data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8b2fae50",
   "metadata": {},
   "source": [
    "# Data Generation for the GTZan Music Genre Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e886f4ed",
   "metadata": {},
   "source": [
    "## Import Statements"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83b590e7",
   "metadata": {},
   "source": [
    "Start by importing necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22138a63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n",
      "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import librosa\n",
    "import pandas as pd\n",
    "from pydub import AudioSegment\n",
    "import tempfile\n",
    "import numpy as np\n",
    "import soundfile as sf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a0285f",
   "metadata": {},
   "source": [
    "## Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16e9fb7d",
   "metadata": {},
   "source": [
    "Convert to MP3 function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e3d8b42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to convert MP3 to WAV\n",
    "def convert_mp3_to_wav(mp3_file):\n",
    "    try:\n",
    "        sound = AudioSegment.from_mp3(mp3_file)\n",
    "        wav_file = tempfile.mktemp(suffix='.wav')\n",
    "        sound.export(wav_file, format=\"wav\")\n",
    "        return wav_file\n",
    "    except Exception as e:\n",
    "        print(f\"Error converting {mp3_file} to WAV: {e}\")\n",
    "        return None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1e514b2",
   "metadata": {},
   "source": [
    "Feature extraction from a segment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5aa55f22",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to extract features from an audio segment\n",
    "def extract_features_from_segment(y, sr, start_time, end_time):\n",
    "    segment = y[start_time:end_time]\n",
    "\n",
    "    chroma_stft = librosa.feature.chroma_stft(y=segment, sr=sr)\n",
    "    rms = librosa.feature.rms(y=segment)\n",
    "    spectral_centroid = librosa.feature.spectral_centroid(y=segment, sr=sr)\n",
    "    spectral_bandwidth = librosa.feature.spectral_bandwidth(y=segment, sr=sr)\n",
    "    rolloff = librosa.feature.spectral_rolloff(y=segment, sr=sr)\n",
    "    zero_crossing_rate = librosa.feature.zero_crossing_rate(y=segment)\n",
    "    harmony, perceptr = librosa.effects.hpss(segment)\n",
    "    tempo, _ = librosa.beat.beat_track(y=segment, sr=sr)\n",
    "    mfcc = librosa.feature.mfcc(y=segment, sr=sr, n_mfcc=20)\n",
    "\n",
    "    features = {\n",
    "        'chroma_stft_mean': chroma_stft.mean() if chroma_stft.size else 0,\n",
    "        'chroma_stft_var': chroma_stft.var() if chroma_stft.size else 0,\n",
    "        'rms_mean': rms.mean() if rms.size else 0,\n",
    "        'rms_var': rms.var() if rms.size else 0,\n",
    "        'spectral_centroid_mean': spectral_centroid.mean() if spectral_centroid.size else 0,\n",
    "        'spectral_centroid_var': spectral_centroid.var() if spectral_centroid.size else 0,\n",
    "        'spectral_bandwidth_mean': spectral_bandwidth.mean() if spectral_bandwidth.size else 0,\n",
    "        'spectral_bandwidth_var': spectral_bandwidth.var() if spectral_bandwidth.size else 0,\n",
    "        'rolloff_mean': rolloff.mean() if rolloff.size else 0,\n",
    "        'rolloff_var': rolloff.var() if rolloff.size else 0,\n",
    "        'zero_crossing_rate_mean': zero_crossing_rate.mean() if zero_crossing_rate.size else 0,\n",
    "        'zero_crossing_rate_var': zero_crossing_rate.var() if zero_crossing_rate.size else 0,\n",
    "        'harmony_mean': harmony.mean() if harmony.size else 0,\n",
    "        'harmony_var': harmony.var() if harmony.size else 0,\n",
    "        'perceptr_mean': perceptr.mean() if perceptr.size else 0,\n",
    "        'perceptr_var': perceptr.var() if perceptr.size else 0,\n",
    "        'tempo': tempo,\n",
    "    }\n",
    "\n",
    "    for i in range(1, 21):\n",
    "        features[f'mfcc{i}_mean'] = mfcc[i-1].mean() if mfcc.shape[0] >= i else 0\n",
    "        features[f'mfcc{i}_var'] = mfcc[i-1].var() if mfcc.shape[0] >= i else 0\n",
    "\n",
    "    return features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dcf0655",
   "metadata": {},
   "source": [
    "Load audio helpfer function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "913bcfba",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to load audio file\n",
