdj = []\n",
    "\n",
    "    for idx, row in df.iterrows():\n",
    "        rec = row['AudioData']\n",
    "        sr = row['SampleRates']\n",
    "        #rec, index = librosa.effects.trim(rec, top_db=30)\n",
    "        threshold = sr * seconds \n",
    "        if len(rec) > threshold:\n",
    "            rec = rec[:threshold]\n",
    "        elif len(rec)<threshold:\n",
    "            rec = np.pad(rec, (0,threshold-len(rec)), mode='constant', constant_values=0)\n",
    "        recListAdj.append(rec)\n",
    "        lenListAdj.append(len(rec)/sr)\n",
    "    df['AudioData'] = recListAdj\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "73a44dbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "def standardise_length_trim(df, seconds):\n",
    "\n",
    "    recListAdj = []\n",
    "    lenListAdj = []\n",
    "\n",
    "    for idx, row in df.iterrows():\n",
    "        rec = row['AudioData']\n",
    "        sr = row['SampleRates']\n",
    "        rec, index = librosa.effects.trim(rec, top_db=30)\n",
    "        threshold = sr * seconds \n",
    "        if len(rec) > threshold:\n",
    "            rec = rec[:threshold]\n",
    "        elif len(rec)<threshold:\n",
    "            rec = np.pad(rec, (0,threshold-len(rec)), mode='constant', constant_values=0)\n",
    "        recListAdj.append(rec)\n",
    "        lenListAdj.append(len(rec)/sr)\n",
    "    df['AudioData'] = recListAdj\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "daed1318",
   "metadata": {},
   "outputs": [],
   "source": [
    "def trim_audio(df, seconds):\n",
    "\n",
    "    recListAdj = []\n",
    "    lenListAdj = []\n",
    "\n",
    "    for idx, row in df.iterrows():\n",
    "        rec = row['AudioData']\n",
    "        sr = row['SampleRates']\n",
    "        rec, index = librosa.effects.trim(rec, top_db=30)\n",
    "        threshold = sr * seconds \n",
    "        if len(rec) > threshold:\n",
    "            rec = rec[:threshold]\n",
    "        recListAdj.append(rec)\n",
    "        lenListAdj.append(len(rec)/sr)\n",
    "    df['AudioData'] = recListAdj\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "74e86885",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Padding no trim\n",
    "df = standardise_length(df, 3)\n",
    "plot_duration_histogram(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d7b919cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Padding and trim\n",
    "df3 = standardise_length_trim(df, 3)\n",
    "plot_duration_histogram(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "93720e6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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p6emSpPXr18vhcGjTpk3KyMhQfX29CgsLtWHDBqWmpkqSNm7cqPj4eG3fvl2TJk3q0WMBAAD+x+czO0ePHlVcXJwSEhL0wAMP6OOPP5YkVVdXy+VyKS0tzay12+0aP368KisrJUlVVVVqbW11q4mLi1NSUpJZ05Hm5mY1NDS4LQAAwJo8CjvV1dVe+eOjR4/WSy+9pDfffFMvvviiXC6Xxo4dq08//VQul0uS5HA43D7jcDjMbS6XS8HBwYqOju60piN5eXmKiooyl/j4eK8cDwAA8D8ehZ0bbrhBd911lzZu3Kgvv/zS4z8+efJk3XfffRo2bJhSU1P1hz/8QdJXX1edZ7PZ3D5jGEa7sQtdrCY7O1v19fXmcuLECY+PAQAA+DePws67776rESNGKCsrS06nUxkZGfrTn/7U7WbCw8M1bNgwHT161LyP58IZmtraWnO2x+l0qqWlRXV1dZ3WdMRutysyMtJtAQAA1uRR2ElKSlJ+fr7+9re/qaioSC6XS+PGjdMtt9yi/Px8/eMf//ComebmZn344YcaOHCgEhIS5HQ6VVZWZm5vaWlReXm5xo4dK0lKTk5WUFCQW01NTY0OHjxo1gAAgL6tWzcoBwYG6t5779Vvf/tbrVixQh999JEWL16sQYMG6eGHH1ZNTU2Xn1+8eLHKy8tVXV2td955R//6r/+qhoYGzZ49WzabTZmZmcrNzVVpaakOHjyoOXPmKCwsTLNmzZIkRUVFad68ecrKytKOHTu0f/9+PfTQQ+bXYgAAAN169Hzfvn36zW9+o5KSEoWHh2vx4sWaN2+eTp06pR//+MeaPn16l19vnTx5Ut/+9rf1ySef6Gtf+5puu+027dmzR4MHD5YkLVmyRE1NTZo/f77q6uo0evRobdu2TREREeY+Vq9ercDAQM2cOVNNTU1KSUlRcXGxAgICunNoAADAIjwKO/n5+SoqKtLhw4d1991366WXXtLdd9+tq676aqIoISFBzz//vG666aYu91NSUtLldpvNppycHOXk5HRaExISooKCAhUUFFz2cQAAAOvzKOysW7dOc+fO1SOPPNLhDwJK0jXXXKPCwsJuNQcAANBdHoWdo0ePXrQmODhYs2fP9mT3AAAAXuPRDcpFRUX63e9+1278d7/7ndtv5AAAAPiaR2Fn+fLlio2NbTc+YMAA5ebmdrspAAAAb/Eo7Bw7dkwJCQntxgcPHqzjx493uykAAABv8SjsDBgwQO+991678XfffVcxMTHdbgoAAMBbPAo7DzzwgH7wgx9o586damtrU1tbm9566y0tXLhQDzzwgLd7BAAA8JhHT2M9/fTTOnbsmFJSUhQY+NUuzp07p4cffph7dgAAgF/xKOwEBwfrlVde0U9/+lO9++67Cg0N1bBhw8xfPgYAAPAX3XpdxI033qgbb7zRW70AAAB4nUdhp62tTcXFxdqxY4dqa2t17tw5t+1vvfWWV5oDAADoLo/CzsKFC1VcXKwpU6YoKSlJNpvN230BAAB4hUdhp6SkRL/97W919