ravdess_savee_analysis.ipynb

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   "file_extension": ".py",
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 "cells": [
  {
   "source": [
    "# RAVDESS and SAVEE Datasets Analysis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Libraries Import"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as pyplot\n",
    "import seaborn as sns"
   ]
  },
  {
   "source": [
    "## Configuration"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "DATASET_FOLDER = \"datasets/ravdess_savee/\"\n",
    "RAVDESS_ID = \"RAVDESS\"\n",
    "SAVEE_ID = \"SAVEE\"\n",
    "COLUMNS = [\"full_path\", \"parent_dataset\", \"sex\", \"emotion\"]\n",
    "ROWS_TO_DISPLAY = 5"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 2,
   "outputs": []
  },
  {
   "source": [
    "## Datasets Concatenation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_file(dataset: str, file_id: str) -> tuple:\n",
    "    sex = None\n",
    "    emotion = None\n",
    "\n",
    "    if (dataset == RAVDESS_ID):\n",
    "        details = file_id.split(\"-\")\n",
    "\n",
    "        # Get the sex\n",
    "        sex_id = int(details[-1])\n",
    "        if (sex_id % 2 == 0):\n",
    "            sex = \"female\"\n",
    "        else:\n",
    "            sex = \"male\"\n",
    "\n",
    "        # Get the emotion\n",
    "        emotion_id = int(details[2])\n",
    "        if (emotion_id == 2):\n",
    "            emotion = \"calm\"\n",
    "        elif (emotion_id == 3):\n",
    "            emotion = \"happy\"\n",
    "        elif (emotion_id == 4):\n",
    "            emotion = \"sad\"\n",
    "        elif (emotion_id == 5):\n",
    "            emotion = \"angry\"\n",
    "        elif (emotion_id == 3):\n",
    "            emotion = \"fearful\"\n",
    "    elif (dataset == SAVEE_ID):\n",
    "        sex = \"male\"\n",
    "\n",
    "        # Get the emotion\n",
    "        if file_id.startswith(\"h\"):\n",
    "            emotion = \"happy\"\n",
    "        elif file_id.startswith(\"sa\"):\n",
    "            emotion = \"sad\"\n",
    "        elif file_id.startswith(\"a\"):\n",
    "            emotion = \"angry\"\n",
    "        elif file_id.startswith(\"f\"):\n",
    "            emotion = \"fearful\"\n",
    "\n",
    "    return (sex, emotion) if emotion is not None else None\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "for subdir, dirs, files in os.walk(DATASET_FOLDER):\n",
    "    for current_filename in files:\n",
    "        # Get the file features\n",
    "        full_path = os.path.join(subdir, current_filename)\n",
    "        parent_dataset = full_path.split(\"/\")[2]\n",
    "\n",
    "        # Get the file basename\n",
    "        file_id = current_filename.replace(\".wav\", \"\")\n",
    "        result = process_file(parent_dataset, file_id)\n",
    "        if result:\n",
    "            sex, emotion = result\n",
    "        else:\n",
    "            continue\n",
    "        data.append([full_path, parent_dataset, sex, emotion])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "speech_df = pd.DataFrame(data, columns=COLUMNS)\n",
    "speech_df[\"label\"] = speech_df[\"sex\"] + \"_\" + speech_df[\"emotion\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                             full_path parent_dataset     sex  \\\n",
       "183            datasets/ravdess_savee/SAVEE/DC/f01.wav          SAVEE    male   \n",
       "284  datasets/ravdess_savee/RAVDESS/Actor_21/03-01-...        RAVDESS    male   \n",
       "618  datasets/ravdess_savee/RAVDESS/Actor_13/03-01-...        RAVDESS    male   \n",
       "195            datasets/ravdess_savee/SAVEE/DC/a11.wav          SAVEE    male   \n",
       "760  datasets/ravdess_savee/RAVDESS/Actor_12/03-01-...        RAVDESS  female   \n",
       "\n",
       "     emotion         label  \n",
       "183  fearful  male_fearful  \n",
       "284    happy    male_happy  \n",
       "618    happy    male_happy  \n",
       "195    angry    male_angry  \n",
       "760    happy  female_happy  "
      ],
      "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>full_path</th>\n      <th>parent_dataset</th>\n      <th>sex</th>\n      <th>emotion</th>\n      <th>label</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>183</th>\n      <td>datasets/ravdess_savee/SAVEE/DC/f01.wav</td>\n      <td>SAVEE</td>\n      <td>male</td>\n      <td>fearful</td>\n      <td>male_fearful</td>\n    </tr>\n    <tr>\n      <th>284</th>\n      <td>datasets/ravdess_savee/RAVDESS/Actor_21/03-01-...</td>\n      <td>RAVDESS</td>\n      <td>male</td>\n      <td>happy</td>\n      <td>male_happy</td>\n    </tr>\n    <tr>\n      <th>618</th>\n      <td>datasets/ravdess_savee/RAVDESS/Actor_13/03-01-...</td>\n      <td>RAVDESS</td>\n      <td>male</td>\n      <td>happy</td>\n      <td>male_happy</td>\n    </tr>\n    <tr>\n      <th>195</th>\n      <td>datasets/ravdess_savee/SAVEE/DC/a11.wav</td>\n      <td>SAVEE</td>\n      <td>male</td>\n      <td>angry</td>\n      <td>male_angry</td>\n    </tr>\n    <tr>\n      <th>760</th>\n      <td>datasets/ravdess_savee/RAVDESS/Actor_12/03-01-...</td>\n      <td>RAVDESS</td>\n      <td>female</td>\n      <td>happy</td>\n      <td>female_happy</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "speech_df.sample(ROWS_TO_DISPLAY)"
   ]
  },
  {
   "source": [
    "## Dataset Analysis"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Total number of samples in dataset is 1008.\n"
     ]
    }
   ],
   "source": [
    "print(\"Total number of samples in dataset is {}.\".format(len(speech_df)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plot = sns.histplot(data=speech_df, x=\"parent_dataset\", shrink=0.5)\n",
    "plot.set_xlabel(\"Parent Dataset\")\n",
    "plot.set_ylabel(\"Samples Count\")\n",
    "plot.set_title(\"Samples Distribution in Datasets\")\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "pyplot.rcParams[\"figure.figsize\"] = (15, 3)\n",
    "\n",
    "plot = sns.histplot(data=speech_df, x=\"label\", multiple=\"dodge\", shrink=0.5)\n",
    "plot.set_xlabel(\"Label\")\n",
    "plot.set_ylabel(\"Samples Count\")\n",
    "plot.set_title(\"Samples Distribution over the Labels\")\n",
    "pyplot.show()"
   ]
  }
 ]
}