Small Model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BNt4eZDyB-GZ"
      },
      "source": [
        "# Test"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e2jruf-Ka3Dn"
      },
      "source": [
        "## Start test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NV61vXXIbaTi",
        "outputId": "808c594b-3f4f-4e0e-b2ce-69a0978601ff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2023-06-27 05:58:10--  https://bark.phon.ioc.ee/voxlingua107/zip_urls.txt\n",
            "Resolving bark.phon.ioc.ee (bark.phon.ioc.ee)... 193.40.251.129\n",
            "Connecting to bark.phon.ioc.ee (bark.phon.ioc.ee)|193.40.251.129|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4819 (4.7K) [text/plain]\n",
            "Saving to: ‘zip_urls.txt’\n",
            "\n",
            "zip_urls.txt        100%[===================>]   4.71K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-06-27 05:58:11 (169 MB/s) - ‘zip_urls.txt’ saved [4819/4819]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "! wget https://bark.phon.ioc.ee/voxlingua107/zip_urls.txt\n",
        "! cat zip_urls.txt |  parallel -j 4 wget --continue"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JiETxI9ibAFt"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "def write_file(name):\n",
        "    df = pd.DataFrame(data = [[1,2],[3,4],[5,6]],\n",
        "    columns = ['col-one', 'col-two'])\n",
        "    df.to_csv(name+'.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sduTtK2b0wF",
        "outputId": "efd2f970-b190-4043-eee7-57c7f16e499d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[None, None, None]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "temp_list= np.array([\"hello\",\"hi\",\"file1\"])\n",
        "[write_file(x) for x in temp_list]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uqwBvN0CTdc2"
      },
      "outputs": [],
      "source": [
        "def create_spect(filename,name, is_train=True):\n",
        "    plt.interactive(False)\n",
        "    clip, sample_rate = librosa.load(filename, sr=None)\n",
        "    fig = plt.figure(figsize=[0.72,0.72])\n",
        "    ax = fig.add_subplot(111)\n",
        "    #ax.axes.get_xaxis().set_visible(False)\n",
        "    #ax.axes.get_yaxis().set_visible(False)\n",
        "    ax.set_frame_on(False)\n",
        "    S = librosa.feature.melspectrogram(y=clip, sr=sample_rate)\n",
        "    #S = librosa.feature.mfcc(y=clip, sr=sample_rate)\n",
        "    librosa.display.specshow(librosa.power_to_db(S, ref=np.max))\n",
        "    prefix= TRAIN_IMG_PATH\n",
        "    if not is_train:\n",
        "        prefix= TEST_IMG_PATH\n",
        "    filename  = prefix + name\n",
        "    plt.savefig(filename, dpi=400, bbox_inches='tight',pad_inches=0)\n",
        "    plt.close()\n",
        "    fig.clf()\n",
        "    plt.close(fig)\n",
        "    plt.close('all')\n",
        "    del filename,name,clip,sample_rate,fig,ax,S"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qIa9GhXCzJQL"
      },
      "outputs": [],
      "source": [
        "def just_spec(filename, name, is_train=True):\n",
        "    plt.interactive(False)\n",
        "    matplotlib.use('Agg')\n",
        "    data, samplerate = sf.read(filename)\n",
        "    #freq, time, Sxx = signal.spectrogram(data, samplerate, scaling='spectrum')\n",
        "    #plt.pcolormesh(time, freq, Sxx)\n",
        "    Pxx, freqs, bins, im = plt.specgram(data, Fs=samplerate)\n",
        "    ax = plt.gca()\n",
        "    #fig = plt.gcf()\n",
        "    ax.axis(\"off\")\n",
        "    prefix= TRAIN_IMG_PATH\n",
        "    if not is_train:\n",
        "        prefix= TEST_IMG_PATH\n",
        "    filename  = prefix + name\n",
        "    plt.savefig(filename, dpi=400, bbox_inches='tight',pad_inches=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OKE7e0xB_acO"
      },
      "outputs": [],
      "source": [
        "import soundfile as sf\n",
        "import scipy.signal as signal\n",
        "import matplotlib\n",
        "from PIL import Image\n",
        "import io\n",
        "plt.interactive(False)\n",
        "import timeit\n",
        "start= [0]*20\n",
        "end= [0]*20\n",
        "cnt=0\n",
        "def upd_time():\n",
        "    global cnt\n",
        "    end[cnt]=timeit.default_timer()\n",
        "    cnt=cnt+1\n",
        "    start[cnt]=timeit.default_timer()\n",
        "\n",
        "def create_greenspec(filename, name, is_train=True):\n",
        "    global cnt\n",
        "    cnt=0\n",
        "    upd_time()\n",
        "    #plt.interactive(False)\n",
        "    matplotlib.use('Agg')\n",
        "    upd_time()\n",
        "    data, samplerate = sf.read(filename)\n",
        "    #freq, time, Sxx = signal.spectrogram(data, samplerate, scaling='spectrum')\n",
        "    #plt.pcolormesh(time, freq, Sxx)\n",
        "    upd_time()\n",
        "    Pxx, freqs, bins, im = plt.specgram(data, Fs=samplerate)\n",
        "    upd_time()\n",
        "    ax = plt.gca()\n",
        "    upd_time()\n",
        "    fig = plt.gcf()\n",
        "    upd_time()\n",
        "    ax.axis(\"off\")\n",
        "    upd_time()\n",
        "    prefix= TRAIN_IMG_PATH\n",
        "    upd_time()\n",
        "    if not is_train:\n",
        "        prefix= TEST_IMG_PATH\n",
        "    upd_time()\n",
        "    filename  = prefix + name\n",
        "    upd_time()\n",
        "    #plt.savefig(filename, dpi=400, bbox_inches='tight',pad_inches=0)\n",