913e7sfAAAAr/Lo0fPg4GDdcMMN3u4FAADA6zwKO1lZWfrFL34hwzC83Q8AAIBXefQ1VkVFhXbu3KmtW7fqlltuUVBQkNv21157zSvNAQAAdJdHYefqq6/Wvffe6+1eAAAAvM6jsFNUVOTtPgAAAK4Ij+7ZkaSzZ89q+/btev7553X69GlJ0qlTp9TY2Oi15gAAALrLo5mdY8eO6Zvf/KaOHz+u5uZmTZw4UREREVq5cqW+/PJLPffcc97uEwAAwCMezewsXLhQo0aNUl1dnUJDQ83xe++9Vzt27PBacwAAAN3l8dNYf/zjHxUcHOw2PnjwYP3tb3/zSmMAAADe4FHYOXfunNra2tqNnzx5UhEREd1uCoB/mDGjosvtmzeP66FOAMBzHn2NNXHiRK1Zs8Zct9lsamxs1FNPPcUrJAAAgF/xaGZn9erVuuuuuzR06FB9+eWXmjVrlo4eParY2Fi9/PLL3u4RAADAYx6Fnbi4OB04cEAvv/yy/vznP+vcuXOaN2+eHnzwQbcblgEAAHzNo7AjSaGhoZo7d67mzp3rzX4AAAC8yqOw89JLL3W5/eGHH/aoGQAAAG/zKOwsXLjQbb21tVVffPGFgoODFRYWRtgBAAB+w6Onserq6tyWxsZGHT58WOPGjeMGZQAA4Fc8fjfWhRITE7V8+fJ2sz4AAAC+5LWwI0kBAQE6deqUN3cJAADQLR7ds7Nlyxa3dcMwVFNTo7Vr1+r222/3SmMAAADe4NHMzowZM9yW9PR05eTkaPjw4frNb37jUSN5eXmy2WzKzMw0xwzDUE5OjuLi4hQaGqoJEybo0KFDbp9rbm7WggULFBsbq/DwcE2bNk0nT570qAcAAGA9HoWdc+fOuS1tbW1yuVzatGmTBg4ceNn727t3r1544QUNHz7cbXzlypXKz8/X2rVrtXfvXjmdTk2cOFGnT582azIzM1VaWqqSkhJVVFSosbFRU6dO7fDdXQAAoO/x6j07nmhsbNSDDz6oF198UdHR0ea4YRhas2aNli5dqvT0dCUlJWn9+vX64osvtGnTJklSfX29CgsLtWrVKqWmpmrEiBHauHGj3n//fW3fvr3Tv9nc3KyGhga3BQAAWJNH9+wsWrTokmvz8/O73P7YY49pypQpSk1N1dNPP22OV1dXy+VyKS0tzRyz2+0aP368KisrlZGRoaqqKrW2trrVxMXFKSkpSZWVlZo0aVKHfzMvL0/Lli275GMAAAC9l0dhZ//+/frzn/+ss2fPasiQIZKkI0eOKCAgQCNHjjTrbDZbl/spKSlRVVWV9u3b126by+WSJDkcDrdxh8OhY8eOmTXBwcFuM0Lna85/viPZ2dluga2hoUHx8fFd9goAAHonj8LOPffco4iICK1fv94MGnV1dXrkkUd0xx13KCsr66L7OHHihBYuXKht27YpJCSk07oLA5NhGBcNURersdvtstvtF+0RAAD0fh7ds7Nq1Srl5eW5zahER0fr6aef1qpVqy5pH1VVVaqtrVVycrICAwMVGBio8vJy/fKXv1RgYKA5o3PhDE1tba25zel0qqWlRXV1dZ3WAACAvs2jsNPQ0KC///3v7cZra2vdnpTqSkpKit5//30dOHDAXEaNGqUHH3xQBw4c0HXXXSen06mysjLzMy0tLSovL9fYsWMlScnJyQoKCnKrqamp0cGDB80aAADQt3n0Nda9996rRx55RKtWrdJtt90mSdqzZ49+9KMfKT09/ZL2ERERoaSkJLex8PBwxcTEmOOZmZnKzc1VYmKiEhMTlZubq7CwMM2aNUuSFBUVpXnz5ikrK0sxMTHq37+/Fi9erGHDhik1NdWTQwMAABbjUdh57rnntHjxYj300ENqbW39akeBgZo3b56eeeYZrzW3ZMkSNTU1af78+aqrq9Po0aO1bds2RUREmDWrV69WYGCgZs6cqaamJqWkpKi4uFgBAQFe6wMAAPReHoWdsLAwPfvss3rmmWf00UcfyTAM3XDDDQoPD+9WM7t27XJbt9lsysnJUU5OTqefCQkJUUFBgQoKCrr1twEAgDV160cFa2pqVFNToxtvvFHh4eEyDMNbfQEAAHiFRzM7n376qWbOnKmdO3fKZrPp6NGjuu666/Td735XV1999SU/kQVYyYwZFb5uAQDQAY9mdn74wx8qKChIx48fV1hYmDn+rW99S2+88YbXmgMAAOguj2Z2tm3bpjfffFODBg1yG09MTDR/3RgAAMAfeDSzc+bMGbcZnfM++eQTfpkYAAD4FY/Czp133qmXXnrJXLfZbDp37pyeeeYZ3XXXXV5rDgAAoLs8+hrrmWee0YQJE7Rv3z61tLRoyZIlOnTokD777DP98Y9/9HaPAAAAHvMo7AwdOlTvvfee1q1bp4CAAJ05c0bp6el67LHHNHDgQG/3CPgFnra6fF2ds82bx/VgJwD6sssOO62trUpLS9Pzzz+vZcuWXYmeAAAAvOay79kJCgrSwYMHZbPZrkQ/AAAAXuXRDcoPP/ywCgsLvd0LAACA13l0z05LS4t+/etfq6ysTKNGjWr3Tqz8/HyvNAf0FdwPBABXzmWFnY8//ljXXnutDh48qJEjR0qSjhw54lbD11vorov9w8+NrQCAy3FZYScxMVE1NTXauXOnpK9eD/HLX/5SDofjijQHAADQXZd1z86FbzXfunWrzpw549WGAAAAvMmjG5TPuzD8AAAA+JvLCjs2m63dPTncowMAAPzZZd2zYxiG5syZY77s88svv9Sjjz7a7mms1157zXsdAgAAdMNlhZ3Zs2e7rT/00ENebQYAAMDbLivsFBUVXak+AAAArohu3aAMAADg7wg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0nwadtatW6fhw4crMjJSkZGRGjNmjLZu3WpuNwxDOTk5iouLU2hoqCZMmKBDhw657aO5uVkLFixQbGyswsPDNW3aNJ08ebKnDwW9xIwZFZ0uAABr8mnYGTRokJYvX659+/Zp3759+sY3vqHp06ebgWblypXKz8/X2rVrtXfvXjmdTk2cOFGnT58295GZmanS0lKVlJSooqJCjY2Nmjp1qtra2nx1WAAAwI8E+vKP33PPPW7rP/vZz7Ru3Trt2bNHQ4cO1Zo1a7R06VKlp6dLktavXy+Hw6FNmzYpIyND9fX1Kiws1IYNG5SamipJ2rhxo+Lj47V9+3ZNmjSpx48J6GnMSgFA1/zmnp22tjaVlJTozJkzGjNmjKqrq+VyuZSWlmbW2O12jR8/XpWVlZKkqqoqtba2utXExcUpKSnJrOlIc3OzGhoa3BYAAGBNPg8777//vvr16ye73a5HH31UpaWlGjp0qFwulyTJ4XC41TscDnOby+VScHCwoqOjO63pSF5enqKioswlPj7ey0cFAAD8hc/DzpAhQ3TgwAHt2bNH3//+9zV79mx98MEH5nabzeZWbxhGu7ELXawmOztb9fX15nLixInuHQQAAPBbPg87wcHBuuGGGzRq1Cjl5eXp1ltv1S9+8Qs5nU5JajdDU1tba872OJ1OtbS0qK6urtOajtjtdvMJsPMLAACwJp+HnQsZhqHm5mYlJCTI6XSqrKzM3NbS0qLy8nKNHTtWkpScnKygoCC3mpqaGh08eNCsAQAAfZtPn8Z68sknNXnyZMXHx+v06dMqKSnRrl279MYbb8hmsykzM1O5ublKTExUYmKicnNzFRYWplmzZkmSoqKiNG/ePGVlZSkmJkb9+/fX4sWLNWzYMPPpLAAA0Lf5NOz8/e9/13e+8x3V1NQoKipKw4cP1xtvvKGJEydKkpYsWaKmpibNnz9fdXV1Gj16tLZt26aIiAhzH6tXr1ZgYKBmzpyppqYmpaSkqLi4WAEBAb46LAAA4Ed8GnYKCwu73G6z2ZSTk6OcnJxOa0JCQlRQUKCCggIvdwcAAKzA7+7ZAQAA8CbCDgAAsDSffo0FAJ3p6jUYmzeP68FOAPR2zOwAAABLI+wAAABL42ssoI/jrekArI6ZHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGmEHQAAYGk8eo4rgl+/BQD4C2Z2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApfHWc/Q6Xb1RHQCACzGzAwAALI2wAwAALI2wAwAALI17dgBYzsXu69q8eVwPdQLAHzCzAwAALM2nYScvL09f//rXFRERoQEDBmjGjBk6fPiwW41hGMrJyVFcXJxCQ0M1YcIEHTp0yK2mublZCxYsUGxsrMLDwzVt2jSdPHmyJw8FAAD4KZ+GnfLycj322GPas2ePysrKdPbsWaWlpenMmTNmzcqVK5Wfn6+1a9dq7969cjqdmjhxok6fPm3WZGZmqrS0VCUlJaqoqFBjY6OmTp2qtrY2XxwWAADwIz69Z+eNN95wWy8qKtKAAQNUVVWlO++8U4ZhaM2aNVq6dKnS09MlSevXr5fD4dCmTZuUkZGh+vp6FRYWasOGDUpNTZUkbdy4UfHx8dq+fbsmTZrU48cFAAD8h1/ds1NfXy9J6t+/vySpurpaLpdLaWlpZo3dbtf48eNVWVkpSaqqqlJra6tbTVxcnJKSksyaCzU3N6uhocFtAQAA1uQ3YccwDC1atEjjxo1TUlKSJMnlckmSHA6HW63D4TC3uVwuBQcHKzo6utOaC+Xl5SkqKspc4uPjvX04AADAT/hN2Hn88cf13nvv6eWXX263zWazua0bhtFu7EJd1WRnZ6u+vt5cTpw44XnjAADAr/nF7+wsWLBAW7Zs0e7duzVo0CBz3Ol0Svpq9mbgwIHmeG1trTnb43Q61dLSorq6OrfZndraWo0dO7bDv2e322W326/EoQB9Cu8pA9Ab+HRmxzAMPf7443rttdf01ltvKSEhwW17QkKCnE6nysrKzLGWlhaVl5ebQSY5OVlBQUFuNTU1NTp48GCnYQcAAPQdPp3Zeeyxx7Rp0yb913/9lyIiIsx7bKKiohQaGiqbzabMzEzl5uYqMTFRiYmJys3NVVhYmGbNmmXWzps3T1lZWYqJiVH//v21ePFiDRs2zHw6CwAA9F0+DTvr1q2TJE2YMMFtvKioSHPmzJEkLVmyRE1NTZo/f77q6uo0evRobdu2TREREWb96tWrFRgYqJkzZ6qpqUkpKSkqLi5WQEBATx0KAADwUz4NO4ZhXLTGZrMpJydHOTk5ndaEhISooKBABQUFXuwOAABYgd88jQUAAHAlEHYAAIClEXYAAIClEXYAAIClEXYAAICl+cUvKAOAv7jYr0Jv3jyuhzoB4C3M7AAAAEsj7AAAAEsj7AAAAEvjnh0APsEb0wH0FGZ2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApfELygB6nd7668u8UR3wDWZ2AACApRF2AACApRF2AACApXHPDnpcb73fAgDQOzGzAwAALI2wAwAALI2wAwAALI17dtAhfg8EAGAVzOwAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABL82nY2b17t+655x7FxcXJZrNp8+bNbtsNw1BOTo7i4uIUGhqqCRMm6NChQ241zc3NWrBggWJjYxUeHq5p06bp5MmTPXgUAADAn/n00fMzZ87o1ltv1SOPPKL77ruv3faVK1cqPz9fxcXFuvHGG/X0009r4sSJOnz4sCIiIiRJmZmZ+u///m+VlJQoJiZGWVlZmjp1qqqqqhQQENDThwSgF+CVJUDf4tOwM3nyZE2ePLnDbYZhaM2aNVq6dKnS09MlSevXr5fD4dCmTZuUkZGh+vp6FRYWasOGDUpNTZUkbdy4UfHx8dq+fbsmTZrU4b6bm5vV3Nxsrjc0NHj5yAAAgL/w2x8VrK6ulsvlUlpamjlmt9s1fvx4VVZWKiMjQ1VVVWptbXWriYuLU1JSkiorKzsNO3l5eVq2bNkVPwYA8KauZqT4oU+gc34bdlwulyTJ4XC4jTscDh07dsysCQ4OVnR0dLua85/vSHZ2thYtWmSuNzQ0KD4+3lut9wl8DYC+il8XB3ofvw0759lsNrd1wzDajV3oYjV2u112u90r/QEAAP/mt4+eO51OSWo3Q1NbW2vO9jidTrW0tKiurq7TGgAA0Lf57cxOQkKCnE6nysrKNGLECElSS0uLysvLtWLFCklScnKygoKCVFZWppkzZ0qSampqdPDgQa1cudJnvQOAv+HrN/RlPg07jY2N+stf/mKuV1dX68CBA+rfv7+uueYaZWZmKjc3V4mJiUpMTFRubq7CwsI0a9YsSVJUVJTmzZunrKwsxcTEqH///lq8eLGGDRtmPp0FAAD6Np+GnX379umuu+4y18/fNDx79mwVFxdryZIlampq0vz581VXV6fRo0dr27Zt5m/sSNLq1asVGBiomTNnqqmpSSkpKSouLuY3dgAAgCQfh50JEybIMIxOt9tsNuXk5CgnJ6fTmpCQEBUUFKigoOAKdAgAAHo7v71BGQAAwBsIOwAAwNL89mksAOiN+MFNwP8wswMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNR88tjBf/AQBA2AEAXAT/4YTejrADABbgrz9mSFCCPyDsAICf8NfAAvR23KAMAAAsjbADAAAsjbADAAAsjbADAAAsjRuU+zBuhgQA9AWEHQCAJfHYO84j7AAA/Hamt6u+CCu4VNyzAwAALI2ZHQBAt/jrrBBwHjM7AADA0gg7AADA0vgaCwCAC3T3SS5urPYvhB0AQK/U3XuFuNeo7+BrLAAAYGnM7Pg5/ssDAIDuIewAANCD+GXnnkfYAQD4DLPX6AmEHQAALhMhrT1/fgKNsAMAgB/pTpDydajwV5YJO88++6yeeeYZ1dTU6JZbbtGaNWt0xx13+LotAAB6DPcDdcwSYeeVV15RZmamnn32Wd1+++16/vnnNXnyZH3wwQe65pprfN0eAAB+wZ+/arqSbIZhGL5uortGjx6tkSNHat26debYzTffrBkzZigvL++in29oaFBUVJTq6+sVGRnp1d74XhcAYAX++KvRl/rvd6+f2WlpaVFVVZWeeOIJt/G0tDRVVlZ2+Jnm5mY1Nzeb6/X19ZK+Omne1tp6xuv7BACgp02Z8qbHn70S/77+//d7sXmbXh92PvnkE7W1tcnhcLiNOxwOuVyuDj+Tl5enZcuWtRuPj4+/Ij0CANCXRUVd2f2fPn1aUV38kV4fds6z2Wxu64ZhtBs7Lzs7W4sWLTLXz507p88++0wxMTGdfqavaGhoUHx8vE6cOOH1r/T6Cs6hd3Aeu49z2H2cw+67kufQMAydPn1acXFxXdb1+rATGxurgICAdrM4tbW17WZ7zrPb7bLb7W5jV1999ZVqsVeKjIzk/9jdxDn0Ds5j93EOu49z2H1X6hx2NaNzXq9/EWhwcLCSk5NVVlbmNl5WVqaxY8f6qCsAAOAvev3MjiQtWrRI3/nOdzRq1CiNGTNGL7zwgo4fP65HH33U160BAAAfs0TY+da3vqVPP/1UP/nJT1RTU6OkpCS9/vrrGjx4sK9b63Xsdrueeuqpdl/z4dJxDr2D89h9nMPu4xx2nz+cQ0v8zg4AAEBnev09OwAAAF0h7AAAAEsj7AAAAEsj7AAAAEsj7PRBzz77rBISEhQSEqLk5GT9z//8T6e1u3btks1ma7f87//+bw927F92796te+65R3FxcbLZbNq8efNFP1NeXq7k5GSFhITouuuu03PPPXflG/Vjl3sOuQ7by8vL09e//nVFRERowIABmjFjhg4fPnzRz3Et/h9PziHXort169Zp+PDh5g8GjhkzRlu3bu3yM764Bgk7fcwrr7yizMxMLV26VPv379cdd9yhyZMn6/jx411+7vDhw6qpqTGXxMTEHurY/5w5c0a33nqr1q5de0n11dXVuvvuu3XHHXdo//79evLJJ/WDH/xAr7766hXu1H9d7jk8j+vw/5SXl+uxxx7Tnj17VFZWprNnzyotLU1nznT+8mGuRXeenMPzuBa/MmjQIC1fvlz79u3Tvn379I1vfEPTp0/XoUOHOqz32TVooE/5l3/5F+PRRx91G7vpppuMJ554osP6nTt3GpKMurq6Huiu95FklJaWdlmzZMkS46abbnIby8jIMG677bYr2FnvcSnnkOvw4mpraw1JRnl5eac1XItdu5RzyLV4cdHR0cavf/3rDrf56hpkZqcPaWlpUVVVldLS0tzG09LSVFlZ2eVnR4wYoYEDByolJUU7d+68km1azttvv93unE+aNEn79u1Ta2urj7rqnbgOO1dfXy9J6t+/f6c1XItdu5RzeB7XYnttbW0qKSnRmTNnNGbMmA5rfHUNEnb6kE8++URtbW3tXpDqcDjavUj1vIEDB+qFF17Qq6++qtdee01DhgxRSkqKdu/e3RMtW4LL5erwnJ89e1affPKJj7rqXbgOu2YYhhYtWqRx48YpKSmp0zquxc5d6jnkWmzv/fffV79+/WS32/Xoo4+qtLRUQ4cO7bDWV9egJV4Xgctjs9nc1g3DaDd23pAhQzRkyBBzfcyYMTpx4oR+/vOf684777yifVpJR+e8o3F0jOuwa48//rjee+89VVRUXLSWa7Fjl3oOuRbbGzJkiA4cOKDPP/9cr776qmbPnq3y8vJOA48vrkFmdvqQ2NhYBQQEtJvFqa2tbZe0u3Lbbbfp6NGj3m7PspxOZ4fnPDAwUDExMT7qqvfjOvzKggULtGXLFu3cuVODBg3qspZrsWOXcw470tevxeDgYN1www0aNWqU8vLydOutt+oXv/hFh7W+ugYJO31IcHCwkpOTVVZW5jZeVlamsWPHXvJ+9u/fr4EDB3q7PcsaM2ZMu3O+bds2jRo1SkFBQT7qqvfr69ehYRh6/PHH9dprr+mtt95SQkLCRT/DtejOk3PYkb5+LV7IMAw1Nzd3uM1n1+AVvf0ZfqekpMQICgoyCgsLjQ8++MDIzMw0wsPDjb/+9a+GYRjGE088YXznO98x61evXm2UlpYaR44cMQ4ePGg88cQThiTj1Vdf9dUh+Nzp06eN/fv3G/v37zckGfn5+cb+/fuNY8eOGYbR/hx+/PHHRlhYmPHDH/7Q+OCDD4zCwkIjKCjI+P3vf++rQ/C5yz2HXIftff/73zeioqKMXbt2GTU1NebyxRdfmDVci13z5BxyLbrLzs42du/ebVRXVxvvvfee8eSTTxpXXXWVsW3bNsMw/OcaJOz0Qb/61a+MwYMHG8HBwcbIkSPdHrOcPXu2MX78eHN9xYoVxvXXX2+EhIQY0dHRxrhx44w//OEPPujaf5x/9PTCZfbs2YZhtD+HhmEYu3btMkaMGGEEBwcb1157rbFu3bqeb9yPXO455Dpsr6PzJ8koKioya7gWu+bJOeRadDd37lzz35Ovfe1rRkpKihl0DMN/rkGbYfy/O4MAAAAsiHt2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AOAyzJkzRzNmzPB1GwAuA2EHgE/U1tYqIyND11xzjex2u5xOpyZNmqS3337b160BsJhAXzcAoG+677771NraqvXr1+u6667T3//+d+3YsUOfffaZr1sDYDHM7ADocZ9//rkqKiq0YsUK3XXXXRo8eLD+5V/+RdnZ2ZoyZYokqb6+Xv/2b/+mAQMGKDIyUt/4xjf07rvvuu1ny5YtGjVqlEJCQhQbG6v09HRzW11dnR5++GFFR0crLCxMkydP1tGjR83txcXFuvrqq/Xmm2/q5ptvVr9+/fTNb35TNTU1Zk1bW5sWLVqkq6++WjExMVqyZIkufJ3g73//ew0bNkyhoaGKiYlRamqqzpw5cyVOGwAPEXYA9Lh+/fqpX79+2rx5s5qbm9ttNwxDU6ZMkcvl0uuvv66qqiqNHDlSKSkp5szPH/7wB6Wnp2vKlCnav3+/duzYoVGjRpn7mDNnjvbt26ctW7bo7bfflmEYuvvuu9Xa2mrWfPHFF/r5z3+uDRs2aPfu3Tp+/LgWL15sbl+1apV+85vfqLCwUBUVFfrss89UWlpqbq+pqdG3v/1tzZ07Vx9++KF27dql9PT0doEIgI9d8feqA0AHfv/73xvR0dFGSEiIMXbsWCM7O9t49913DcMwjB07dhiRkZHGl19+6faZ66+/3nj++ecNwzCMMWPGGA8++GCH+z5y5IghyfjjH/9ojn3yySdGaGio8dvf/tYwDMMoKioyJBl/+ctfzJpf/epXhsPhMNcHDhxoLF++3FxvbW01Bg0aZEyfPt0wDMOoqqoyJBl//etfu3EmAFxpzOwA8In77rtPp06d0pYtWzRp0iTt2rVLI0eOVHFxsaqqqtTY2KiYmBhzFqhfv36qrq7WRx99JEk6cOCAUlJSOtz3hx9+qMDAQI0ePdoci4mJ0ZAhQ/Thhx+aY2FhYbr++uvN9YEDB6q2tlbSV1+j1dTUaMyYMeb2wMBAt9mjW2+9VSkpKRo2bJjuv/9+vfjii6qrq/POCQLgNYQdAD4TEhKiiRMn6sc//rEqKys1Z84cPfXUUzp37pwGDhyoAwcOuC2HDx/Wj370I0lSaGhop/s1OvkayTAM2Ww2cz0oKMhtu81mu6yvoAICAlRWVqatW7dq6NChKigo0JAhQ1RdXX3J+wBw5RF2APiNoUOH6syZMxo5cqRcLpcCAwN1ww03uC2xsbGSpOHDh2vHjh2d7ufs2bN65513zLFPP/1UR44c0c0333xJvURFRWngwIHas2ePOXb27FlVVVW51dlsNt1+++1atmyZ9u/fr+DgYLf7egD4Ho+eA+hxn376qe6//37NnTtXw4cPV0REhPbt26eVK1dq+vTpSk1N1ZgxYzRjxgytWLFCQ4YM0alTp/T6669rxowZGjVqlJ566imlpKTo+uuv1wMPPKCzZ89q69atWrJkiRITEzV9+nR973vf0/PPP6+IiAg98cQT+qd/+idNnz79kvtcuHChli9frsTERN18883Kz8/X559/bm5/5513tGPHDqWlpWnAgAF655139I9//OOSAxWAnkHYAdDj+vXrp9GjR2v16tX66KOP1Nraqvj4eH3ve9/Tk08+KZvNptdff11Lly7V3Llz9Y9//ENOp1N33nmnHA6HJGnChAn63e9+p5/+9Kdavny5IiMjdeedd5p/o6ioSAsXLtTUqVPV0tKiO++8U6+//nq7r666kpWVpZqaGs2ZM0dXXXWV5s6dq3vvvVf19fWSpMjISO3evVtr1qxRQ0ODBg8erFWrVmny5MnePWEAusVmXM4X1AAAAL0M9+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABL+/8Aa320+KvZTAoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Trim no padding\n",
    "dfTrim = trim_audio(df, 3)\n",
    "plot_duration_histogram(dfTrim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2063634b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_all_audios(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f2c709cf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c7729fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To be able to compare the different blocks of the spectrogram in a meaningful way\n",
    "# the audios should all have the same length --> padding?\n",
    "# for each audio shorter than the threshold, we add 'silence' at the end by adding \n",
    "# zeros to the audio array "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6f1e7533",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_split_spectrogram(audio, sr, n_blocks):\n",
    "    frequencies, times, spec = spectrogram(audio, fs=sr)\n",
    "    times = (times * 1000).astype(int) #milliseconds\n",
    "    frequencies = frequencies.astype(int)\n",
    "    fig, ax = plt.subplots(1,1)\n",
    "    plt.pcolormesh(times, frequencies, 10*np.log10(spec)) #changed the scale to dB\n",
    "    plt.colorbar()\n",
    "    time_bounds = list()\n",
    "    freq_bounds = list()\n",
    "    #n_blocks is the same vertically and horizontally\n",
    "    #starting from vertical lines to create time bins:\n",
    "    v_step = int(times[len(times)-1] / n_blocks)\n",
    "    \n",
    "    for v in range(0, times[len(times)-1], v_step):\n",
    "        ax.axvline(v, color = 'red')\n",
    "        time_bounds.append(v)\n",
    "    #and then horizontal lines to create frequency bins:\n",
    "    h_step = int(frequencies[len(frequencies)-1]/n_blocks)\n",
    "    for h in range(0, frequencies[len(frequencies)-1], h_step):\n",
    "        ax.axhline(h, color = 'red')\n",
    "        freq_bounds.append(h)\n",
    "    plt.xlabel(\"Time (ms)\")\n",
    "    plt.xlim(left=0)\n",
    "    plt.ylabel(\"Frequency (Hz)\")\n",
    "    plt.show()\n",
    "\n",
    "def split_spectrogram(audio, sr, n_blocks):\n",
    "    frequencies, times, spec = spectrogram(audio, fs=sr)\n",
    "    times = (times * 1000).astype(int) #milliseconds\n",
    "    frequencies = frequencies.astype(int)\n",
    "        \n",
    "    \n",
    "    # Split the matrix into sub-arrays of size N along the first axis (rows)\n",
    "    rows_blocks = np.array_split(spec, n_blocks, axis=0)\n",
    "    #this is a list: each element of the list is a sub-matrix obtained slicing horizontally the spectrogram\n",
    "    # into n_blocks slices\n",
    "\n",
    "    # Split the sub_matrices into sub-arrays of size NxN along the second axis (columns)\n",
    "    blocks = [ np.array_split(block, n_blocks, axis=1) for block in rows_blocks]\n",
    "    #This is, instead, a list of lists: the first element is a list containing all the blocks obtained from \n",
    "    #the first vertical slice\n",
    "\n",
    "\n",
    "    # Initialize arrays to store the mean and standard deviation of each block\n",
    "    block_means = np.zeros((n_blocks,n_blocks))\n",
    "    block_stds = np.zeros((n_blocks,n_blocks))\n",
    "\n",
    "    \n",
    "\n",
    "    # Iterate over blocks\n",
    "    for i in range(0, n_blocks):\n",
    "        for j in range(0, n_blocks):\n",
    "            # Compute mean and standard deviation of the block\n",
    "             #i is the index of the horizontal split  (indicating which column)\n",
    "            block = blocks[i][j] #j is the index of the vertical split (indicating which row)\n",
    "            mean = np.mean(block)\n",
    "            std = np.std(block)\n",
    "            minimum = np.min(block)\n",
    "            maximum = np.max(block)\n",
    "            block_means[i,j] = mean\n",
    "            block_stds[i,j] = std\n",
    "    return block_means.tolist(), block_stds.tolist(), blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "128290f8",
   "metadata": {},
   "outputs": [],
   "source": [
    " avg, std_dev, blocks = split_spectrogram(df['AudioData'].loc[0], df['SampleRates'].loc[0], 7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c44bea81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0015354873336479171,\n",
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       " [5.877106301268341e-06,\n",
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       "  0.0],\n",
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       "  0.0],\n",
       " [5.038768720027236e-05,\n",
       "  9.11414046140584e-06,\n",
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       "  0.0,\n",
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       "  0.0],\n",
       " [3.726761154301474e-05,\n",
       "  4.227371327967359e-06,\n",
       "  5.150716230666736e-06,\n",
       "  2.3843202373484145e-08,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0],\n",
       " [1.6438292622475263e-05,\n",
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       "  0.0,\n",
       "  0.0,\n",
       "  0.0],\n",
       " [5.667409042484858e-06,\n",
       "  5.026882314622631e-07,\n",
       "  8.702464900518787e-07,\n",
       "  1.384530894683735e-09,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0]]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4ed46055",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.004708645295534316,\n",
