Sound Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9b77feed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n",
      "Recording...\n",
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: pyaudio in c:\\programdata\\anaconda3\\lib\\site-packages (0.2.14)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 44\u001b[0m\n\u001b[0;32m     42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m     43\u001b[0m     file_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrecorded_audio.wav\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 44\u001b[0m     \u001b[43mrecord_audio\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     45\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAudio recorded and saved as \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfile_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "Cell \u001b[1;32mIn[1], line 23\u001b[0m, in \u001b[0;36mrecord_audio\u001b[1;34m(file_name, duration, channels, sample_rate, chunk_size, format)\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[38;5;66;03m# Record audio\u001b[39;00m\n\u001b[0;32m     22\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;28mint\u001b[39m(sample_rate \u001b[38;5;241m/\u001b[39m chunk_size \u001b[38;5;241m*\u001b[39m duration)):\n\u001b[1;32m---> 23\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mstream\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchunk_size\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     24\u001b[0m     frames\u001b[38;5;241m.\u001b[39mappend(data)\n\u001b[0;32m     26\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFinished recording.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\pyaudio\\__init__.py:570\u001b[0m, in \u001b[0;36mPyAudio.Stream.read\u001b[1;34m(self, num_frames, exception_on_overflow)\u001b[0m\n\u001b[0;32m    567\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_is_input:\n\u001b[0;32m    568\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIOError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNot input stream\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m    569\u001b[0m                   paCanNotReadFromAnOutputOnlyStream)\n\u001b[1;32m--> 570\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mpa\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread_stream\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_frames\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    571\u001b[0m \u001b[43m                      \u001b[49m\u001b[43mexception_on_overflow\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "!pip install pyaudio\n",
    "### Audio Classification using Machine Learing\n",
    "import pyaudio\n",
    "import wave\n",
    "\n",
    "def record_audio(file_name, duration=6, channels=1, sample_rate=44100, chunk_size=1024, format=pyaudio.paInt16):\n",
    "    # Initialize PyAudio\n",
    "    audio = pyaudio.PyAudio()\n",
    "\n",
    "    # Open stream\n",
    "    stream = audio.open(format=format,\n",
    "                        channels=channels,\n",
    "                        rate=sample_rate,\n",
    "                        input=True,\n",
    "                        frames_per_buffer=chunk_size)\n",
    "\n",
    "    print(\"Recording...\")\n",
    "\n",
    "    frames = []\n",
    "\n",
    "    # Record audio\n",
    "    for i in range(0, int(sample_rate / chunk_size * duration)):\n",
    "        data = stream.read(chunk_size)\n",
    "        frames.append(data)\n",
    "\n",
    "    print(\"Finished recording.\")\n",
    "\n",
    "    # Stop and close the stream\n",
    "    stream.stop_stream()\n",
    "    stream.close()\n",
    "\n",
    "    # Terminate PyAudio\n",
    "    audio.terminate()\n",
    "\n",
    "    # Save the recorded audio as a WAV file\n",
    "    with wave.open(file_name, 'wb') as wf:\n",
    "        wf.setnchannels(channels)\n",
    "        wf.setsampwidth(audio.get_sample_size(format))\n",
    "        wf.setframerate(sample_rate)\n",
    "        wf.writeframes(b''.join(frames))\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    file_name = \"recorded_audio.wav\"\n",
    "    record_audio(file_name)\n",
    "    print(f\"Audio recorded and saved as {file_name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fdcb71fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: librosa in c:\\programdata\\anaconda3\\lib\\site-packages (0.10.2.post1)\n",
      "Requirement already satisfied: audioread>=2.1.9 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (3.0.1)\n",
      "Requirement already satisfied: numpy!=1.22.0,!=1.22.1,!=1.22.2,>=1.20.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (1.26.4)\n",
      "Requirement already satisfied: scipy>=1.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (1.13.1)\n",
      "Requirement already satisfied: scikit-learn>=0.20.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (1.4.2)\n",
      "Requirement already satisfied: joblib>=0.14 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (1.4.2)\n",
      "Requirement already satisfied: decorator>=4.3.0 in c:\\users\\hp\\appdata\\roaming\\python\\python312\\site-packages (from librosa) (5.1.1)\n",
      "Requirement already satisfied: numba>=0.51.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (0.59.1)\n",
      "Requirement already satisfied: soundfile>=0.12.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (0.12.1)\n",
      "Requirement already satisfied: pooch>=1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (1.8.2)\n",
      "Requirement already satisfied: soxr>=0.3.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (0.4.0)\n",
      "Requirement already satisfied: typing-extensions>=4.1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (4.11.0)\n",
      "Requirement already satisfied: lazy-loader>=0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (0.4)\n",
      "Requirement already satisfied: msgpack>=1.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (1.0.3)\n",
      "Requirement already satisfied: packaging in c:\\users\\hp\\appdata\\roaming\\python\\python312\\site-packages (from lazy-loader>=0.1->librosa) (24.1)\n",
      "Requirement already satisfied: llvmlite<0.43,>=0.42.0dev0 in c:\\programdata\\anaconda3\\lib\\site-packages (from numba>=0.51.0->librosa) (0.42.0)\n",
      "Requirement already satisfied: platformdirs>=2.5.0 in c:\\users\\hp\\appdata\\roaming\\python\\python312\\site-packages (from pooch>=1.1->librosa) (4.2.2)\n",
      "Requirement already satisfied: requests>=2.19.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pooch>=1.1->librosa) (2.32.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20.0->librosa) (2.2.0)\n",
      "Requirement already satisfied: cffi>=1.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from soundfile>=0.12.1->librosa) (1.16.0)\n",
      "Requirement already satisfied: pycparser in c:\\programdata\\anaconda3\\lib\\site-packages (from cffi>=1.0->soundfile>=0.12.1->librosa) (2.21)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch>=1.1->librosa) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch>=1.1->librosa) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch>=1.1->librosa) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch>=1.1->librosa) (2024.7.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install librosa\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47f880cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "32826a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename='dog-bark1.wav'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5625dc50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: librosa in c:\\programdata\\anaconda3\\lib\\site-packages (0.10.2.post1)\n",
      "Requirement already satisfied: audioread>=2.1.9 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (3.0.1)\n",
      "Requirement already satisfied: numpy!=1.22.0,!=1.22.1,!=1.22.2,>=1.20.3 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (1.26.4)\n",
      "Requirement already satisfied: scipy>=1.2.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (1.13.1)\n",
      "Requirement already satisfied: scikit-learn>=0.20.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (1.4.2)\n",
      "Requirement already satisfied: joblib>=0.14 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (1.4.2)\n",
      "Requirement already satisfied: decorator>=4.3.0 in c:\\users\\hp\\appdata\\roaming\\python\\python312\\site-packages (from librosa) (5.1.1)\n",
      "Requirement already satisfied: numba>=0.51.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (0.59.1)\n",
      "Requirement already satisfied: soundfile>=0.12.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (0.12.1)\n",
      "Requirement already satisfied: pooch>=1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (1.8.2)\n",
      "Requirement already satisfied: soxr>=0.3.2 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (0.4.0)\n",
      "Requirement already satisfied: typing-extensions>=4.1.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (4.11.0)\n",
      "Requirement already satisfied: lazy-loader>=0.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (0.4)\n",
      "Requirement already satisfied: msgpack>=1.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from librosa) (1.0.3)\n",
      "Requirement already satisfied: packaging in c:\\users\\hp\\appdata\\roaming\\python\\python312\\site-packages (from lazy-loader>=0.1->librosa) (24.1)\n",
      "Requirement already satisfied: llvmlite<0.43,>=0.42.0dev0 in c:\\programdata\\anaconda3\\lib\\site-packages (from numba>=0.51.0->librosa) (0.42.0)\n",
      "Requirement already satisfied: platformdirs>=2.5.0 in c:\\users\\hp\\appdata\\roaming\\python\\python312\\site-packages (from pooch>=1.1->librosa) (4.2.2)\n",
      "Requirement already satisfied: requests>=2.19.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from pooch>=1.1->librosa) (2.32.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20.0->librosa) (2.2.0)\n",
      "Requirement already satisfied: cffi>=1.0 in c:\\programdata\\anaconda3\\lib\\site-packages (from soundfile>=0.12.1->librosa) (1.16.0)\n",
      "Requirement already satisfied: pycparser in c:\\programdata\\anaconda3\\lib\\site-packages (from cffi>=1.0->soundfile>=0.12.1->librosa) (2.21)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch>=1.1->librosa) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch>=1.1->librosa) (3.7)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch>=1.1->librosa) (2.2.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from requests>=2.19.0->pooch>=1.1->librosa) (2024.7.4)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Dog sound \n",
    "!pip install librosa\n",
    "import IPython.display as ipd\n",
    "# from IPython.display import HTML\n",
    "# HTML(\"<h1>Hello, world!</h1>\")\n",
    "\n",
    "plt.figure(figsize=(14,5)) \n",
    "import librosa\n",
    "import librosa.display # type: ignore\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f32a6f2e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\tempfile.py:256\u001b[0m, in \u001b[0;36m_mkstemp_inner\u001b[1;34m(dir, pre, suf, flags, output_type)\u001b[0m\n\u001b[0;32m    255\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 256\u001b[0m     fd \u001b[38;5;241m=\u001b[39m \u001b[43m_os\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0o600\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n",
      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'C:\\\\ProgramData\\\\anaconda3\\\\Lib\\\\site-packages\\\\librosa\\\\core\\\\__pycache__\\\\tmp44kp2cf3'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[11], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m filename\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdog-bark1.wav\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m----> 2\u001b[0m librosa_data,librosa_sample_rate\u001b[38;5;241m=\u001b[39m\u001b[43mlibrosa\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m(filename)\n\u001b[0;32m      3\u001b[0m librosa\u001b[38;5;241m.\u001b[39mdisplay\u001b[38;5;241m.\u001b[39mwaveshow(librosa_data,sr\u001b[38;5;241m=\u001b[39mlibrosa_sample_rate)\n\u001b[0;32m      4\u001b[0m ipd\u001b[38;5;241m.\u001b[39mAudio(filename) \u001b[38;5;66;03m# shows the file directly of audio \u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\lazy_loader\\__init__.py:83\u001b[0m, in \u001b[0;36mattach.<locals>.__getattr__\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m     81\u001b[0m submod_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpackage_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr_to_modules[name]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     82\u001b[0m submod \u001b[38;5;241m=\u001b[39m importlib\u001b[38;5;241m.\u001b[39mimport_module(submod_path)\n\u001b[1;32m---> 83\u001b[0m attr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msubmod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     85\u001b[0m \u001b[38;5;66;03m# If the attribute lives in a file (module) with the same\u001b[39;00m\n\u001b[0;32m     86\u001b[0m \u001b[38;5;66;03m# name as the attribute, ensure that the attribute and *not*\u001b[39;00m\n\u001b[0;32m     87\u001b[0m \u001b[38;5;66;03m# the module is accessible on the package.\u001b[39;00m\n\u001b[0;32m     88\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;241m==\u001b[39m attr_to_modules[name]:\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\lazy_loader\\__init__.py:82\u001b[0m, in \u001b[0;36mattach.<locals>.__getattr__\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m     80\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m attr_to_modules:\n\u001b[0;32m     81\u001b[0m     submod_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpackage_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr_to_modules[name]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 82\u001b[0m     submod \u001b[38;5;241m=\u001b[39m \u001b[43mimportlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubmod_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     83\u001b[0m     attr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(submod, name)\n\u001b[0;32m     85\u001b[0m     \u001b[38;5;66;03m# If the attribute lives in a file (module) with the same\u001b[39;00m\n\u001b[0;32m     86\u001b[0m     \u001b[38;5;66;03m# name as the attribute, ensure that the attribute and *not*\u001b[39;00m\n\u001b[0;32m     87\u001b[0m     \u001b[38;5;66;03m# the module is accessible on the package.\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\importlib\\__init__.py:90\u001b[0m, in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m     88\u001b[0m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m     89\u001b[0m         level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:1387\u001b[0m, in \u001b[0;36m_gcd_import\u001b[1;34m(name, package, level)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:1360\u001b[0m, in \u001b[0;36m_find_and_load\u001b[1;34m(name, import_)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:1331\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:935\u001b[0m, in \u001b[0;36m_load_unlocked\u001b[1;34m(spec)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap_external>:995\u001b[0m, in \u001b[0;36mexec_module\u001b[1;34m(self, module)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:488\u001b[0m, in \u001b[0;36m_call_with_frames_removed\u001b[1;34m(f, *args, **kwds)\u001b[0m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\librosa\\core\\audio.py:20\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumba\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m jit, stencil, guvectorize\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_fftlib\n\u001b[1;32m---> 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvert\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frames_to_samples, time_to_samples\n\u001b[0;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_cache\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cache\n\u001b[0;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m util\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\librosa\\core\\convert.py:7\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mre\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m notation\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ParameterError\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdecorators\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m vectorize\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\librosa\\core\\notation.py:1008\u001b[0m\n\u001b[0;32m   1001\u001b[0m     acc_str \u001b[38;5;241m=\u001b[39m acc_map_inv[np\u001b[38;5;241m.\u001b[39msign(acc_index) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mint\u001b[39m(\n\u001b[0;32m   1002\u001b[0m         \u001b[38;5;28mabs\u001b[39m(acc_index) \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[0;32m   1003\u001b[0m     ) \u001b[38;5;241m+\u001b[39m acc_map_inv[np\u001b[38;5;241m.