    "def load_audio(file_path):\n",
    "    try:\n",
    "        y, sr = librosa.load(file_path, sr=None)\n",
    "    except sf.LibsndfileError:\n",
    "        print(f\"LibsndfileError: {file_path}\")\n",
    "        return None, None\n",
    "    except Exception as e:\n",
    "        print(f\"Error loading {file_path}: {e}\")\n",
    "        return None, None\n",
    "    return y, sr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfa5f717",
   "metadata": {},
   "source": [
    "Segment data and call feature extraction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "75e1c6b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to extract features from an audio file\n",
    "def extract_features(audio_file, segment_duration=10):\n",
    "    try:\n",
    "        y, sr = load_audio(audio_file)\n",
    "        if y is None:\n",
    "            return []\n",
    "\n",
    "        total_duration = len(y) / sr\n",
    "        segment_length = int(sr * segment_duration)\n",
    "\n",
    "        features_list = []\n",
    "\n",
    "        for start in range(0, len(y), segment_length):\n",
    "            end = start + segment_length\n",
    "            if end <= len(y):\n",
    "                segment_features = extract_features_from_segment(y, sr, start, end)\n",
    "                all_features = segment_features\n",
    "                all_features['filename'] = os.path.basename(audio_file)\n",
    "                all_features['start'] = start / sr\n",
    "                all_features['end'] = end / sr\n",
    "                features_list.append(all_features)\n",
    "\n",
    "        return features_list\n",
    "\n",
    "    except Exception as e:\n",
    "        print(f\"Error extracting features from {audio_file}: {e}\")\n",
    "        return []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3851f10",
   "metadata": {},
   "source": [
    "## Define Data Paths"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96da33c7",
   "metadata": {},
   "source": [
    "Specify the paths to the CSV files containing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0784aec2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# List of genres\n",
    "genres = [\"blues\", \"classical\", \"country\", \"disco\", \"hiphop\", \"jazz\", \"metal\", \"pop\", \"reggae\", \"rock\"]\n",
    "\n",
    "# Base folder containing genre subfolders\n",
    "base_folder_path = '/Users/isaiah/Desktop/Career/Projects/music-genre-detector/GTZan/genres_original'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "953974b0",
   "metadata": {},
   "source": [
    "## Process Input Audio Files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e0d4edc",
   "metadata": {},
   "source": [
    "Process input files and label the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec7af356",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to process a folder of audio files\n",
    "def process_audio_folder(folder_path, genre_label):\n",
    "    results = []\n",
    "    for filename in os.listdir(folder_path):\n",
    "        if filename.endswith('.wav') or filename.endswith('.mp3'):\n",
    "            file_path = os.path.join(folder_path, filename)\n",
    "            if filename.endswith('.mp3'):\n",
    "                file_path = convert_mp3_to_wav(file_path)\n",
    "            features_list = extract_features(file_path)\n",
    "            for features in features_list:\n",
    "                features['genre'] = genre_label\n",
    "                results.append(features)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a872c4cd",
   "metadata": {},
   "source": [
    "## Create Singular CSV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c9fc8b6",
   "metadata": {},
   "source": [
    "Aggregate results of the feature extraction into a singular CSV."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c7412610",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing genre: blues\n",
      "Completed processing genre: blues\n",
      "Processing genre: classical\n",
      "Completed processing genre: classical\n",
      "Processing genre: country\n",
      "Completed processing genre: country\n",
      "Processing genre: disco\n",
      "Completed processing genre: disco\n",
      "Processing genre: hiphop\n",
      "Completed processing genre: hiphop\n",
