training.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.2"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0qSV8jtCLJMU",
        "outputId": "89c43f0a-583b-4ee2-f77e-0ba2ac344f62"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ],
      "id": "0qSV8jtCLJMU",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nguSEkkY3ojl",
        "outputId": "252b3b10-24ab-4e04-fc90-9abc9f0709f2"
      },
      "source": [
        "!pip install librosa numpy matplotlib pydub audiomentations pySmartDL "
      ],
      "id": "nguSEkkY3ojl",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: librosa in /usr/local/lib/python3.7/dist-packages (0.8.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.19.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.2.2)\n",
            "Collecting pydub\n",
            "  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
            "Collecting audiomentations\n",
            "  Downloading audiomentations-0.18.0-py3-none-any.whl (29 kB)\n",
            "Collecting pySmartDL\n",
            "  Downloading pySmartDL-1.3.4-py3-none-any.whl (20 kB)\n",
            "Requirement already satisfied: resampy>=0.2.2 in /usr/local/lib/python3.7/dist-packages (from librosa) (0.2.2)\n",
            "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (2.1.9)\n",
            "Requirement already satisfied: joblib>=0.14 in /usr/local/lib/python3.7/dist-packages (from librosa) (1.0.1)\n",
            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (0.22.2.post1)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (1.4.1)\n",
            "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (4.4.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (21.0)\n",
            "Requirement already satisfied: pooch>=1.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (1.5.1)\n",
            "Requirement already satisfied: numba>=0.43.0 in /usr/local/lib/python3.7/dist-packages (from librosa) (0.51.2)\n",
            "Requirement already satisfied: soundfile>=0.10.2 in /usr/local/lib/python3.7/dist-packages (from librosa) (0.10.3.post1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba>=0.43.0->librosa) (57.4.0)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba>=0.43.0->librosa) (0.34.0)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->librosa) (2.4.7)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from pooch>=1.0->librosa) (2.23.0)\n",
            "Requirement already satisfied: appdirs in /usr/local/lib/python3.7/dist-packages (from pooch>=1.0->librosa) (1.4.4)\n",
            "Requirement already satisfied: six>=1.3 in /usr/local/lib/python3.7/dist-packages (from resampy>=0.2.2->librosa) (1.15.0)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.7/dist-packages (from soundfile>=0.10.2->librosa) (1.14.6)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.0->soundfile>=0.10.2->librosa) (2.20)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.3.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->pooch>=1.0->librosa) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->pooch>=1.0->librosa) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->pooch>=1.0->librosa) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->pooch>=1.0->librosa) (2021.5.30)\n",
            "Installing collected packages: pySmartDL, pydub, audiomentations\n",
            "Successfully installed audiomentations-0.18.0 pySmartDL-1.3.4 pydub-0.25.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1e28654f"
      },
      "source": [
        "import librosa\n",
        "import librosa.display # for waveplots, spectograms, etc\n",
        "import IPython.display as ipd # for playing files within pythonimport numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "%matplotlib inline"
      ],
      "id": "1e28654f",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-ESVZEzgF1k"
      },
      "source": [
        "SAMPLE_RATE = 16000\n",
        "AUDIO_WINDOW = 1.0 #seconds\n",
        "AUDIO_LENGTH = int(AUDIO_WINDOW * SAMPLE_RATE)"
      ],
      "id": "p-ESVZEzgF1k",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_qCGuUry9t7",
        "outputId": "d76e987d-19c3-4582-84cc-3668be401b19"
      },
      "source": [
        "import base64\n",
        "from os import system\n",
        "from time import sleep\n",
        "\n",
        "def create_onedrive_directdownload (onedrive_link):\n",
        "    data_bytes64 = base64.b64encode(bytes(onedrive_link, 'utf-8'))\n",
        "    data_bytes64_String = data_bytes64.decode('utf-8').replace('/','_').replace('+','-').rstrip(\"=\")\n",
        "    resultUrl = f\"https://api.onedrive.com/v1.0/shares/u!{data_bytes64_String}/root/content\"\n",
        "    return resultUrl\n",
        "\n",
        "dataset_url = create_onedrive_directdownload(\"https://1drv.ms/u/s!AuXsUYf_hDUcgq4xD30EZ1Xanf6uhw?e=sv9u8e\")\n",
        "noise_url = create_onedrive_directdownload(\"https://1drv.ms/u/s!AuXsUYf_hDUcgq57tQZxYjXmoYHSVw?e=a6ZLW8\")\n",
        "\n",
        "from pySmartDL import SmartDL # or ‘~/Downloads/’ on linux\n",
        "\n",
        "for i in range(3):\n",
        "  try:\n",
        "    obj = SmartDL(dataset_url, \"/content/dataset_format_fixed.zip\") \n",
        "    print(obj.start())\n",
        "    break\n",
        "  except :\n",
        "    sleep(0.5)\n",
        "\n",
        "for i in range(3):\n",
        "  try:\n",
        "    obj = SmartDL(noise_url, \"/content/noise_format_fixed.zip\")\n",
        "    obj.start()\n",
        "  except :\n",
        "    sleep(0.5)"
      ],
      "id": "1_qCGuUry9t7",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 286.0 MB / 344.0 MB @ 79.7 MB/s [##############----] [83.1%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 294.4 MB / 344.0 MB @ 79.7 MB/s [###############---] [85.6%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 302.2 MB / 344.0 MB @ 79.6 MB/s [###############---] [87.8%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 310.4 MB / 344.0 MB @ 79.6 MB/s [################--] [90.2%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 319.6 MB / 344.0 MB @ 80.1 MB/s [################--] [92.9%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 329.8 MB / 344.0 MB @ 81.2 MB/s [#################-] [95.9%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 330.3 MB / 344.0 MB @ 78.8 MB/s [#################-] [96.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 330.3 MB / 344.0 MB @ 76.2 MB/s [#################-] [96.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 333.3 MB / 344.0 MB @ 74.3 MB/s [#################-] [96.9%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 343.2 MB / 344.0 MB @ 73.8 MB/s [#################-] [99.8%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 344.0 MB / 344.0 MB @ 73.8 MB/s [##################] [100%, 0s left]    \n",
            "None\n",
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\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 0 bytes / 233.6 MB @ 0 bytes/s [------------------] [0.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 0 bytes / 233.6 MB @ 0 bytes/s [------------------] [0.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 0 bytes / 233.6 MB @ 0 bytes/s [------------------] [0.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 816 kB / 233.6 MB @ 907 kB/s [------------------] [0.3%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 6.1 MB / 233.6 MB @ 6.1 MB/s [------------------] [2.6%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 13.8 MB / 233.6 MB @ 12.6 MB/s [#-----------------] [5.9%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 21.7 MB / 233.6 MB @ 18.1 MB/s [#-----------------] [9.3%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 28.8 MB / 233.6 MB @ 22.2 MB/s [##----------------] [12.3%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 37.8 MB / 233.6 MB @ 27.0 MB/s [##----------------] [16.2%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 45.9 MB / 233.6 MB @ 30.6 MB/s [###---------------] [19.6%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 53.3 MB / 233.6 MB @ 33.3 MB/s [####--------------] [22.8%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 60.6 MB / 233.6 MB @ 35.6 MB/s [####--------------] [25.9%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 67.5 MB / 233.6 MB @ 37.5 MB/s [#####-------------] [28.9%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 75.0 MB / 233.6 MB @ 39.5 MB/s [#####-------------] [32.1%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 82.1 MB / 233.6 MB @ 41.1 MB/s [######------------] [35.1%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 90.9 MB / 233.6 MB @ 43.3 MB/s [#######-----------] [38.9%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 98.9 MB / 233.6 MB @ 45.0 MB/s [#######-----------] [42.4%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 107.7 MB / 233.6 MB @ 46.8 MB/s [########----------] [46.1%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 116.6 MB / 233.6 MB @ 48.6 MB/s [########----------] [49.9%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 125.1 MB / 233.6 MB @ 50.0 MB/s [#########---------] [53.6%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 133.6 MB / 233.6 MB @ 51.4 MB/s [##########--------] [57.2%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 140.5 MB / 233.6 MB @ 52.0 MB/s [##########--------] [60.2%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 148.4 MB / 233.6 MB @ 53.0 MB/s [###########-------] [63.5%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 156.5 MB / 233.6 MB @ 54.0 MB/s [############------] [67.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 163.6 MB / 233.6 MB @ 54.5 MB/s [############------] [70.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 171.2 MB / 233.6 MB @ 57.1 MB/s [#############-----] [73.3%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 178.2 MB / 233.6 MB @ 59.4 MB/s [#############-----] [76.3%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 185.6 MB / 233.6 MB @ 61.9 MB/s [##############----] [79.4%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 193.1 MB / 233.6 MB @ 64.4 MB/s [##############----] [82.7%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 200.7 MB / 233.6 MB @ 66.9 MB/s [###############---] [85.9%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 209.0 MB / 233.6 MB @ 69.7 MB/s [################--] [89.5%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 216.7 MB / 233.6 MB @ 72.2 MB/s [################--] [92.8%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 225.5 MB / 233.6 MB @ 75.2 MB/s [#################-] [96.5%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 233.6 MB / 233.6 MB @ 75.2 MB/s [##################] [100%, 0s left]    \n",
