creation_nouvelle_base-2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "creation_nouvelle_base.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Création d'une nouvelle base d'entraînement et d'évaluation à partir de la base de donnée LibriSpeech : \n",
        "\n",
        "On prend aléatoirement un nombre de speakers entre 2 et 4, on prend pour chacun des speakers un nombre d'intervention aléatoire (2 à 4). Chaque intervention est une portion aléatoire du speech initiale. On recole les blocs de façon aléatoire pour avoir un nouvel extrait. \n",
        "\n",
        "On réécrit à la fin des nouveaux fichiers wav."
      ],
      "metadata": {
        "id": "UMwCwnlMkogJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "buwuMRfsen65"
      },
      "outputs": [],
      "source": [
        "import glob\n",
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import shutil\n",
        "import librosa\n",
        "from tqdm import tqdm\n",
        "from scipy.io.wavfile import write"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import ast"
      ],
      "metadata": {
        "id": "8S-tpYp1lU6C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gNtpYcBPfZlN",
        "outputId": "87005916-b938-461b-b241-18c93d3e8911"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/drive/MyDrive/hackaton twelve/AudioMNIST/LibriSpeech/LibriSpeech\""
      ],
      "metadata": {
        "id": "fFNOggnEfZ7B"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = pd.DataFrame(columns = [\"file_id\",\"freq\",\"sampling_rate\"])\n",
        "\n",
        "\n",
        "for fichier in os.listdir(path):\n",
        "  if '.flac'==fichier[-5:]:\n",
        "        res = librosa.load(os.path.join(path, fichier))\n",
        "        freq, sampling_rate = res[0],res[1]\n",
        "        line_df = pd.DataFrame([[fichier, freq, sampling_rate]], columns = df2.columns)\n",
        "        df2 = pd.concat([df2, line_df], ignore_index = True)\n",
        "        "
      ],
      "metadata": {
        "id": "dbVU0ROphQTi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2['ID'] = df2[\"file_id\"].apply(lambda x: x.split('-')[0])\n",
        "df2[\"ID\"] = df2[\"ID\"].astype('int64')"
      ],
      "metadata": {
        "id": "QIFXr3IQggaN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "label = pd.read_csv(path + \"/label\",sep=',')\n",
        "label = label.drop([\"Unnamed: 0\"],axis =1)\n",
        "label.columns = ['ID','SEX']"
      ],
      "metadata": {
        "id": "ZaV6cFyXhA6y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df2.merge(label, left_on='ID', right_on='ID')"
      ],
      "metadata": {
        "id": "6NdNGyLti5-q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random"
      ],
      "metadata": {
        "id": "_WUJdaxgvZos"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = pd.DataFrame(columns = [\"freq\",\"nb_pers\",\"nb_f\",\"nb_h\", \"timing\"])\n",
        "\n",
        "for i in range(200):\n",
        "  nb_speaker = random.randint(2, 4)\n",
        "  nb_blocs = [random.randint(1,3) for i in range(nb_speaker)]\n",
        "\n",
        "  liste_decoup = []\n",
        "  for i in range(nb_speaker):\n",
        "    decoup = []\n",
        "    for j in range(nb_blocs[i]):\n",
        "      decoup.append(np.random.uniform(0,1))\n",
        "      liste_decoup.append(sorted(decoup))\n",
        "  \n",
        "  df_sample = df2.sample(n=nb_speaker)\n",
        "  df_sample_freq = df_sample[\"freq\"].values\n",
        "  df_sample_sex = df_sample[\"SEX\"].values\n",
        "\n",
        "\n",
        "  liste_morceaux = []\n",
        "\n",
        "  for k in range(nb_speaker):\n",
        "    sex = df_sample_sex[k]\n",
        "    i = liste_decoup[k]\n",
        "    if len(i) == 1:\n",
        "      n = round(i[0] * len(df_sample_freq[k]) )\n",
        "      morceau = df_sample_freq[k][:n]\n",
        "\n",
        "      liste_morceaux.append((np.array(morceau),k,sex))\n",
        "   \n",
        "    if len(i) == 2:\n",
        "      n = round( i[0] * len(df_sample_freq[k]) )\n",
        "      n1 = round( i[1] * len(df_sample_freq[k]) )\n",
        "      morceau = df_sample_freq[k][:n]\n",
        "      morceau1 = df_sample_freq[k][n+1:n1]\n",
        "      liste_morceaux.append((np.array(morceau),k,sex))\n",
        "      liste_morceaux.append((np.array(morceau1),k,sex))\n",
        "    \n",
        "    if len(i) == 3:\n",
        "      n = round( i[0] * len(df_sample_freq[k]) )\n",
        "      n1 = round( i[1] * len(df_sample_freq[k]) )\n",
        "      n2 = round( i[2] * len(df_sample_freq[k]) )\n",
        "      morceau = df_sample_freq[k][:n]\n",
        "      morceau1 = df_sample_freq[k][n+1:n1]\n",
        "      morceau2 = df_sample_freq[k][n1+1:n2]\n",
        "      liste_morceaux.append((np.array(morceau),k,sex))\n",
        "      liste_morceaux.append((np.array(morceau1),k,sex))\n",
        "      liste_morceaux.append((np.array(morceau2),k,sex))\n",
        "\n",