       "  0.001673676957373711,\n",
       "  0.0010361933771979059,\n",
       "  2.2594232403332512e-09,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0],\n",
       " [1.7945071686125354e-05,\n",
       "  0.0012431110969773611,\n",
       "  2.8036462349376026e-05,\n",
       "  9.351449454982846e-09,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0],\n",
       " [0.0015218133100028002,\n",
       "  0.00010426295992468413,\n",
       "  0.0001550709333545003,\n",
       "  6.743640351809547e-08,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0],\n",
       " [0.0001384574928403315,\n",
       "  1.9945925425433543e-05,\n",
       "  3.2409527925321176e-05,\n",
       "  3.7113896261343515e-07,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0],\n",
       " [0.00010035444032989864,\n",
       "  2.1363785195057494e-05,\n",
       "  1.5148908279550007e-05,\n",
       "  1.7753695747564414e-07,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0],\n",
       " [5.227678225996793e-05,\n",
       "  7.963207503507668e-06,\n",
       "  3.3918742256134243e-06,\n",
       "  1.1671341006863846e-08,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0],\n",
       " [1.6182683409087007e-05,\n",
       "  1.6200721109281529e-06,\n",
       "  1.7191023367378207e-06,\n",
       "  1.228518409613944e-08,\n",
       "  0.0,\n",
       "  0.0,\n",
       "  0.0]]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std_dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "9c7f11cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#adding the statistics of the blocks into the dataframe\n",
    "\n",
    "def add_stats_df(df,sr,n_blocks):\n",
    "    for i in range(0,n_blocks):\n",
    "        for j in range(0, n_blocks):\n",
    "            idx = j+n_blocks*i\n",
    "            col_name_mean = 'block_mean_'+str(idx)\n",
    "            col_name_dev = 'block_dev_'+str(idx)\n",
    "            df[col_name_mean] = ''\n",
    "            df[col_name_dev] = ''\n",
    "    all_means = list()\n",
    "    all_std_devs = list()\n",
    "    all_mins = list()\n",
    "    all_maxs = list()\n",
    "\n",
    "    for index, row in df.iterrows():\n",
    "        audio = row['AudioData']\n",
    "        meanTemp, stdTemp, blocksTemps = split_spectrogram(audio, sr, n_blocks)\n",
    "        for i in range(0,n_blocks):\n",
    "            for j in range(0, n_blocks):\n",
    "                idx = j+n_blocks*i\n",
    "                col_name_mean = 'block_mean_'+str(idx)\n",
    "                col_name_dev = 'block_dev_'+str(idx)\n",
    "                df.loc[index, col_name_mean] = meanTemp[i][j]\n",
    "                df.loc[index, col_name_dev] = stdTemp[i][j]\n",
    "        \n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "42d15d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "add_stats_df(df, 16000, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "1a88f642",
   "metadata": {},
   "outputs": [],
   "source": [
    "add_stats_df(dfTrim, 16000, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a010427d",
   "metadata": {},
   "outputs": [],
   "source": [
    "add_stats_df(df3, 16000, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "958f3e20",
   "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>Id</th>\n",
       "      <th>path</th>\n",
       "      <th>speakerId</th>\n",
       "      <th>action</th>\n",
       "      <th>object</th>\n",
       "      <th>Self-reported fluency level</th>\n",
       "      <th>First Language spoken</th>\n",
       "      <th>Current language used for work/school</th>\n",
       "      <th>gender</th>\n",
       "      <th>ageRange</th>\n",
       "      <th>...</th>\n",
       "      <th>block_mean_20</th>\n",
       "      <th>block_dev_20</th>\n",
       "      <th>block_mean_21</th>\n",
       "      <th>block_dev_21</th>\n",
       "      <th>block_mean_22</th>\n",
       "      <th>block_dev_22</th>\n",
       "      <th>block_mean_23</th>\n",
       "      <th>block_dev_23</th>\n",
       "      <th>block_mean_24</th>\n",
       "      <th>block_dev_24</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0a312...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>change language</td>\n",