            "[*] 0 bytes / 233.6 MB @ 0 bytes/s [------------------] [0.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 0 bytes / 233.6 MB @ 0 bytes/s [------------------] [0.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 0 bytes / 233.6 MB @ 0 bytes/s [------------------] [0.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 0 bytes / 233.6 MB @ 0 bytes/s [------------------] [0.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 0 bytes / 233.6 MB @ 0 bytes/s [------------------] [0.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 0 bytes / 233.6 MB @ 0 bytes/s [------------------] [0.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 0 bytes / 233.6 MB @ 0 bytes/s [------------------] [0.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 0 bytes / 233.6 MB @ 0 bytes/s [------------------] [0.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 448 kB / 233.6 MB @ 498 kB/s [------------------] [0.2%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 5.7 MB / 233.6 MB @ 5.7 MB/s [------------------] [2.4%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 14.2 MB / 233.6 MB @ 12.9 MB/s [#-----------------] [6.1%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 22.4 MB / 233.6 MB @ 18.7 MB/s [#-----------------] [9.6%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 30.4 MB / 233.6 MB @ 23.4 MB/s [##----------------] [13.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 38.2 MB / 233.6 MB @ 27.3 MB/s [##----------------] [16.4%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 46.2 MB / 233.6 MB @ 30.8 MB/s [###---------------] [19.8%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 54.2 MB / 233.6 MB @ 33.9 MB/s [####--------------] [23.2%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 62.2 MB / 233.6 MB @ 36.6 MB/s [####--------------] [26.6%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 71.1 MB / 233.6 MB @ 39.5 MB/s [#####-------------] [30.4%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 79.7 MB / 233.6 MB @ 42.0 MB/s [######------------] [34.1%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 88.0 MB / 233.6 MB @ 44.0 MB/s [######------------] [37.7%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 96.5 MB / 233.6 MB @ 45.9 MB/s [#######-----------] [41.3%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 104.0 MB / 233.6 MB @ 47.3 MB/s [########----------] [44.5%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 112.9 MB / 233.6 MB @ 49.1 MB/s [########----------] [48.3%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 120.0 MB / 233.6 MB @ 50.0 MB/s [#########---------] [51.4%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 129.8 MB / 233.6 MB @ 51.9 MB/s [#########---------] [55.6%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 138.0 MB / 233.6 MB @ 53.1 MB/s [##########--------] [59.1%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 146.6 MB / 233.6 MB @ 54.3 MB/s [###########-------] [62.8%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 155.3 MB / 233.6 MB @ 55.5 MB/s [###########-------] [66.5%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 162.7 MB / 233.6 MB @ 56.1 MB/s [############------] [69.6%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 170.6 MB / 233.6 MB @ 56.9 MB/s [#############-----] [73.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 177.5 MB / 233.6 MB @ 59.2 MB/s [#############-----] [76.0%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 185.0 MB / 233.6 MB @ 61.7 MB/s [##############----] [79.2%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 192.8 MB / 233.6 MB @ 64.3 MB/s [##############----] [82.5%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 201.2 MB / 233.6 MB @ 67.1 MB/s [###############---] [86.1%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 210.1 MB / 233.6 MB @ 70.0 MB/s [################--] [89.9%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 218.6 MB / 233.6 MB @ 72.9 MB/s [################--] [93.6%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 227.4 MB / 233.6 MB @ 75.8 MB/s [#################-] [97.3%, 0s left]   \b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b [*] 233.6 MB / 233.6 MB @ 75.8 MB/s [##################] [100%, 0s left]    \n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ye2Q8qJThnDw"
      },
      "source": [
        "!unzip -qq dataset_format_fixed.zip\n",
        "!unzip -qq noise_format_fixed.zip\n",
        "!rm *.zip"
      ],
      "id": "ye2Q8qJThnDw",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a7692b5e"
      },
      "source": [
        "import random\n",
        "import numpy as np\n",
        "import soundfile as sf\n",
        "\n",
        "def randomCrop(x:np.array,length=AUDIO_LENGTH)->np.array :\n",
        "    assert(x.shape[0]>length)\n",
        "    frontBits = random.randint(0,x.shape[0]-length) \n",
        "    return x[frontBits:frontBits+length]\n",
        "\n",
        "def addPadding(x:np.array,length=AUDIO_LENGTH)->np.array :\n",
        "    assert(x.shape[0]<length)\n",
        "    bitCountToBeAdded = length - x.shape[0]\n",
        "    frontBits = random.randint(0,bitCountToBeAdded)\n",
        "    #print(frontBits, bitCountToBeAdded-frontBits)\n",
        "    new_x = np.append(np.zeros(frontBits),x)\n",
        "    new_x = np.append(new_x,np.zeros(bitCountToBeAdded-frontBits))\n",
        "    return new_x\n",
        "\n",
        "def removeExistingPadding(x:np.array)->np.array:\n",
        "    lastZeroBitBeforeAudio = 0 \n",
        "    firstZeroBitAfterAudio = len(x)\n",
        "    for i in range(len(x)):\n",
        "      if x[i]==0:\n",
        "        lastZeroBitBeforeAudio = i\n",
        "      else:\n",
        "        break\n",
        "    for i in range(len(x)-1,1,-1):\n",
        "      if x[i]==0:\n",
        "        firstZeroBitAfterAudio = i\n",
        "      else:\n",
        "        break\n",
        "    return x[lastZeroBitBeforeAudio:firstZeroBitAfterAudio]\n",
        "\n",
        "\n",
        "def fixPaddingIssues(x:np.array,length=AUDIO_LENGTH)-> np.array:\n",
        "    x = removeExistingPadding(x)\n",
        "    #x = randomAugumentation(x)\n",
        "    #print(\"Preprocessing Shape\",x.shape[0])\n",
        "    if(x.shape[0]>length):\n",
        "      return randomCrop(x,length=length)\n",
        "    elif(x.shape[0]<length):\n",
        "      return addPadding(x,length=length)\n",
        "    else:\n",
        "      return x\n",
        "\n",
        "def addNoise(x:np.array,noise:np.array,noise_factor = 0.4)-> np.array:\n",
        "  assert(x.shape[0]==noise.shape[0])\n",
        "  out = (1-noise_factor)*x/x.max() + noise_factor*(noise/noise.max())\n",
        "  return out/out.max()\n",
        "\n",
        "def splitNoiseFileToChunks(filename:str,target_folder:str,count=100,sr=16000):\n",
        "  #print(filename)\n",
        "  noiseAudio, _ = librosa.load(filename,sr=sr)\n",
        "  for i in range(count):\n",
        "    noiseAudioCrop = randomCrop(noiseAudio)\n",
        "    outFilePath = target_folder+\"/\"+(f\"{'.'.join(filename.split('.')[:-1])}_{i}.wav\").split(\"/\")[-1]\n",
        "    #print(filename,outFilePath)\n",
        "    sf.write(outFilePath, noiseAudioCrop, sr,'PCM_24')\n",
        "    count=count+1\n",
        "\n"
      ],
      "id": "a7692b5e",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hS-_VbyeQKrO",
        "outputId": "6ca0c7d7-1c00-4b5e-d841-7b50a3f5ebba"
      },
      "source": [
        "from os import listdir, mkdir\n",
        "from os.path import isdir\n",
        "from os import system as run_sys_command\n",
        "import librosa\n",
        "\n",
        "\n",
        "if(isdir(\"./NoiseChunked\")): \n",
        "  run_sys_command(\"rm -r NoiseChunked/\")\n",
        "\n",
        "mkdir(\"./NoiseChunked\") \n",
        "\n",
        "print(f\"Splitting Noise into {AUDIO_WINDOW} second(s) Chunks for quicker loading\")\n",
        "\n",
        "noise_categories = listdir(\"./noise_files_format_fixed/\") \n",
        "for i , noise_category in enumerate(noise_categories):\n",
        "    source_path = \"./noise_files_format_fixed/\" + noise_category\n",
        "    target_path = \"./NoiseChunked/\" + noise_category\n",
        "    print(f\"{len(noise_categories)-i} noise categories left\")\n",
        "    if(isdir(source_path)):\n",
        "        mkdir(target_path)\n",
        "        audioFiles = listdir(source_path)\n",
        "\n",
        "        for j , audioFile in enumerate(audioFiles):\n",
        "            print(f\"{j+1} out of {len(audioFiles)} \")\n",
        "            srcFilePath = f\"{source_path}/{audioFile}\"\n",
        "            splitNoiseFileToChunks(srcFilePath,target_path)\n",
        "\n",
        "print(\"\\nSplitted Up Amigo\")"
      ],
      "id": "hS-_VbyeQKrO",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Splitting Noise into 1.0 second(s) Chunks for quicker loading\n",
            "4 noise categories left\n",
            "1 out of 5 \n",
            "2 out of 5 \n",
            "3 out of 5 \n",
            "4 out of 5 \n",
            "5 out of 5 \n",
            "3 noise categories left\n",
            "1 out of 6 \n",
            "2 out of 6 \n",
            "3 out of 6 \n",
            "4 out of 6 \n",
            "5 out of 6 \n",
            "6 out of 6 \n",
            "2 noise categories left\n",
            "1 out of 5 \n",
            "2 out of 5 \n",
            "3 out of 5 \n",
            "4 out of 5 \n",
            "5 out of 5 \n",
            "1 noise categories left\n",
            "\n",
            "Splitted Up Amigo\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "A8kIJuPFlgEm",
        "outputId": "87fe5b84-c927-45f0-8575-7c5edf9188bf"
      },
      "source": [
        "import random\n",
        "from audiomentations import Compose , TimeStretch , PitchShift , Shift \n",
        "\n",
        "TimeStretchMin = Compose([\n",
        "    TimeStretch(min_rate=0.97,max_rate=0.99,p=0.3),\n",
        "])\n",
