Kapre_.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Kapre_.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMJLzOawIpEUDE+jPix/vVh",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ithingv/AudioProcessing/blob/main/Kapre_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/MyDrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Lx2kKHwkPVl",
        "outputId": "00b9a4e6-9dcf-441b-e8a2-df882492d153"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/MyDrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bOwP0od9fbLL",
        "outputId": "5a33f3af-3eac-4aec-a31a-e20eb301ec64"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kapre in /usr/local/lib/python3.7/dist-packages (0.3.6)\n",
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          ]
        }
      ],
      "source": [
        "!pip install kapre"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# How to use Kapre - example"
      ],
      "metadata": {
        "id": "B1Je4pXckbSw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import librosa\n",
        "import kapre\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "import numpy as np\n",
        "\n",
        "from datetime import datetime\n",
        "now = datetime.now()\n",
        "\n",
        "print('%s/%s/%s' % (now.year, now.month, now.day))\n",
        "print('Tensorflow: {}'.format(tf.__version__))\n",
        "print('Librosa: {}'.format(librosa.__version__))\n",
        "print('Image data format: {}'.format(tf.keras.backend.image_data_format()))\n",
        "print('Kapre: {}'.format(kapre.__version__))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udiaFbfmji3b",
        "outputId": "7a0424fe-be49-4704-f2e2-f05d07749c43"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022/1/6\n",
            "Tensorflow: 2.7.0\n",
            "Librosa: 0.8.1\n",
            "Image data format: channels_last\n",
            "Kapre: 0.3.6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading an mp3 file"
      ],
      "metadata": {
        "id": "y0P3tmm5ket4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "audio_path = '/content/MyDrive/MyDrive/[해커톤]DeepASMR/Dataset/asmr/wave/0.wav'\n",
        "src, sr = librosa.load(audio_path, sr=None, mono=True)\n",
        "print('Audio length: %d samples, %04.2f seconds. \\n' % (len(src), len(src) / sr) +\n",
        "      'Audio sample rate: %d Hz' % sr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Iy8OtZNkjVD",
        "outputId": "ef4facbb-cb3a-4ff9-d74e-d8c29f46e7f9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Audio length: 220500 samples, 5.00 seconds. \n",
            "Audio sample rate: 44100 Hz\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "src.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aev8B8ZilncR",
        "outputId": "4857602f-e6e7-452a-cd94-2ec53db62871"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(220500,)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Trim it and make it a 2d.\n",
        "If your file is mono, librosa.load returns a 1D array. Kapre always expects 2d array, so make it 2d.\n",
        "\n",
        "On my computer, I use default `image_data_format == channels_last`. I'll keep it in that way for the audio data, too."
      ],
      "metadata": {
        "id": "OSA9lJqPlV4c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len_second = 1.0 \n",
        "src = src[:int(sr*len_second)]\n",
        "src = np.expand_dims(src, axis=1)\n",
        "input_shape = src.shape\n",
        "print('The shape of an item', input_shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RWLstNgClysb",
        "outputId": "65b91072-90b7-4277-9a23-f7bcd14d1475"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The shape of an item (44100, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Let's make it a batch of 4 items"
      ],
      "metadata": {
        "id": "h-zsITbxly14"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array([src] * 4)\n",
        "print('The shape of a batch: ',x.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMWdZNz-mCM9",
        "outputId": "9c547518-a3db-41af-b970-e91d59653ada"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The shape of a batch:  (4, 44100, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# A Keras Model\n",
        "\n",
        "A simple model with 10-class and single-label classification."
      ],
      "metadata": {
        "id": "xyogUjGxmJ3q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from kapre.time_frequency import STFT, Magnitude, MagnitudeToDecibel\n",
        "\n",
        "\n",
        "model = Sequential()\n",
        "# A STFT layer\n",
        "model.add(STFT(n_fft=2048, win_length=2018, hop_length=1024,\n",
        "               window_name=None, pad_end=False,\n",
        "               input_data_format='channels_last', output_data_format='channels_last',\n",
        "               input_shape=input_shape,\n",
        "              name='stft-layer'))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3K-a1B1nmbBg",
        "outputId": "cb0aa2df-19a3-48e9-a88a-4251eef57462"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " stft-layer (STFT)           (None, 42, 1025, 1)       0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 0\n",
            "Trainable params: 0\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- The model has no trainable parameters because STFT layer uses tf.signal.stft() function which is just an implementation of FFT-based short-time Fourier transform.\n",
        "- The output shape is (batch, time, frequency, channels).\n",
        "    - 42 (time) is the number of STFT frames. A shorter hop length would make it (nearly) proportionally longer. If pad_end=True, the input audio signals become a little longer, hence the number of frames would get longer, too.\n",
        "    - 1025 is the number of STFT bins and decided as n_fft // 2 + 1.\n",
        "    - 1 channel: because the input signal was single-channel.\n",
        "- The output of STFT layer is complex number."
      ],
      "metadata": {
        "id": "W02rgINWmoqU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, BatchNormalization, ReLU, GlobalAveragePooling2D, Dense, Softmax\n",
        "from kapre import STFT, Magnitude, MagnitudeToDecibel\n",
        "from kapre.composed import get_melspectrogram_layer, get_log_frequency_spectrogram_layer"
      ],
      "metadata": {
        "id": "O7vNy-4vgaRG"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(Magnitude())\n",
        "model.add(MagnitudeToDecibel())\n",
        "model.add(Conv2D(32, (3, 3), strides=(2, 2)))\n",
        "model.add(BatchNormalization())\n",
        "model.add(ReLU())\n",
        "model.add(GlobalAveragePooling2D())\n",
        "model.add(Dense(10))\n",
        "model.add(Softmax())\n",
        "\n",
        "# Compile the model\n",
        "model.compile('adam', 'categorical_crossentropy') # if single-label classification"
      ],
      "metadata": {
        "id": "-6AYZOHMgwnR"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CyGfngeUg_Et",
        "outputId": "c5d58020-ee65-46ab-bca8-92c02e55ad41"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " stft-layer (STFT)           (None, 42, 1025, 1)       0         \n",
            "                                                                 \n",
            " magnitude (Magnitude)       (None, 42, 1025, 1)       0         \n",
            "                                                                 \n",
            " magnitude_to_decibel (Magni  (None, 42, 1025, 1)      0         \n",
            " tudeToDecibel)                                                  \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 20, 512, 32)       320       \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 20, 512, 32)      128       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " re_lu (ReLU)                (None, 20, 512, 32)       0         \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 32)               0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dense (Dense)               (None, 10)                330       \n",
            "                                                                 \n",
            " softmax (Softmax)           (None, 10)                0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 778\n",
            "Trainable params: 714\n",
            "Non-trainable params: 64\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- I added Magnitude() which is a simple abs() operation on the complex numbers.\n",
        "- MagnitudeToDecibel maps the numbers to a decibel scale."
      ],
      "metadata": {
        "id": "_xK6d7Qsg_xs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "cS6V2LZWnAoV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}