       "      <td>none</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.000022</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0ee42...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>activate</td>\n",
       "      <td>music</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/1d9f3...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>deactivate</td>\n",
       "      <td>lights</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/269fc...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>increase</td>\n",
       "      <td>volume</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/5bbda...</td>\n",
       "      <td>2BqVo8kVB2Skwgyb</td>\n",
       "      <td>increase</td>\n",
       "      <td>volume</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id                                               path         speakerId  \\\n",
       "0   0  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0a312...  2BqVo8kVB2Skwgyb   \n",
       "1   1  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/0ee42...  2BqVo8kVB2Skwgyb   \n",
       "2   2  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/1d9f3...  2BqVo8kVB2Skwgyb   \n",
       "3   3  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/269fc...  2BqVo8kVB2Skwgyb   \n",
       "4   4  dsl_data/audio/speakers/2BqVo8kVB2Skwgyb/5bbda...  2BqVo8kVB2Skwgyb   \n",
       "\n",
       "            action  object  Self-reported fluency level   \\\n",
       "0  change language    none                             1   \n",
       "1         activate   music                             1   \n",
       "2       deactivate  lights                             1   \n",
       "3         increase  volume                             1   \n",
       "4         increase  volume                             1   \n",
       "\n",
       "   First Language spoken  Current language used for work/school  gender  \\\n",
       "0                      0                                      0       0   \n",
       "1                      0                                      0       0   \n",
       "2                      0                                      0       0   \n",
       "3                      0                                      0       0   \n",
       "4                      0                                      0       0   \n",
       "\n",
       "   ageRange  ...  block_mean_20 block_dev_20 block_mean_21 block_dev_21  \\\n",
       "0         0  ...       0.000006     0.000022      0.000001     0.000002   \n",
       "1         0  ...       0.000019     0.000083           0.0          0.0   \n",
       "2         0  ...            0.0          0.0      0.000015      0.00005   \n",
       "3         0  ...            0.0          0.0      0.000002      0.00001   \n",
       "4         0  ...            0.0          0.0           0.0          0.0   \n",
       "\n",
       "  block_mean_22 block_dev_22 block_mean_23 block_dev_23 block_mean_24  \\\n",
       "0           0.0     0.000001           0.0          0.0           0.0   \n",
       "1           0.0          0.0           0.0          0.0           0.0   \n",
       "2      0.000003     0.000017           0.0          0.0           0.0   \n",
       "3           0.0          0.0           0.0          0.0           0.0   \n",
       "4           0.0          0.0           0.0          0.0           0.0   \n",
       "\n",
       "  block_dev_24  \n",
       "0          0.0  \n",
       "1          0.0  \n",
       "2          0.0  \n",
       "3          0.0  \n",
       "4          0.0  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7b79f09",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c8a5f5f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['action'] + ' ' + df['object']\n",
    "X = df.drop(['action', 'object', 'path', 'speakerId', 'AudioData'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f71bf540",
   "metadata": {},
   "outputs": [],
   "source": [
    "y2 = dfTrim['action'] + ' ' + dfTrim['object']\n",
    "X2 = dfTrim.drop(['action', 'object', 'path', 'speakerId', 'AudioData'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ba23a9b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "y3 = df3['action'] + ' ' + df3['object']\n",