        "\n",
        "TimeStretchMax = Compose([\n",
        "    TimeStretch(min_rate=1.1,max_rate=1.2,p=1),\n",
        "])\n",
        "\n",
        "PitchShiftMin = Compose([\n",
        "    PitchShift(min_semitones=-2,max_semitones=-1,p=1),\n",
        "])\n",
        "\n",
        "\n",
        "PitchShiftMax = Compose([\n",
        "    PitchShift(min_semitones=1,max_semitones=2,p=0.3),\n",
        "])\n",
        "\n",
        "def empty(x,sample_rate=SAMPLE_RATE):\n",
        "  x\n",
        "\n",
        "Augumentations = [\n",
        "    TimeStretchMin,TimeStretchMax,PitchShiftMin,PitchShiftMax\n",
        "]\n",
        "\n",
        "def randomAugumentation(x,sr=SAMPLE_RATE):\n",
        "    choice1 = random.randint(0,1)\n",
        "    choice2 = random.randint(0,1)\n",
        "    return Augumentations[choice1](\n",
        "        Augumentations[2+choice2](\n",
        "            x,\n",
        "            sample_rate=sr\n",
        "        ),\n",
        "        sample_rate=sr\n",
        "    )\n",
        "x, _ = librosa.load(\"/content/dataset_format_fixed/acquire/Veena_SIRI.wav\",sr=SAMPLE_RATE)\n",
        "ipd.Audio(Augumentations[2](x,sample_rate=SAMPLE_RATE),rate=SAMPLE_RATE)\n",
        "#ipd.Audio(x,rate=SAMPLE_RATE)"
      ],
      "id": "A8kIJuPFlgEm",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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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>"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u0lSyA1mN5q4",
        "outputId": "9db83be9-3735-4f79-c91f-e6ee1c9f2451"
      },
      "source": [
        "##Tflite Compatible audio to log mel spectrogram converter\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import math\n",
        "import fractions\n",
        "import datetime\n",
        "\n",
        "LOG_MEL_MEAN = 1.4\n",
        "LOG_MEL_STD = 1.184\n",
        "\n",
        "\n",
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "# import fractions\n",
        "import math\n",
        "\n",
        "\n",
        "\n",
        "def _dft_matrix(dft_length):\n",
        "  \"\"\"Calculate the full DFT matrix in numpy.\"\"\"\n",
        "  omega = (0 + 1j) * 2.0 * np.pi / float(dft_length)\n",
        "  # Don't include 1/sqrt(N) scaling, tf.signal.rfft doesn't apply it.\n",
        "  return np.exp(omega * np.outer(np.arange(dft_length), np.arange(dft_length)))\n",
        "\n",
        "\n",
        "def _naive_rdft(signal_tensor, fft_length, padding='center'):\n",
        "    \"\"\"Implement real-input Fourier Transform by matmul.\"\"\"\n",
        "    # We are right-multiplying by the DFT matrix, and we are keeping\n",
        "    # only the first half (\"positive frequencies\").\n",
        "    # So discard the second half of rows, but transpose the array for\n",
        "    # right-multiplication.\n",
        "    # The DFT matrix is symmetric, so we could have done it more\n",
        "    # directly, but this reflects our intention better.\n",
        "    complex_dft_matrix_kept_values = _dft_matrix(fft_length)[:(fft_length // 2 + 1), :].transpose()\n",
        "    real_dft_tensor = tf.constant(np.real(complex_dft_matrix_kept_values).astype(np.float32), name='real_dft_matrix')\n",
        "    imag_dft_tensor = tf.constant(np.imag(complex_dft_matrix_kept_values).astype(np.float32), name='imaginary_dft_matrix')\n",
        "    signal_frame_length = signal_tensor.shape[-1]#.value\n",
        "    half_pad = (fft_length - signal_frame_length) // 2\n",
        "\n",
        "    if padding == 'center':\n",
        "        # Center-padding.\n",
        "        pad_values = tf.concat([\n",
        "            tf.zeros([tf.rank(signal_tensor) - 1, 2], tf.int32),\n",
        "            [[half_pad, fft_length - signal_frame_length - half_pad]]\n",
        "        ], axis=0)\n",
        "    elif padding == 'right':\n",
        "        # Right-padding.\n",
        "        pad_values = tf.concat([\n",
        "            tf.zeros([tf.rank(signal_tensor) - 1, 2], tf.int32),\n",
        "            [[0, fft_length - signal_frame_length]]\n",
        "        ], axis=0)\n",
        "\n",
        "    padded_signal = tf.pad(signal_tensor, pad_values)\n",
        "    \n",
        "    result_real_part = tf.matmul(padded_signal, real_dft_tensor)\n",
        "    result_imag_part = tf.matmul(padded_signal, imag_dft_tensor)\n",
        "    \n",
        "    return result_real_part, result_imag_part\n",
        "\n",
        "\n",
        "def _fixed_frame(signal, frame_length, frame_step, first_axis=False):\n",
        "    \"\"\"tflite-compatible tf.signal.frame for fixed-size input.\n",
        "    Args:\n",
        "        signal: Tensor containing signal(s).\n",
        "        frame_length: Number of samples to put in each frame.\n",
        "        frame_step: Sample advance between successive frames.\n",
        "        first_axis: If true, framing is applied to first axis of tensor; otherwise,\n",
        "        it is applied to last axis.\n",
        "    Returns:\n",
        "        A new tensor where the last axis (or first, if first_axis) of input\n",
        "        signal has been replaced by a (num_frames, frame_length) array of individual\n",
        "        frames where each frame is drawn frame_step samples after the previous one.\n",
        "    Raises:\n",
        "        ValueError: if signal has an undefined axis length.  This routine only\n",
        "        supports framing of signals whose shape is fixed at graph-build time.\n",
        "    \"\"\"\n",
        "    signal_shape = signal.shape.as_list()\n",
        "    \n",
        "    if first_axis:\n",
        "        length_samples = signal_shape[0]\n",
        "    else:\n",
        "        length_samples = signal_shape[-1]\n",
        "    \n",
        "    if length_samples <= 0:\n",
        "        raise ValueError('fixed framing requires predefined constant signal length')\n",
        "    \n",
        "    num_frames = max(0, 1 + (length_samples - frame_length) // frame_step)\n",
        "    \n",
        "    if first_axis:\n",
        "        inner_dimensions = signal_shape[1:]\n",
        "        result_shape = [num_frames, frame_length] + inner_dimensions\n",
        "        gather_axis = 0\n",
        "    else:\n",
        "        outer_dimensions = signal_shape[:-1]\n",
        "        result_shape = outer_dimensions + [num_frames, frame_length]\n",
        "        # Currently tflite's gather only supports axis==0, but that may still\n",
        "        # work if we want the last of 1 axes.\n",
        "        gather_axis = len(outer_dimensions)\n",
        "\n",
        "    subframe_length = math.gcd(frame_length, frame_step)  # pylint: disable=deprecated-method\n",
        "    subframes_per_frame = frame_length // subframe_length\n",
        "    subframes_per_hop = frame_step // subframe_length\n",
        "    num_subframes = length_samples // subframe_length\n",
        "\n",
        "    if first_axis:\n",
        "        trimmed_input_size = [num_subframes * subframe_length] + inner_dimensions\n",
        "        subframe_shape = [num_subframes, subframe_length] + inner_dimensions\n",
        "    else:\n",
        "        trimmed_input_size = outer_dimensions + [num_subframes * subframe_length]\n",
        "        subframe_shape = outer_dimensions + [num_subframes, subframe_length]\n",
        "    subframes = tf.reshape(\n",
        "        tf.slice(\n",
        "            signal,\n",
        "            begin=np.zeros(len(signal_shape), np.int32),\n",
        "            size=trimmed_input_size), subframe_shape)\n",
        "\n",
        "    # frame_selector is a [num_frames, subframes_per_frame] tensor\n",
        "    # that indexes into the appropriate frame in subframes. For example:\n",
        "    # [[0, 0, 0, 0], [2, 2, 2, 2], [4, 4, 4, 4]]\n",
        "    frame_selector = np.reshape(np.arange(num_frames) * subframes_per_hop, [num_frames, 1])\n",
        "\n",
        "    # subframe_selector is a [num_frames, subframes_per_frame] tensor\n",
        "    # that indexes into the appropriate subframe within a frame. For example:\n",
        "    # [[0, 1, 2, 3], [0, 1, 2, 3], [0, 1, 2, 3]]\n",
        "    subframe_selector = np.reshape(np.arange(subframes_per_frame), [1, subframes_per_frame])\n",
        "\n",
        "    # Adding the 2 selector tensors together produces a [num_frames,\n",
        "    # subframes_per_frame] tensor of indices to use with tf.gather to select\n",
        "    # subframes from subframes. We then reshape the inner-most subframes_per_frame\n",
        "    # dimension to stitch the subframes together into frames. For example:\n",
        "    # [[0, 1, 2, 3], [2, 3, 4, 5], [4, 5, 6, 7]].\n",
        "    selector = frame_selector + subframe_selector\n",
        "    frames = tf.reshape(tf.gather(subframes, selector.astype(np.int32), axis=gather_axis), result_shape)\n",
        "    \n",
        "    return frames\n",
        "\n",
        "\n",
        "def _stft_tflite(signal, frame_length, frame_step, fft_length):\n",
        "    \"\"\"tflite-compatible implementation of tf.signal.stft.\n",
        "    Compute the short-time Fourier transform of a 1D input while avoiding tf ops\n",
        "    that are not currently supported in tflite (Rfft, Range, SplitV).\n",
        "    fft_length must be fixed. A Hann window is of frame_length is always\n",
        "    applied.\n",
        "    Since fixed (precomputed) framing must be used, signal.shape[-1] must be a\n",
        "    specific value (so \"?\"/None is not supported).\n",
        "    Args:\n",
        "        signal: 1D tensor containing the time-domain waveform to be transformed.\n",
        "        frame_length: int, the number of points in each Fourier frame.\n",
        "        frame_step: int, the number of samples to advance between successive frames.\n",
        "        fft_length: int, the size of the Fourier transform to apply.\n",
        "    Returns:\n",
        "        Two (num_frames, fft_length) tensors containing the real and imaginary parts\n",
        "        of the short-time Fourier transform of the input signal.\n",
        "    \"\"\"\n",
        "    # Make the window be shape (1, frame_length) instead of just frame_length\n",