      "Processing genre: jazz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/np/q9rxp7h96v19y1wfjlnwbjzr0000gn/T/ipykernel_65088/3579487775.py:4: UserWarning: PySoundFile failed. Trying audioread instead.\n",
      "  y, sr = librosa.load(file_path, sr=None)\n",
      "/Users/isaiah/opt/anaconda3/envs/ELEC292/lib/python3.9/site-packages/librosa/core/audio.py:184: FutureWarning: librosa.core.audio.__audioread_load\n",
      "\tDeprecated as of librosa version 0.10.0.\n",
      "\tIt will be removed in librosa version 1.0.\n",
      "  y, sr_native = __audioread_load(path, offset, duration, dtype)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error loading /Users/isaiah/Desktop/Career/Projects/music-genre-detector/GTZan/genres_original/jazz/jazz.00054.wav: \n",
      "Completed processing genre: jazz\n",
      "Processing genre: metal\n",
      "Completed processing genre: metal\n",
      "Processing genre: pop\n",
      "Completed processing genre: pop\n",
      "Processing genre: reggae\n",
      "Completed processing genre: reggae\n",
      "Processing genre: rock\n",
      "Completed processing genre: rock\n",
      "Writing results to CSV file...\n",
      "CSV file generation completed.\n"
     ]
    },
    {
     "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>chroma_stft_mean</th>\n",
       "      <th>chroma_stft_var</th>\n",
       "      <th>rms_mean</th>\n",
       "      <th>rms_var</th>\n",
       "      <th>spectral_centroid_mean</th>\n",
       "      <th>spectral_centroid_var</th>\n",
       "      <th>spectral_bandwidth_mean</th>\n",
       "      <th>spectral_bandwidth_var</th>\n",
       "      <th>rolloff_mean</th>\n",
       "      <th>rolloff_var</th>\n",
       "      <th>...</th>\n",
       "      <th>mfcc18_mean</th>\n",
       "      <th>mfcc18_var</th>\n",
       "      <th>mfcc19_mean</th>\n",
       "      <th>mfcc19_var</th>\n",
       "      <th>mfcc20_mean</th>\n",
       "      <th>mfcc20_var</th>\n",
       "      <th>filename</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.399429</td>\n",
       "      <td>0.092919</td>\n",
       "      <td>0.061375</td>\n",
       "      <td>0.001746</td>\n",
       "      <td>574.917649</td>\n",
       "      <td>126695.325130</td>\n",
       "      <td>968.464834</td>\n",
       "      <td>87971.594698</td>\n",
       "      <td>982.858326</td>\n",
       "      <td>5.887872e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060722</td>\n",
       "      <td>46.816097</td>\n",
       "      <td>4.530915</td>\n",
       "      <td>42.765888</td>\n",
       "      <td>1.700291</td>\n",
       "      <td>35.366920</td>\n",
       "      <td>blues.00093.wav</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.382725</td>\n",
       "      <td>0.096942</td>\n",
       "      <td>0.059265</td>\n",
       "      <td>0.001327</td>\n",
       "      <td>571.488368</td>\n",
       "      <td>65396.649681</td>\n",
       "      <td>1001.270756</td>\n",
       "      <td>64480.456959</td>\n",
       "      <td>914.161912</td>\n",
       "      <td>3.057253e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>1.393839</td>\n",
       "      <td>25.180780</td>\n",
       "      <td>1.342265</td>\n",
       "      <td>48.229031</td>\n",
       "      <td>-5.838692</td>\n",
       "      <td>68.770432</td>\n",
       "      <td>blues.00093.wav</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.355013</td>\n",
       "      <td>0.099065</td>\n",
       "      <td>0.076845</td>\n",
       "      <td>0.002158</td>\n",
       "      <td>567.393583</td>\n",
       "      <td>125956.638174</td>\n",
       "      <td>1022.309635</td>\n",
       "      <td>105444.049669</td>\n",
       "      <td>898.848857</td>\n",
       "      <td>6.315897e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.191103</td>\n",
       "      <td>29.066435</td>\n",
       "      <td>0.475051</td>\n",
       "      <td>32.250404</td>\n",
       "      <td>-4.556223</td>\n",
       "      <td>48.543552</td>\n",
       "      <td>blues.00093.wav</td>\n",
       "      <td>20.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.319351</td>\n",
       "      <td>0.094813</td>\n",
       "      <td>0.155237</td>\n",
       "      <td>0.002587</td>\n",
       "      <td>1379.320718</td>\n",
       "      <td>541760.685298</td>\n",
       "      <td>1829.221092</td>\n",
       "      <td>173513.186918</td>\n",