\u001b[39msign(acc_index)] \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mabs\u001b[39m(acc_index) \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m   1005\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m raw_output \u001b[38;5;241m+\u001b[39m acc_str\n\u001b[1;32m-> 1008\u001b[0m \u001b[38;5;129;43m@jit\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnopython\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnogil\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m   1009\u001b[0m \u001b[38;5;28;43;01mdef\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;21;43m__o_fold\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m   1010\u001b[0m \u001b[38;5;250;43m    \u001b[39;49m\u001b[38;5;124;43;03m\"\"\"Compute the octave-folded interval.\u001b[39;49;00m\n\u001b[0;32m   1011\u001b[0m \n\u001b[0;32m   1012\u001b[0m \u001b[38;5;124;43;03m    This maps intervals to the range [1, 2).\u001b[39;49;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1016\u001b[0m \u001b[38;5;124;43;03m    documentation.\u001b[39;49;00m\n\u001b[0;32m   1017\u001b[0m \u001b[38;5;124;43;03m    \"\"\"\u001b[39;49;00m\n\u001b[0;32m   1018\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mreturn\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2.0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog2\u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\decorators.py:229\u001b[0m, in \u001b[0;36m_jit.<locals>.wrapper\u001b[1;34m(func)\u001b[0m\n\u001b[0;32m    225\u001b[0m disp \u001b[38;5;241m=\u001b[39m dispatcher(py_func\u001b[38;5;241m=\u001b[39mfunc, \u001b[38;5;28mlocals\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlocals\u001b[39m,\n\u001b[0;32m    226\u001b[0m                   targetoptions\u001b[38;5;241m=\u001b[39mtargetoptions,\n\u001b[0;32m    227\u001b[0m                   \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdispatcher_args)\n\u001b[0;32m    228\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache:\n\u001b[1;32m--> 229\u001b[0m     \u001b[43mdisp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_caching\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sigs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    231\u001b[0m     \u001b[38;5;66;03m# Register the Dispatcher to the type inference mechanism,\u001b[39;00m\n\u001b[0;32m    232\u001b[0m     \u001b[38;5;66;03m# even though the decorator hasn't returned yet.\u001b[39;00m\n\u001b[0;32m    233\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumba\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m typeinfer\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\dispatcher.py:856\u001b[0m, in \u001b[0;36mDispatcher.enable_caching\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    855\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_caching\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 856\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache \u001b[38;5;241m=\u001b[39m \u001b[43mFunctionCache\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpy_func\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\caching.py:601\u001b[0m, in \u001b[0;36mCache.__init__\u001b[1;34m(self, py_func)\u001b[0m\n\u001b[0;32m    599\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrepr\u001b[39m(py_func)\n\u001b[0;32m    600\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_py_func \u001b[38;5;241m=\u001b[39m py_func\n\u001b[1;32m--> 601\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_impl \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_impl_class\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpy_func\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    602\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_impl\u001b[38;5;241m.\u001b[39mlocator\u001b[38;5;241m.\u001b[39mget_cache_path()\n\u001b[0;32m    603\u001b[0m \u001b[38;5;66;03m# This may be a bit strict but avoids us maintaining a magic number\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\caching.py:333\u001b[0m, in \u001b[0;36mCacheImpl.__init__\u001b[1;34m(self, py_func)\u001b[0m\n\u001b[0;32m    331\u001b[0m source_path \u001b[38;5;241m=\u001b[39m inspect\u001b[38;5;241m.\u001b[39mgetfile(py_func)\n\u001b[0;32m    332\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_locator_classes:\n\u001b[1;32m--> 333\u001b[0m     locator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpy_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    334\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m locator \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    335\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\caching.py:180\u001b[0m, in \u001b[0;36m_SourceFileBackedLocatorMixin.from_function\u001b[1;34m(cls, py_func, py_file)\u001b[0m\n\u001b[0;32m    178\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(py_func, py_file)\n\u001b[0;32m    179\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 180\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mensure_cache_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    181\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[0;32m    182\u001b[0m     \u001b[38;5;66;03m# Cannot ensure the cache directory exists or is writable\u001b[39;00m\n\u001b[0;32m    183\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\caching.py:107\u001b[0m, in \u001b[0;36m_CacheLocator.ensure_cache_path\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    105\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(path, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    106\u001b[0m \u001b[38;5;66;03m# Ensure the directory is writable by trying to write a temporary file\u001b[39;00m\n\u001b[1;32m--> 107\u001b[0m \u001b[43mtempfile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTemporaryFile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\tempfile.py:582\u001b[0m, in \u001b[0;36mNamedTemporaryFile\u001b[1;34m(mode, buffering, encoding, newline, suffix, prefix, dir, delete, errors, delete_on_close)\u001b[0m\n\u001b[0;32m    580\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fd\n\u001b[0;32m    581\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 582\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[43m_io\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffering\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbuffering\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    583\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnewline\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    584\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mopener\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopener\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    585\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    586\u001b[0m         raw \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(file, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer\u001b[39m\u001b[38;5;124m'\u001b[39m, file)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\tempfile.py:579\u001b[0m, in \u001b[0;36mNamedTemporaryFile.<locals>.opener\u001b[1;34m(*args)\u001b[0m\n\u001b[0;32m    577\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopener\u001b[39m(\u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m    578\u001b[0m     \u001b[38;5;28;01mnonlocal\u001b[39;00m name\n\u001b[1;32m--> 579\u001b[0m     fd, name \u001b[38;5;241m=\u001b[39m \u001b[43m_mkstemp_inner\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprefix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuffix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    580\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fd\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\tempfile.py:262\u001b[0m, in \u001b[0;36m_mkstemp_inner\u001b[1;34m(dir, pre, suf, flags, output_type)\u001b[0m\n\u001b[0;32m    258\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m    \u001b[38;5;66;03m# try again\u001b[39;00m\n\u001b[0;32m    259\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mPermissionError\u001b[39;00m:\n\u001b[0;32m    260\u001b[0m     \u001b[38;5;66;03m# This exception is thrown when a directory with the chosen name\u001b[39;00m\n\u001b[0;32m    261\u001b[0m     \u001b[38;5;66;03m# already exists on windows.\u001b[39;00m\n\u001b[1;32m--> 262\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (_os\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnt\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[43m_os\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misdir\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    263\u001b[0m         _os\u001b[38;5;241m.\u001b[39maccess(\u001b[38;5;28mdir\u001b[39m, _os\u001b[38;5;241m.\u001b[39mW_OK)):\n\u001b[0;32m    264\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m    265\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "filename='dog-bark1.wav'\n",
    "librosa_data,librosa_sample_rate=librosa.load(filename)\n",
    "librosa.display.waveshow(librosa_data,sr=librosa_sample_rate)\n",
    "ipd.Audio(filename) # shows the file directly of audio "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0207ab5e",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\tempfile.py:256\u001b[0m, in \u001b[0;36m_mkstemp_inner\u001b[1;34m(dir, pre, suf, flags, output_type)\u001b[0m\n\u001b[0;32m    255\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 256\u001b[0m     fd \u001b[38;5;241m=\u001b[39m \u001b[43m_os\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0o600\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n",
      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'C:\\\\ProgramData\\\\anaconda3\\\\Lib\\\\site-packages\\\\librosa\\\\core\\\\__pycache__\\\\tmp00ngdshe'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[14], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m filename\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mUrbanSound8K/audio/fold1/9031-3-2-0.wav\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m----> 2\u001b[0m librosa_audio_data,librosa_sample_rate\u001b[38;5;241m=\u001b[39m\u001b[43mlibrosa\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload\u001b[49m(filename)\n\u001b[0;32m      3\u001b[0m librosa\u001b[38;5;241m.\u001b[39mdisplay\u001b[38;5;241m.\u001b[39mwaveshow(librosa_data,sr\u001b[38;5;241m=\u001b[39mlibrosa_sample_rate)\n\u001b[0;32m      4\u001b[0m ipd\u001b[38;5;241m.\u001b[39mAudio(filename)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\lazy_loader\\__init__.py:83\u001b[0m, in \u001b[0;36mattach.<locals>.__getattr__\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m     81\u001b[0m submod_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpackage_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr_to_modules[name]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     82\u001b[0m submod \u001b[38;5;241m=\u001b[39m importlib\u001b[38;5;241m.\u001b[39mimport_module(submod_path)\n\u001b[1;32m---> 83\u001b[0m attr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msubmod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     85\u001b[0m \u001b[38;5;66;03m# If the attribute lives in a file (module) with the same\u001b[39;00m\n\u001b[0;32m     86\u001b[0m \u001b[38;5;66;03m# name as the attribute, ensure that the attribute and *not*\u001b[39;00m\n\u001b[0;32m     87\u001b[0m \u001b[38;5;66;03m# the module is accessible on the package.\u001b[39;00m\n\u001b[0;32m     88\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;241m==\u001b[39m attr_to_modules[name]:\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\lazy_loader\\__init__.py:82\u001b[0m, in \u001b[0;36mattach.<locals>.__getattr__\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m     80\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m attr_to_modules:\n\u001b[0;32m     81\u001b[0m     submod_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpackage_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr_to_modules[name]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 82\u001b[0m     submod \u001b[38;5;241m=\u001b[39m \u001b[43mimportlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubmod_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     83\u001b[0m     attr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(submod, name)\n\u001b[0;32m     85\u001b[0m     \u001b[38;5;66;03m# If the attribute lives in a file (module) with the same\u001b[39;00m\n\u001b[0;32m     86\u001b[0m     \u001b[38;5;66;03m# name as the attribute, ensure that the attribute and *not*\u001b[39;00m\n\u001b[0;32m     87\u001b[0m     \u001b[38;5;66;03m# the module is accessible on the package.\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\importlib\\__init__.py:90\u001b[0m, in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m     88\u001b[0m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m     89\u001b[0m         level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:1387\u001b[0m, in \u001b[0;36m_gcd_import\u001b[1;34m(name, package, level)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:1360\u001b[0m, in \u001b[0;36m_find_and_load\u001b[1;34m(name, import_)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:1331\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:935\u001b[0m, in \u001b[0;36m_load_unlocked\u001b[1;34m(spec)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap_external>:995\u001b[0m, in \u001b[0;36mexec_module\u001b[1;34m(self, module)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:488\u001b[0m, in \u001b[0;36m_call_with_frames_removed\u001b[1;34m(f, *args, **kwds)\u001b[0m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\librosa\\core\\audio.py:20\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumba\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m jit, stencil, guvectorize\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfft\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_fftlib\n\u001b[1;32m---> 20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvert\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m frames_to_samples, time_to_samples\n\u001b[0;32m     21\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_cache\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cache\n\u001b[0;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m util\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\librosa\\core\\convert.py:7\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mre\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m notation\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ParameterError\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdecorators\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m vectorize\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\librosa\\core\\notation.py:1008\u001b[0m\n\u001b[0;32m   1001\u001b[0m     acc_str \u001b[38;5;241m=\u001b[39m acc_map_inv[np\u001b[38;5;241m.\u001b[39msign(acc_index) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mint\u001b[39m(\n\u001b[0;32m   1002\u001b[0m         \u001b[38;5;28mabs\u001b[39m(acc_index) \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[0;32m   1003\u001b[0m     ) \u001b[38;5;241m+\u001b[39m acc_map_inv[np\u001b[38;5;241m.\u001b[39msign(acc_index)] \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mabs\u001b[39m(acc_index) \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m   1005\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m raw_output \u001b[38;5;241m+\u001b[39m acc_str\n\u001b[1;32m-> 1008\u001b[0m \u001b[38;5;129;43m@jit\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnopython\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnogil\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m   1009\u001b[0m \u001b[38;5;28;43;01mdef\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;21;43m__o_fold\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m   1010\u001b[0m \u001b[38;5;250;43m    \u001b[39;49m\u001b[38;5;124;43;03m\"\"\"Compute the octave-folded interval.\u001b[39;49;00m\n\u001b[0;32m   1011\u001b[0m \n\u001b[0;32m   1012\u001b[0m \u001b[38;5;124;43;03m    This maps intervals to the range [1, 2).\u001b[39;49;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1016\u001b[0m \u001b[38;5;124;43;03m    documentation.\u001b[39;49;00m\n\u001b[0;32m   1017\u001b[0m \u001b[38;5;124;43;03m    \"\"\"\u001b[39;49;00m\n\u001b[0;32m   1018\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mreturn\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2.0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog2\u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\decorators.py:229\u001b[0m, in \u001b[0;36m_jit.<locals>.wrapper\u001b[1;34m(func)\u001b[0m\n\u001b[0;32m    225\u001b[0m disp \u001b[38;5;241m=\u001b[39m dispatcher(py_func\u001b[38;5;241m=\u001b[39mfunc, \u001b[38;5;28mlocals\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlocals\u001b[39m,\n\u001b[0;32m    226\u001b[0m                   targetoptions\u001b[38;5;241m=\u001b[39mtargetoptions,\n\u001b[0;32m    227\u001b[0m                   \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdispatcher_args)\n\u001b[0;32m    228\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache:\n\u001b[1;32m--> 229\u001b[0m     \u001b[43mdisp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_caching\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sigs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    231\u001b[0m     \u001b[38;5;66;03m# Register the Dispatcher to the type inference mechanism,\u001b[39;00m\n\u001b[0;32m    232\u001b[0m     \u001b[38;5;66;03m# even though the decorator hasn't returned yet.