        "    # in an effort to help the tflite broadcast logic.\n",
        "    window = tf.reshape(\n",
        "        tf.constant(\n",
        "            (0.5 - 0.5 * np.cos(2 * np.pi * np.arange(0, 1.0, 1.0 / frame_length))\n",
        "            ).astype(np.float32),\n",
        "            name='window'), [1, frame_length])\n",
        "    \n",
        "    framed_signal = _fixed_frame(signal, frame_length, frame_step, first_axis=False)\n",
        "    framed_signal *= window\n",
        "    \n",
        "    real_spectrogram, imag_spectrogram = _naive_rdft(framed_signal, fft_length)\n",
        "    \n",
        "    return real_spectrogram, imag_spectrogram\n",
        "\n",
        "\n",
        "def _stft_magnitude_tflite(signals, frame_length, frame_step, fft_length):\n",
        "    \"\"\"Calculate spectrogram avoiding tflite incompatible ops.\"\"\"\n",
        "    real_stft, imag_stft = _stft_tflite(signals, frame_length, frame_step, fft_length)\n",
        "    stft_magnitude = tf.sqrt(tf.add(real_stft * real_stft, imag_stft * imag_stft), name='magnitude_spectrogram')\n",
        "    \n",
        "    return stft_magnitude\n",
        "\n",
        "\n",
        "def build_mel_calculation_graph(waveform):\n",
        "    \"\"\"Compute log mel spectrogram of a 1-D waveform.\"\"\"\n",
        "    with tf.name_scope('log_mel_features'):\n",
        "        # waveform has shape [<# samples>]\n",
        "\n",
        "        window_length_samples = 400 #int(round(params.SAMPLE_RATE * params.STFT_WINDOW_SECONDS))\n",
        "        hop_length_samples = 160    #int(round(params.SAMPLE_RATE * params.STFT_HOP_SECONDS))\n",
        "        MEL_BANDS   = 64            #params.MEL_BANDS\n",
        "        SAMPLE_RATE = 16000         #params.SAMPLE_RATE\n",
        "        LOG_OFFSET  = 0.001         #params.LOG_OFFSET\n",
        "        MEL_MIN_HZ  = 50            #params.MEL_MIN_HZ\n",
        "        MEL_MAX_HZ  = 8000         #params.MEL_MAX_HZ\n",
        "        fft_length  = 2 ** int(np.ceil(np.log(window_length_samples) / np.log(2.0)))\n",
        "        num_spectrogram_bins = fft_length // 2 + 1\n",
        "        magnitude_spectrogram = _stft_magnitude_tflite(\n",
        "            signals=waveform, \n",
        "            frame_length=window_length_samples, \n",
        "            frame_step=hop_length_samples, \n",
        "            fft_length=fft_length)\n",
        "\n",
        "        linear_to_mel_weight_matrix = tf.signal.linear_to_mel_weight_matrix(\n",
        "            num_mel_bins=MEL_BANDS,\n",
        "            num_spectrogram_bins=num_spectrogram_bins,\n",
        "            sample_rate=SAMPLE_RATE,\n",
        "            lower_edge_hertz=MEL_MIN_HZ,\n",
        "            upper_edge_hertz=MEL_MAX_HZ)\n",
        "\n",
        "        mel_spectrogram = tf.matmul(\n",
        "            magnitude_spectrogram, linear_to_mel_weight_matrix)\n",
        "        log_mel_spectrogram = (tf.math.log(\n",
        "            mel_spectrogram + LOG_OFFSET) - LOG_MEL_MEAN)/LOG_MEL_STD\n",
        "        # log_mel_spectrogram has shape [<# STFT frames>, MEL_BANDS]\n",
        "\n",
        "        return np.expand_dims(log_mel_spectrogram,axis=-1)\n",
        "\n",
        "t=np.random.random(16000)\n",
        "t=tf.constant(t,dtype=tf.float32)\n",
        "t=build_mel_calculation_graph(t)\n",
        "print(t.shape)"
      ],
      "id": "u0lSyA1mN5q4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(98, 64, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-FsvAoeb1NQK"
      },
      "source": [
        "from enum import Enum\n",
        "import random, os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "#wordsInDataset = os.listdir(datasetPath)\n",
        "#wordsInDataset = random.sample(wordsInDataset,len(wordsInDataset)) #Shuffling the word list\n",
        "\n",
        "def omitHiddenFiles(inpArray):\n",
        "  return [x for x in inpArray if '.'!=x[0]]\n",
        "\n",
        "class CustomDataGen(tf.keras.utils.Sequence):\n",
        "    \n",
        "    def __init__(self, chunkedNoisePath,datasetPath,\n",
        "                 batch_size = 16,\n",
        "                 training_generator = True,\n",
        "                 max_noise_factor = 0.2 ,\n",
        "                 min_noise_factor = 0.05,\n",
        "                 sampling_rate = 16000,\n",
        "                 spectrogram = True,\n",
        "                 shuffle=True,\n",
        "                 print_words = False):\n",
        "      \n",
        "        self.batch_size = batch_size\n",
        "        self.spectrogram = spectrogram\n",
        "        #self.input_size = input_size\n",
        "        self.shuffle = shuffle\n",
        "        self.sampling_rate = sampling_rate\n",
        "        self.chunkedNoisePath = chunkedNoisePath\n",
        "        self.typesOfNoise = omitHiddenFiles(os.listdir(self.chunkedNoisePath))\n",
        "        self.datasetPath = datasetPath\n",
        "        self.max_noise_factor = max_noise_factor\n",
        "        self.min_noise_factor = min_noise_factor\n",
        "        self.wordsInDataset = omitHiddenFiles(os.listdir(datasetPath))\n",
        "        self.print_words = print_words\n",
        "        countOfWords = len(self.wordsInDataset)\n",
        "\n",
        "        \n",
        "        if training_generator :\n",
        "          self.wordsInDataset = self.wordsInDataset[:int(0.9*countOfWords)]\n",
        "          print(\"train size: \",len(self.wordsInDataset))\n",
        "        else:\n",
        "          self.wordsInDataset = self.wordsInDataset[int(0.9*countOfWords):]\n",
        "          print(\"test size: \",len(self.wordsInDataset))\n",
        "\n",
        "        self.wordsInDataset = random.sample(\n",
        "            self.wordsInDataset,\n",
        "            len(self.wordsInDataset)\n",
        "        )\n",
        "\n",
        "       \n",
        "        self.n = 2 * len(self.wordsInDataset) #Because each word compares with itself for a +ve pair and compares with other for -ve pair making 2 such pairs for every word\n",
        "\n",
        "    def on_epoch_end(self):\n",
        "        #Randomizing word order on epoch end\n",
        "        self.wordsInDataset = random.sample(\n",
        "            self.wordsInDataset,\n",
        "            len(self.wordsInDataset)\n",
        "        )\n",
        "    \n",
        "    def giveJoinedAudio(self,word1:str,word2:str):\n",
        "      if(self.print_words):\n",
        "        print(word1,word2)\n",
        "      if(word1 == word2):\n",
        "        sample1, sample2 = random.sample(omitHiddenFiles(os.listdir(self.datasetPath+\"/\"+word1)),2)\n",
        "      else:\n",
        "        sample1 = random.choice(omitHiddenFiles(os.listdir(self.datasetPath+\"/\"+word1)))\n",
        "        sample2 = random.choice(omitHiddenFiles(os.listdir(self.datasetPath+\"/\"+word2)))\n",
        "\n",
        "      voiceVector1, _ = librosa.load(self.datasetPath+\"/\"+word1+\"/\"+sample1,self.sampling_rate)\n",
        "      voiceVector2, _ = librosa.load(self.datasetPath+\"/\"+word2+\"/\"+sample2,self.sampling_rate)\n",
        "\n",
        "      voiceVector1 = fixPaddingIssues(voiceVector1)\n",
        "      voiceVector2 = fixPaddingIssues(voiceVector2)\n",
        "\n",
        "      randomNoiseType1, randomNoiseType2 = random.sample(self.typesOfNoise,2)\n",
        "\n",
        "      randomNoise1 = random.choice(omitHiddenFiles(os.listdir(self.chunkedNoisePath+\"/\"+randomNoiseType1+\"/\")))\n",
        "      randomNoise2 = random.choice(omitHiddenFiles(os.listdir(self.chunkedNoisePath+\"/\"+randomNoiseType2+\"/\")))\n",
        "\n",
        "      noiseVector1, _ = librosa.load(self.chunkedNoisePath+\"/\"+randomNoiseType1+\"/\"+randomNoise1,self.sampling_rate)\n",
        "      noiseVector2, _ = librosa.load(self.chunkedNoisePath+\"/\"+randomNoiseType2+\"/\"+randomNoise2,self.sampling_rate)\n",
        "      \n",
        "      # noiseVector1 = fixPaddingIssues(noiseVector1)\n",
        "      # noiseVector2 = fixPaddingIssues(noiseVector2)\n",
        "      randomNoiseFactor1 = random.uniform(self.min_noise_factor, self.max_noise_factor)\n",
        "      randomNoiseFactor2 = random.uniform(self.min_noise_factor, self.max_noise_factor)\n",
        "\n",
        "      voice_with_noise1 = addNoise(voiceVector1, noiseVector1, randomNoiseFactor1)\n",
        "      voice_with_noise2 = addNoise(voiceVector2, noiseVector2, randomNoiseFactor2)\n",
        "      #print(type(voice_with_noise1))\n",
        "      \n",
        "      if self.spectrogram:\n",
        "        voice_with_noise_spectrogram1 = build_mel_calculation_graph(tf.constant(voice_with_noise1,dtype=tf.float32))\n",
        "        voice_with_noise_spectrogram2 = build_mel_calculation_graph(tf.constant(voice_with_noise2,dtype=tf.float32))\n",
        "        return np.array([voice_with_noise_spectrogram1, voice_with_noise_spectrogram2])\n",
        "          \n",
        "      return np.array([voice_with_noise1, voice_with_noise2])\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        firstPairIndex = index*self.batch_size\n",
        "        X,Y=[],[]\n",
        "\n",
        "        for i in range(firstPairIndex,firstPairIndex+self.batch_size):\n",
        "          # X.append(\n",
        "          #     (\n",
        "          #         self.wordsInDataset[i//2],\n",
        "          #         self.wordsInDataset[\n",
        "          #           (i//2+i%2)%(self.n//2)\n",
        "          #           ]\n",
        "          #     )\n",
        "          #     )\n",
        "          X.append(\n",
        "              self.giveJoinedAudio(\n",
        "                  self.wordsInDataset[i//2],\n",
        "                  self.wordsInDataset[(i//2+i%2)%(self.n//2)]\n",
        "          ))\n",
        "          \n",
        "         \n",
        "          Y.append(1.0 if i%2==0 else 0.0) # 0 if 2 words arent same , 1 if 2 words are same\n",
        "\n",
        "        X = np.array(X)\n",
        "        Y = np.array(Y)\n",
        "        return X,Y\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.n // self.batch_size\n"
      ],