        "    #upd_time()\n",
        "\n",
        "    #return\n",
        "    buf = io.BytesIO()\n",
        "    fig.canvas.print_jpg(buf)\n",
        "    buf.seek(0)\n",
        "    plot_image = Image.open(buf)\n",
        "\n",
        "\n",
        "    upd_time()\n",
        "    # Render the plot to a PIL image\n",
        "    #plot_image = Image.frombytes('RGB', canvas.get_width_height(), canvas.tostring_rgb())\n",
        "    #upd_time()\n",
        "    plot_image.save(filename)\n",
        "    upd_time()\n",
        "    # Close the plot and PIL image\n",
        "    #plt.close(fig)\n",
        "    #upd_time()\n",
        "    #plot_image.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "UN16c9-0bNBR",
        "outputId": "50423bc6-022f-4f05-b7d4-54a50cff0592"
      },
      "outputs": [
        {
          "ename": "LibsndfileError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mLibsndfileError\u001b[0m                           Traceback (most recent call last)",
            "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2302\u001b[0m             \u001b[0mvargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_n\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_n\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2304\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vectorize_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2306\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_vectorize_call\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m   2380\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2381\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2382\u001b[0;31m             \u001b[0mufunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0motypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2384\u001b[0m             \u001b[0;31m# Convert args to object arrays first\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_get_ufunc_and_otypes\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m   2340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2341\u001b[0m             \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2342\u001b[0;31m             \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2344\u001b[0m             \u001b[0;31m# Performance note: profiling indicates that -- for simple\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-78-a0ea5c7619f3>\u001b[0m in \u001b[0;36mjust_spec\u001b[0;34m(filename, name, is_train)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minteractive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Agg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamplerate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0;31m#freq, time, Sxx = signal.spectrogram(data, samplerate, scaling='spectrum')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;31m#plt.pcolormesh(time, freq, Sxx)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/soundfile.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(file, frames, start, stop, dtype, always_2d, fill_value, out, samplerate, channels, format, subtype, endian, closefd)\u001b[0m\n\u001b[1;32m    283\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    284\u001b[0m     \"\"\"\n\u001b[0;32m--> 285\u001b[0;31m     with SoundFile(file, 'r', samplerate, channels,\n\u001b[0m\u001b[1;32m    286\u001b[0m                    subtype, endian, format, closefd) as f:\n\u001b[1;32m    287\u001b[0m         \u001b[0mframes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/soundfile.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, file, mode, samplerate, channels, subtype, endian, format, closefd)\u001b[0m\n\u001b[1;32m    656\u001b[0m         self._info = _create_info_struct(file, mode, samplerate, channels,\n\u001b[1;32m    657\u001b[0m                                          format, subtype, endian)\n\u001b[0;32m--> 658\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode_int\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclosefd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    659\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0missuperset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'r+'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseekable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    660\u001b[0m             \u001b[0;31m# Move write position to 0 (like in Python file objects)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/soundfile.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, file, mode_int, closefd)\u001b[0m\n\u001b[1;32m   1214\u001b[0m             \u001b[0;31m# get the actual error code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1215\u001b[0m             \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_snd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msf_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mLibsndfileError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Error opening {0!r}: \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1217\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode_int\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_snd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSFM_WRITE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1218\u001b[0m             \u001b[0;31m# Due to a bug in libsndfile version <= 1.0.25, frames != 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mLibsndfileError\u001b[0m: Error opening '/content/train/train/es_m_b8e0e6f56f02e6f8f79cc360958e5982.fragment25.speed6.flac': System error."