\u001b[39;00m\n\u001b[0;32m    233\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumba\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m typeinfer\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\dispatcher.py:856\u001b[0m, in \u001b[0;36mDispatcher.enable_caching\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    855\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_caching\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 856\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache \u001b[38;5;241m=\u001b[39m \u001b[43mFunctionCache\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpy_func\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\caching.py:601\u001b[0m, in \u001b[0;36mCache.__init__\u001b[1;34m(self, py_func)\u001b[0m\n\u001b[0;32m    599\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrepr\u001b[39m(py_func)\n\u001b[0;32m    600\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_py_func \u001b[38;5;241m=\u001b[39m py_func\n\u001b[1;32m--> 601\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_impl \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_impl_class\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpy_func\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    602\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_impl\u001b[38;5;241m.\u001b[39mlocator\u001b[38;5;241m.\u001b[39mget_cache_path()\n\u001b[0;32m    603\u001b[0m \u001b[38;5;66;03m# This may be a bit strict but avoids us maintaining a magic number\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\caching.py:333\u001b[0m, in \u001b[0;36mCacheImpl.__init__\u001b[1;34m(self, py_func)\u001b[0m\n\u001b[0;32m    331\u001b[0m source_path \u001b[38;5;241m=\u001b[39m inspect\u001b[38;5;241m.\u001b[39mgetfile(py_func)\n\u001b[0;32m    332\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_locator_classes:\n\u001b[1;32m--> 333\u001b[0m     locator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpy_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    334\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m locator \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    335\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\caching.py:180\u001b[0m, in \u001b[0;36m_SourceFileBackedLocatorMixin.from_function\u001b[1;34m(cls, py_func, py_file)\u001b[0m\n\u001b[0;32m    178\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(py_func, py_file)\n\u001b[0;32m    179\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 180\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mensure_cache_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    181\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[0;32m    182\u001b[0m     \u001b[38;5;66;03m# Cannot ensure the cache directory exists or is writable\u001b[39;00m\n\u001b[0;32m    183\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\caching.py:107\u001b[0m, in \u001b[0;36m_CacheLocator.ensure_cache_path\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    105\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(path, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    106\u001b[0m \u001b[38;5;66;03m# Ensure the directory is writable by trying to write a temporary file\u001b[39;00m\n\u001b[1;32m--> 107\u001b[0m \u001b[43mtempfile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTemporaryFile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\tempfile.py:582\u001b[0m, in \u001b[0;36mNamedTemporaryFile\u001b[1;34m(mode, buffering, encoding, newline, suffix, prefix, dir, delete, errors, delete_on_close)\u001b[0m\n\u001b[0;32m    580\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fd\n\u001b[0;32m    581\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 582\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[43m_io\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffering\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbuffering\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    583\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnewline\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    584\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mopener\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopener\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    585\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    586\u001b[0m         raw \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(file, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer\u001b[39m\u001b[38;5;124m'\u001b[39m, file)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\tempfile.py:579\u001b[0m, in \u001b[0;36mNamedTemporaryFile.<locals>.opener\u001b[1;34m(*args)\u001b[0m\n\u001b[0;32m    577\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopener\u001b[39m(\u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m    578\u001b[0m     \u001b[38;5;28;01mnonlocal\u001b[39;00m name\n\u001b[1;32m--> 579\u001b[0m     fd, name \u001b[38;5;241m=\u001b[39m \u001b[43m_mkstemp_inner\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprefix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuffix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    580\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fd\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\tempfile.py:262\u001b[0m, in \u001b[0;36m_mkstemp_inner\u001b[1;34m(dir, pre, suf, flags, output_type)\u001b[0m\n\u001b[0;32m    258\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m    \u001b[38;5;66;03m# try again\u001b[39;00m\n\u001b[0;32m    259\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mPermissionError\u001b[39;00m:\n\u001b[0;32m    260\u001b[0m     \u001b[38;5;66;03m# This exception is thrown when a directory with the chosen name\u001b[39;00m\n\u001b[0;32m    261\u001b[0m     \u001b[38;5;66;03m# already exists on windows.\u001b[39;00m\n\u001b[1;32m--> 262\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (_os\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnt\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[43m_os\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misdir\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    263\u001b[0m         _os\u001b[38;5;241m.\u001b[39maccess(\u001b[38;5;28mdir\u001b[39m, _os\u001b[38;5;241m.\u001b[39mW_OK)):\n\u001b[0;32m    264\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m    265\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "filename='UrbanSound8K/audio/fold1/9031-3-2-0.wav'\n",
    "librosa_audio_data,librosa_sample_rate=librosa.load(filename)\n",
    "librosa.display.waveshow(librosa_data,sr=librosa_sample_rate)\n",
    "ipd.Audio(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb0dcf74",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'librosa_sample_rate' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[10], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mlibrosa_sample_rate\u001b[49m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'librosa_sample_rate' is not defined"
     ]
    }
   ],
   "source": [
    "librosa_sample_rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4167cb08",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.io import wavfile as wav\n",
    "wave_sample_rate,wave_audio=wav.read(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "676d529d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import soundfile as sf\n",
    "\n",
    "# Read the WAV file using soundfile\n",
    "sf_wave_audio_data, sf_wave_sample_rate = sf.read(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e521eddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "44100"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_wave_sample_rate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "46e9af36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.07455444, 0.07434082, 0.07345581, ..., 0.07290649, 0.07373047,\n",
       "       0.07318115])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf_wave_audio_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0798c68e",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'librosa_data' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[24], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mlibrosa_data\u001b[49m \u001b[38;5;66;03m# from librosa \u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# Have range different from -1 to 1\u001b[39;00m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'librosa_data' is not defined"
     ]
    }
   ],
   "source": [
    "librosa_data # from librosa \n",
    "# Have range different from -1 to 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c1d7b7f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "librosa_audio_data=librosa_data\n",
    "import numpy as np\n",
    "# Normalize the audio data to the range [-1, 1]\n",
    "normalized_audio = librosa_audio_data / np.max(np.abs(librosa_audio_data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8be35dcb",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'normalized_audio' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[26], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mnormalized_audio\u001b[49m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'normalized_audio' is not defined"
     ]
    }
   ],
   "source": [
    "normalized_audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d92fe8b7",
   "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>slice_file_name</th>\n",
       "      <th>fsID</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>salience</th>\n",
       "      <th>fold</th>\n",
       "      <th>classID</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100032-3-0-0.wav</td>\n",
       "      <td>100032</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.317551</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>dog_bark</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100263-2-0-117.wav</td>\n",
       "      <td>100263</td>\n",
       "      <td>58.500000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>children_playing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100263-2-0-121.wav</td>\n",
       "      <td>100263</td>\n",
       "      <td>60.500000</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>children_playing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100263-2-0-126.wav</td>\n",
       "      <td>100263</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>children_playing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100263-2-0-137.wav</td>\n",
       "      <td>100263</td>\n",
       "      <td>68.500000</td>\n",
       "      <td>72.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>children_playing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100263-2-0-143.wav</td>\n",
       "      <td>100263</td>\n",
       "      <td>71.500000</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>children_playing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100263-2-0-161.wav</td>\n",
       "      <td>100263</td>\n",
       "      <td>80.500000</td>\n",
       "      <td>84.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>children_playing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100263-2-0-3.wav</td>\n",
       "      <td>100263</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>children_playing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100263-2-0-36.wav</td>\n",
       "      <td>100263</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>children_playing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>100648-1-0-0.wav</td>\n",
       "      <td>100648</td>\n",
       "      <td>4.823402</td>\n",
       "      <td>5.471927</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>car_horn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      slice_file_name    fsID      start        end  salience  fold  classID  \\\n",
       "0    100032-3-0-0.wav  100032   0.000000   0.317551         1     5        3   \n",
       "1  100263-2-0-117.wav  100263  58.500000  62.500000         1     5        2   \n",
       "2  100263-2-0-121.wav  100263  60.500000  64.500000         1     5        2   \n",
       "3  100263-2-0-126.wav  100263  63.000000  67.000000         1     5        2   \n",
       "4  100263-2-0-137.wav  100263  68.500000  72.500000         1     5        2   \n",
       "5  100263-2-0-143.wav  100263  71.500000  75.500000         1     5        2   \n",
       "6  100263-2-0-161.wav  100263  80.500000  84.500000         1     5        2   \n",
       "7    100263-2-0-3.wav  100263   1.500000   5.500000         1     5        2   \n",
       "8   100263-2-0-36.wav  100263  18.000000  22.000000         1     5        2   \n",
       "9    100648-1-0-0.wav  100648   4.823402   5.471927         2    10        1   \n",
       "\n",
       "              class  \n",
       "0          dog_bark  \n",
       "1  children_playing  \n",
       "2  children_playing  \n",
       "3  children_playing  \n",
       "4  children_playing  \n",
       "5  children_playing  \n",
       "6  children_playing  \n",
       "7  children_playing  \n",
       "8  children_playing  \n",
       "9          car_horn  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "metadata=pd.read_csv('UrbanSound8K/metadata/UrbanSound8K.csv')\n",
    "metadata.head(10) #to read first 10 enteries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "efb6ca34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "dog_bark            1000\n",
       "children_playing    1000\n",
       "air_conditioner     1000\n",
       "street_music        1000\n",
       "engine_idling       1000\n",
       "jackhammer          1000\n",
       "drilling            1000\n",
       "siren                929\n",
       "car_horn             429\n",
       "gun_shot             374\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata['class'].value_counts() # finds the c|ount of all the enteries in all dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e6da6276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f6dc855940>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cYQmda8GEItyPHazCCruAWT7y0IJr7y7aqX/QA4IAk5WTX+lpRZJ09/SfvZgGAAD4Aguz+bEqLo4O+C3DMPTM1yeHJ96c0lBJMdVNTgQA8CWDUVUAAojXe8KnTJmipk2bKioqSikpKVq4cGGlnvfTTz8pLCxMF110kXcD+rGt+8veVxnwB3uO5KnPpPmuOVCVnRt7ooi9YgEAZ/fhsnSNmbnG7BgAUIpXi/CZM2dq9OjRevLJJ7V69Wr16NFDV111ldLTK154wOFwaMiQIerdu7c34/m9pTsqP4QRsJpnvt6o7dnH9MTn682OAviVvEJW0AUq6/PVe5XFtoAALMarRfikSZM0dOhQDRs2TG3atNHkyZOVlJSkqVOnVvi8++67T4MGDVLXrl29GQ+AifKLWIAEOBcHclkPBMHnfBZmc7INAQCL8VoRXlhYqJUrVyo1NdXteGpqqhYvXlzu89577z39+uuveuqppyp1noKCAuXk5Lh9BaojxwvNjgAAAAAAOA9eK8IPHjyokpISxcfHux2Pj49XVlZWmc/Ztm2bHn/8cc2YMUNhYZVbM27ixImy2+2ur6SkpPPOblULth0wOwLgMWfuc8+iO0Dl7D6Up+ycfBUUsz4CAAD+yOsLs9nOWMLbMIxSxySppKREgwYN0tNPP62WLVtW+vXHjRsnh8Ph+srI8Mx2SFY0bdFOj7zO4eOFMhiaBRP9sHm/luw45HYsv5ILrv24hX2SEdyGTFuuzs9/rz6T5psdBQAAnAOvFeGxsbEKDQ0t1eudnZ1dqndcknJzc7VixQqNHDlSYWFhCgsL04QJE7R27VqFhYXphx9+KPM8kZGRio6OdvsKRIZhaO0eh0de6+Jn0vTkFxs88lrAuXj403Vu369OP1Lp/cT/vWS3NyIBfifj8AmzIwB+gW4HAFbjtSI8IiJCKSkpSktLczuelpambt26lWofHR2t9evXa82aNa6v4cOHq1WrVlqzZo26dOnirah+YWOmZ+e6n9oWCrCCSWlbzY4AAAAA+ETlJl6fo7Fjx2rw4MHq1KmTunbtqrfeekvp6ekaPny4pJNDyffu3at///vfCgkJUXJystvz4+LiFBUVVep4MDpRyNw/QKr8fuIAgMCy48AxsyMAgEd4tQgfMGCADh06pAkTJigzM1PJycmaPXu2GjduLEnKzMw8657hOGnPEYYdwv/l5hfpljeW6DAr/QMAqujKV1gHAebZkpVrdgQEEK8W4ZI0YsQIjRgxoszHpk+fXuFzx48fr/Hjx3s+lJ/Zuj9Xo2euMTsGUCVrM44qMtx9xst/lu7W5nP4I8Y8cADAuTr3HcaB3w14a4nZERBAvF6E4/x9v4nVoOFfMg7n6Y//+qnU8eKSsseSL9x2sMLXe+rLXzySCwAQfJjFBE84mlfk1dfftj9XxSVOhYV6ffMqWAD/lwF4XI+X5pkdAQAAwG98sWYfI1+DCEW4HzC4hwsAAIIZY8oRBL5el2l2BPgIRbjFGYahl+ZsMTsGAAAAAMADKMIt7pd9nt0fHAAAwO8wKBBAAKEIt7iCYvYHBwAAOFeGQQUP/5FXWGx2BPgARbjF8XcDcMeHKfijjZmMagLOh5P3fgSJW99kK7RgQBFuYcUlTr32w3azYwBV4jjh3S08+BgGf7NsxyGvvZdvorhHkDhRdO4jA202VnWD/9iwl/f1YEARblGGYajPpPmav/WA2VGAKvn34l1mRwAs5fvN2V577ezcAq+9NgAA8A6KcIv6afsh7TqUZ3YMoMqKnPRVAwCq7kQh6+AACA5hZgdA2bZn55odAQBgcSdYwAcB4p9zt+kfc7d65bVZSwTn65Xv2C4YnkVPOAAAfmr4B6vMjgCctx0Hjp21AM/N54YTzMMaTfA0inAAPsPSOACAM1WmwB43a70PkgCAb1CEA/CZVelHzvs1GFUIAKiKjMMnzI4AVMmKXYeZRhHgKMIBeNTPOw+X+9i8Lee/2r/BJmUAEFC8/a4+a9UeL58B8Kyb31iiH7y4swbMRxFuUd7c0gbwpiU7Dnn19ffnFCgn37t7kQMAfOd/a/aaHQGwnLmbqAUCGUW4RS3cdtDsCIBlvbNwp9kRgEqbu2m/2REAS/vk5wyvvv6nK/dow16HV88BAFVBEQ7A7xQUs5cs/MeOA8fNjgAEvaHv/2x2BABwoQgHAACAaXyx0sf+nALlF3EDF4A1UIQDAADANHmFvimOp/z4q0/OA3iCjX1dAxpFOAAAXsIWM4B1bM/ONTsCAEiiCAcAwCtmrdqjpuNme/08e47kUewDQIDhbT2wUYQDAOAFYz9Z65PzXPbiPP31fxt8ci4AAHD+KMIB+B2bmCgFnO6DpelmRwAAeBBzwgMbRbgFHTleaHYEwNIMn6ylC5y7lbsPmx0BwBnyCku0Kv0I0zcAmI4i3ILmbcn2yXkcJ4p8ch7A0/IK2GYG1rbzYJ7ZEQCc4cctB3TjlMX6ZEWG2VEABDmK8CA2ZuYasyMA52TxrwfNjgAA8FP/XbnH7AjwExmH8/TtL1lmx0AACjM7AMzzw+ZsGYYhG5NO4Gd+PXDc7AhAhRiODgD+r8dL88yOgABFT3iQe+W7rWZHAICA89FyhrsClXEgt8DsCADgcxThFuTL9UJen7fddydDwNuw12F2BACAH/lm3T6zIwCAz1GEA/CYjMMsRgUAqDzWKQfKtn3/MbMjwIsowgEAAGAKdguDVZm9ZfDyXawtEsgowi3o1R+2+fR8BcVs9wT/M2+zb7byAwB4z/tLdpkdASjTjoMsAgvvoQi3oN2HfDuk96Kn03x6PgQuX3Zo3D39Zx+eDQDgDb7+zAMAVuD1InzKlClq2rSpoqKilJKSooULF5bbdtasWfrDH/6gevXqKTo6Wl27dtW3337r7YhB70QRPeHwjJwTRWZHAAAAACzNq0X4zJkzNXr0aD355JNavXq1evTooauuukrp6elltl+wYIH+8Ic/aPbs2Vq5cqV69eqla6+9VqtXr/ZmTAAe8vis9WZHAEyXm2/OzajV6UdMOS/gbw6ZPNcXALxahE+aNElDhw7VsGHD1KZNG02ePFlJSUmaOnVqme0nT56sRx99VJdccolatGih559/Xi1atNBXX33lzZgAAHjMepO26rthymJTzgv4mz2HT5gdAX5g7qb9ZkdAAPNaEV5YWKiVK1cqNTXV7XhqaqoWL67cBwWn06nc3FzFxMSU26agoEA5OTluXwAAAEBZCkucZkeAH5j6469mR0AA81oRfvDgQZWUlCg+Pt7teHx8vLKysir1Gq+88oqOHz+uW2+9tdw2EydOlN1ud30lJSWdV24AAAAEthOFrIcDwDxeX5jNZrO5fW8YRqljZfnoo480fvx4zZw5U3FxceW2GzdunBwOh+srIyPjvDMDAHCuFmw9aHYEAGex9yirsgMwT5i3Xjg2NlahoaGler2zs7NL9Y6faebMmRo6dKg+/fRT9enTp8K2kZGRioyMPO+8AAB4whvzGcIIAADK57We8IiICKWkpCgtzX0P6rS0NHXr1q3c53300Ue666679OGHH6p///7eimdZ6/YcNTsC4Dd2HTxudgQAwDnKzsk3OwJgadm5/I4EKq8ORx87dqzeeecdTZs2TZs2bdKYMWOUnp6u4cOHSzo5lHzIkCGu9h999JGGDBmiV155RZdeeqmysrKUlZUlh8OclWbNsDkr1+wIgN+44uUfVcwCOwDglw4eY6swoCK9X55vdgR4iVeL8AEDBmjy5MmaMGGCLrroIi1YsECzZ89W48aNJUmZmZlue4a/+eabKi4u1gMPPKD69eu7vh566CFvxrSUz1ftNTsC4FdY5RZWYhiG2REAv+Hk9wWoUG5BsX49cMzsGPACr80JP2XEiBEaMWJEmY9Nnz7d7fsff/zR23Esb8mOQ2ZHAPyKTWdf6BHwlV/2sU0mUFnU4MDZXTV5oV68uZ1u6NjQ7CjwIK+vjg4gOOw0aX52bkGRKecFylLiNLeqGDFjpannB6ri89WM/gPOprDEqTEz12pV+hGzo8CDKMIBeMTBYwWmnLfzc9/ruW82mnJuwGpmr886eyPAIqb9tNO0cz/7zSYW90S5zL6hWpbdh7heAwlFOACPeGnOZtPO/fZC8z7IAafLdJi/ku0X9C4CZ/XjlgO64uUfzY4Biyp2Wm+9mRAb0+8CCUU4AI/4eZe5w6QOH2eVXZhv+AfmDwef8DUjQ2B927PZDQZA8KIIBxAQUv+xwOwICHLseQxUXp9JvGcDVWGjJzygUIRbyGoTF1wY9v4K084N/+c4Yf7iaGbNSQdO2ZZtjW1kDh8vZKs0oJKOFxSbHQGolD1H8rRg6wGzY8BDKMIt5O/fbjHt3HM37Zcjr0hb9+fKacHFKGBteYXW+BCzePtBsyMAlvDtL1naT888LGr2+kyzI7j8sDnb7AiwoBUmT7Ery0tztmjItOX6eddhs6PAAyjCLWTxr+buET7iw5VK/ccCvWjiAlvwT1a5bzPonWXKOJxndgwEKTNHM51p+Aer1OX575VlgYXigDONmLHK7AguD360WpPnbjU7Bizmo+XpZkco19qMoyousd7CcagainC4/LT95E2ANxfsMDkJ/M3W/dZZYKf3K/PNjoAg9fJ31vsgv4jRIcBZTZ67TTn5RZr5c7oOMbUJktZkHDU7QrlW7j6iVn+do0nfmTeCFuePIhxlOtWbmF9Uoj9/slZzNrD3LMr3wmzrjJ4oLHEq/dDvveGOE0X69YA15uoicB2x6Or8f/vfBrMjAH6h07Nz9dhn63Xne8vNjgKTrck4qj1HTpgdo1z/tyFLJU5Dr/6w3ewoOA8U4SjTPdN/1ro9RzVl3nZ9tmqPJbbdgXWVWGwRqL+eVnh0ePo79X5lvt5a8KuJiRDozJ5OVJ68whJ69oBKKCw+Obx3w94ck5PAbEss+n5eFqfTUH5RidkxcA4owlGmbdnHdN3rP3GXDZVitcX85m894PpAdcrzFuqtB3wp5dm5uva1RXpnIVONgMoqLHbq+037dYzV04OOP+0E9sd//aR247/16XW6ePtBNXn8G03/aafPzhmIKMIBnDenxXrCJanlX/5P/SazDy0gSev3OvTsN5s0cfYms6MAfuGlOZs19P0VGjr9Z7OjAOVav9ehohJDy3f6rvd+0DvLJEnjv9ros3MGIopwVFq/yQvOabXIvUdP6IvVey3XWwrPser/2s1Z7gvGcQ3CWwz5x7X15oId2rDXoaN5hTpWUKxNmQy9Bcoyc0WGJGnZzsP6z9LdJqcBrMOfRgpYWZjZAeA/Nmflatys9bqtc6NKP8cwDHV/4QfX8x+/qrW34sFEJX5S3BY7DUWE8NcDwe2a1xa5ff/vezrr8pb1TEoDWE+Tx79x+/6vX2xQwzrVdFnzWIWH0n8V6NL9cKvT937apStbx/vkXDUjwpTLNI3zxjsJqmzmz+nadfB4pdrO25Lt+u835p9cGMsXBZthGKX2UMwvKlGLJ2fr1e+3ef38weaEnywK0vIv/1fm8WHvr1CTx79Rk8e/sfTeoIA3fLF6r7Jz87U246jf3FADfO3u937W4HeX6fPVe3Qgl8UOA9mHy/zvc8DCbQe1cvdhSdKB3AJt25+rORuyZHhhuiAFuGfQE44qe+yz9ZKkXS/0L/PxwmKnIsJO3t9Zk37U7bElvx7SbW8vrfD5FXE6DfV4aZ6ycvK1aUI/13lOZxiGmo6bLUmaM7qHWidES5IGvLVURSWGJqVt1ajeLap8bpTvzEXQrGx79jE1j6spSUo/lKe3F+7Q3E37XY9XdbQHIEmHjllzi7LKmLV6r2at3uv6vtsFdfXiTe2VFFPdxFSA9SzdcVhLd5wsdH56/Eo1qF3N5ETA726aukSjerdw62yaPOAiXd+xgYmpUB56wi0iOzff7AhVtmGvQ8PeX6EOT3+nP76+SGszjmr+1gNq+Zf/07uLTq6YuDHTfU7uqQJckhx5RaVe80Bugd6Y/6v2Hi29P+N7P+1Usydma+/REypxGnp81royc73wf7+vgt1v8kLXfzc67QMlW/Z4VrHTf4rwPpPmSzq5VsHlf59X5ly/N+b/qg+XpeuJz9eXGlHxyYoMXfbiD8rNL3397s/J1+Ys5tj6UnGJU00e/0bfrMs0NcdTX/5i6vk9afGvh9TjpXl676edKig+Ocpl/R6Hbn1ziRZvP2hyOsAaRn64SgPfWuIaRfXs1xu1PydfeYW/9xIahqGDxwpKrUfijd5JQFKp0Z6jZ65xXaNTftyuLEe+XpqzWU0e/0aP/net1mQcLfU5pzynruOo8N/LR67lc2czAuxfLycnR3a7XQ6HQ9HR0WbHqbTnZ2/SWwuCa/uYV27poJtSGur7Tfs19P0V5bbr3CRGD1zZXHdOW17qsVO96ccKipX81Lfq0yberVfz9Dan/xu/cUeK+iUnuNpM+XG7XpqzRZL0aL9WGnFF8/P74Twsv6hE2/YfU3KDaNksuCLGmfPnrO612zrqhf/bXObNnrLMHdtTn6/eo5+2H9KajKOu46eurZfmbNaUH0vvQ77osV5qWMec3sTCYqdr+P21HRL12m0dTcnhbadfezuev1ohFcz5X7rjkA4eK1D/dvU9/nvkb78D5+v1QR11JK9If/1ig2YM66KuzepKUoX//sApwfL70rhude0+9Pv84mVP9NaKXUf08KdrdVW7BD1/QztFhYeamBBlCZbr85TOTWP0xh0peuyzdbrp4obql5yg3PwinSgsUVx0lCRpz5E8XfbiPDWNraGdp01JvePSRnr2+nalXvP52Zu0dX+u3rvrEkt+bvWWqtShFOEmOlZQrJwTRUqsXU3PfL3R1XuMytv1Qn8dzSvURRPSynw80R6lxeN6S5JGf7xaX6zZ5/b4zD9dqgFvLS31vCtbx2naXZd4JOPBYwWKjgqXJB0vKFadGhGu/64RWbkZIeX9QfhgaBd1vaCuQk3+4Btsf7BOWfBILx06XqAbpiyusF3ftvH6+y0d5HQa2p59TDabTd/+kuW6KdSzZT29dHN71YoK08Z9ObLZpFveWFJq1flNE/qpWkTFH9jmbcnW3e+VvaVORVNASpyGHvp4tb7+rUf5pZvb66aLG3rk2vr3kl3KzS/W1e3qq2lsjQrbGoahA7kFyi9yqlHds9/AOP3aG9SlkZ6/ofSHgQlfbdS0M/YznTWimy5uVKdS+TMO56nHS/MqfE6w/g6cqVPjOlqx+4hqRIRqwh+T1aNFrOrWjNQXq/fKcaJI32/er50Hjqt781g90Ku5NuxzqFp4qK5oFee61opLnAo7Y/GrEqdR6WsxN79Ie46cUEyNCNmrhVeqyDEMQ1k5+UqIjnL7wJhxOE9hoTZFhIaoxDBUr2akJCknv1j2auEqLnGqoNgpp2Eov8ipqPAQ1/Orh4dW+qaEYRhlflAt77hZDMNQsdNQWIjtnHOdummO34WH2vRQ7xa6olWcGtSupuqRocovdOqXTIekkyMHL2kao7o1Iix1PQSi7dm56jMpuLc3bd/QrnV7Tl57D6e21MvfbS237cBLknR1u/rq0SLWdW0u3XFIA0/7bP3t6MtVPSJUSTHVVVzi1N+/3aI3F+zQ3d2b6G/XXBhQ1zRFuB8U4W/O/1UTTxs2jXOz4/mr1eyJ2RW22f7cVQoLDdHt7yzVT9urvo9i28Ro1a0Zqdz8Iq0+Y447YCUX1KuhXw9UvGji5mf6uYqSohKn3l20020Kx9mkNK6jlbuPnFfO0701OEW/HjiuF+d45v3w1I2GhdsOaPC7pUfPnM2nw7vqQG6BRsxYVWG7sBCbilnEDAAAt8Ldm7Y820+RYdYdPUIR7gdF+DfrMvXAhxV/yMPZnTnUqyzXtK/v6t0Dgs0fLoxX2sb9Z28YIDY/00+t/zrH7BgAAMDDxl97oe7q3tTsGOWqSh3Kwmwm6d++vtkRAsLZCnBJlSrAq0eEatcL/c9pxXbglM/u76ZrOySqR4tYs6NIOjlS5O0hnfRArwvMjuIz5RXgrRNqadcL/bVz4tU+TgQAADzhilZxZkfwGHrCLYL5hFKz2Br6fER3zd20X/9eultrT1sAqzLq26M0Z/TlenfhDg3q0liXTvz+rM+xVwvX/x7oriZnzFN1Og21+dscFZSz9dZ7d12iHi1iXXPjQmw2hYTY5HQayi8u0bGCYkWGhurQ8QI1q1ezwgxOp1HhvMHTt1wry8JHe7m2EipxGlqw7YDeXbhTE29sp5qRYdp79IQurB8tQ3LNqTQMQyeKShRisykyLETFTkOhv/0Mpxz/bR/InQePK7F2NR08VqBmsTVcczVPzdsscRq64CxTAoLB8id7K65WVKnjxSVObc7K1TWvLXI7vvDRXqoZGaZDxwv0/aZsNahTTSE2m65sHVfhHNYThSevL3u1cIWH2uQ0pOU7D7vtPFDWzaSc/CK1H/9dua+7c+LV5c7LMgxDy3ce1t3Tf1Ze4cnVsm/s2ED7c/P12m0Xq071cLfnGoYhp+F+vZ16fNfB43p93nZt3JejjZm/ryTfp0283h6SUipDcYlTh48XKqZGhMJCQ8qcI7s5K8dtJ4TTVXRjrbDYqfs/WKn1ex2a9/AVanvGPNVdL/Qvdb7s3Hx1fu7ke8v4ay/UZS3qqXlcTd7Dq6hZvRraceC4buvcSOv3HlXm0Xx9OryroquFK/Novq59/fffl9s6J2nije2148AxZeXka+mOwwoLsSmxdjVdlFRb/1myS1+u3acjeUW6u3sTPXVtW7dznXq/23nwuFrG11J4aPD0P+QXlajEabjWHykqcarEaZT5HlNQXCLDOPleEVM9otS8fE/i90V64cZ2enzW+grbvHxLB61OP6IZldi3+uHUlvpxywGdKCrRL/tOvreO6dNSR/IKtS07V8N7XqB/L9mtLk1j1LdtgjbsdchpSF2axeh/a/apqMSp/1ufqWevb6f6taOUc6JIDetUV3joyfn/p/7mn3pPPLVOQ4nTUIhNrvfJnPwi5ReVSIa05+gJdUyq7XqsxGmooLhEOw4c1/KdhzVvS7aGdG2ilvE11Simumw2mwzDUFHJyd/Z6hGhKnEa+nTlHu04cEztG9p1UVIdxdaMUHHJyes6IixExwuK5TQMhYWEKCsnX3uPnFDt6uFqnVDL7Toua72J0xWXODV98S49+82ms/57o2LT7uqke6afXHR57tieGvvJGq3b49Bbg1P0hwvjXdfErweOaVNmjkZ+uLrc11o6rrfs1cIVGRZi+cU/GY7uh0X4Y/9dp5krMsyO4XHP3ZCsmy5ueNbhoQ1qV9NPj1/pduzV77fJMKR/zHVfEOKxfq3LnD96ZhFR0QdzSVo3PtW1YJrVHSso1sKtB5TaNkGn3n+stJCFv3+guqFjA32+eq96taqn9+7uLMl9T/vTvXFHioZ/sLLU8bONojAMQ+8v3qW2Dey6pEmMZ4L/5vQbIQnRUVr6RO9y2xYUlyjjcJ4a1qkeMKvylnWjauuzVykizHfF1thP1mjWqr1nbxggpt3VSY1iamjGst26vGU9NYqprkR7NUWFhyi/yOlaQLDEacgwDOXm/74oJeDvfzMqEhkWos3P9HPdxD/1Prt+j0NPf/WLnrk+WW3q+8/n02AUyNdnRW7v0sh1w2fho730wdLd6tU6TvHRUaoVFabYmpHKcuTrg6W79fq87WW+RrCPKKUI98MiPP1Qni7/+zyzY3jMt6MvV8v4mq5C8cw3tLlje6p5XE1tycrVhr0O3Xhxg3KLymMFxaoWHqoDuQU6dLxAbRPtpV5vTJ+WeqhPi1LPLeuNdP34VElSLT8pwP2Bv//B2vxMP/20/aAubVa33BXrC4udrqLO6TTcFgRc8Zc+iv1t1WSz/PWLDfrP0t0V9mgHstOvwZG9muvhvq18ev41GUd1/b9+8uk5faF1Qi091LuFHv50rU4UlahXqziN6NVcKY0rt7I8UBZ//5shSdPvvkQ9W9ZTsdOQYUgRYWWP1IH/CYTr83T/HHiRHvp4TZmPTbyxnXq3iVNxiaHE2tXkyCtSraiws/Y4b9jrcI3wG3hJkgZ1aaR2DexBf/1XpQ6t3P5I8LoGdaqZHaHKnrshWU9+vkGS9MvTfTV98S41rFNNf7gwXtUjKr60msedHKLdKqGWWiXUqrBtzd+KogR7lBLspYf7StL9V5Q95/XUHblXvtui1344edeO4js4hYfaVFRS+p7jRUm1FRUeqt5t4it8/um9qiEhNv37ns5ynCjSNe09v+f0uXjm+mQ9c32y2TFMc2pBtgevbK4/p/q2AJdOTm3xZ81ia6h5XE39/ZYO+nh5ujok1dalv+39LUn9khMkWWsEDmCG+y5vplG9W7hu2IaH/v47we8HrOaz+7sqpXGM/nhRA9exot+medWIDHN9xj7FXr1yf8uSG9j13ZjL9f2mbA29rKlPR54FCnrCLcRf7rw91q+1ereJU8v4iovn0505VPN8h6uc/m+19m+plX7TgHd8uXafRn1U/nwes/VtG6/H+rXWla/Mdzs+80+X6pImMZafYwTr23f0hLq98IPZMc5JdFSY1o3va3YMBJHrXl/kk+2MPMlmk74Y0V0dkmqbHQVetvPgcfV6+UezY5yzqbdfrPtnrNJf+rfRsB7NzI4TVOgJh1ckN4jWwEsa6Y5LG1f5uQ/1buHR+ZKbn+mn615fpPuvuIAC3AKu65Bo6SL8zcGdJEm3pDTUpyv3uI53Oa2nDzgfibX9bzSTJMVHR2rWiO5mx0CQSYiO0jr5TxEe7PNcg03TMxbr9ReTbu2g44Uluqpdfa5ZP0ARjkr7+sEe5/zcxnU9+4YWFR6q78b09OhrIvD9/ZYOriK8b9uKh58Dgeimixvqs1W/34j66bErvboKNlAWfxqC2ayefxZkCD43XtzQ7AioAopwlKl787r6afshDbwkSQePFejmFM/9Yjf0w/nvCBxr/5aq7zZmuea4AsHilVs66PqODdSkbnV1bhrDSBCYZkyflkrbuN/sGGU6Na379dsuVmRYiMd3swA86f4rLtDUH3/Vu3d2MjsKqogi3EJ6tqyn+VsPmB1DkvSfe7q47SvtCVue7aft2cfUNtHusdcEzuaNO1LcvrdXD9ctnZJMSoNA1qtVPc3bYo338DONurK5bvrtZuqDvUvvJAH4kpUXo905kWG8OLl17t6jJ8yOcVaP9Wuth1NbefTzOnyDMWgWMv3uS8yO4GKzebYAl6TIsFAKcPjcqZX4AW979baOZkcopW/beL01OEVj/tDS7CiAC4uIw+qeu8F/dhuhAPdPFOEWYqWtLayUBaiqZvVqaECnJD2c2pIiHD5TKypcCx/tZXYMNy3iaim1bQLv6bCUaAtuFfqny5tp0WPW+v2FeS5vUc/sCOW6opV1s6HyvF6ET5kyRU2bNlVUVJRSUlK0cOHCCtvPnz9fKSkpioqKUrNmzfTGG294O6KlvHJLB7MjAOfkNQv1ArapH60Xb26vkVcy7Ba+lRRT3ewIbu7ryfY0sKZdL/S31ArOT1zdRg3rWOv3F+ax8talPSx8gwCV59UifObMmRo9erSefPJJrV69Wj169NBVV12l9PT0Mtvv3LlTV199tXr06KHVq1friSee0KhRo/TZZ595M6al3OTBBdCq6qWb26tv23h9PqKbaRngv7o3jzU7AoAz1LJgjyNwukf6tjI7AuA32iZGa0jXxnqkbyt9OZLtJf2ZV4vwSZMmaejQoRo2bJjatGmjyZMnKykpSVOnTi2z/RtvvKFGjRpp8uTJatOmjYYNG6Z77rlHL7/8sjdj4je3dkrSm4M7qWOjOmZHgR+qw37tgCTp1+evNjsC4Df6t6tvdgTAb9hsUnhoiB7o1VztG9Y2Ow7Og9eK8MLCQq1cuVKpqalux1NTU7V48eIyn7NkyZJS7fv27asVK1aoqKiozOcUFBQoJyfH7QuA7zHnFDjJKovkTLn9YrMjAGfVJJZ9uAEEH68V4QcPHlRJSYni4+PdjsfHxysrK6vM52RlZZXZvri4WAcPHizzORMnTpTdbnd9JSWx9RAQ7BrWtu72NwgO/xhg3voet3VupAGdktSvbYJpGQDA3z1xdWuzIyCAeX1htjN7xwzDqLDHrKz2ZR0/Zdy4cXI4HK6vjIyM80wMwJ8NvCSJfZBhuu4XmLdGwsQb2+nFm9tbemEhwEr+eFGi2RFgQX+6/AKzI0hiul+g8loRHhsbq9DQ0FK93tnZ2aV6u09JSEgos31YWJjq1q1b5nMiIyMVHR3t9uXv+GMAVE23C06+P7x0U3u9cFN71YwMMzkRgl1cdJTZEQBUwgs3ttM/B1pndw9YixVW8F/9t9+n6trEzdVA4bUiPCIiQikpKUpLS3M7npaWpm7dyl59u2vXrqXaf/fdd+rUqZPCw4PnLtC9PdhSBv6pZ0tzts348N5LteuF/rr1EqajAAAAwNq8Ohx97NixeueddzRt2jRt2rRJY8aMUXp6uoYPHy7p5FDyIUOGuNoPHz5cu3fv1tixY7Vp0yZNmzZN7777rh5++GFvxrSc5AZ2syMA52R4T2sM3QKCFSOpAACwPq+O2RwwYIAOHTqkCRMmKDMzU8nJyZo9e7YaN24sScrMzHTbM7xp06aaPXu2xowZo3/9619KTEzUq6++qptuusmbMSGpSd3qZkdAAAgPZZgUYKYaTMUAgIDFRjSBw+t/rUeMGKERI0aU+dj06dNLHevZs6dWrVrl5VQ4k2F2AASE+qxKDrgMu6yp3lm006fnPLWYKYDKoajB2Yy/9kKN/2qjqRnu7NpY7y/ZrYdTW5maA57j9dXRcW7iakX69Hwt4mr69HwITA0owgGXJ65u4/NzOp0+PyXg13q0MGctE/iPu7o3NTuCxl/XVuvGp+pyk9begedRhFuUrxdnG/OHlj49HwAEOjO2CGM4OlA51cJDtfIvfZTIzWP4AZvNpuio4FmkOhhQhFtUv+QEn56PLZ0AwPP6tCl7S05vGdW7uU/PB/iryPAQ1a3p21GHAHAKRbhFJcWwUBoA+Lu3h6T49Hy1q0f49HyAJ5gxL7tVfC3fnxSoonbsmBSwKMIt7JP7upodAbC0ni3r6d07O5kdAyiXjVWfgLNa+1Sqz8/5z4EdfX5OoCpevKmd3r+ns9kx4CWMQbawzk1jzI4AWNaIKy7Qo/1amx0DAHCeavl4StyIKy5Qgj3Kp+cEqmrAJY3MjgAvoiccgF+6tFldsyMAAAAAVUYRbnEf3Xup2REAS2KULwAEBl9P2zB8ejYAKI0i3OK6XlBXU2+/2OwYAAAAAAAPoAj3A1e1q+/1cxjcFoaH3NqpodkRAAAAAMuiCAfgUR0b1TE7AgAAgMf4evHANwf7dntL+B5FeBB75vpk138zvxaewqgKAAAQSGr4uAjv2zbBp+eD71GEB7HrL0o0OwIAAABgaf8ceJHZERBgKMKDWI2I3+/qsV8mPMXw0bqz9LgD7prF1jA7AgAEpC5siwoPowgPYiEhNm2c0Ffrx6cqMizU7DgIEIn2amZHACzl3h5NfXKe9+6+xCfnAbxh2RO9z9qmR4tYHyQBAO+jCA9y1SPCVCsq3OwYCCBXtKrnk/OwjgH8xZP9L9T/Huju1XO8cksHNa5LTzj8V3z02UfkvXNnJ4+cq0+bOI+8DgCcK4rwIPX6oI5mR0CAslEdAz619dmrdFMKWwMi8Hlq1F5K4xiPvA6Cy/d/7ml2BAQQivAgFUKhBAABISKMP+UA4G0X1KtpdgQEEP5yA/CZ+6+4QAmVGHIIAAAQjOzVmCYaDCjCAXjcxY1ql3m8X9sELa3E4jtAoGkZX8vsCEBQa1iHRUNhfbd3aaSl4/icFAx8u/M8LIPtneBN793dWXM37tefP11bqfYjezXX6/O2ezkVYJ5qEexAAZyPkPOcRXdb50aKCA1R1wvYagrWFR8dxd+LIEFPOACPs1cL100pDTVrRDe34zUiy77v1zyu8vOserasp0R7lC5pwsI6ABAsznfRzxCbTfde3kzJDeweSgR43qAujcyOAB+hJ9xPRISFqLDY6bHXM0RXOLzv9I9Mo65sXm6xXZXrcfrdl8hpSKHn2y0CAPAb3ejBRoALC7Eptmak2THgI/SE+4nZo3rovsubeez1GI4OX2hQ+/c5eGNTW5XbrkZE5e8H2mw2CnDgNyN7NTc7AuAx9WqVX4C8eFN7HyYBfC+EzzZBhSLcTzSPq6lxV7cxOwZQJXHRUfp0eFfNHtWjwnbsLQ6cm4f7ln9zC/Bnd3Zt7PZ97eqsGA3zxdSIMDsCAgTD0QF4FXO3AQBVFRri2X4iOhnhCdFRYTp8vNArr80lGlzoCQ9SjEaHlZztD0/jutV9kgMAYJ6KCmXbeZYot7HgFTzgCUalwkMowv3YBfVqmB0B8IkHfpv32r9dfZOTAAC8ZeodKYqpEaFXbumgsNDfi+6+bePPum1TWAUV/KwR3RQdxXB2nL8GXtxvnpl5wYXh6EHKYGU2mCg81Kaikspdg+/f01k9W9ZTl6YxaliHHnHglHeGdDI7AuBRFzeqo5V/6SObzaZ/pG11HX9z8O/XepO61bXrUJ5qRYWpV6s4fbl2n6SKR/hd3KiOtyIDHnO+oz3gX+gJ92Mh3DKDn/r4T5e6bVdW0aXcs2U9SVLjujVYFR1+be7Ynh59vT4Xxnv09QArONtCnf8Z2kV3dm2s2aN66NXbOvooFXAShTI8hSIcgM+lNI7R3LE9dWunhrqwfrR6tKhndiTA65rH1dS2567SvwZdbHYUwG8lxVTX039MVlKM+8io8kb4nb5VJnC+Tu9AAM4Hw9EBmOalmzuYHQHwqfDQEPVvX18PfGh2EsDaPDXYr1YUH3XhORFh9F/CM7iS/Nj5/IFiSjgAAAgUfKyBr3x2fzevvC6zTIMLRbgfY14KAt2NHRuYHQEAYIKqdhac2f7UsOFrOyR6KBFwEqMr4AleK8KPHDmiwYMHy263y263a/DgwTp69Gi57YuKivTYY4+pXbt2qlGjhhITEzVkyBDt27fPWxH93nn1hHPPGH6goRe3AgH8GVtUAif1bh0nSbrjUvd9wP87vKum3dVJ913ezIxYCGAtvDQvnKHuwcVr/7cHDRqkNWvWaM6cOZozZ47WrFmjwYMHl9s+Ly9Pq1at0l//+letWrVKs2bN0tatW3Xdddd5K2JQYzg6/AJjs4AyseUSAl1l3/5fG9RR7911if56zYVux2tXj9CVreMVFkphA8862wr+5+rp69p65XVhTV4ZT7Fp0ybNmTNHS5cuVZcuXSRJb7/9trp27aotW7aoVatWpZ5jt9uVlpbmduy1115T586dlZ6erkaNGpV6TrC7vGU9bc7KNTsGAMDHuI8KnFQ9Iky9fusNB/xZk7qMcAomXrk9uGTJEtntdlcBLkmXXnqp7Ha7Fi9eXOnXcTgcstlsql27drltCgoKlJOT4/YVLO7s1sTsCAAAAADOU53qEWZHgA95pQjPyspSXFzpu5JxcXHKysqq1Gvk5+fr8ccf16BBgxQdHV1uu4kTJ7rmndvtdiUlJZ1zbn8QFvL7EJjz2fuS4egAAMCqakWFn/Nzb0lp6MEkgPc1rFNNjepWNzsGfKhKRfj48eNls9kq/FqxYoWksudLGIZRqXkURUVFGjhwoJxOp6ZMmVJh23HjxsnhcLi+MjIyqvIj+R1qZwSqVvG1JEm3d2HqCQLfZ/d3VXKDaP13eFezowCWdHuXRurdOk7P3ZBc5ede3rKeFxIB3nNrp8DuRERpVZoTPnLkSA0cOLDCNk2aNNG6deu0f//+Uo8dOHBA8fHxFT6/qKhIt956q3bu3Kkffvihwl5wSYqMjFRkZOTZw8MNxTys5tP7u2pdhkNdL6irGcvSzY4DeFVK4xh9/WAPs2MAlhUVHqp377rE7BgA4BVVKsJjY2MVGxt71nZdu3aVw+HQ8uXL1blzZ0nSsmXL5HA41K1b+RvcnyrAt23bpnnz5qlu3bpViYcqMBiPDouJjgrXZS3c31/aJNQyKQ1gbTd2bGB2BMBy2tSP1qbMHPVocfbPqgBgJq+sjt6mTRv169dP9957r958801J0p/+9Cddc801biujt27dWhMnTtQNN9yg4uJi3XzzzVq1apW+/vprlZSUuOaPx8TEKCKCxQokimcEh29GXaZ1exzql5xgdhTAUjY/00+Zjnw1jWUVXeBMXz94mQqLnaoWEWp2FACokFeKcEmaMWOGRo0apdTUVEnSddddp9dff92tzZYtW+RwOCRJe/bs0ZdffilJuuiii9zazZs3T1dccYW3ogKwmLaJdrVNtJsdA7CcqPBQCnCgHKEhNgpwAH7Ba0V4TEyMPvjggwrbnN6r26RJE3p5vahlfE1t3X/M9T3/0gBgDT1b1tP8rQfMjgEA8KEHr2yu137YLkmKrcn6VsHGa0U4vOPM4nlU7xZ69fttFT6nb9t4RYaFuhXhAABraFDn3LebBAD4pz+ntlLrhGgt2n5Qt3RiW71g45V9wuE7V7Q6+zYcYaEh9HwDgB+Y/8gVuvFiFl0DgGDQv319TbyxncJDKcmCDf/H/cyZI/YvblTnHF/o/LMAAM6f7bT/bly3hibdepFZUQAAgA9QhAeJM+fbG1ThAGBZbw5OUUyN33cFuSiptr4aeZmJiQAAgKdQhAcJSm4AsKay3p/7tk3Qyr/0cX3/7PXJateQHQMAAAgEFOF+plPjk8PPq7KKoq2MYyxEDwDWZrOV9e4NAAD8HUW4n5ly+8X60+XN9Nn9Xav0vM5NYryUCABwPioqtVvG11Sd6uFqEV/TZ3kAABWbPOAi/fGiRLNjwI9RhPuZuOgoPXF1GzWuW6NKz7u9SyO9dFN7L6UCAHjD/z10uZY/2UeRYaFmRwEA/Ob6jg30z4Edy3zszOL8mvb1VT2C93C4owgPEmGhIbr1kiSF/Nbl0rkpPeMAYAWtE2qV+1hoiI2tawDAj5xZnI+/rq1+ebqvSWlgVWFmB4Bvrf5bqg4fL1TT2Kr1pAMAvOO2zo10rKBE3ZvXNTsKAMALWOMDZ6IIDwKnr8FmrxYue7Vw07IAANyFhYbo/isuMDsGAADwEca4AQAAAADgIxThAAAAAOBFj/ZrZXYEWAhFOAAAAAB4wanZ4PG1olzHXrqZHYuCHUV4EGApCAAAAMAabu2UZHYEmIwiHAAAAACq6K5uTcyOAD9FEQ4AAAAAVfTE1W00Y1iXUsfZiQhnQxEOAAAAAFUUERai7s1jSx1//57OJqSBP6EIBwAAAAAPaRxT3fXfoSEnV2cyzAoDSwozOwAAAAAABIo6NSJ0Z9fGstlsql09wuw4sCCKcAAAAADwoKf/mGx2BFgYw9EBAAAAAPARinAAAAAAAHyEIjwI2Gw2syMAAAAAAEQRHhAe6HWBJOkv/duYnAQAAAAAUBGK8ADwcGorLR3XW8N6NDM7CgAAAACgAhThAcBmsynBHmV2DAAAAABlSGlcx+wIsBC2KAMAAAAAL2oaW0NpYy5XTA32DQdFeFAwDMPsCAAAAEBQaxFfy+wIsAiGowMAAAAA4CMU4QAAAAAA+AhFeIB5c3CK2REAAAAAAOWgCA8wfdsmlDpms9lMSAIAAAAEvtiaJxdba5sYbXIS+AsWZgMAAACAc/Tf4d00ffEu/enyZmZHgZ/wWk/4kSNHNHjwYNntdtntdg0ePFhHjx6t9PPvu+8+2Ww2TZ482VsRAQAAAOC8NImtofHXtVVi7WpmR4Gf8FoRPmjQIK1Zs0Zz5szRnDlztGbNGg0ePLhSz/3iiy+0bNkyJSYmeiseAAAAAAA+55Xh6Js2bdKcOXO0dOlSdenSRZL09ttvq2vXrtqyZYtatWpV7nP37t2rkSNH6ttvv1X//v29EQ8AAAAAAFN4pSd8yZIlstvtrgJcki699FLZ7XYtXry43Oc5nU4NHjxYjzzyiNq2bVupcxUUFCgnJ8ftCwAAAAAAK/JKEZ6VlaW4uLhSx+Pi4pSVlVXu81588UWFhYVp1KhRlT7XxIkTXfPO7Xa7kpKSzikzAAAAAADeVqUifPz48bLZbBV+rVixQlLZ22IZhlHudlkrV67UP//5T02fPr1KW2qNGzdODofD9ZWRkVGVHykoGIZhdgQAAAAAgKo4J3zkyJEaOHBghW2aNGmidevWaf/+/aUeO3DggOLj48t83sKFC5Wdna1GjRq5jpWUlOjPf/6zJk+erF27dpX5vMjISEVGRlb+hwAAAAAAwCRVKsJjY2MVGxt71nZdu3aVw+HQ8uXL1blzZ0nSsmXL5HA41K1btzKfM3jwYPXp08ftWN++fTV48GDdfffdVYkJAAAAAIAleWVOeJs2bdSvXz/de++9Wrp0qZYuXap7771X11xzjdvK6K1bt9bnn38uSapbt66Sk5PdvsLDw5WQkFDhauoorX/7+m7fV2V4PwAAAADAe7y2T/iMGTPUrl07paamKjU1Ve3bt9d//vMftzZbtmyRw+HwVoSg9fptHfXr81ebHQMAAAAAcAav7BMuSTExMfrggw8qbHO2BcPKmweOitlsNoXS+Q0AAAAAluO1nnAAAAAAAOCOIhwAAAAAAB+hCAcAAAAAwEcowgEAAAAA8BGKcAAAAAAAfIQiPIA1iqkuSbrmjH3DAQAAAADm8NoWZTDf7Id6aOeB40puEG12FAAAAACAKMIDWs3IMLVraDc7BgAAAADgNwxHBwAAAADARyjCAQAAAADwEYpwAAAAAAB8hCIcAAAAAAAfoQgHAAAAAMBHKMIBAAAAAPARinAAAAAAAHyEIhwAAAAAAB+hCAcAAAAAwEcowgEAAAAA8JEwswN4mmEYkqScnByTkwAAAAAAgsGp+vNUPVqRgCvCc3NzJUlJSUkmJwEAAAAABJPc3FzZ7fYK29iMypTqfsTpdGrfvn2qVauWbDab2XEqlJOTo6SkJGVkZCg6OtrsOIAL1yasimsTVsR1Cavi2oRVBeK1aRiGcnNzlZiYqJCQimd9B1xPeEhIiBo2bGh2jCqJjo4OmIsPgYVrE1bFtQkr4rqEVXFtwqoC7do8Ww/4KSzMBgAAAACAj1CEAwAAAADgIxThJoqMjNRTTz2lyMhIs6MAbrg2YVVcm7AirktYFdcmrCrYr82AW5gNAAAAAACroiccAAAAAAAfoQgHAAAAAMBHKMIBAAAAAPARinAAAAAAAHyEIhwAAAAAAB+hCDfJlClT1LRpU0VFRSklJUULFy40OxL81MSJE3XJJZeoVq1aiouL0/XXX68tW7a4tTEMQ+PHj1diYqKqVaumK664Qr/88otbm4KCAj344IOKjY1VjRo1dN1112nPnj1ubY4cOaLBgwfLbrfLbrdr8ODBOnr0qFub9PR0XXvttapRo4ZiY2M1atQoFRYWeuVnh3+ZOHGibDabRo8e7TrGtQmz7N27V3fccYfq1q2r6tWr66KLLtLKlStdj3NtwgzFxcX6y1/+oqZNm6patWpq1qyZJkyYIKfT6WrDtQlfWLBgga699lolJibKZrPpiy++cHvcatfh+vXr1bNnT1WrVk0NGjTQhAkTZOlNwAz43Mcff2yEh4cbb7/9trFx40bjoYceMmrUqGHs3r3b7GjwQ3379jXee+89Y8OGDcaaNWuM/v37G40aNTKOHTvmavPCCy8YtWrVMj777DNj/fr1xoABA4z69esbOTk5rjbDhw83GjRoYKSlpRmrVq0yevXqZXTo0MEoLi52tenXr5+RnJxsLF682Fi8eLGRnJxsXHPNNa7Hi4uLjeTkZKNXr17GqlWrjLS0NCMxMdEYOXKkb/4xYFnLly83mjRpYrRv39546KGHXMe5NmGGw4cPG40bNzbuuusuY9myZcbOnTuNuXPnGtu3b3e14dqEGZ599lmjbt26xtdff23s3LnT+PTTT42aNWsakydPdrXh2oQvzJ4923jyySeNzz77zJBkfP75526PW+k6dDgcRnx8vDFw4EBj/fr1xmeffWbUqlXLePnll733D3SeKMJN0LlzZ2P48OFux1q3bm08/vjjJiVCIMnOzjYkGfPnzzcMwzCcTqeRkJBgvPDCC642+fn5ht1uN9544w3DMAzj6NGjRnh4uPHxxx+72uzdu9cICQkx5syZYxiGYWzcuNGQZCxdutTVZsmSJYYkY/PmzYZhnHzDDgkJMfbu3etq89FHHxmRkZGGw+Hw3g8NS8vNzTVatGhhpKWlGT179nQV4VybMMtjjz1mXHbZZeU+zrUJs/Tv39+455573I7deOONxh133GEYBtcmzHFmEW6163DKlCmG3W438vPzXW0mTpxoJCYmGk6n04P/Ep7DcHQfKyws1MqVK5Wamup2PDU1VYsXLzYpFQKJw+GQJMXExEiSdu7cqaysLLdrLjIyUj179nRdcytXrlRRUZFbm8TERCUnJ7vaLFmyRHa7XV26dHG1ufTSS2W3293aJCcnKzEx0dWmb9++KigocBvmieDywAMPqH///urTp4/bca5NmOXLL79Up06ddMsttyguLk4dO3bU22+/7XqcaxNmueyyy/T9999r69atkqS1a9dq0aJFuvrqqyVxbcIarHYdLlmyRD179lRkZKRbm3379mnXrl2e/wfwgDCzAwSbgwcPqqSkRPHx8W7H4+PjlZWVZVIqBArDMDR27FhddtllSk5OliTXdVXWNbd7925Xm4iICNWpU6dUm1PPz8rKUlxcXKlzxsXFubU58zx16tRRREQE13eQ+vjjj7Vq1Sr9/PPPpR7j2oRZduzYoalTp2rs2LF64okntHz5co0aNUqRkZEaMmQI1yZM89hjj8nhcKh169YKDQ1VSUmJnnvuOd12222SeN+ENVjtOszKylKTJk1KnefUY02bNj2XH9OrKMJNYrPZ3L43DKPUMaCqRo4cqXXr1mnRokWlHjuXa+7MNmW1P5c2CA4ZGRl66KGH9N133ykqKqrcdlyb8DWn06lOnTrp+eeflyR17NhRv/zyi6ZOnaohQ4a42nFtwtdmzpypDz74QB9++KHatm2rNWvWaPTo0UpMTNSdd97pase1CSuw0nVYVpbynmsFDEf3sdjYWIWGhpa6g5idnV3qLg9QFQ8++KC+/PJLzZs3Tw0bNnQdT0hIkKQKr7mEhAQVFhbqyJEjFbbZv39/qfMeOHDArc2Z5zly5IiKioq4voPQypUrlZ2drZSUFIWFhSksLEzz58/Xq6++qrCwMLe71Kfj2oS31a9fXxdeeKHbsTZt2ig9PV0S75swzyOPPKLHH39cAwcOVLt27TR48GCNGTNGEydOlMS1CWuw2nVYVpvs7GxJpXvrrYIi3MciIiKUkpKitLQ0t+NpaWnq1q2bSangzwzD0MiRIzVr1iz98MMPpYbcNG3aVAkJCW7XXGFhoebPn++65lJSUhQeHu7WJjMzUxs2bHC16dq1qxwOh5YvX+5qs2zZMjkcDrc2GzZsUGZmpqvNd999p8jISKWkpHj+h4el9e7dW+vXr9eaNWtcX506ddLtt9+uNWvWqFmzZlybMEX37t1LbeW4detWNW7cWBLvmzBPXl6eQkLcP56Hhoa6tijj2oQVWO067Nq1qxYsWOC2bdl3332nxMTEUsPULcN3a8DhlFNblL377rvGxo0bjdGjRxs1atQwdu3aZXY0+KH777/fsNvtxo8//mhkZma6vvLy8lxtXnjhBcNutxuzZs0y1q9fb9x2221lbiPRsGFDY+7cucaqVauMK6+8ssxtJNq3b28sWbLEWLJkidGuXbsyt5Ho3bu3sWrVKmPu3LlGw4YN2c4ELqevjm4YXJswx/Lly42wsDDjueeeM7Zt22bMmDHDqF69uvHBBx+42nBtwgx33nmn0aBBA9cWZbNmzTJiY2ONRx991NWGaxO+kJuba6xevdpYvXq1IcmYNGmSsXr1ateWyla6Do8ePWrEx8cbt912m7F+/Xpj1qxZRnR0NFuUobR//etfRuPGjY2IiAjj4osvdm0nBVSVpDK/3nvvPVcbp9NpPPXUU0ZCQoIRGRlpXH755cb69evdXufEiRPGyJEjjZiYGKNatWrGNddcY6Snp7u1OXTokHH77bcbtWrVMmrVqmXcfvvtxpEjR9za7N692+jfv79RrVo1IyYmxhg5cqTblhEIbmcW4VybMMtXX31lJCcnG5GRkUbr1q2Nt956y+1xrk2YIScnx3jooYeMRo0aGVFRUUazZs2MJ5980igoKHC14dqEL8ybN6/Mz5d33nmnYRjWuw7XrVtn9OjRw4iMjDQSEhKM8ePHW3Z7MsMwDJth/DZrHQAAAAAAeBVzwgEAAAAA8BGKcAAAAAAAfIQiHAAAAAAAH6EIBwAAAADARyjCAQAAAADwEYpwAAAAAAB8hCIcAAAAAAAfoQgHAAAAAMBHKMIBAAAAAPARinAAAAAAAHyEIhwAAAAAAB/5f1ZBV8MTbBUqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "\n",