       "      <td>2890.545244</td>\n",
       "      <td>2.591938e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.237206</td>\n",
       "      <td>53.630169</td>\n",
       "      <td>2.549892</td>\n",
       "      <td>49.117638</td>\n",
       "      <td>-5.811831</td>\n",
       "      <td>82.158409</td>\n",
       "      <td>blues.00087.wav</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.345845</td>\n",
       "      <td>0.094060</td>\n",
       "      <td>0.171401</td>\n",
       "      <td>0.003239</td>\n",
       "      <td>1425.317034</td>\n",
       "      <td>276693.251662</td>\n",
       "      <td>1844.788630</td>\n",
       "      <td>116117.705916</td>\n",
       "      <td>3012.799879</td>\n",
       "      <td>1.621920e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>2.447671</td>\n",
       "      <td>72.564873</td>\n",
       "      <td>3.575914</td>\n",
       "      <td>55.017838</td>\n",
       "      <td>-5.471438</td>\n",
       "      <td>67.686737</td>\n",
       "      <td>blues.00087.wav</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   chroma_stft_mean  chroma_stft_var  rms_mean   rms_var  \\\n",
       "0          0.399429         0.092919  0.061375  0.001746   \n",
       "1          0.382725         0.096942  0.059265  0.001327   \n",
       "2          0.355013         0.099065  0.076845  0.002158   \n",
       "3          0.319351         0.094813  0.155237  0.002587   \n",
       "4          0.345845         0.094060  0.171401  0.003239   \n",
       "\n",
       "   spectral_centroid_mean  spectral_centroid_var  spectral_bandwidth_mean  \\\n",
       "0              574.917649          126695.325130               968.464834   \n",
       "1              571.488368           65396.649681              1001.270756   \n",
       "2              567.393583          125956.638174              1022.309635   \n",
       "3             1379.320718          541760.685298              1829.221092   \n",
       "4             1425.317034          276693.251662              1844.788630   \n",
       "\n",
       "   spectral_bandwidth_var  rolloff_mean   rolloff_var  ...  mfcc18_mean  \\\n",
       "0            87971.594698    982.858326  5.887872e+05  ...     0.060722   \n",
       "1            64480.456959    914.161912  3.057253e+05  ...     1.393839   \n",
       "2           105444.049669    898.848857  6.315897e+05  ...    -1.191103   \n",
       "3           173513.186918   2890.545244  2.591938e+06  ...    -1.237206   \n",
       "4           116117.705916   3012.799879  1.621920e+06  ...     2.447671   \n",
       "\n",
       "   mfcc18_var  mfcc19_mean  mfcc19_var  mfcc20_mean  mfcc20_var  \\\n",
       "0   46.816097     4.530915   42.765888     1.700291   35.366920   \n",
       "1   25.180780     1.342265   48.229031    -5.838692   68.770432   \n",
       "2   29.066435     0.475051   32.250404    -4.556223   48.543552   \n",
       "3   53.630169     2.549892   49.117638    -5.811831   82.158409   \n",
       "4   72.564873     3.575914   55.017838    -5.471438   67.686737   \n",
       "\n",
       "          filename  start   end  genre  \n",
       "0  blues.00093.wav    0.0  10.0  blues  \n",
       "1  blues.00093.wav   10.0  20.0  blues  \n",
       "2  blues.00093.wav   20.0  30.0  blues  \n",
       "3  blues.00087.wav    0.0  10.0  blues  \n",
       "4  blues.00087.wav   10.0  20.0  blues  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_results = []\n",
    "\n",
    "for genre in genres:\n",
    "    print(f\"Processing genre: {genre}\")\n",
    "    folder_path = os.path.join(base_folder_path, genre)\n",
    "    genre_results = process_audio_folder(folder_path, genre)\n",
    "    all_results.extend(genre_results)\n",
    "    print(f\"Completed processing genre: {genre}\")\n",
    "\n",
    "print(\"Writing results to CSV file...\")\n",
    "df = pd.DataFrame(all_results)\n",
    "csv_file_path = '/Users/isaiah/Desktop/Career/Projects/music-genre-detector/all_genres_audio_features.csv'\n",
    "df.to_csv(csv_file_path, index=False)\n",
    "print(\"CSV file generation completed.\")\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f0d23c",
   "metadata": {},
   "source": [
    "## Examine Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9f555dd",
   "metadata": {},
   "source": [
    "Gain insights into data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "05cf96d0",