      "id": "-FsvAoeb1NQK",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kOIyBVuXpytl"
      },
      "source": [],
      "id": "kOIyBVuXpytl"
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "ye1XYZIYaJvo",
        "outputId": "6204e30f-4166-476f-d36a-0527bcaea340"
      },
      "source": [
        "chunkedNoisePath = \"/content/NoiseChunked\"\n",
        "datasetPath = \"/content/dataset_format_fixed\"\n",
        "\n",
        "giveSpectrogram = False\n",
        "\n",
        "generator = CustomDataGen(\n",
        "    chunkedNoisePath,\n",
        "    datasetPath,\n",
        "    batch_size = 1,\n",
        "    max_noise_factor=0.18,\n",
        "    min_noise_factor=0.12, \n",
        "    spectrogram = giveSpectrogram,\n",
        "    print_words=True\n",
        "    )\n",
        "\n",
        "X1,Y1 = generator[72]\n",
        "data1 = X1[0][0]\n",
        "data2 = X1[0][1]\n",
        "\n",
        "print(data1.shape)\n",
        "\n",
        "ipd.Audio(np.concatenate((data1,data2)),rate=SAMPLE_RATE)\n"
      ],
      "id": "ye1XYZIYaJvo",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train size:  2424\n",
            "decade decade\n",
            "(16000,)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "d7W_u-6LapmG",
        "outputId": "cf26eb20-288e-41b6-d5c4-358388560b9d"
      },
      "source": [
        "fig , ax = plt.subplots(1,2)\n",
        "ax[0].imshow(\n",
        "    build_mel_calculation_graph(tf.constant(data1,dtype=tf.float32))[...,0]\n",
        "    )\n",
        "ax[1].imshow(\n",
        "    build_mel_calculation_graph(tf.constant(data2,dtype=tf.float32))[...,0]\n",
        "    )"
      ],
      "id": "d7W_u-6LapmG",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f43b902afd0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jx-uPX4Ivs7z",
        "outputId": "93b99b08-3cf4-4c19-b45d-655f1efec2d3"
      },
      "source": [
        "inpShape=(148,64,1)\n",
        "model = tf.keras.applications.EfficientNetB0(include_top=False,weights=None,input_shape=inpShape)\n",
        "model.summary()"
      ],
      "id": "jx-uPX4Ivs7z",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"efficientnetb0\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_3 (InputLayer)            [(None, 148, 64, 1)] 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "rescaling_1 (Rescaling)         (None, 148, 64, 1)   0           input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "normalization_1 (Normalization) (None, 148, 64, 1)   3           rescaling_1[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv_pad (ZeroPadding2D)   (None, 149, 65, 1)   0           normalization_1[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "stem_conv (Conv2D)              (None, 74, 32, 32)   288         stem_conv_pad[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "stem_bn (BatchNormalization)    (None, 74, 32, 32)   128         stem_conv[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "stem_activation (Activation)    (None, 74, 32, 32)   0           stem_bn[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "block1a_dwconv (DepthwiseConv2D (None, 74, 32, 32)   288         stem_activation[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block1a_bn (BatchNormalization) (None, 74, 32, 32)   128         block1a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block1a_activation (Activation) (None, 74, 32, 32)   0           block1a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_squeeze (GlobalAvera (None, 32)           0           block1a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_reshape (Reshape)    (None, 1, 1, 32)     0           block1a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_reduce (Conv2D)      (None, 1, 1, 8)      264         block1a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_expand (Conv2D)      (None, 1, 1, 32)     288         block1a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_se_excite (Multiply)    (None, 74, 32, 32)   0           block1a_activation[0][0]         \n",
            "                                                                 block1a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_project_conv (Conv2D)   (None, 74, 32, 16)   512         block1a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block1a_project_bn (BatchNormal (None, 74, 32, 16)   64          block1a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_conv (Conv2D)    (None, 74, 32, 96)   1536        block1a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_bn (BatchNormali (None, 74, 32, 96)   384         block2a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block2a_expand_activation (Acti (None, 74, 32, 96)   0           block2a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_dwconv_pad (ZeroPadding (None, 75, 33, 96)   0           block2a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block2a_dwconv (DepthwiseConv2D (None, 37, 16, 96)   864         block2a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_bn (BatchNormalization) (None, 37, 16, 96)   384         block2a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block2a_activation (Activation) (None, 37, 16, 96)   0           block2a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_squeeze (GlobalAvera (None, 96)           0           block2a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_reshape (Reshape)    (None, 1, 1, 96)     0           block2a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_reduce (Conv2D)      (None, 1, 1, 4)      388         block2a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_expand (Conv2D)      (None, 1, 1, 96)     480         block2a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_se_excite (Multiply)    (None, 37, 16, 96)   0           block2a_activation[0][0]         \n",
            "                                                                 block2a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_project_conv (Conv2D)   (None, 37, 16, 24)   2304        block2a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2a_project_bn (BatchNormal (None, 37, 16, 24)   96          block2a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_conv (Conv2D)    (None, 37, 16, 144)  3456        block2a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_bn (BatchNormali (None, 37, 16, 144)  576         block2b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block2b_expand_activation (Acti (None, 37, 16, 144)  0           block2b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_dwconv (DepthwiseConv2D (None, 37, 16, 144)  1296        block2b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block2b_bn (BatchNormalization) (None, 37, 16, 144)  576         block2b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block2b_activation (Activation) (None, 37, 16, 144)  0           block2b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_squeeze (GlobalAvera (None, 144)          0           block2b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_reshape (Reshape)    (None, 1, 1, 144)    0           block2b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block2b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block2b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_se_excite (Multiply)    (None, 37, 16, 144)  0           block2b_activation[0][0]         \n",
            "                                                                 block2b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_project_conv (Conv2D)   (None, 37, 16, 24)   3456        block2b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block2b_project_bn (BatchNormal (None, 37, 16, 24)   96          block2b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block2b_drop (Dropout)          (None, 37, 16, 24)   0           block2b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block2b_add (Add)               (None, 37, 16, 24)   0           block2b_drop[0][0]               \n",
            "                                                                 block2a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_conv (Conv2D)    (None, 37, 16, 144)  3456        block2b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_bn (BatchNormali (None, 37, 16, 144)  576         block3a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3a_expand_activation (Acti (None, 37, 16, 144)  0           block3a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_dwconv_pad (ZeroPadding (None, 41, 19, 144)  0           block3a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block3a_dwconv (DepthwiseConv2D (None, 19, 8, 144)   3600        block3a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_bn (BatchNormalization) (None, 19, 8, 144)   576         block3a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block3a_activation (Activation) (None, 19, 8, 144)   0           block3a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_squeeze (GlobalAvera (None, 144)          0           block3a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_reshape (Reshape)    (None, 1, 1, 144)    0           block3a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_reduce (Conv2D)      (None, 1, 1, 6)      870         block3a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_expand (Conv2D)      (None, 1, 1, 144)    1008        block3a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_se_excite (Multiply)    (None, 19, 8, 144)   0           block3a_activation[0][0]         \n",
            "                                                                 block3a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_project_conv (Conv2D)   (None, 19, 8, 40)    5760        block3a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3a_project_bn (BatchNormal (None, 19, 8, 40)    160         block3a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_conv (Conv2D)    (None, 19, 8, 240)   9600        block3a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_bn (BatchNormali (None, 19, 8, 240)   960         block3b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block3b_expand_activation (Acti (None, 19, 8, 240)   0           block3b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_dwconv (DepthwiseConv2D (None, 19, 8, 240)   6000        block3b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block3b_bn (BatchNormalization) (None, 19, 8, 240)   960         block3b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block3b_activation (Activation) (None, 19, 8, 240)   0           block3b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_squeeze (GlobalAvera (None, 240)          0           block3b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_reshape (Reshape)    (None, 1, 1, 240)    0           block3b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block3b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block3b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_se_excite (Multiply)    (None, 19, 8, 240)   0           block3b_activation[0][0]         \n",