    "#orignal audio with 2 channel \n",
    "plt.figure(figsize=(12,4))\n",
    "plt.plot(sf_wave_audio_data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d618d891",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mPermissionError\u001b[0m                           Traceback (most recent call last)",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\tempfile.py:256\u001b[0m, in \u001b[0;36m_mkstemp_inner\u001b[1;34m(dir, pre, suf, flags, output_type)\u001b[0m\n\u001b[0;32m    255\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 256\u001b[0m     fd \u001b[38;5;241m=\u001b[39m \u001b[43m_os\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0o600\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    257\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mFileExistsError\u001b[39;00m:\n",
      "\u001b[1;31mPermissionError\u001b[0m: [Errno 13] Permission denied: 'C:\\\\ProgramData\\\\anaconda3\\\\Lib\\\\site-packages\\\\librosa\\\\core\\\\__pycache__\\\\tmpbyhr1wsb'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[34], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# for a single file\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m mfccs\u001b[38;5;241m=\u001b[39m\u001b[43mlibrosa\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmfcc\u001b[49m(y\u001b[38;5;241m=\u001b[39mlibrosa_audio_data,sr\u001b[38;5;241m=\u001b[39mlibrosa_sample_rate,n_mfcc\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m40\u001b[39m)\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(mfccs\u001b[38;5;241m.\u001b[39mshape)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\lazy_loader\\__init__.py:82\u001b[0m, in \u001b[0;36mattach.<locals>.__getattr__\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m     80\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m attr_to_modules:\n\u001b[0;32m     81\u001b[0m     submod_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpackage_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr_to_modules[name]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 82\u001b[0m     submod \u001b[38;5;241m=\u001b[39m \u001b[43mimportlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[43msubmod_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     83\u001b[0m     attr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(submod, name)\n\u001b[0;32m     85\u001b[0m     \u001b[38;5;66;03m# If the attribute lives in a file (module) with the same\u001b[39;00m\n\u001b[0;32m     86\u001b[0m     \u001b[38;5;66;03m# name as the attribute, ensure that the attribute and *not*\u001b[39;00m\n\u001b[0;32m     87\u001b[0m     \u001b[38;5;66;03m# the module is accessible on the package.\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\importlib\\__init__.py:90\u001b[0m, in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m     88\u001b[0m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m     89\u001b[0m         level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:1387\u001b[0m, in \u001b[0;36m_gcd_import\u001b[1;34m(name, package, level)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:1360\u001b[0m, in \u001b[0;36m_find_and_load\u001b[1;34m(name, import_)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:1331\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:935\u001b[0m, in \u001b[0;36m_load_unlocked\u001b[1;34m(spec)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap_external>:995\u001b[0m, in \u001b[0;36mexec_module\u001b[1;34m(self, module)\u001b[0m\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:488\u001b[0m, in \u001b[0;36m_call_with_frames_removed\u001b[1;34m(f, *args, **kwds)\u001b[0m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\librosa\\feature\\spectral.py:11\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfftpack\u001b[39;00m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m util\n\u001b[1;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m filters\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ParameterError\n\u001b[0;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvert\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fft_frequencies\n",
      "File \u001b[1;32m<frozen importlib._bootstrap>:1412\u001b[0m, in \u001b[0;36m_handle_fromlist\u001b[1;34m(module, fromlist, import_, recursive)\u001b[0m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\lazy_loader\\__init__.py:79\u001b[0m, in \u001b[0;36mattach.<locals>.__getattr__\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m     77\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__getattr__\u001b[39m(name):\n\u001b[0;32m     78\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m submodules:\n\u001b[1;32m---> 79\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mimportlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimport_module\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mpackage_name\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mname\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     80\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m attr_to_modules:\n\u001b[0;32m     81\u001b[0m         submod_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpackage_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mattr_to_modules[name]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\importlib\\__init__.py:90\u001b[0m, in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m     88\u001b[0m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m     89\u001b[0m         level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m---> 90\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_bootstrap\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gcd_import\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m[\u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpackage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\librosa\\filters.py:59\u001b[0m\n\u001b[0;32m     56\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ParameterError\n\u001b[0;32m     57\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdecorators\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m deprecated\n\u001b[1;32m---> 59\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvert\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m note_to_hz, hz_to_midi, midi_to_hz, hz_to_octs\n\u001b[0;32m     60\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconvert\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m fft_frequencies, mel_frequencies\n\u001b[0;32m     61\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtyping\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ArrayLike, DTypeLike\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\librosa\\core\\convert.py:7\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mre\u001b[39;00m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m notation\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ParameterError\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdecorators\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m vectorize\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\librosa\\core\\notation.py:1008\u001b[0m\n\u001b[0;32m   1001\u001b[0m     acc_str \u001b[38;5;241m=\u001b[39m acc_map_inv[np\u001b[38;5;241m.\u001b[39msign(acc_index) \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mint\u001b[39m(\n\u001b[0;32m   1002\u001b[0m         \u001b[38;5;28mabs\u001b[39m(acc_index) \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[0;32m   1003\u001b[0m     ) \u001b[38;5;241m+\u001b[39m acc_map_inv[np\u001b[38;5;241m.\u001b[39msign(acc_index)] \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mint\u001b[39m(\u001b[38;5;28mabs\u001b[39m(acc_index) \u001b[38;5;241m%\u001b[39m \u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m   1005\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m raw_output \u001b[38;5;241m+\u001b[39m acc_str\n\u001b[1;32m-> 1008\u001b[0m \u001b[38;5;129;43m@jit\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnopython\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnogil\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m   1009\u001b[0m \u001b[38;5;28;43;01mdef\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;21;43m__o_fold\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m   1010\u001b[0m \u001b[38;5;250;43m    \u001b[39;49m\u001b[38;5;124;43;03m\"\"\"Compute the octave-folded interval.\u001b[39;49;00m\n\u001b[0;32m   1011\u001b[0m \n\u001b[0;32m   1012\u001b[0m \u001b[38;5;124;43;03m    This maps intervals to the range [1, 2).\u001b[39;49;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1016\u001b[0m \u001b[38;5;124;43;03m    documentation.\u001b[39;49;00m\n\u001b[0;32m   1017\u001b[0m \u001b[38;5;124;43;03m    \"\"\"\u001b[39;49;00m\n\u001b[0;32m   1018\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mreturn\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43md\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2.0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloor\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog2\u001b[49m\u001b[43m(\u001b[49m\u001b[43md\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\decorators.py:229\u001b[0m, in \u001b[0;36m_jit.<locals>.wrapper\u001b[1;34m(func)\u001b[0m\n\u001b[0;32m    225\u001b[0m disp \u001b[38;5;241m=\u001b[39m dispatcher(py_func\u001b[38;5;241m=\u001b[39mfunc, \u001b[38;5;28mlocals\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlocals\u001b[39m,\n\u001b[0;32m    226\u001b[0m                   targetoptions\u001b[38;5;241m=\u001b[39mtargetoptions,\n\u001b[0;32m    227\u001b[0m                   \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mdispatcher_args)\n\u001b[0;32m    228\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cache:\n\u001b[1;32m--> 229\u001b[0m     \u001b[43mdisp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menable_caching\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sigs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    231\u001b[0m     \u001b[38;5;66;03m# Register the Dispatcher to the type inference mechanism,\u001b[39;00m\n\u001b[0;32m    232\u001b[0m     \u001b[38;5;66;03m# even though the decorator hasn't returned yet.\u001b[39;00m\n\u001b[0;32m    233\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnumba\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m typeinfer\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\dispatcher.py:856\u001b[0m, in \u001b[0;36mDispatcher.enable_caching\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    855\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21menable_caching\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 856\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache \u001b[38;5;241m=\u001b[39m \u001b[43mFunctionCache\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpy_func\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\caching.py:601\u001b[0m, in \u001b[0;36mCache.__init__\u001b[1;34m(self, py_func)\u001b[0m\n\u001b[0;32m    599\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mrepr\u001b[39m(py_func)\n\u001b[0;32m    600\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_py_func \u001b[38;5;241m=\u001b[39m py_func\n\u001b[1;32m--> 601\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_impl \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_impl_class\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpy_func\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    602\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_impl\u001b[38;5;241m.\u001b[39mlocator\u001b[38;5;241m.\u001b[39mget_cache_path()\n\u001b[0;32m    603\u001b[0m \u001b[38;5;66;03m# This may be a bit strict but avoids us maintaining a magic number\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\caching.py:333\u001b[0m, in \u001b[0;36mCacheImpl.__init__\u001b[1;34m(self, py_func)\u001b[0m\n\u001b[0;32m    331\u001b[0m source_path \u001b[38;5;241m=\u001b[39m inspect\u001b[38;5;241m.\u001b[39mgetfile(py_func)\n\u001b[0;32m    332\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_locator_classes:\n\u001b[1;32m--> 333\u001b[0m     locator \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpy_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    334\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m locator \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    335\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\caching.py:180\u001b[0m, in \u001b[0;36m_SourceFileBackedLocatorMixin.from_function\u001b[1;34m(cls, py_func, py_file)\u001b[0m\n\u001b[0;32m    178\u001b[0m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mcls\u001b[39m(py_func, py_file)\n\u001b[0;32m    179\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 180\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mensure_cache_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    181\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[0;32m    182\u001b[0m     \u001b[38;5;66;03m# Cannot ensure the cache directory exists or is writable\u001b[39;00m\n\u001b[0;32m    183\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\site-packages\\numba\\core\\caching.py:107\u001b[0m, in \u001b[0;36m_CacheLocator.ensure_cache_path\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    105\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(path, exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m    106\u001b[0m \u001b[38;5;66;03m# Ensure the directory is writable by trying to write a temporary file\u001b[39;00m\n\u001b[1;32m--> 107\u001b[0m \u001b[43mtempfile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTemporaryFile\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\tempfile.py:582\u001b[0m, in \u001b[0;36mNamedTemporaryFile\u001b[1;34m(mode, buffering, encoding, newline, suffix, prefix, dir, delete, errors, delete_on_close)\u001b[0m\n\u001b[0;32m    580\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fd\n\u001b[0;32m    581\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 582\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[43m_io\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbuffering\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbuffering\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    583\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mnewline\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnewline\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    584\u001b[0m \u001b[43m                    \u001b[49m\u001b[43mopener\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mopener\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    585\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    586\u001b[0m         raw \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(file, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbuffer\u001b[39m\u001b[38;5;124m'\u001b[39m, file)\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\tempfile.py:579\u001b[0m, in \u001b[0;36mNamedTemporaryFile.<locals>.opener\u001b[1;34m(*args)\u001b[0m\n\u001b[0;32m    577\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopener\u001b[39m(\u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m    578\u001b[0m     \u001b[38;5;28;01mnonlocal\u001b[39;00m name\n\u001b[1;32m--> 579\u001b[0m     fd, name \u001b[38;5;241m=\u001b[39m \u001b[43m_mkstemp_inner\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprefix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msuffix\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moutput_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    580\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fd\n",