        "  random.shuffle(liste_morceaux)\n",
        "  liste_morceaux2 = [x[0] for x in liste_morceaux]\n",
        "  liste_N = [ len(i) for i in liste_morceaux2 ]\n",
        "  liste_duree = [ x/22050 for x in liste_N ]\n",
        "  \n",
        "  res = []\n",
        "  t = 0\n",
        "  for p in range(len(liste_morceaux2)):\n",
        "    t+= liste_duree[p]\n",
        "    res.append((liste_morceaux[p][1],t,liste_morceaux[p][2]))\n",
        "  \n",
        "  liste_morceaux2 = np.concatenate(liste_morceaux2)\n",
        "  # On compte le nombre de voix de femmes de d'hommes piochés \n",
        "  compteur_f = 0 \n",
        "  compteur_h = 0 \n",
        "\n",
        "  genres = df_sample[\"SEX\"].values\n",
        "\n",
        "  for i in genres:\n",
        "    if i == ' M ':\n",
        "      compteur_h += 1\n",
        "    if i == ' F ':\n",
        "      compteur_f += 1\n",
        "\n",
        "  line_df = pd.DataFrame([[ liste_morceaux2, nb_speaker , compteur_f, compteur_h,res ]], columns = df3.columns)\n",
        "  df3 = pd.concat([df3, line_df], ignore_index = True)"
      ],
      "metadata": {
        "id": "Q8vsLnpHjXrG"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "timings = []\n",
        "\n",
        "for i in range(200):\n",
        "  liste = df3['timing'].values[i]\n",
        "\n",
        "  new_list = []\n",
        "  (speak_id, start_time, sex) = liste.pop(0)\n",
        "\n",
        "  while liste :\n",
        "    new_speak_id, new_start_time, new_sex = liste.pop(0)\n",
        "\n",
        "    if speak_id == new_speak_id :\n",
        "      start_time += new_start_time\n",
        "\n",
        "    else :\n",
        "      new_list.append((speak_id, start_time, sex))\n",
        "      speak_id, start_time, sex = new_speak_id, new_start_time, new_sex\n",
        "  timings.append(new_list)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kh8HZgR_kTt-",
        "outputId": "a059a184-2a8b-4ac0-e11e-3d356325843d"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "[(1, 2.0940589569160997, ' F '), (0, 6.315691609977324, ' F '), (1, 14.306213151927437, ' F ')]\n",
            "[(0, 10.072879818594105, ' F '), (1, 16.139455782312925, ' F '), (1, 19.96013605442177, ' F '), (2, 34.56512471655329, ' M '), (3, 42.652789115646264, ' F '), (3, 48.299501133786855, ' F ')]\n",
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            "[(1, 2.4979591836734696, ' F '), (0, 15.016780045351474, ' M '), (2, 16.01360544217687, ' M ')]\n",
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            "[(2, 3.3536507936507935, ' F '), (2, 3.8346485260770975, ' F '), (2, 4.343401360544218, ' F '), (0, 8.627981859410431, ' F '), (1, 11.961950113378684, ' M '), (1, 12.404263038548752, ' M ')]\n",
            "[(3, 0.10476190476190476, ' M '), (0, 6.716643990929705, ' M '), (2, 11.754376417233559, ' M '), (1, 17.308072562358277, ' M '), (3, 28.240090702947846, ' M '), (1, 33.48040816326531, ' M ')]\n",
            "[(0, 6.108027210884353, ' M '), (1, 10.974331065759637, ' F '), (1, 18.137414965986395, ' F '), (2, 27.575918367346937, ' M ')]\n",
            "[(1, 12.453378684807257, ' F '), (0, 13.130975056689342, ' M ')]\n",
            "[(0, 3.377233560090703, ' M '), (1, 6.4491156462585035, ' F '), (1, 11.497687074829933, ' F ')]\n",
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            "[(0, 14.253469387755102, ' M '), (1, 26.40566893424036, ' M '), (1, 28.87492063492063, ' M ')]\n",
            "[(0, 8.831065759637188, ' M '), (1, 10.651156462585034, ' M ')]\n",
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            "[(1, 2.399909297052154, ' F '), (1, 15.095283446712019, ' F '), (2, 17.007755102040818, ' M '), (0, 31.487437641723357, ' F '), (2, 31.90825396825397, ' M '), (2, 44.25151927437642, ' M ')]\n",
            "[(0, 4.624807256235828, ' M '), (1, 5.721179138321996, ' M '), (1, 7.702721088435375, ' M ')]\n",
            "[(1, 4.382312925170068, ' F '), (0, 8.321678004535148, ' M ')]\n",
            "[(3, 0.7653061224489796, ' F '), (0, 1.4161451247165533, ' F '), (1, 11.526439909297052, ' F '), (3, 14.251972789115646, ' F '), (2, 23.003038548752833, ' F '), (3, 24.129614512471655, ' F '), (2, 25.868117913832197, ' F ')]\n",
            "[(1, 7.658956916099773, ' F '), (0, 10.081496598639456, ' F '), (1, 12.858684807256235, ' F ')]\n",
            "[(1, 5.987301587301587, ' F '), (1, 13.654512471655329, ' F '), (0, 20.977414965986394, ' M ')]\n",
            "[(2, 0.07102040816326531, ' M '), (1, 1.6411791383219954, ' F '), (1, 1.7052154195011338, ' F '), (2, 3.4463038548752833, ' M '), (2, 15.856825396825398, ' M '), (0, 16.41015873015873, ' M ')]\n",
            "[(2, 2.1297505668934242, ' M '), (1, 9.030521541950113, ' M '), (1, 15.720090702947846, ' M '), (2, 16.220362811791382, ' M '), (0, 28.647845804988663, ' M '), (2, 30.34453514739229, ' M ')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "timings2 = []\n",
        "\n",
        "for i in timings:\n",