   "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>chroma_stft_mean</th>\n",
       "      <th>chroma_stft_var</th>\n",
       "      <th>rms_mean</th>\n",
       "      <th>rms_var</th>\n",
       "      <th>spectral_centroid_mean</th>\n",
       "      <th>spectral_centroid_var</th>\n",
       "      <th>spectral_bandwidth_mean</th>\n",
       "      <th>spectral_bandwidth_var</th>\n",
       "      <th>rolloff_mean</th>\n",
       "      <th>rolloff_var</th>\n",
       "      <th>...</th>\n",
       "      <th>mfcc18_mean</th>\n",
       "      <th>mfcc18_var</th>\n",
       "      <th>mfcc19_mean</th>\n",
       "      <th>mfcc19_var</th>\n",
       "      <th>mfcc20_mean</th>\n",
       "      <th>mfcc20_var</th>\n",
       "      <th>filename</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2.988000e+03</td>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2.988000e+03</td>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2.988000e+03</td>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2.988000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2988</td>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2988.000000</td>\n",
       "      <td>2988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blues.00093.wav</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>blues</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.378823</td>\n",
       "      <td>0.085846</td>\n",
       "      <td>0.130570</td>\n",
       "      <td>2.870646e-03</td>\n",
       "      <td>2200.838173</td>\n",
       "      <td>4.496328e+05</td>\n",
       "      <td>2241.994650</td>\n",
       "      <td>1.291835e+05</td>\n",
       "      <td>4569.365666</td>\n",
       "      <td>1.759079e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.603902</td>\n",
       "      <td>60.373924</td>\n",
       "      <td>-2.408998</td>\n",
       "      <td>62.671871</td>\n",
       "      <td>-1.031245</td>\n",
       "      <td>65.967857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.969880</td>\n",
       "      <td>19.969880</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.084776</td>\n",
       "      <td>0.008636</td>\n",
       "      <td>0.066960</td>\n",
       "      <td>3.593415e-03</td>\n",
       "      <td>729.637169</td>\n",
       "      <td>4.146715e+05</td>\n",
       "      <td>533.451980</td>\n",
       "      <td>9.992376e+04</td>\n",
       "      <td>1600.754328</td>\n",
       "      <td>1.456471e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>4.325673</td>\n",
       "      <td>39.036507</td>\n",
       "      <td>4.259204</td>\n",
       "      <td>40.920700</td>\n",
       "      <td>4.362487</td>\n",
       "      <td>47.345730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.160125</td>\n",
       "      <td>8.160125</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.126113</td>\n",
       "      <td>0.014554</td>\n",
       "      <td>0.001251</td>\n",
       "      <td>1.780778e-07</td>\n",
       "      <td>518.949680</td>\n",
       "      <td>2.389426e+03</td>\n",
       "      <td>695.900448</td>\n",
       "      <td>1.960306e+03</td>\n",
       "      <td>713.768228</td>\n",
       "      <td>1.260120e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>-15.889191</td>\n",
       "      <td>10.180762</td>\n",
       "      <td>-20.310465</td>\n",
       "      <td>11.670246</td>\n",
       "      <td>-21.439188</td>\n",
       "      <td>0.528655</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.318897</td>\n",
       "      <td>0.081522</td>\n",
       "      <td>0.085172</td>\n",
       "      <td>8.013145e-04</td>\n",
       "      <td>1641.892948</td>\n",
       "      <td>1.586539e+05</td>\n",
       "      <td>1892.997981</td>\n",
       "      <td>5.984662e+04</td>\n",
       "      <td>3386.701969</td>\n",
       "      <td>7.048487e+05</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.127181</td>\n",
       "      <td>36.199359</td>\n",
       "      <td>-5.087884</td>\n",
       "      <td>37.353881</td>\n",
       "      <td>-3.614868</td>\n",
       "      <td>37.319559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.384743</td>\n",
       "      <td>0.086310</td>\n",
       "      <td>0.121440</td>\n",
       "      <td>1.714807e-03</td>\n",
       "      <td>2219.993089</td>\n",
       "      <td>3.165817e+05</td>\n",
       "      <td>2225.534119</td>\n",
       "      <td>1.041495e+05</td>\n",