            "                                                                 block3b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_project_conv (Conv2D)   (None, 19, 8, 40)    9600        block3b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block3b_project_bn (BatchNormal (None, 19, 8, 40)    160         block3b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block3b_drop (Dropout)          (None, 19, 8, 40)    0           block3b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block3b_add (Add)               (None, 19, 8, 40)    0           block3b_drop[0][0]               \n",
            "                                                                 block3a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_conv (Conv2D)    (None, 19, 8, 240)   9600        block3b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_bn (BatchNormali (None, 19, 8, 240)   960         block4a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4a_expand_activation (Acti (None, 19, 8, 240)   0           block4a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_dwconv_pad (ZeroPadding (None, 21, 9, 240)   0           block4a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4a_dwconv (DepthwiseConv2D (None, 10, 4, 240)   2160        block4a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_bn (BatchNormalization) (None, 10, 4, 240)   960         block4a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4a_activation (Activation) (None, 10, 4, 240)   0           block4a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_squeeze (GlobalAvera (None, 240)          0           block4a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_reshape (Reshape)    (None, 1, 1, 240)    0           block4a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_reduce (Conv2D)      (None, 1, 1, 10)     2410        block4a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_expand (Conv2D)      (None, 1, 1, 240)    2640        block4a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_se_excite (Multiply)    (None, 10, 4, 240)   0           block4a_activation[0][0]         \n",
            "                                                                 block4a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_project_conv (Conv2D)   (None, 10, 4, 80)    19200       block4a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4a_project_bn (BatchNormal (None, 10, 4, 80)    320         block4a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_conv (Conv2D)    (None, 10, 4, 480)   38400       block4a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_bn (BatchNormali (None, 10, 4, 480)   1920        block4b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4b_expand_activation (Acti (None, 10, 4, 480)   0           block4b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_dwconv (DepthwiseConv2D (None, 10, 4, 480)   4320        block4b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4b_bn (BatchNormalization) (None, 10, 4, 480)   1920        block4b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4b_activation (Activation) (None, 10, 4, 480)   0           block4b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_squeeze (GlobalAvera (None, 480)          0           block4b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_se_excite (Multiply)    (None, 10, 4, 480)   0           block4b_activation[0][0]         \n",
            "                                                                 block4b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_project_conv (Conv2D)   (None, 10, 4, 80)    38400       block4b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4b_project_bn (BatchNormal (None, 10, 4, 80)    320         block4b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4b_drop (Dropout)          (None, 10, 4, 80)    0           block4b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4b_add (Add)               (None, 10, 4, 80)    0           block4b_drop[0][0]               \n",
            "                                                                 block4a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_conv (Conv2D)    (None, 10, 4, 480)   38400       block4b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_bn (BatchNormali (None, 10, 4, 480)   1920        block4c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block4c_expand_activation (Acti (None, 10, 4, 480)   0           block4c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_dwconv (DepthwiseConv2D (None, 10, 4, 480)   4320        block4c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block4c_bn (BatchNormalization) (None, 10, 4, 480)   1920        block4c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block4c_activation (Activation) (None, 10, 4, 480)   0           block4c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_squeeze (GlobalAvera (None, 480)          0           block4c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_reshape (Reshape)    (None, 1, 1, 480)    0           block4c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block4c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block4c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_se_excite (Multiply)    (None, 10, 4, 480)   0           block4c_activation[0][0]         \n",
            "                                                                 block4c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_project_conv (Conv2D)   (None, 10, 4, 80)    38400       block4c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block4c_project_bn (BatchNormal (None, 10, 4, 80)    320         block4c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block4c_drop (Dropout)          (None, 10, 4, 80)    0           block4c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block4c_add (Add)               (None, 10, 4, 80)    0           block4c_drop[0][0]               \n",
            "                                                                 block4b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_conv (Conv2D)    (None, 10, 4, 480)   38400       block4c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_bn (BatchNormali (None, 10, 4, 480)   1920        block5a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5a_expand_activation (Acti (None, 10, 4, 480)   0           block5a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_dwconv (DepthwiseConv2D (None, 10, 4, 480)   12000       block5a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5a_bn (BatchNormalization) (None, 10, 4, 480)   1920        block5a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5a_activation (Activation) (None, 10, 4, 480)   0           block5a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_squeeze (GlobalAvera (None, 480)          0           block5a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_reshape (Reshape)    (None, 1, 1, 480)    0           block5a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_reduce (Conv2D)      (None, 1, 1, 20)     9620        block5a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_expand (Conv2D)      (None, 1, 1, 480)    10080       block5a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_se_excite (Multiply)    (None, 10, 4, 480)   0           block5a_activation[0][0]         \n",
            "                                                                 block5a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_project_conv (Conv2D)   (None, 10, 4, 112)   53760       block5a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5a_project_bn (BatchNormal (None, 10, 4, 112)   448         block5a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_conv (Conv2D)    (None, 10, 4, 672)   75264       block5a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_bn (BatchNormali (None, 10, 4, 672)   2688        block5b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5b_expand_activation (Acti (None, 10, 4, 672)   0           block5b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_dwconv (DepthwiseConv2D (None, 10, 4, 672)   16800       block5b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5b_bn (BatchNormalization) (None, 10, 4, 672)   2688        block5b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5b_activation (Activation) (None, 10, 4, 672)   0           block5b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_squeeze (GlobalAvera (None, 672)          0           block5b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_se_excite (Multiply)    (None, 10, 4, 672)   0           block5b_activation[0][0]         \n",
            "                                                                 block5b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_project_conv (Conv2D)   (None, 10, 4, 112)   75264       block5b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5b_project_bn (BatchNormal (None, 10, 4, 112)   448         block5b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5b_drop (Dropout)          (None, 10, 4, 112)   0           block5b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5b_add (Add)               (None, 10, 4, 112)   0           block5b_drop[0][0]               \n",