        "  new_list = []\n",
        "  start_time = 0\n",
        "  (speaker, duration, sex) = i[0]\n",
        "  new_list.append((speaker,0,duration,sex))\n",
        "  for j in range(1, len(i)-1):\n",
        "    duration2 = i[j+1][1] - i[j][1]\n",
        "    new_start_time = start_time + duration \n",
        "\n",
        "    (speaker, start_time, duration, sex) = i[j][0], new_start_time, duration2, i[j][2]\n",
        "    \n",
        "    new_list.append((speaker, start_time, duration, sex))\n",
        "  timings2.append(new_list)\n",
        "\n",
        "timings2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FhRIOk7eXelF",
        "outputId": "8bdb45f7-dbcf-470c-9f61-5dc7b32af4f3"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " [(1, 0, 7.658956916099773, ' F ')],\n",
              " [(1, 0, 19.641814058956918, ' F ')],\n",
              " [(2, 0, 0.07102040816326531, ' M '),\n",
              "  (1, 0.07102040816326531, 15.956734693877554, ' F ')],\n",
              " [(2, 0, 2.1297505668934242, ' M '),\n",
              "  (1, 2.1297505668934242, -8.530249433106576, ' M '),\n",
              "  (2, -6.400498866213152, 12.42748299319728, ' M ')]]"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df3.timing = timings2"
      ],
      "metadata": {
        "id": "87Mg3NPakUs6"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3.to_csv('nouvelle_base_2.csv')"
      ],
      "metadata": {
        "id": "dKmpZ47VlfGE"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df3.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "A2VeIxxelvk9",
        "outputId": "8106442b-ecd5-4df3-882e-35110eed27c2"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                freq nb_pers nb_f nb_h  \\\n",
              "0  [0.050662443, 0.05233018, 0.053912535, 0.05480...       4    1    3   \n",
              "1  [-0.0011938515, -0.007182852, -0.012882587, -0...       3    1    2   \n",
              "2  [-0.00023306884, -9.711788e-05, -0.00018447044...       4    2    2   \n",
              "3  [-0.00083981245, -0.0009235549, -0.00045078128...       3    1    2   \n",
              "4  [-2.6354796e-06, -1.4959975e-06, 7.119993e-06,...       4    1    3   \n",
              "\n",
              "                                              timing  len_timling  \n",
              "0  [(2, 0, 1.4854421768707482,  M ), (3, 1.485442...            4  \n",
              "1  [(2, 0, 1.7310657596371881,  M ), (0, 1.731065...            3  \n",
              "2  [(3, 0, 15.741315192743766,  M ), (0, 15.74131...            4  \n",
              "3                   [(2, 0, 7.847709750566893,  F )]            1  \n",
              "4  [(2, 0, 6.035555555555556,  M ), (3, 6.0355555...            2  "
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              "      <th>freq</th>\n",
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              "      <th>1</th>\n",
              "      <td>[-0.0011938515, -0.007182852, -0.012882587, -0...</td>\n",
              "      <td>3</td>\n",
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              "      <td>3</td>\n",
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              "      <th>2</th>\n",
              "      <td>[-0.00023306884, -9.711788e-05, -0.00018447044...</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
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              "      <td>4</td>\n",
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              "      <th>3</th>\n",
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              "      <td>3</td>\n",
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              "      <td>2</td>\n",
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              "      <td>1</td>\n",
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              "      <th>4</th>\n",
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              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
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              "      <td>2</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bc54a61f-9467-4c8a-9b42-372ba1fa74fc')\"\n",
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              "\n",
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bc54a61f-9467-4c8a-9b42-372ba1fa74fc button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bc54a61f-9467-4c8a-9b42-372ba1fa74fc');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.io.wavfile import write\n",
        "\n",
        "data = df3[\"freq\"][5]\n",
        "samplerate = 22050"
      ],
      "metadata": {
        "id": "zewb4yzdmTy-"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(200):\n",
        "  data = df3['freq'][i]\n",
        "  write(f'/content/drive/MyDrive/hackaton twelve/nouvelle base 2/new_{i}.wav', samplerate, data)\n",
        "\n"
      ],
      "metadata": {
        "id": "EkXTRbv6numr"
      },
      "execution_count": 109,
      "outputs": []
    }
  ]
}