          ]
        }
      ],
      "source": [
        "%%time\n",
        "#for file in train_data_dir[i:i+train_size]:\n",
        "#    name = file.split('/')[-1]\n",
        "#    train_id.append(name+\".jpg\")\n",
        "#    create_spectrogram(file,name)\n",
        "\n",
        "traindf = pd.DataFrame(train_data_dir[0:10],columns = ['file'])\n",
        "traindf['ID']= traindf['file'].apply(lambda x: x.split('/')[-1]+'.jpg')\n",
        "#traindf.apply(lambda x: create_spectrogram(x.file, x.ID), axis=1)\n",
        "np.vectorize(just_spec)(traindf['file'], traindf['ID'])\n",
        "traindf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K9-inzaHk4Tk",
        "outputId": "034dec32-f505-45ee-feb8-789e8cbf3507"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[6624.770487622, 4.377999994176207e-05, 0.011670042000332614, 0.02217329100039933, 5.486000191012863e-06, 2.04900061362423e-06, 2.3611999495187774e-05, 1.8509999790694565e-06, 4.060002538608387e-07, 1.4940005712560378e-06, 0.9924033429997507, 0.0007784659992466914, -6625.797607543, 0, 0, 0, 0, 0, 0, 0]\n"
          ]
        }
      ],
      "source": [
        "how = [end[i]-start[i] for i in range(20)]\n",
        "print(how)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "moqIYZHH6eQ8"
      },
      "outputs": [],
      "source": [
        "!cp $TRAIN_IMG_PATH $DRIVE_PATH -r"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pHBU3DJf6xR7"
      },
      "outputs": [],
      "source": [
        "print(\"done\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "JukZI3_zbsdT",
        "outputId": "a5bc6fdc-9b29-46f9-c854-6d84fdada1df"
      },
      "outputs": [
        {
          "data": {
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              "                                                 file  \\\n",
              "0   /content/train/train/en_f_386ee651f6f1539ff562...   \n",
              "1   /content/train/train/es_f_53b555eab2b3baada380...   \n",
              "2   /content/train/train/es_m_534a0cf6f6aea8e35667...   \n",
              "3   /content/train/train/de_m_df90bffac026f705f4a9...   \n",
              "4   /content/train/train/en_m_61764486dd621ce03fa4...   \n",
              "..                                                ...   \n",
              "95  /content/train/train/en_f_10134f409d9b7b0b95fe...   \n",
              "96  /content/train/train/de_m_df90bffac026f705f4a9...   \n",
              "97  /content/train/train/es_m_d5b91a4ffb1ead826b79...   \n",
              "98  /content/train/train/es_f_bac9be13a058d1c859ed...   \n",
              "99  /content/train/train/en_m_81995ee8a5e990193b78...   \n",
              "\n",
              "                                                   ID  \n",
              "0   en_f_386ee651f6f1539ff5622c55e234e5a4.fragment...  \n",
              "1   es_f_53b555eab2b3baada380f7d3ede20b20.fragment...  \n",
              "2   es_m_534a0cf6f6aea8e35667a9147d2d0f8d.fragment...  \n",
              "3   de_m_df90bffac026f705f4a99871d71684f1.fragment...  \n",
              "4   en_m_61764486dd621ce03fa461e92cd9e2b3.fragment...  \n",
              "..                                                ...  \n",
              "95  en_f_10134f409d9b7b0b95fed6e025febcad.fragment...  \n",
              "96  de_m_df90bffac026f705f4a99871d71684f1.fragment...  \n",
              "97  es_m_d5b91a4ffb1ead826b7968ec19cbfa1c.fragment...  \n",
              "98  es_f_bac9be13a058d1c859ed5d2e3a0dc27b.fragment...  \n",
              "99  en_m_81995ee8a5e990193b7858ec4b158e48.fragment...  \n",
              "\n",
              "[100 rows x 2 columns]"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "traindf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d7dZQ-cb9nII",
        "outputId": "aedd9cbc-bb43-428c-b12c-d7c8e7ec67d1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "es_m_b8e0e6f56f02e6f8f79cc360958e5982.fragment25.speed6.flac.jpg\n",
            "/content/train/train/es_m_b8e0e6f56f02e6f8f79cc360958e5982.fragment25.speed6.flac\n"
          ]
        }
      ],
      "source": [
        "print(traindf.ID[0])\n",
        "print(traindf.file[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "Ix3Rvh7kB64z",
        "outputId": "4c1064f7-e8bd-4db5-d943-6599999c3e14"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "create_greenspec(train_data_dir[0], 'that.jpg')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "id": "n2wTEsPdOUKW",
        "outputId": "56ac00c2-3ef4-410a-f165-596e34273add"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/train/train/en_f_386ee651f6f1539ff5622c55e234e5a4.fragment5.speed1.flac\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import soundfile as sf\n",