      "File \u001b[1;32mC:\\ProgramData\\anaconda3\\Lib\\tempfile.py:262\u001b[0m, in \u001b[0;36m_mkstemp_inner\u001b[1;34m(dir, pre, suf, flags, output_type)\u001b[0m\n\u001b[0;32m    258\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m    \u001b[38;5;66;03m# try again\u001b[39;00m\n\u001b[0;32m    259\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mPermissionError\u001b[39;00m:\n\u001b[0;32m    260\u001b[0m     \u001b[38;5;66;03m# This exception is thrown when a directory with the chosen name\u001b[39;00m\n\u001b[0;32m    261\u001b[0m     \u001b[38;5;66;03m# already exists on windows.\u001b[39;00m\n\u001b[1;32m--> 262\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (_os\u001b[38;5;241m.\u001b[39mname \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnt\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[43m_os\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpath\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43misdir\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mdir\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    263\u001b[0m         _os\u001b[38;5;241m.\u001b[39maccess(\u001b[38;5;28mdir\u001b[39m, _os\u001b[38;5;241m.\u001b[39mW_OK)):\n\u001b[0;32m    264\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[0;32m    265\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# for a single file\n",
    "\n",
    "mfccs=librosa.feature.mfcc(y=librosa_audio_data,sr=22050,n_mfcc=40)\n",
    "print(mfccs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50ba06a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "mfccs\n",
    "plt.figure(figsize=(25,10))\n",
    "librosa.display.specshow(mfccs,x_axis='time',sr=librosa_sample_rate)\n",
    "plt.colorbar(format=\"%+2f\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9a31acd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>slice_file_name</th>\n",
       "      <th>fsID</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>salience</th>\n",
       "      <th>fold</th>\n",
       "      <th>classID</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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      ],
      "text/plain": [
       "      slice_file_name    fsID  start        end  salience  fold  classID  \\\n",
       "0    100032-3-0-0.wav  100032    0.0   0.317551         1     5        3   \n",
       "1  100263-2-0-117.wav  100263   58.5  62.500000         1     5        2   \n",
       "2  100263-2-0-121.wav  100263   60.5  64.500000         1     5        2   \n",
       "3  100263-2-0-126.wav  100263   63.0  67.000000         1     5        2   \n",
       "4  100263-2-0-137.wav  100263   68.5  72.500000         1     5        2   \n",
       "\n",
       "              class  \n",
       "0          dog_bark  \n",
       "1  children_playing  \n",
       "2  children_playing  \n",
       "3  children_playing  \n",
       "4  children_playing  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting MFCC's for every audio file\n",
    "import pandas as pd\n",
    "import os \n",
    "import librosa\n",
    "\n",
    "audio_dataset_path='UrbanSound8K/audio/'\n",
    "metadata=pd.read_csv('UrbanSound8K/metadata/UrbanSound8K.csv')\n",
    "metadata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "219415d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def features_extractor(file_name):\n",
    "   audio_data, sample_rate = librosa.load(file_name)\n",
    "   mfccs_features = librosa.feature.mfcc(y=audio_data, sr=sample_rate, n_mfcc=40)\n",
    "   mfccs_scaled_features = np.mean(mfccs_features.T,axis=0)\n",
    "   return mfccs_scaled_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3387eb08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeableNote: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "Requirement already satisfied: resampy in c:\\programdata\\anaconda3\\lib\\site-packages (0.4.3)\n",
      "Requirement already satisfied: numpy>=1.17 in c:\\programdata\\anaconda3\\lib\\site-packages (from resampy) (1.26.4)\n",
      "Requirement already satisfied: numba>=0.53 in c:\\programdata\\anaconda3\\lib\\site-packages (from resampy) (0.59.1)\n",
      "Requirement already satisfied: llvmlite<0.43,>=0.42.0dev0 in c:\\programdata\\anaconda3\\lib\\site-packages (from numba>=0.53->resampy) (0.42.0)\n"
     ]
    }
   ],
   "source": [
    "%pip install resampy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ab2b92d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "0it [00:00, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HP\\Desktop\\My resume Project\\BTP PRoject\\UrbanSound8K\\audio\\fold5/100032-3-0-0.wav\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "### Now we iterate through every audio file and extract features\n",
    "##$ using Mel-frequency Cepstral Coefficients\n",
    "extracted_features=[]\n",
    "for index_num,row in tqdm(metadata.iterrows()):\n",
    "    file_name = os.path.join(os.path.abspath(audio_dataset_path),'fold'+str(row[\"fold\"])+'/',str(row[\"slice_file_name\"]))\n",
    "    print(file_name)\n",
    "    final_class_labels=row['class']\n",
    "#     print(final_class_labels)\n",
    "    data=features_extractor(file_name)\n",
    "    extracted_features.append([data,final_class_labels])\n",
    "\n",
    "# import pickle\n",
    "\n",
    "# Assuming mfcc_scaled_features is your variable containing the scaled MFCC features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9df2bd91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to convert it into a Dataframe with two columns\n",
    "extracted_features_df=pd.DataFrame(extracted_features,columns=['features','class'])\n",
    "extracted_features_df.head(100)\n",
    "with open('extracted_features.pkl', 'wb') as f:\n",
    "    pickle.dump(extracted_features, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98703833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# seprating both the columns \n",
    "x=np.array(extracted_features_df['features'].tolist())\n",
    "y=np.array(extracted_features_df['class'].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fade952c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x.shape # mel frequency features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4be857a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f0086ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "ee88fb91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Requirement already satisfied: tensorflow in c:\\programdata\\anaconda3\\lib\\site-packages (2.17.0)\n",
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     ]
    }
   ],
   "source": [
    "#label encoding \n",
    "# y=np.array(pd.get_dummies(y))\n",
    "### label encoder\n",
    "!pip install tensorflow\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder=LabelEncoder()\n",
    "# doing for a reason\n",
    "y=to_categorical(labelencoder.fit_transform(y)) # to encode all the classes firstly standerize and then fiting it in a range "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "71c2e705",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8732, 10)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "dac7e4d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train test split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0) # Here it is done to split the dataset into two parts Here \n",
    "# 20 % of the whole dataset would be used for testing purpose and rest is for training purpose  therefore X_train is now 6985 not 8732"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "adf9f0d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6985, 40)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape # here 40 is the number of extracted features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "d73a20cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1747, 40)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "d4c22a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6985, 10)"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "859d09cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1747, 10)"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "cffebd53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.24484680e+02,  1.03169846e+02, -1.33165474e+01, ...,\n",
       "         4.72908735e+00, -2.91925478e+00,  4.36318684e+00],\n",
       "       [-5.13921928e+00,  7.90478210e+01,  4.16248989e+00, ...,\n",
       "        -3.44017529e+00, -5.19278336e+00, -1.54999781e+00],\n",
       "       [-4.03939552e+01, -1.31092958e+01, -7.20987606e+00, ...,\n",
       "         1.99475384e+00, -8.70601594e-01,  2.89561915e+00],\n",
       "       ...,\n",
       "       [-4.26655701e+02,  9.15513382e+01,  4.19170380e+00, ...,\n",
       "         9.74400103e-01,  5.15445411e-01,  9.15374815e-01],\n",
       "       [-1.37251968e+02,  1.24268539e+02, -2.15997734e+01, ...,\n",
       "         1.25008631e+00, -1.89575124e+00, -9.01497185e-01],\n",
       "       [-4.18389374e+02,  2.06862335e+02,  7.23461866e+00, ...,\n",
       "        -3.98612142e+00, -4.71784496e+00, -2.78925896e-01]], dtype=float32)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e5b10a9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\aggar\\anaconda3\\lib\\site-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.1.0)\n"
     ]
    }
   ],
   "source": [
    "# Model Creation using Tensor Flow\n",
    "%pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "358eaa8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.17.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "3ff3b44e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "# below is used to create ANN\n",
    "from tensorflow.keras.layers import Dense,Dropout,Activation,Flatten\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "d1051198",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "# print(y.shape)\n",
    "num_labels=y.shape[1] # since classes are 10 in number\n",
    "print(num_labels)\n",
    "# Dense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "f00a1df1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "model=Sequential()\n",
    "## First layer\n",
    "model.add(Dense(100,input_shape=(40,))) # Here since 40 features therfore we did 40\n",
    "model.add(Activation('relu')) # activation function \n",
    "model.add(Dropout(0.2)) # why dropout is used \n",
    "#Dropout is used with a rate of 0.2, meaning that during training, 20% of the units in the layer will be randomly dropped out,\n",
    "#which helps prevent overfitting by reducing interdependent learning among neurons.\n",
    "\n",
    "#second layer\n",
    "model.add(Dense(200)) \n",
    "model.add(Activation('relu')) # activation function  Here relu is used  # for applying the non -linearity \n",
    "model.add(Dropout(0.2)) # why dropout is used\n",
    "\n",
    "#third layer\n",
    "model.add(Dense(100))  # Dense layer of full interconnected 100 units \n",
    "model.add(Activation('relu')) # activation function\n",
    "model.add(Dropout(0.2)) \n",
    "\n",
    "#final layer\n",
    "model.add(Dense(num_labels)) \n",
    "# softmax since multiclass classification\n",
    "model.add(Activation('softmax')) # soft max activation function used for formation of output probabilities and\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "947b2ba2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_3\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,100</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">200</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,100</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,010</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ dense_12 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │         \u001b[38;5;34m4,100\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_12 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_13 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m200\u001b[0m)            │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_13 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m200\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_10 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m200\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_14 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │        \u001b[38;5;34m20,100\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_14 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_11 (\u001b[38;5;33mDropout\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_15 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m1,010\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ activation_15 (\u001b[38;5;33mActivation\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">45,410</span> (177.38 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m45,410\u001b[0m (177.38 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">45,410</span> (177.38 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m45,410\u001b[0m (177.38 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "784387ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for compiling  the model\n",
    "model.compile(loss='categorical_crossentropy',metrics=['accuracy'],optimizer='adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "d8e82433",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (6985, 40)\n",
      "y_train shape: (6985, 10)\n",
      "X_test shape: (1747, 40)\n",
      "y_test shape: (1747, 10)\n",
      "Epoch 1/100\n",
      "\u001b[1m544/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.1396 - loss: 6.4415\n",
      "Epoch 1: val_loss improved from inf to 2.18816, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.1399 - loss: 6.2390 - val_accuracy: 0.1620 - val_loss: 2.1882\n",
      "Epoch 2/100\n",
      "\u001b[1m555/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.1919 - loss: 2.2219\n",
      "Epoch 2: val_loss improved from 2.18816 to 1.82975, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.1931 - loss: 2.2184 - val_accuracy: 0.3320 - val_loss: 1.8298\n",
      "Epoch 3/100\n",
      "\u001b[1m572/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.2997 - loss: 1.8962\n",
      "Epoch 3: val_loss improved from 1.82975 to 1.55985, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.3002 - loss: 1.8951 - val_accuracy: 0.4322 - val_loss: 1.5598\n",
      "Epoch 4/100\n",
      "\u001b[1m561/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.3958 - loss: 1.6628\n",
      "Epoch 4: val_loss improved from 1.55985 to 1.43877, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.3960 - loss: 1.6621 - val_accuracy: 0.4906 - val_loss: 1.4388\n",
      "Epoch 5/100\n",
      "\u001b[1m568/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.4595 - loss: 1.5043\n",
      "Epoch 5: val_loss improved from 1.43877 to 1.25321, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.4601 - loss: 1.5030 - val_accuracy: 0.5753 - val_loss: 1.2532\n",
      "Epoch 6/100\n",
      "\u001b[1m573/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5391 - loss: 1.3418\n",
      "Epoch 6: val_loss improved from 1.25321 to 1.09145, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.5393 - loss: 1.3410 - val_accuracy: 0.6314 - val_loss: 1.0915\n",
      "Epoch 7/100\n",
      "\u001b[1m546/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.5940 - loss: 1.1830\n",
      "Epoch 7: val_loss improved from 1.09145 to 0.97836, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.5941 - loss: 1.1831 - val_accuracy: 0.6846 - val_loss: 0.9784\n",
      "Epoch 8/100\n",
      "\u001b[1m567/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6208 - loss: 1.1098\n",
      "Epoch 8: val_loss improved from 0.97836 to 0.96573, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.6212 - loss: 1.1089 - val_accuracy: 0.6892 - val_loss: 0.9657\n",
      "Epoch 9/100\n",
      "\u001b[1m575/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6566 - loss: 1.0442\n",
      "Epoch 9: val_loss improved from 0.96573 to 0.83696, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6566 - loss: 1.0438 - val_accuracy: 0.7321 - val_loss: 0.8370\n",
      "Epoch 10/100\n",
      "\u001b[1m565/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6753 - loss: 0.9467\n",
      "Epoch 10: val_loss improved from 0.83696 to 0.78359, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.6755 - loss: 0.9464 - val_accuracy: 0.7361 - val_loss: 0.7836\n",
      "Epoch 11/100\n",
      "\u001b[1m557/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7019 - loss: 0.8746\n",
      "Epoch 11: val_loss improved from 0.78359 to 0.69839, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7023 - loss: 0.8742 - val_accuracy: 0.7710 - val_loss: 0.6984\n",
      "Epoch 12/100\n",
      "\u001b[1m573/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7273 - loss: 0.8335\n",
      "Epoch 12: val_loss improved from 0.69839 to 0.67312, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7274 - loss: 0.8335 - val_accuracy: 0.7836 - val_loss: 0.6731\n",
      "Epoch 13/100\n",
      "\u001b[1m574/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7312 - loss: 0.7984\n",