            "                                                                 block5a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_conv (Conv2D)    (None, 10, 4, 672)   75264       block5b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_bn (BatchNormali (None, 10, 4, 672)   2688        block5c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block5c_expand_activation (Acti (None, 10, 4, 672)   0           block5c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_dwconv (DepthwiseConv2D (None, 10, 4, 672)   16800       block5c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block5c_bn (BatchNormalization) (None, 10, 4, 672)   2688        block5c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block5c_activation (Activation) (None, 10, 4, 672)   0           block5c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_squeeze (GlobalAvera (None, 672)          0           block5c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_reshape (Reshape)    (None, 1, 1, 672)    0           block5c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block5c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block5c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_se_excite (Multiply)    (None, 10, 4, 672)   0           block5c_activation[0][0]         \n",
            "                                                                 block5c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_project_conv (Conv2D)   (None, 10, 4, 112)   75264       block5c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block5c_project_bn (BatchNormal (None, 10, 4, 112)   448         block5c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block5c_drop (Dropout)          (None, 10, 4, 112)   0           block5c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block5c_add (Add)               (None, 10, 4, 112)   0           block5c_drop[0][0]               \n",
            "                                                                 block5b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_conv (Conv2D)    (None, 10, 4, 672)   75264       block5c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_bn (BatchNormali (None, 10, 4, 672)   2688        block6a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6a_expand_activation (Acti (None, 10, 4, 672)   0           block6a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_dwconv_pad (ZeroPadding (None, 13, 7, 672)   0           block6a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6a_dwconv (DepthwiseConv2D (None, 5, 2, 672)    16800       block6a_dwconv_pad[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_bn (BatchNormalization) (None, 5, 2, 672)    2688        block6a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6a_activation (Activation) (None, 5, 2, 672)    0           block6a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_squeeze (GlobalAvera (None, 672)          0           block6a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_reshape (Reshape)    (None, 1, 1, 672)    0           block6a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_reduce (Conv2D)      (None, 1, 1, 28)     18844       block6a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_expand (Conv2D)      (None, 1, 1, 672)    19488       block6a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_se_excite (Multiply)    (None, 5, 2, 672)    0           block6a_activation[0][0]         \n",
            "                                                                 block6a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_project_conv (Conv2D)   (None, 5, 2, 192)    129024      block6a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6a_project_bn (BatchNormal (None, 5, 2, 192)    768         block6a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_conv (Conv2D)    (None, 5, 2, 1152)   221184      block6a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_bn (BatchNormali (None, 5, 2, 1152)   4608        block6b_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6b_expand_activation (Acti (None, 5, 2, 1152)   0           block6b_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_dwconv (DepthwiseConv2D (None, 5, 2, 1152)   28800       block6b_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6b_bn (BatchNormalization) (None, 5, 2, 1152)   4608        block6b_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6b_activation (Activation) (None, 5, 2, 1152)   0           block6b_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_squeeze (GlobalAvera (None, 1152)         0           block6b_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6b_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6b_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6b_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_se_excite (Multiply)    (None, 5, 2, 1152)   0           block6b_activation[0][0]         \n",
            "                                                                 block6b_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_project_conv (Conv2D)   (None, 5, 2, 192)    221184      block6b_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6b_project_bn (BatchNormal (None, 5, 2, 192)    768         block6b_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6b_drop (Dropout)          (None, 5, 2, 192)    0           block6b_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6b_add (Add)               (None, 5, 2, 192)    0           block6b_drop[0][0]               \n",
            "                                                                 block6a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_conv (Conv2D)    (None, 5, 2, 1152)   221184      block6b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_bn (BatchNormali (None, 5, 2, 1152)   4608        block6c_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6c_expand_activation (Acti (None, 5, 2, 1152)   0           block6c_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_dwconv (DepthwiseConv2D (None, 5, 2, 1152)   28800       block6c_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6c_bn (BatchNormalization) (None, 5, 2, 1152)   4608        block6c_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6c_activation (Activation) (None, 5, 2, 1152)   0           block6c_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_squeeze (GlobalAvera (None, 1152)         0           block6c_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6c_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6c_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6c_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_se_excite (Multiply)    (None, 5, 2, 1152)   0           block6c_activation[0][0]         \n",
            "                                                                 block6c_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_project_conv (Conv2D)   (None, 5, 2, 192)    221184      block6c_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6c_project_bn (BatchNormal (None, 5, 2, 192)    768         block6c_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6c_drop (Dropout)          (None, 5, 2, 192)    0           block6c_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6c_add (Add)               (None, 5, 2, 192)    0           block6c_drop[0][0]               \n",
            "                                                                 block6b_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_conv (Conv2D)    (None, 5, 2, 1152)   221184      block6c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_bn (BatchNormali (None, 5, 2, 1152)   4608        block6d_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block6d_expand_activation (Acti (None, 5, 2, 1152)   0           block6d_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_dwconv (DepthwiseConv2D (None, 5, 2, 1152)   28800       block6d_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block6d_bn (BatchNormalization) (None, 5, 2, 1152)   4608        block6d_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block6d_activation (Activation) (None, 5, 2, 1152)   0           block6d_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_squeeze (GlobalAvera (None, 1152)         0           block6d_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block6d_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block6d_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block6d_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_se_excite (Multiply)    (None, 5, 2, 1152)   0           block6d_activation[0][0]         \n",
            "                                                                 block6d_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_project_conv (Conv2D)   (None, 5, 2, 192)    221184      block6d_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block6d_project_bn (BatchNormal (None, 5, 2, 192)    768         block6d_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block6d_drop (Dropout)          (None, 5, 2, 192)    0           block6d_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block6d_add (Add)               (None, 5, 2, 192)    0           block6d_drop[0][0]               \n",
            "                                                                 block6c_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_conv (Conv2D)    (None, 5, 2, 1152)   221184      block6d_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_bn (BatchNormali (None, 5, 2, 1152)   4608        block7a_expand_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block7a_expand_activation (Acti (None, 5, 2, 1152)   0           block7a_expand_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_dwconv (DepthwiseConv2D (None, 5, 2, 1152)   10368       block7a_expand_activation[0][0]  \n",
            "__________________________________________________________________________________________________\n",
            "block7a_bn (BatchNormalization) (None, 5, 2, 1152)   4608        block7a_dwconv[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block7a_activation (Activation) (None, 5, 2, 1152)   0           block7a_bn[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_squeeze (GlobalAvera (None, 1152)         0           block7a_activation[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_reshape (Reshape)    (None, 1, 1, 1152)   0           block7a_se_squeeze[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_reduce (Conv2D)      (None, 1, 1, 48)     55344       block7a_se_reshape[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_expand (Conv2D)      (None, 1, 1, 1152)   56448       block7a_se_reduce[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_se_excite (Multiply)    (None, 5, 2, 1152)   0           block7a_activation[0][0]         \n",