       "      <td>4660.726922</td>\n",
       "      <td>1.335184e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>0.550131</td>\n",
       "      <td>49.636463</td>\n",
       "      <td>-2.512369</td>\n",
       "      <td>51.855022</td>\n",
       "      <td>-1.112768</td>\n",
       "      <td>53.529154</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.438246</td>\n",
       "      <td>0.091423</td>\n",
       "      <td>0.175636</td>\n",
       "      <td>3.326403e-03</td>\n",
       "      <td>2701.593712</td>\n",
       "      <td>5.957259e+05</td>\n",
       "      <td>2586.507099</td>\n",
       "      <td>1.709418e+05</td>\n",
       "      <td>5570.548470</td>\n",
       "      <td>2.421513e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>3.445446</td>\n",
       "      <td>71.917171</td>\n",
       "      <td>0.162183</td>\n",
       "      <td>75.648439</td>\n",
       "      <td>1.648025</td>\n",
       "      <td>78.926550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.715664</td>\n",
       "      <td>0.112509</td>\n",
       "      <td>0.428984</td>\n",
       "      <td>2.833341e-02</td>\n",
       "      <td>5430.693430</td>\n",
       "      <td>3.347463e+06</td>\n",
       "      <td>3668.873197</td>\n",
       "      <td>1.354849e+06</td>\n",
       "      <td>9388.726368</td>\n",
       "      <td>9.884046e+06</td>\n",
       "      <td>...</td>\n",
       "      <td>22.642700</td>\n",
       "      <td>588.701050</td>\n",
       "      <td>21.863365</td>\n",
       "      <td>560.703064</td>\n",
       "      <td>23.908262</td>\n",
       "      <td>712.592102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        chroma_stft_mean  chroma_stft_var     rms_mean       rms_var  \\\n",
       "count        2988.000000      2988.000000  2988.000000  2.988000e+03   \n",
       "unique               NaN              NaN          NaN           NaN   \n",
       "top                  NaN              NaN          NaN           NaN   \n",
       "freq                 NaN              NaN          NaN           NaN   \n",
       "mean            0.378823         0.085846     0.130570  2.870646e-03   \n",
       "std             0.084776         0.008636     0.066960  3.593415e-03   \n",
       "min             0.126113         0.014554     0.001251  1.780778e-07   \n",
       "25%             0.318897         0.081522     0.085172  8.013145e-04   \n",
       "50%             0.384743         0.086310     0.121440  1.714807e-03   \n",
       "75%             0.438246         0.091423     0.175636  3.326403e-03   \n",
       "max             0.715664         0.112509     0.428984  2.833341e-02   \n",
       "\n",
       "        spectral_centroid_mean  spectral_centroid_var  \\\n",
       "count              2988.000000           2.988000e+03   \n",
       "unique                     NaN                    NaN   \n",
       "top                        NaN                    NaN   \n",
       "freq                       NaN                    NaN   \n",
       "mean               2200.838173           4.496328e+05   \n",
       "std                 729.637169           4.146715e+05   \n",
       "min                 518.949680           2.389426e+03   \n",
       "25%                1641.892948           1.586539e+05   \n",
       "50%                2219.993089           3.165817e+05   \n",
       "75%                2701.593712           5.957259e+05   \n",
       "max                5430.693430           3.347463e+06   \n",
       "\n",
       "        spectral_bandwidth_mean  spectral_bandwidth_var  rolloff_mean  \\\n",
       "count               2988.000000            2.988000e+03   2988.000000   \n",
       "unique                      NaN                     NaN           NaN   \n",
       "top                         NaN                     NaN           NaN   \n",
       "freq                        NaN                     NaN           NaN   \n",
       "mean                2241.994650            1.291835e+05   4569.365666   \n",
       "std                  533.451980            9.992376e+04   1600.754328   \n",
       "min                  695.900448            1.960306e+03    713.768228   \n",
       "25%                 1892.997981            5.984662e+04   3386.701969   \n",
       "50%                 2225.534119            1.041495e+05   4660.726922   \n",
       "75%                 2586.507099            1.709418e+05   5570.548470   \n",