      "Epoch 13: val_loss improved from 0.67312 to 0.65576, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.7312 - loss: 0.7984 - val_accuracy: 0.7836 - val_loss: 0.6558\n",
      "Epoch 14/100\n",
      "\u001b[1m573/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7559 - loss: 0.7113\n",
      "Epoch 14: val_loss improved from 0.65576 to 0.64148, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.7558 - loss: 0.7117 - val_accuracy: 0.7848 - val_loss: 0.6415\n",
      "Epoch 15/100\n",
      "\u001b[1m555/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7552 - loss: 0.7445\n",
      "Epoch 15: val_loss improved from 0.64148 to 0.57926, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.7552 - loss: 0.7436 - val_accuracy: 0.8082 - val_loss: 0.5793\n",
      "Epoch 16/100\n",
      "\u001b[1m577/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7778 - loss: 0.6773\n",
      "Epoch 16: val_loss improved from 0.57926 to 0.57420, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.7778 - loss: 0.6774 - val_accuracy: 0.8140 - val_loss: 0.5742\n",
      "Epoch 17/100\n",
      "\u001b[1m556/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7797 - loss: 0.6684\n",
      "Epoch 17: val_loss improved from 0.57420 to 0.56559, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.7797 - loss: 0.6685 - val_accuracy: 0.8231 - val_loss: 0.5656\n",
      "Epoch 18/100\n",
      "\u001b[1m542/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7820 - loss: 0.6414\n",
      "Epoch 18: val_loss improved from 0.56559 to 0.52161, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.7822 - loss: 0.6412 - val_accuracy: 0.8220 - val_loss: 0.5216\n",
      "Epoch 19/100\n",
      "\u001b[1m561/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7920 - loss: 0.6150\n",
      "Epoch 19: val_loss improved from 0.52161 to 0.50795, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.7917 - loss: 0.6156 - val_accuracy: 0.8243 - val_loss: 0.5080\n",
      "Epoch 20/100\n",
      "\u001b[1m582/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7948 - loss: 0.6197\n",
      "Epoch 20: val_loss did not improve from 0.50795\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.7948 - loss: 0.6196 - val_accuracy: 0.8300 - val_loss: 0.5220\n",
      "Epoch 21/100\n",
      "\u001b[1m564/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7932 - loss: 0.6172\n",
      "Epoch 21: val_loss did not improve from 0.50795\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.7933 - loss: 0.6171 - val_accuracy: 0.8277 - val_loss: 0.5244\n",
      "Epoch 22/100\n",
      "\u001b[1m574/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8041 - loss: 0.5598\n",
      "Epoch 22: val_loss improved from 0.50795 to 0.50339, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8041 - loss: 0.5599 - val_accuracy: 0.8414 - val_loss: 0.5034\n",
      "Epoch 23/100\n",
      "\u001b[1m576/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8038 - loss: 0.5843\n",
      "Epoch 23: val_loss improved from 0.50339 to 0.48882, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8039 - loss: 0.5842 - val_accuracy: 0.8437 - val_loss: 0.4888\n",
      "Epoch 24/100\n",
      "\u001b[1m580/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8134 - loss: 0.5656\n",
      "Epoch 24: val_loss improved from 0.48882 to 0.48648, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8134 - loss: 0.5656 - val_accuracy: 0.8552 - val_loss: 0.4865\n",
      "Epoch 25/100\n",
      "\u001b[1m555/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8215 - loss: 0.5238\n",
      "Epoch 25: val_loss did not improve from 0.48648\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8212 - loss: 0.5253 - val_accuracy: 0.8420 - val_loss: 0.4896\n",
      "Epoch 26/100\n",
      "\u001b[1m543/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8225 - loss: 0.5214\n",
      "Epoch 26: val_loss improved from 0.48648 to 0.47983, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8224 - loss: 0.5226 - val_accuracy: 0.8369 - val_loss: 0.4798\n",
      "Epoch 27/100\n",
      "\u001b[1m554/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8362 - loss: 0.5050\n",
      "Epoch 27: val_loss improved from 0.47983 to 0.47740, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8357 - loss: 0.5058 - val_accuracy: 0.8517 - val_loss: 0.4774\n",
      "Epoch 28/100\n",
      "\u001b[1m575/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8308 - loss: 0.4987\n",
      "Epoch 28: val_loss improved from 0.47740 to 0.44136, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8306 - loss: 0.4990 - val_accuracy: 0.8632 - val_loss: 0.4414\n",
      "Epoch 29/100\n",
      "\u001b[1m574/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8245 - loss: 0.5082\n",
      "Epoch 29: val_loss did not improve from 0.44136\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8245 - loss: 0.5084 - val_accuracy: 0.8489 - val_loss: 0.4587\n",
      "Epoch 30/100\n",
      "\u001b[1m558/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8368 - loss: 0.4900\n",
      "Epoch 30: val_loss did not improve from 0.44136\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8364 - loss: 0.4911 - val_accuracy: 0.8552 - val_loss: 0.4505\n",
      "Epoch 31/100\n",
      "\u001b[1m582/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8365 - loss: 0.4843\n",
      "Epoch 31: val_loss did not improve from 0.44136\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8365 - loss: 0.4844 - val_accuracy: 0.8552 - val_loss: 0.4632\n",
      "Epoch 32/100\n",
      "\u001b[1m572/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8390 - loss: 0.4920\n",
      "Epoch 32: val_loss improved from 0.44136 to 0.43487, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8389 - loss: 0.4920 - val_accuracy: 0.8598 - val_loss: 0.4349\n",
      "Epoch 33/100\n",
      "\u001b[1m560/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8367 - loss: 0.4939\n",
      "Epoch 33: val_loss did not improve from 0.43487\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8369 - loss: 0.4937 - val_accuracy: 0.8569 - val_loss: 0.4463\n",
      "Epoch 34/100\n",
      "\u001b[1m536/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8413 - loss: 0.4628\n",
      "Epoch 34: val_loss improved from 0.43487 to 0.43159, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8411 - loss: 0.4639 - val_accuracy: 0.8672 - val_loss: 0.4316\n",
      "Epoch 35/100\n",
      "\u001b[1m544/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8471 - loss: 0.4587\n",
      "Epoch 35: val_loss improved from 0.43159 to 0.41944, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8469 - loss: 0.4592 - val_accuracy: 0.8695 - val_loss: 0.4194\n",
      "Epoch 36/100\n",
      "\u001b[1m570/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8465 - loss: 0.4632\n",
      "Epoch 36: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8464 - loss: 0.4635 - val_accuracy: 0.8626 - val_loss: 0.4575\n",
      "Epoch 37/100\n",
      "\u001b[1m572/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8378 - loss: 0.4932\n",
      "Epoch 37: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8379 - loss: 0.4927 - val_accuracy: 0.8661 - val_loss: 0.4442\n",
      "Epoch 38/100\n",
      "\u001b[1m544/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8481 - loss: 0.4311\n",
      "Epoch 38: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8479 - loss: 0.4328 - val_accuracy: 0.8552 - val_loss: 0.4459\n",
      "Epoch 39/100\n",
      "\u001b[1m552/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8433 - loss: 0.4525\n",
      "Epoch 39: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8433 - loss: 0.4530 - val_accuracy: 0.8718 - val_loss: 0.4407\n",
      "Epoch 40/100\n",
      "\u001b[1m540/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8577 - loss: 0.4191\n",
      "Epoch 40: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8572 - loss: 0.4215 - val_accuracy: 0.8638 - val_loss: 0.4300\n",
      "Epoch 41/100\n",
      "\u001b[1m578/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8521 - loss: 0.4497\n",
      "Epoch 41: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8521 - loss: 0.4497 - val_accuracy: 0.8661 - val_loss: 0.4415\n",
      "Epoch 42/100\n",
      "\u001b[1m580/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8547 - loss: 0.4333\n",
      "Epoch 42: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8547 - loss: 0.4334 - val_accuracy: 0.8615 - val_loss: 0.4434\n",
      "Epoch 43/100\n",
      "\u001b[1m574/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8546 - loss: 0.4293\n",
      "Epoch 43: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8546 - loss: 0.4295 - val_accuracy: 0.8678 - val_loss: 0.4313\n",
      "Epoch 44/100\n",
      "\u001b[1m567/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8606 - loss: 0.4174\n",
      "Epoch 44: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8606 - loss: 0.4176 - val_accuracy: 0.8718 - val_loss: 0.4328\n",
      "Epoch 45/100\n",
      "\u001b[1m555/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8534 - loss: 0.4548\n",
      "Epoch 45: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8535 - loss: 0.4544 - val_accuracy: 0.8632 - val_loss: 0.4399\n",
      "Epoch 46/100\n",
      "\u001b[1m574/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8638 - loss: 0.4071\n",
      "Epoch 46: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8636 - loss: 0.4077 - val_accuracy: 0.8626 - val_loss: 0.4424\n",
      "Epoch 47/100\n",
      "\u001b[1m574/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8603 - loss: 0.4277\n",
      "Epoch 47: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8605 - loss: 0.4272 - val_accuracy: 0.8752 - val_loss: 0.4277\n",
      "Epoch 48/100\n",
      "\u001b[1m576/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8656 - loss: 0.3971\n",
      "Epoch 48: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8655 - loss: 0.3973 - val_accuracy: 0.8689 - val_loss: 0.4379\n",
      "Epoch 49/100\n",
      "\u001b[1m555/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8629 - loss: 0.4101\n",
      "Epoch 49: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8628 - loss: 0.4103 - val_accuracy: 0.8666 - val_loss: 0.4685\n",
      "Epoch 50/100\n",
      "\u001b[1m571/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8572 - loss: 0.4285\n",
      "Epoch 50: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8572 - loss: 0.4288 - val_accuracy: 0.8786 - val_loss: 0.4294\n",
      "Epoch 51/100\n",
      "\u001b[1m574/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8636 - loss: 0.3924\n",
      "Epoch 51: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8636 - loss: 0.3926 - val_accuracy: 0.8638 - val_loss: 0.4553\n",
      "Epoch 52/100\n",
      "\u001b[1m559/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8470 - loss: 0.4629\n",
      "Epoch 52: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8476 - loss: 0.4614 - val_accuracy: 0.8741 - val_loss: 0.4361\n",
      "Epoch 53/100\n",
      "\u001b[1m566/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8657 - loss: 0.3957\n",
      "Epoch 53: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8657 - loss: 0.3961 - val_accuracy: 0.8792 - val_loss: 0.4300\n",
      "Epoch 54/100\n",
      "\u001b[1m572/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8578 - loss: 0.4163\n",
      "Epoch 54: val_loss did not improve from 0.41944\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8580 - loss: 0.4162 - val_accuracy: 0.8741 - val_loss: 0.4396\n",
      "Epoch 55/100\n",
      "\u001b[1m574/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8562 - loss: 0.4236\n",
      "Epoch 55: val_loss improved from 0.41944 to 0.40019, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8562 - loss: 0.4235 - val_accuracy: 0.8781 - val_loss: 0.4002\n",
      "Epoch 56/100\n",
      "\u001b[1m553/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8632 - loss: 0.4043\n",
      "Epoch 56: val_loss did not improve from 0.40019\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8633 - loss: 0.4041 - val_accuracy: 0.8764 - val_loss: 0.4148\n",
      "Epoch 57/100\n",
      "\u001b[1m555/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8754 - loss: 0.3755\n",
      "Epoch 57: val_loss did not improve from 0.40019\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8754 - loss: 0.3759 - val_accuracy: 0.8735 - val_loss: 0.4302\n",
      "Epoch 58/100\n",
      "\u001b[1m549/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8564 - loss: 0.4358\n",
      "Epoch 58: val_loss did not improve from 0.40019\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - accuracy: 0.8567 - loss: 0.4351 - val_accuracy: 0.8809 - val_loss: 0.4136\n",
      "Epoch 59/100\n",
      "\u001b[1m573/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8800 - loss: 0.3628\n",
      "Epoch 59: val_loss did not improve from 0.40019\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8798 - loss: 0.3632 - val_accuracy: 0.8821 - val_loss: 0.4363\n",
      "Epoch 60/100\n",
      "\u001b[1m565/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8586 - loss: 0.4502\n",
      "Epoch 60: val_loss did not improve from 0.40019\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8587 - loss: 0.4493 - val_accuracy: 0.8741 - val_loss: 0.4377\n",
      "Epoch 61/100\n",
      "\u001b[1m563/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8752 - loss: 0.3748\n",
      "Epoch 61: val_loss did not improve from 0.40019\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8751 - loss: 0.3752 - val_accuracy: 0.8746 - val_loss: 0.4324\n",
      "Epoch 62/100\n",
      "\u001b[1m571/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8710 - loss: 0.3744\n",
      "Epoch 62: val_loss improved from 0.40019 to 0.39836, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8710 - loss: 0.3745 - val_accuracy: 0.8895 - val_loss: 0.3984\n",
      "Epoch 63/100\n",
      "\u001b[1m563/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8796 - loss: 0.3580\n",
      "Epoch 63: val_loss improved from 0.39836 to 0.38935, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8795 - loss: 0.3585 - val_accuracy: 0.8930 - val_loss: 0.3893\n",
      "Epoch 64/100\n",
      "\u001b[1m559/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8744 - loss: 0.3739\n",
      "Epoch 64: val_loss improved from 0.38935 to 0.38733, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8742 - loss: 0.3743 - val_accuracy: 0.8804 - val_loss: 0.3873\n",
      "Epoch 65/100\n",
      "\u001b[1m560/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8779 - loss: 0.3666\n",
      "Epoch 65: val_loss did not improve from 0.38733\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8777 - loss: 0.3671 - val_accuracy: 0.8758 - val_loss: 0.4057\n",
      "Epoch 66/100\n",
      "\u001b[1m568/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8781 - loss: 0.3726\n",
      "Epoch 66: val_loss did not improve from 0.38733\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8779 - loss: 0.3732 - val_accuracy: 0.8741 - val_loss: 0.4238\n",
      "Epoch 67/100\n",
      "\u001b[1m580/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8677 - loss: 0.4013\n",
      "Epoch 67: val_loss did not improve from 0.38733\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8678 - loss: 0.4012 - val_accuracy: 0.8712 - val_loss: 0.4111\n",
      "Epoch 68/100\n",
      "\u001b[1m559/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8730 - loss: 0.3795\n",
      "Epoch 68: val_loss did not improve from 0.38733\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8729 - loss: 0.3796 - val_accuracy: 0.8746 - val_loss: 0.4027\n",
      "Epoch 69/100\n",
      "\u001b[1m568/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8740 - loss: 0.3755\n",
      "Epoch 69: val_loss did not improve from 0.38733\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8741 - loss: 0.3755 - val_accuracy: 0.8884 - val_loss: 0.3926\n",
      "Epoch 70/100\n",
      "\u001b[1m576/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8880 - loss: 0.3474\n",
      "Epoch 70: val_loss did not improve from 0.38733\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8879 - loss: 0.3477 - val_accuracy: 0.8735 - val_loss: 0.4250\n",
      "Epoch 71/100\n",
      "\u001b[1m573/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8657 - loss: 0.4089\n",
      "Epoch 71: val_loss did not improve from 0.38733\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8658 - loss: 0.4087 - val_accuracy: 0.8827 - val_loss: 0.3943\n",
      "Epoch 72/100\n",
      "\u001b[1m564/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8792 - loss: 0.3478\n",
      "Epoch 72: val_loss improved from 0.38733 to 0.37916, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8793 - loss: 0.3478 - val_accuracy: 0.8867 - val_loss: 0.3792\n",
      "Epoch 73/100\n",
      "\u001b[1m575/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8779 - loss: 0.3682\n",
      "Epoch 73: val_loss did not improve from 0.37916\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8779 - loss: 0.3683 - val_accuracy: 0.8764 - val_loss: 0.4195\n",
      "Epoch 74/100\n",
      "\u001b[1m567/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8760 - loss: 0.3877\n",
