Feature_Extraction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BDA Project: Create Your Own Spotify Experience\n",
    "\n",
    "### Feature Extraction and Transformation\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Group members:\n",
    "\n",
    "- Aaqib Ahmed Nazir (i22-1920),\n",
    "- Arhum Khan (i22-1967),\n",
    "- Ammar Khasif (i22-1968)\n",
    "\n",
    "##### Section: DS-D\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Libraries Used:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pymongo\n",
    "import librosa\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "from joblib import Parallel, delayed, Memory\n",
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the file paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_4219/3360183426.py:1: DtypeWarning: Columns (0,1,5,6,8,12,18,20,21,22,24,33,34,38,39,44,47,49) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  tracks = pd.read_csv('fma_metadata/tracks.csv')\n",
      "/tmp/ipykernel_4219/3360183426.py:3: DtypeWarning: Columns (0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473,474,475,476,477,478,479,480,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496,497,498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  features = pd.read_csv('fma_metadata/features.csv')\n",
      "/tmp/ipykernel_4219/3360183426.py:4: DtypeWarning: Columns (0,1,2,3,4,5,6,7,8,11,13,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  echonest = pd.read_csv('fma_metadata/echonest.csv')\n",
      "/tmp/ipykernel_4219/3360183426.py:9: DtypeWarning: Columns (0,1,2,3,4,5,6,7,8,11,13,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  raw_echonest = pd.read_csv('fma_metadata/raw_echonest.csv')\n"
     ]
    }
   ],
   "source": [
    "tracks = pd.read_csv('fma_metadata/tracks.csv')\n",
    "genres = pd.read_csv('fma_metadata/genres.csv')\n",
    "features = pd.read_csv('fma_metadata/features.csv')\n",
    "echonest = pd.read_csv('fma_metadata/echonest.csv')\n",
    "raw_albums = pd.read_csv('fma_metadata/raw_albums.csv')\n",
    "raw_artists = pd.read_csv('fma_metadata/raw_artists.csv')\n",
    "raw_genres = pd.read_csv('fma_metadata/raw_genres.csv')\n",
    "raw_tracks = pd.read_csv('fma_metadata/raw_tracks.csv')\n",
    "raw_echonest = pd.read_csv('fma_metadata/raw_echonest.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to get artist name \n",
    "for testing purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Artist found\n"
     ]
    }
   ],
   "source": [
    "def find_artist(artist_name):\n",
    "    artist_name = artist_name.lower()\n",
    "    raw_artists[\"artist_name\"] = raw_artists[\"artist_name\"].str.lower()\n",
    "\n",
    "    # Checking if artist is in the dataset\n",
    "    if artist_name.lower() in raw_artists[\"artist_name\"].values:\n",
    "        print(\"Artist found\")\n",
    "    else:\n",
    "        print(\"Artist not found\")\n",
    "\n",
    "\n",
    "artist_name = \"lucky dragons\"\n",
    "find_artist(artist_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Function to extract features from the audio files and Normalize them\n",
    "loads an audio file using Librosa library, then extracts three features: MFCC, spectral centroid, and zero-crossing rate. It caches the results for faster access later. If an error occurs, it prints an error message and returns empty arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_features(file):\n",
    "    try:\n",
    "        y, sr = librosa.load(file, sr=None)\n",
    "        mfcc = librosa.feature.mfcc(y=y, sr=sr)\n",
    "        spectral_centroid = librosa.feature.spectral_centroid(y=y, sr=sr)\n",
    "        zero_crossing_rate = librosa.feature.zero_crossing_rate(y)\n",
    "        \n",
    "        # Normalizing features\n",
    "        scaler = MinMaxScaler()\n",
    "        mfcc_normalized = scaler.fit_transform(mfcc.T).T\n",
    "        spectral_centroid_normalized = scaler.fit_transform(spectral_centroid.T).T\n",
    "        zero_crossing_rate_normalized = scaler.fit_transform(zero_crossing_rate.T).T\n",
    "        \n",
    "        feature_doc = {\n",
    "            \"mfcc\": mfcc_normalized.tolist(),\n",
    "            \"spectral_centroid\": spectral_centroid_normalized.tolist(),\n",
    "            \"zero_crossing_rate\": zero_crossing_rate_normalized.tolist(),\n",
    "            \"file\": os.path.basename(file),\n",
    "        }\n",
    "        return feature_doc\n",
    "    except Exception as e:\n",
    "        print(f\"Error loading file {file}: {e}\")\n",
    "        return None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parallel Audio Feature Extraction and Adding the Features to MongoDB \n",
    "utilizes parallel processing to extract audio features from each file concurrently, enhancing computational efficiency. It employs the os.walk function for directory traversal, Parallel from joblib for parallelism, and tqdm for a progress bar display."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/386 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "100%|██████████| 386/386 [00:42<00:00,  9.02it/s]\n"
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      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = pymongo.MongoClient(\"mongodb://localhost:27017/\")\n",
    "db = client[\"audio_features\"]\n",
    "collection = db[\"features\"]\n",
    "\n",
    "AUDIO_DIR = r\"fma_small1\"\n",
    "\n",
    "# Getting all audio files\n",
    "audio_files = []\n",
    "for root, dirs, files in os.walk(AUDIO_DIR):\n",
    "    for file in files:\n",
    "        if file.endswith(\".mp3\"):\n",
    "            audio_files.append(os.path.join(root, file))\n",
    "            \n",
    "# Using joblib to extract features in parallel\n",
    "features = Parallel(n_jobs=-1)(\n",
    "    delayed(extract_features)(file) for file in tqdm(audio_files, total=len(audio_files))\n",
    ")\n",
    "            \n",
    "features = [f for f in features if f is not None]\n",
    "\n",
    "# Insert features into MongoDB\n",
    "collection.insert_many(features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Printing the extracted features \n",
    "for testing purposes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# priting the first 5 records\n",
    "# for feature in collection.find().limit(5):\n",
    "#     print(feature)"
   ]
  }
 ],
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