            "                                                                 block7a_se_expand[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_project_conv (Conv2D)   (None, 5, 2, 320)    368640      block7a_se_excite[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block7a_project_bn (BatchNormal (None, 5, 2, 320)    1280        block7a_project_conv[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "top_conv (Conv2D)               (None, 5, 2, 1280)   409600      block7a_project_bn[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "top_bn (BatchNormalization)     (None, 5, 2, 1280)   5120        top_conv[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "top_activation (Activation)     (None, 5, 2, 1280)   0           top_bn[0][0]                     \n",
            "==================================================================================================\n",
            "Total params: 4,048,991\n",
            "Trainable params: 4,006,972\n",
            "Non-trainable params: 42,019\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qtcpKK3nXYf"
      },
      "source": [
        "def l2_reg_output(x):\n",
        "  return tf.keras.backend.l2_normalize(x,axis=1)\n",
        "\n",
        "outputLayer = model.get_layer('block4c_add').output\n",
        "x = tf.keras.layers.Conv2D(32,3,padding=\"same\",name=\"extra_conv1\",kernel_regularizer=\"l2\")(outputLayer)\n",
        "x = tf.keras.layers.BatchNormalization()(x)\n",
        "x = tf.keras.layers.MaxPooling2D()(x)\n",
        "\n",
        "x = tf.keras.layers.Conv2D(64,3,padding=\"same\",name = \"extra_conv2\",kernel_regularizer=\"l2\")(x)\n",
        "x = tf.keras.layers.AveragePooling2D(pool_size=(x.shape[1]//2, x.shape[2]//2), strides=None, padding=\"valid\")(x)\n",
        "x = tf.keras.layers.Flatten()(x)\n",
        "x = tf.keras.layers.Dense(128,kernel_regularizer=\"l2\")(x)\n",
        "x = tf.keras.layers.Lambda(l2_reg_output)(x)\n",
        "\n",
        "baseNetwork = tf.keras.Model(inputs=model.input,outputs=x,name=\"basemodel\")\n",
        "baseNetwork.summary()\n"
      ],
      "id": "0qtcpKK3nXYf",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pY7-R2kapYZP"
      },
      "source": [
        "from tensorflow.keras import backend as K\n",
        "def euclidean_distance(featsA,featsB):\n",
        "\t# compute the sum of squared distances between the vectors\n",
        "\tsumSquared = K.sum(K.square(featsA - featsB), axis=1,\n",
        "\t\tkeepdims=True)\n",
        "\t# return the euclidean distance between the vectors\n",
        "\treturn K.sqrt(K.maximum(sumSquared, K.epsilon()))\n",
        "\n",
        "def cosine_distance(featsA:np.array,featsB:np.array):\n",
        "\treturn tf.matmul(featsA,featsB.T)\n",
        "\t"
      ],
      "id": "pY7-R2kapYZP",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZlRsYGqjrtU7",
        "outputId": "a93e2fa2-5a22-4316-c9a6-f964e4d22ddf"
      },
      "source": [
        "baseNetwork = tf.keras.models.load_model(\"/content/drive/MyDrive/Siamese/modelCheckpoints_old/model-8-01-0.96.h5\").get_layer(\"basemodel\")\n",
        "inputLayer = tf.keras.layers.Input(shape=(2,98,64,1))\n",
        "\n",
        "#v = 0.5 \n",
        "#q = 0.1\n",
        "\n",
        "input1 = inputLayer[:,0]\n",
        "input2 = inputLayer[:,1]\n",
        "\n",
        "output1 = baseNetwork(input1)\n",
        "output2 = baseNetwork(input2)\n",
        "\n",
        "#distance = 1 - tf.expand_dims(tf.keras.losses.cosine_similarity(output1,output2),axis=-1)\n",
        "distance = euclidean_distance(output1,output2)\n",
        "#Fx = ((1-v)/q)*(distance-1)+1\n",
        "#Sx = (v/(2-q))*(distance+1)\n",
        "\n",
        "#cubed = tf.math.pow(distance,3)\n",
        "#threshold = 0.2\n",
        "#finalOutput = 1 - (cubed/(threshold**3+cubed)) \n",
        "#finalOutput = tf.maximum(Fx,Sx)\n",
        "#finalOutput = tf.cond(distance>(1-q),lambda:Fx,lambda:Sx)\n",
        "\n",
        "siamese_model = tf.keras.Model(inputs=inputLayer,outputs=distance)\n",
        "siamese_model.summary()"
      ],
      "id": "ZlRsYGqjrtU7",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            [(None, 2, 98, 64, 1 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.getitem_4 (Sli (None, 98, 64, 1)    0           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "tf.__operators__.getitem_5 (Sli (None, 98, 64, 1)    0           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "basemodel (Functional)          (None, 128)          364183      tf.__operators__.getitem_4[0][0] \n",
            "                                                                 tf.__operators__.getitem_5[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.subtract_2 (TFOpLambda) (None, 128)          0           basemodel[2][0]                  \n",
            "                                                                 basemodel[3][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.square_1 (TFOpLambda)   (None, 128)          0           tf.math.subtract_2[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.reduce_sum_1 (TFOpLambd (None, 1)            0           tf.math.square_1[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.maximum_2 (TFOpLambda)  (None, 1)            0           tf.math.reduce_sum_1[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.maximum_3 (TFOpLambda)  (None, 1)            0           tf.math.maximum_2[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "tf.math.sqrt_1 (TFOpLambda)     (None, 1)            0           tf.math.maximum_3[0][0]          \n",
            "==================================================================================================\n",
            "Total params: 364,183\n",
            "Trainable params: 355,924\n",
            "Non-trainable params: 8,259\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GIePVyStfDWo",
        "outputId": "b1f07686-94bc-401f-8854-48c47c4ed82a"
      },
      "source": [
        "def triplet_loss(y_true,y_pred):\n",
        "  #matchloss = -2ln(1-x/2)\n",
        "  #mismatchloss = max(-ln(x/0.2),0)\n",
        "  match_loss =  y_true * -2.0 * tf.math.log( 1 - y_pred/2 ) \n",
        "  mismatch_loss =  tf.maximum(\n",
        "        (1 - y_true) * ( -tf.math.log(y_pred/0.2) ) ,\n",
        "        0\n",
        "  )\n",
        "\n",
        "  return tf.reduce_mean( match_loss + mismatch_loss )\n",
        "\n",
        "def accuracy(y_true ,y_pred):\n",
        "  #y_true = tf.equal(y_true,1.0)\n",
        "  threshold_check = tf.cast(tf.less_equal(y_pred,0.2),dtype=tf.float32)\n",
        "  #matches = tf.cast(tf.equal(y_true,threshold_check),dtype=tf.float32)\n",
        "  return tf.keras.metrics.binary_accuracy(y_true,threshold_check)\n",
        "\n",
        "##Test code\n",
        "t = tf.constant((1.0,1.0,1.0,1.0,1.0,1.0))\n",
        "q = t - 0\n",
        "\n",
        "print(triplet_loss(t,q))\n",
        "print(accuracy(t,q))"
      ],
      "id": "GIePVyStfDWo",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tf.Tensor(1.3862944, shape=(), dtype=float32)\n",
            "tf.Tensor(0.0, shape=(), dtype=float32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LECAZs4n1IfO",
        "outputId": "7ba06ba0-d6cf-49e3-ef26-a9e659d60f6c"
      },
      "source": [
        "BATCH_SIZE = 64 \n",
        "NUMBER_OF_EPOCHS = 15\n",
        "CROSSED_NO_EPOCHS = 0\n",
        "\n",
        "model = tf.keras.models.load_model(\n",
        "    \"/content/drive/MyDrive/Siamese/modelCheckpoints/model-4-15-loss_0.072_val_acc0.947.h5\",\n",
        "    custom_objects={\"triplet_loss\":triplet_loss})\n",
        "#model = siamese_model\n",
        "\n",
        "chunkedNoisePath = \"/content/NoiseChunked\"\n",
        "        \n",
        "datasetPath = \"/content/dataset_format_fixed\"\n",
        "\n",
        "training_generator = CustomDataGen(chunkedNoisePath, datasetPath, batch_size = BATCH_SIZE)\n",
        "test_generator = CustomDataGen(chunkedNoisePath, datasetPath, batch_size = BATCH_SIZE, training_generator=False)\n",
        "\n",
        "my_callbacks = [\n",
        "    tf.keras.callbacks.EarlyStopping(patience=5),\n",
        "    tf.keras.callbacks.ModelCheckpoint(\n",
        "        filepath='/content/drive/MyDrive/Siamese/modelCheckpoints/model-5-{epoch:02d}-loss_{loss:.3f}_val_acc{val_accuracy:.3f}.h5',\n",
        "        monitor = 'val_accuracy',\n",
        "        save_best_only = False\n",
        "        ),\n",
        "    tf.keras.callbacks.ReduceLROnPlateau(\n",
        "        monitor = 'loss',\n",
        "        factor = 0.1,\n",
        "        patience = 2,\n",
        "        min_lr = 1e-5\n",
        "    ),\n",
        "]\n",
        "\n",
        "adam = tf.keras.optimizers.Adam(1e-5)\n",
        "\n",
        "#To continue from lastpoint\n",
        "#model = tf.keras.models.load_model(\"/content/drive/MyDrive/Siamese/ref_model7-1-07-0.91.h5\")\n",
        "#CROSSED_NO_EPOCHS = 14\n",
        "\n",
        "model.compile(\n",
        "    #loss=\"binary_crossentropy\",\n",
        "    loss = triplet_loss ,\n",
        "    optimizer=adam,\n",
        "\tmetrics=[accuracy]\n",
        ")\n",
        "\n",
        "history1 = model.fit(\n",
        "    training_generator,\n",
        "    epochs = 25,\n",
        "    validation_data=test_generator,\n",
        "    callbacks=my_callbacks)\n"
      ],
      "id": "LECAZs4n1IfO",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train size:  2424\n",
            "test size:  270\n",
            "Epoch 1/25\n",
            "75/75 [==============================] - 935s 12s/step - loss: 0.0711 - accuracy: 0.9287 - val_loss: 0.0651 - val_accuracy: 0.9453\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py:497: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
            "  category=CustomMaskWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 2/25\n",
            "75/75 [==============================] - 911s 12s/step - loss: 0.0696 - accuracy: 0.9377 - val_loss: 0.0714 - val_accuracy: 0.9297\n",
            "Epoch 3/25\n",
            "75/75 [==============================] - 912s 12s/step - loss: 0.0684 - accuracy: 0.9319 - val_loss: 0.0668 - val_accuracy: 0.9355\n",
            "Epoch 4/25\n",
            "75/75 [==============================] - ETA: 0s - loss: 0.0693 - accuracy: 0.9304 "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TQ0ygqyJFMU9"
      },
      "source": [
        "print(history1)"
      ],
      "id": "TQ0ygqyJFMU9",
      "execution_count": null,
      "outputs": []
    }
  ]
}