      "Epoch 74: val_loss did not improve from 0.37916\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8761 - loss: 0.3877 - val_accuracy: 0.8815 - val_loss: 0.3877\n",
      "Epoch 75/100\n",
      "\u001b[1m562/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8827 - loss: 0.3507\n",
      "Epoch 75: val_loss did not improve from 0.37916\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8828 - loss: 0.3506 - val_accuracy: 0.8867 - val_loss: 0.3831\n",
      "Epoch 76/100\n",
      "\u001b[1m578/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8894 - loss: 0.3559\n",
      "Epoch 76: val_loss improved from 0.37916 to 0.37371, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8894 - loss: 0.3560 - val_accuracy: 0.8884 - val_loss: 0.3737\n",
      "Epoch 77/100\n",
      "\u001b[1m571/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8844 - loss: 0.3430\n",
      "Epoch 77: val_loss did not improve from 0.37371\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8844 - loss: 0.3433 - val_accuracy: 0.8918 - val_loss: 0.3872\n",
      "Epoch 78/100\n",
      "\u001b[1m572/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8868 - loss: 0.3321\n",
      "Epoch 78: val_loss did not improve from 0.37371\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8867 - loss: 0.3324 - val_accuracy: 0.8821 - val_loss: 0.4122\n",
      "Epoch 79/100\n",
      "\u001b[1m568/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8768 - loss: 0.3775\n",
      "Epoch 79: val_loss did not improve from 0.37371\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8768 - loss: 0.3774 - val_accuracy: 0.8838 - val_loss: 0.3893\n",
      "Epoch 80/100\n",
      "\u001b[1m566/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8949 - loss: 0.3320\n",
      "Epoch 80: val_loss did not improve from 0.37371\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8947 - loss: 0.3326 - val_accuracy: 0.8855 - val_loss: 0.3837\n",
      "Epoch 81/100\n",
      "\u001b[1m567/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8858 - loss: 0.3554\n",
      "Epoch 81: val_loss improved from 0.37371 to 0.37307, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8858 - loss: 0.3553 - val_accuracy: 0.8952 - val_loss: 0.3731\n",
      "Epoch 82/100\n",
      "\u001b[1m582/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8874 - loss: 0.3656\n",
      "Epoch 82: val_loss improved from 0.37307 to 0.35841, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8873 - loss: 0.3656 - val_accuracy: 0.8907 - val_loss: 0.3584\n",
      "Epoch 83/100\n",
      "\u001b[1m567/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8967 - loss: 0.3101\n",
      "Epoch 83: val_loss did not improve from 0.35841\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8965 - loss: 0.3110 - val_accuracy: 0.8792 - val_loss: 0.4334\n",
      "Epoch 84/100\n",
      "\u001b[1m549/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8847 - loss: 0.3489\n",
      "Epoch 84: val_loss did not improve from 0.35841\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8848 - loss: 0.3493 - val_accuracy: 0.8844 - val_loss: 0.3961\n",
      "Epoch 85/100\n",
      "\u001b[1m575/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8838 - loss: 0.3567\n",
      "Epoch 85: val_loss did not improve from 0.35841\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8838 - loss: 0.3567 - val_accuracy: 0.8878 - val_loss: 0.3636\n",
      "Epoch 86/100\n",
      "\u001b[1m576/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8861 - loss: 0.3604\n",
      "Epoch 86: val_loss improved from 0.35841 to 0.35661, saving model to saved_models/audio_classification.keras\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.8860 - loss: 0.3604 - val_accuracy: 0.8952 - val_loss: 0.3566\n",
      "Epoch 87/100\n",
      "\u001b[1m573/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8892 - loss: 0.3350\n",
      "Epoch 87: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8891 - loss: 0.3353 - val_accuracy: 0.8890 - val_loss: 0.3901\n",
      "Epoch 88/100\n",
      "\u001b[1m566/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8941 - loss: 0.3231\n",
      "Epoch 88: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8939 - loss: 0.3237 - val_accuracy: 0.9038 - val_loss: 0.3749\n",
      "Epoch 89/100\n",
      "\u001b[1m572/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8900 - loss: 0.3354\n",
      "Epoch 89: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8900 - loss: 0.3355 - val_accuracy: 0.8930 - val_loss: 0.3983\n",
      "Epoch 90/100\n",
      "\u001b[1m552/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8863 - loss: 0.3411\n",
      "Epoch 90: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8865 - loss: 0.3406 - val_accuracy: 0.8941 - val_loss: 0.3996\n",
      "Epoch 91/100\n",
      "\u001b[1m564/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8916 - loss: 0.3155\n",
      "Epoch 91: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8915 - loss: 0.3160 - val_accuracy: 0.8918 - val_loss: 0.3739\n",
      "Epoch 92/100\n",
      "\u001b[1m558/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8937 - loss: 0.3111\n",
      "Epoch 92: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8934 - loss: 0.3122 - val_accuracy: 0.8901 - val_loss: 0.3624\n",
      "Epoch 93/100\n",
      "\u001b[1m561/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8866 - loss: 0.3255\n",
      "Epoch 93: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8865 - loss: 0.3266 - val_accuracy: 0.8970 - val_loss: 0.3758\n",
      "Epoch 94/100\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8883 - loss: 0.3244\n",
      "Epoch 94: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8883 - loss: 0.3244 - val_accuracy: 0.8970 - val_loss: 0.3612\n",
      "Epoch 95/100\n",
      "\u001b[1m565/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8900 - loss: 0.3386\n",
      "Epoch 95: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8899 - loss: 0.3387 - val_accuracy: 0.8804 - val_loss: 0.3917\n",
      "Epoch 96/100\n",
      "\u001b[1m578/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8907 - loss: 0.3432\n",
      "Epoch 96: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8907 - loss: 0.3432 - val_accuracy: 0.8861 - val_loss: 0.3824\n",
      "Epoch 97/100\n",
      "\u001b[1m578/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8876 - loss: 0.3541\n",
      "Epoch 97: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.8876 - loss: 0.3540 - val_accuracy: 0.8918 - val_loss: 0.3751\n",
      "Epoch 98/100\n",
      "\u001b[1m575/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8995 - loss: 0.3037\n",
      "Epoch 98: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8994 - loss: 0.3042 - val_accuracy: 0.9027 - val_loss: 0.3590\n",
      "Epoch 99/100\n",
      "\u001b[1m568/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.8879 - loss: 0.3417\n",
      "Epoch 99: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.8880 - loss: 0.3412 - val_accuracy: 0.8912 - val_loss: 0.4379\n",
      "Epoch 100/100\n",
      "\u001b[1m573/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.9026 - loss: 0.3136\n",
      "Epoch 100: val_loss did not improve from 0.35661\n",
      "\u001b[1m583/583\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.9026 - loss: 0.3137 - val_accuracy: 0.8952 - val_loss: 0.3772\n",
      "Training completed in time: 0:01:52.692661\n"
     ]
    }
   ],
   "source": [
    "# training my model\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from datetime import datetime # to calculate time\n",
    "\n",
    "num_epochs=100\n",
    "num_batch_size=12\n",
    "checkpointer=ModelCheckpoint(filepath='saved_models/audio_classification.keras', verbose=1, save_best_only=True)\n",
    "\n",
    "start=datetime.now()\n",
    "\n",
    "# Print shapes of training and testing data\n",
    "print(\"X_train shape:\", X_train.shape)\n",
    "print(\"y_train shape:\", y_train.shape)\n",
    "print(\"X_test shape:\", X_test.shape)\n",
    "print(\"y_test shape:\", y_test.shape)\n",
    "\n",
    "# Fit the model\n",
    "model.fit(X_train, y_train, batch_size=num_batch_size, epochs=num_epochs, validation_data=(X_test, y_test), callbacks=[checkpointer])\n",
    "\n",
    "# Measure training duration\n",
    "duration = datetime.now() - start\n",
    "print(\"Training completed in time:\", duration)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "9f15c9fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.37720656394958496, 0.8952490091323853]\n"
     ]
    }
   ],
   "source": [
    "test_accuracy=model.evaluate(X_test,y_test,verbose=0)\n",
    "print(test_accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "1808d784",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-2.56894684e+02  1.13383400e+02 -4.88289452e+01  2.50609818e+01\n",
      " -2.57628899e+01  1.54200613e+00 -7.10614204e+00 -1.81738347e-01\n",
      " -1.47560425e-02 -7.82581758e+00 -2.37405705e+00 -1.72822118e+00\n",
      "  1.71940759e-01 -1.68439019e+00 -3.64913177e+00 -4.18054819e+00\n",
      " -9.37811470e+00  4.13374329e+00  1.45985734e+00 -4.83061522e-01\n",
      " -9.82409763e+00 -2.10626602e+00 -1.14755929e+00  8.88540804e-01\n",
      " -2.32707357e+00  3.00393514e-02 -2.34027958e+00  9.47026134e-01\n",
      " -9.86084223e-01  4.21894550e+00 -5.89243285e-02  2.22682309e+00\n",
      " -1.44474268e+00  4.89486027e+00  4.91630650e+00  5.02855730e+00\n",
      "  1.70231068e+00  3.72608805e+00 -1.90717041e+00  2.17339486e-01]\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step\n"
     ]
    }
   ],
   "source": [
    "filename=\"9223-2-0-10.wav\"\n",
    "prediction_feature=features_extractor(filename)\n",
    "print(prediction_feature)\n",
    "prediction_feature=prediction_feature.reshape(1,-1)\n",
    "predictions=model.predict(prediction_feature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "db06b330",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['dog_bark', 'children_playing', 'car_horn', 'air_conditioner',\n",
       "       'street_music', 'gun_shot', 'siren', 'engine_idling', 'jackhammer',\n",
       "       'drilling'], dtype=object)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata['class'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "4178db8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4.9768126e-04 1.1901873e-05 9.0722626e-01 2.8128926e-02 1.7410173e-03\n",
      "  5.7925778e-03 4.0987100e-02 4.1820196e-05 2.1601305e-03 1.3412564e-02]]\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "ans=np.argmax(predictions)\n",
    "print(predictions)\n",
    "print(ans)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "ec70b517",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MFCCs Features Shape: (40,)\n",
      "Reshaped MFCCs Features Shape: (40,)\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 17ms/step\n",
      "Predicted Label (Probabilities): [[1.2531088e-02 4.1798208e-04 5.7747019e-03 1.0363485e-02 4.2196922e-03\n",
      "  5.5376799e-03 8.9350915e-01 3.1282976e-02 3.0337036e-02 6.0261870e-03]]\n",
      "Predicted Class: ['gun_shot']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <audio  controls=\"controls\" >\n",
       "                    <source 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\" type=\"audio/wav\" />\n",
       "                    Your browser does not support the audio element.\n",
       "                </audio>\n",
       "              "
      ],
      "text/plain": [
       "<IPython.lib.display.Audio object>"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filename=\"dog-bark1.wav\"\n",
    "# audio, sample_rate = librosa.load(filename)\n",
    "# mfccs_features = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40)\n",
    "# mfccs_scaled_features = np.mean(mfccs_features.T,axis=0)\n",
    "# print(mfccs_scaled_features)\n",
    "# mfccs_scaled_features=mfccs_scaled_features.reshape(-1,1)\n",
    "# mfccs_scaled_features = mfccs_scaled_features.squeeze()  # Remove the extra dimension\n",
    "# print(mfccs_scaled_features.shape)  # Verify the shape is now (N,)\n",
    "\n",
    "# predicted_label=model.predict(mfccs_scaled_features)\n",
    "# print(predicted_label)\n",
    "\n",
    "# prediction_class=labelencoder.inverse_transform(predicted_label)\n",
    "# prediction_class\n",
    "\n",
    "# import pyaudio\n",
    "# import wave\n",
    "\n",
    "# def record_audio(file_name, duration=10, channels=1, sample_rate=44100, chunk_size=1024, format=pyaudio.paInt16):\n",
    "#     # Initialize PyAudio\n",
    "#     audio = pyaudio.PyAudio()\n",
    "\n",
    "#     # Open stream\n",
    "#     stream = audio.open(format=format,\n",
    "#                         channels=channels,\n",
    "#                         rate=sample_rate,\n",
    "                        \n",
    "#                         input=True,\n",
    "#                         frames_per_buffer=chunk_size)\n",
    "\n",
    "#     print(\"Recording...\")\n",
    "\n",
    "#     frames = []\n",
    "\n",
    "#     # Record audio\n",
    "#     for i in range(0, int(sample_rate / chunk_size * duration)):\n",
    "#         data = stream.read(chunk_size)\n",
    "#         frames.append(data)\n",
    "\n",
    "#     print(\"Finished recording.\")\n",
    "\n",
    "#     # Stop and close the stream\n",
    "#     stream.stop_stream()\n",
    "#     stream.close()\n",
    "\n",
    "#     # Terminate PyAudio\n",
    "#     audio.terminate()\n",
    "\n",
    "#     # Save the recorded audio as a WAV file\n",
    "#     with wave.open(file_name, 'wb') as wf:\n",
    "#         wf.setnchannels(channels)\n",
    "#         wf.setsampwidth(audio.get_sample_size(format))\n",
    "#         wf.setframerate(sample_rate)\n",
    "#         wf.writeframes(b''.join(frames))\n",
    "\n",
    "# if __name__ == \"__main__\":\n",
    "#     file_name = \"recorded_audio.wav\"\n",
    "#     record_audio(file_name)\n",
    "#     print(f\"Audio recorded and saved as {file_name}\")\n",
    "    \n",
    "    \n",
    "filename = \"7064-6-1-0.wav\" # for gunshot \n",
    "# filename = \"dog-bark1.wav\" # for dog bark\n",
    "# filename=\"9223-2-0-10.wav\" # for children Playing \n",
    "\n",
    "\n",
    "\n",
    "audio, sample_rate = librosa.load(filename)\n",
    "mfccs_features = librosa.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40)\n",
    "mfccs_scaled_features = np.mean(mfccs_features.T, axis=0)\n",
    "print(\"MFCCs Features Shape:\", mfccs_scaled_features.shape)\n",
    "\n",
    "# Reshape MFCC features and remove extra dimension\n",
    "mfccs_scaled_features = mfccs_scaled_features.reshape(-1, 1)\n",
    "mfccs_scaled_features = mfccs_scaled_features.squeeze()\n",
    "print(\"Reshaped MFCCs Features Shape:\", mfccs_scaled_features.shape)\n",
    "\n",
    "# Make predictions using the trained model\n",
    "predicted_label = model.predict(mfccs_scaled_features.reshape(1, -1))\n",
    "print(\"Predicted Label (Probabilities):\", predicted_label)\n",
    "\n",
    "\n",
    "# Decode the predicted label using the label encoder\n",
    "predicted_class = labelencoder.inverse_transform(np.argmax(predicted_label, axis=1))\n",
    "print(\"Predicted Class:\", predicted_class)\n",
    "# ipd.Audio(filename) # shows the file directly of audio \n",
    "\n",
    "\n",
    "import pickle\n",
    "with open('model_audio_classification.pkl', 'wb') as file:\n",
    "    pickle.dump(model, file)\n",
    "# predicted_class=\"Engine Idling\"\n",
    "\n",
    "from win32com.client import Dispatch\n",
    "\n",
    "speak = Dispatch(\"SAPI.SpVoice\").Speak\n",
    "  \n",
    "speak(f\"It is a  {predicted_class}\")\n",
    "\n",
    "\n",
    "if(predicted_class == 'gun_shot'):\n",
    "    speak(\" gunshot Detected  !!! \\n Calling the Police \\n !!! Closing all the gates  \\n Mark Your Position \\n\")\n",
    "\n",
    "ipd.Audio(filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "0cf97f88",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('model_audio_classification.pkl', 'rb') as file:\n",
    "    loaded_model = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "81b640ec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m55/55\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
      "[[8.9952355e-06 2.7942160e-06 3.3377412e-06 ... 6.3878791e-10\n",
      "  5.5768751e-08 9.4764746e-06]\n",
      " [2.9399114e-20 1.5190315e-04 1.0288749e-09 ... 2.7173601e-17\n",
      "  5.2216975e-10 3.0408903e-06]\n",
      " [1.1323504e-09 1.2934469e-08 7.3538563e-06 ... 2.5119977e-03\n",
      "  3.6837525e-12 3.8320632e-05]\n",
      " ...\n",
      " [8.2576390e-10 9.9999881e-01 6.8337336e-09 ... 3.1734698e-16\n",
      "  1.8163733e-09 4.1641960e-07]\n",
      " [3.8759462e-03 2.7926973e-04 6.4804238e-01 ... 2.8910610e-04\n",
      "  3.7308672e-04 3.4338757e-01]\n",
      " [1.6126921e-04 1.0518594e-04 5.1373172e-01 ... 6.4235778e-08\n",
      "  2.3913207e-03 1.9133529e-03]]\n"
     ]
    }
   ],
   "source": [
    "predictions = loaded_model.predict(X_test)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4296ced0-e79e-4651-80c8-be3fce3ac354",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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