       "max                 3668.873197            1.354849e+06   9388.726368   \n",
       "\n",
       "         rolloff_var  ...  mfcc18_mean   mfcc18_var  mfcc19_mean   mfcc19_var  \\\n",
       "count   2.988000e+03  ...  2988.000000  2988.000000  2988.000000  2988.000000   \n",
       "unique           NaN  ...          NaN          NaN          NaN          NaN   \n",
       "top              NaN  ...          NaN          NaN          NaN          NaN   \n",
       "freq             NaN  ...          NaN          NaN          NaN          NaN   \n",
       "mean    1.759079e+06  ...     0.603902    60.373924    -2.408998    62.671871   \n",
       "std     1.456471e+06  ...     4.325673    39.036507     4.259204    40.920700   \n",
       "min     1.260120e+03  ...   -15.889191    10.180762   -20.310465    11.670246   \n",
       "25%     7.048487e+05  ...    -2.127181    36.199359    -5.087884    37.353881   \n",
       "50%     1.335184e+06  ...     0.550131    49.636463    -2.512369    51.855022   \n",
       "75%     2.421513e+06  ...     3.445446    71.917171     0.162183    75.648439   \n",
       "max     9.884046e+06  ...    22.642700   588.701050    21.863365   560.703064   \n",
       "\n",
       "        mfcc20_mean   mfcc20_var         filename        start          end  \\\n",
       "count   2988.000000  2988.000000             2988  2988.000000  2988.000000   \n",
       "unique          NaN          NaN              999          NaN          NaN   \n",
       "top             NaN          NaN  blues.00093.wav          NaN          NaN   \n",
       "freq            NaN          NaN                3          NaN          NaN   \n",
       "mean      -1.031245    65.967857              NaN     9.969880    19.969880   \n",
       "std        4.362487    47.345730              NaN     8.160125     8.160125   \n",
       "min      -21.439188     0.528655              NaN     0.000000    10.000000   \n",
       "25%       -3.614868    37.319559              NaN     0.000000    10.000000   \n",
       "50%       -1.112768    53.529154              NaN    10.000000    20.000000   \n",
       "75%        1.648025    78.926550              NaN    20.000000    30.000000   \n",
       "max       23.908262   712.592102              NaN    20.000000    30.000000   \n",
       "\n",
       "        genre  \n",
       "count    2988  \n",
       "unique     10  \n",
       "top     blues  \n",
       "freq      300  \n",
       "mean      NaN  \n",
       "std       NaN  \n",
       "min       NaN  \n",
       "25%       NaN  \n",
       "50%       NaN  \n",
       "75%       NaN  \n",
       "max       NaN  \n",
       "\n",
       "[11 rows x 61 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get summary statistics\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd076ba0",
   "metadata": {},
   "source": [
    "## Process single mp3 for validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7caeb238",
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_single_mp3(mp3_file_path):\n",
    "    wav_file_path = convert_mp3_to_wav(mp3_file_path)\n",
    "    if wav_file_path:\n",
    "        features_list = extract_features(wav_file_path)\n",
    "        if features_list:\n",
    "            df_new = pd.DataFrame(features_list)\n",
    "            output_csv_filename = os.path.basename(mp3_file_path).replace('.mp3', '_features.csv')\n",
    "            output_csv_path = os.path.join(os.path.dirname(mp3_file_path), '..', output_csv_filename)\n",
    "            df_new.to_csv(output_csv_path, index=False)\n",
    "            print(f\"Features extracted and saved to {output_csv_path}\")\n",
    "        else:\n",
    "            print(\"No features extracted.\")\n",
    "    else:\n",
    "        print(\"Failed to convert MP3 to WAV.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce2f092b",
   "metadata": {},
   "source": [
    "Usage of validation data generator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2e6104b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Features extracted and saved to /Users/isaiah/Desktop/Career/Projects/music-genre-detector/inputs/../file_features.csv\n"
     ]
    }
   ],
   "source": [
    "# Example usage\n",
    "mp3_file_path = '/Users/isaiah/Desktop/Career/Projects/music-genre-detector/inputs/file.mp3'\n",
    "process_single_mp3(mp3_file_path)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}