    "X3 = df3.drop(['action', 'object', 'path', 'speakerId', 'AudioData'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "298d6ac8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "increase volume         2614\n",
      "decrease volume         2386\n",
      "increase heat           1209\n",
      "decrease heat           1189\n",
      "change language none    1113\n",
      "activate music           791\n",
      "deactivate lights        552\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "print(y.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "fa2c1f63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2614\n",
       "1    2386\n",
       "2    1209\n",
       "3    1189\n",
       "4    1113\n",
       "5     791\n",
       "6     552\n",
       "dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#encoding also the actions/objects in y \n",
    "\n",
    "label_dictionary = {'increase volume': 0, 'decrease volume': 1, 'increase heat': 2, 'decrease heat':3, 'change language none':4,'activate music':5, 'deactivate lights':6 }\n",
    "\n",
    "y = y.replace(label_dictionary)\n",
    "\n",
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "46e1fe2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c34b5a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train2, X_test2, y_train, y_test = train_test_split(X2, y, test_size=0.2, random_state=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d4724b24",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train3, X_test3, y_train, y_test = train_test_split(X3, y, test_size=0.2, random_state=31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "43c5268c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7883, 57)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "98daf940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1971, 57)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a5ca3c57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 0.3713850837138508\n",
      "20 0.37493658041603245\n",
      "30 0.3906646372399797\n",
      "40 0.4003044140030441\n",
      "50 0.40842212075088785\n",
      "60 0.4063926940639269\n",
      "70 0.3997970573313039\n",
      "80 0.4079147640791476\n",
      "90 0.4058853373921867\n",
      "100 0.4074074074074074\n"
     ]
    }
   ],
   "source": [
    "for n_estimators in range(10, 101, 10):\n",
    "    clf = RandomForestClassifier(n_estimators, max_features='sqrt')\n",
    "    clf.fit(X_train, y_train)\n",
    "    print(n_estimators, accuracy_score(y_test, clf.predict(X_test)))\n",
    "# Padding no trim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f1250b72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 0.4530695078640284\n",
      "20 0.4698122780314561\n",
      "30 0.487062404870624\n",
      "40 0.5027904616945713\n",
      "50 0.5093860984271943\n",
      "60 0.504312531709792\n",
      "70 0.5230847285641806\n",
      "80 0.5230847285641806\n",
      "90 0.5154743784880771\n",
      "100 0.5169964485032978\n"
     ]
    }
   ],
   "source": [
    "for n_estimators in range(10, 101, 10):\n",
    "    clf = RandomForestClassifier(n_estimators, max_features='sqrt')\n",
    "    clf.fit(X_train2, y_train)\n",
    "    print(n_estimators, accuracy_score(y_test, clf.predict(X_test2)))\n",
    "# Trim no padding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "87837b6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 0.3617453069507864\n",
      "20 0.36884830035514965\n",
      "30 0.3952308472856418\n",
      "40 0.39928970065956365\n",
      "50 0.38711314053779805\n",
      "60 0.39776763064434295\n",
      "70 0.39472349061390155\n",
      "80 0.404363267376966\n",
      "90 0.40690005073566715\n",
      "100 0.40233384069000505\n"
     ]
    }
   ],
   "source": [
    "for n_estimators in range(10, 101, 10):\n",
    "    clf = RandomForestClassifier(n_estimators, max_features='sqrt')\n",
    "    clf.fit(X_train3, y_train)\n",
    "    print(n_estimators, accuracy_score(y_test, clf.predict(X_test3)))\n",
    "# Trim and padding"
   ]
  }
 ],
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