CreateDataFiles.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CreateDataFiles.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/QColeman97/AudioTagger/blob/master/CreateDataFiles.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0uVrZrFDFSmq",
        "colab_type": "code",
        "outputId": "d1a4116c-e8cd-4d78-838b-0e9cf78ae2f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "# This cell contains preferred data pre-processing methods\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import librosa\n",
        "from librosa import display\n",
        "import os\n",
        "import glob\n",
        "\n",
        "\n",
        "input_path = 'drive/My Drive/AudioTaggerData/'\n",
        "\n",
        "train_files_path = input_path + 'FSDKaggle2018.audio_train'\n",
        "test_files_path = input_path + 'FSDKaggle2018.audio_test'\n",
        "train_csv_path = (input_path +\n",
        "                  'FSDKaggle2018.meta/train_post_competition.csv')\n",
        "test_csv_path = (input_path +\n",
        "                 'FSDKaggle2018.meta/' +\n",
        "                 'test_post_competition_scoring_clips.csv')\n",
        "\n",
        "# Data preprocessing part\n",
        "\n",
        "df_train = pd.read_csv(train_csv_path)\n",
        "df_test = pd.read_csv(test_csv_path)\n",
        "\n",
        "unique_labels = df_train.label.unique()\n",
        "num_class = len(unique_labels)\n",
        "label2index = {label: index for index, label in enumerate(unique_labels)}\n",
        "\n",
        "train_dict = pd.Series.from_csv(train_csv_path, header = 0).to_dict()\n",
        "test_dict = pd.Series.from_csv(test_csv_path, header = 0).to_dict()\n",
        "\n",
        "\n",
        "#array of labels in number form (0 = hi-hat, 1 = saxophone, etc)\n",
        "label_emb_indices = np.array([label2index[label] for label in df_train.label])\n",
        "\n",
        "label_emb_test_indices = np.array([label2index[label] for label in df_test.label])\n",
        "\n",
        "\n",
        "def pre_process(pathname):\n",
        "    sampling_rate = 32000\n",
        "    hop_length = 192\n",
        "    fmax = None\n",
        "    n_mels = 32\n",
        "    n_fft = 1024\n",
        "\n",
        "\n",
        "    # y, sr = librosa.load(pathname, sr = None)\n",
        "    y, sr = librosa.load(pathname, sr = sampling_rate)\n",
        "    # print('Y len:', len(y))\n",
        "    y, (trim_begin, trim_end) = librosa.effects.trim(y)\n",
        "\n",
        "    # Stretch for low time-scale (for 32 n_mels and 64 time restriction)\n",
        "    y = librosa.effects.time_stretch(y, 2.0)\n",
        "    \n",
        "    # Amplitudes of STFT\n",
        "    stft = np.abs(librosa.stft(y, n_fft = n_fft, hop_length = hop_length,\n",
        "                               window = 'hann', center = True,\n",
        "                               pad_mode = 'reflect'))\n",
        "\n",
        "    # Get the fast fourier transform of frequencies over time\n",
        "    freqs = librosa.core.fft_frequencies(sr = sampling_rate, n_fft = n_fft)\n",
        "    # Perceptually weight the spectrogram - better visual\n",
        "    stft = librosa.perceptual_weighting(stft*2, freqs, ref = 1.0, amin = 1e-10,\n",
        "                                        top_db = 99.0)\n",
        "\n",
        "    # Convert into a mel-scaled spectrogram\n",
        "    # (Power param is set to 2 (for power) by default)\n",
        "    mel_spect = librosa.feature.melspectrogram(S = stft, sr = sampling_rate,\n",
        "                                               n_mels = n_mels, fmax = fmax)\n",
        "\n",
        "    # Convert mel-scaled spectrogram into log-mel spectrogram\n",
        "    log_mel_spect = librosa.core.power_to_db(mel_spect)\n",
        "\n",
        "    return np.asarray(log_mel_spect)\n",
        "\n",
        "\n",
        "# Get data in the form of a file from .wav files (preferred)\n",
        "def get_data(pathname, training = True):\n",
        "    file_list = glob.glob(os.path.join(pathname, '*.wav'))\n",
        "\n",
        "    if training:\n",
        "        data_f = open('Audio.train', 'w')\n",
        "    else:\n",
        "        data_f = open('Audio.test', 'w')\n",
        "\n",
        "    spectrograms, times = [], []\n",
        "    for i, file in enumerate(file_list):\n",
        "        print(\"%04d / %d | %s\" % (i + 1, len(file_list), file))\n",
        "\n",
        "        spectrogram = pre_process(file)\n",
        "\n",
        "        time_restriction = 64\n",
        "        if time_restriction >= spectrogram.shape[1]:\n",
        "            pad_amount = time_restriction - spectrogram.shape[1]\n",
        "            # Use avg or max time\n",
        "            spectrogram = np.pad(spectrogram, ((0, 0), (0, pad_amount)),\n",
        "                                 'minimum')\n",
        "        else:\n",
        "            spectrogram = spectrogram[:, :time_restriction]\n",
        "\n",
        "        spectrogram = spectrogram.transpose()\n",
        "\n",
        "        np.savetxt(data_f, spectrogram)\n",
        "        data_f.write('\\n')\n",
        "\n",
        "    data_f.close()\n",
        "\n",
        "\n",
        "# Get data in the form of a file from .csv file (preferred)\n",
        "def get_labels(pathname, training = True):\n",
        "    file_list = glob.glob(os.path.join(pathname, '*.wav'))\n",
        "\n",
        "    if training:\n",
        "        labels_f = open('Labels.train', 'w')\n",
        "    else:\n",
        "        labels_f = open('Labels.test', 'w')\n",
        "\n",
        "#     labels = []\n",
        "    for i, file in enumerate(file_list):\n",
        "        label = np.zeros((41,))\n",
        "        categ = (train_dict[file.split('/')[-1]] if\n",
        "            (training) else test_dict[file.split('/')[-1]])\n",
        "        hot_index = label2index[categ]\n",
        "        label[hot_index] = 1\n",
        "        # labels.append(label)\n",
        "        labels_f.write(' '.join([str(x) for x in label]) + '\\n')\n",
        "\n",
        "    # return np.array(labels)\n",
        "    labels_f.close()\n",
        "\n",
        "\n",
        "# Run to get these files: Audio.train, Audio.test\n",
        "get_data(train_files_path)\n",
        "get_data(test_files_path, training = False)\n",
        "\n",
        "# Run to get these files: Labels.train, Labels.test\n",
        "get_labels(train_files_path)\n",
        "get_labels(test_files_path, training = False)\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pandas/core/series.py:4141: FutureWarning: from_csv is deprecated. Please use read_csv(...) instead. Note that some of the default arguments are different, so please refer to the documentation for from_csv when changing your function calls\n",
            "  infer_datetime_format=infer_datetime_format)\n"
          ],
          "name": "stderr"
        }
      ]
    }
  ]
}