        "import scipy.signal as signal\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "print(train_data_dir[0])\n",
        "data, samplerate = sf.read(train_data_dir[0])\n",
        "freq, time, Sxx = signal.spectrogram(data, samplerate, scaling='spectrum')\n",
        "plt.pcolormesh(time, freq, Sxx)\n",
        "\n",
        "#fig = plt.figure(figsize=[0.72,0.72])\n",
        "#ax = fig.add_subplot(111)\n",
        "#ax.axes.get_xaxis().set_visible(False)\n",
        "#ax.axes.get_yaxis().set_visible(False)\n",
        "\n",
        "ax = plt.gca()\n",
        "\n",
        "#hide x-axis\n",
        "#ax.get_xaxis().set_visible(False)\n",
        "#ax.get_yaxis().set_visible(False)\n",
        "\n",
        "#hide y-axis\n",
        "#ax.set_frame_on(False)\n",
        "ax.axis(\"off\")\n",
        "# add axis labels\n",
        "#plt.ylabel('Frequency [Hz]')\n",
        "#plt.xlabel('Time [sec]')\n",
        "plt.savefig(\"./this.jpg\", dpi=400, bbox_inches='tight',pad_inches=0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "Vsw2LbAHWKC9",
        "outputId": "b6a87012-014a-43c9-8aad-8b2ed8ed2c7c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "Pxx, freqs, bins, im = plt.specgram(data, Fs=samplerate)\n",
        "\n",
        "# add axis labels\n",
        "plt.ylabel('Frequency [Hz]')\n",
        "plt.xlabel('Time [sec]')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "id": "H7n6DDtMFKU8",
        "outputId": "5d368936-ec68-4ff2-89d0-fd425b3b8630"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 72x72 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import soundfile as sf\n",
        "import scipy.signal as signal\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "data, samplerate = sf.read(\"./audio.flac\")\n",
        "freq, time, Sxx = signal.spectrogram(data, samplerate, scaling='spectrum')\n",
        "plt.pcolormesh(time, freq, Sxx)\n",
        "\n",
        "# add axis labels\n",
        "plt.ylabel('Frequency [Hz]')\n",
        "plt.xlabel('Time [sec]')\n",
        "\n",
        "plt.show() #added second time"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hTvW1PYpGbWn",
        "outputId": "36302f32-d6f2-43a3-8dc2-100fe1a56d0a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Time [sec]')"
            ]
          },
          "execution_count": 45,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "Pxx, freqs, bins, im = plt.specgram(data, Fs=samplerate)\n",
        "\n",
        "# add axis labels\n",
        "plt.ylabel('Frequency [Hz]')\n",
        "plt.xlabel('Time [sec]')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-zZ98U9wa2qn"
      },
      "source": [
        "## End Test\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MXKdJj4qH2Fw"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "icastHjNVXCG"
      },
      "source": [
        "# Run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u9QJw4y73hD7"
      },
      "source": [
        "## check GPU availabilty"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "0f1G9cgB3lbE",
        "outputId": "a257c04e-2e80-499f-f5ca-60354c3c4835"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/device:GPU:0'"
            ]
          },
          "execution_count": 1,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "tf.test.gpu_device_name()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUGyMYnSh5vP",
        "outputId": "a959a082-a2da-4d1a-d38b-7c900b764e96"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of GPUs Available:  1\n"
          ]
        }
      ],
      "source": [
        "physical_devices = tf.config.experimental.list_physical_devices('GPU')\n",
        "print(\"Number of GPUs Available: \", len(physical_devices))\n",
        "if len(physical_devices) > 0:\n",
        "    tf.config.experimental.set_memory_growth(physical_devices[0], True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zvW1K9G_Qfoc"
      },
      "source": [
        "## Paths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SgP9QBEnQhZb"
      },
      "outputs": [],
      "source": [
        "CUR_DIR = '/content/' #this may be different runnig local\n",
        "MAT_DIR= CUR_DIR+'mat/'\n",
        "TRAIN_MAT_PATH = MAT_DIR + 'train/train/'\n",
        "TEST_MAT_PATH = MAT_DIR + 'test/test/'\n",
        "VOICE_DIR = CUR_DIR+'voice/'\n",
        "TRAIN_VOICE_PATH = VOICE_DIR+'train/train/'\n",
        "TEST_VOICE_PATH = VOICE_DIR+'test/test/'\n",
        "DRIVE_PATH = CUR_DIR+'drive/MyDrive/Bachelor_Project/'\n",
        "#TRAIN_VOICE_OLD_PATH = CUR_DIR+'train/train/'\n",
        "#TEST_VOICE_OLD_PATH = CUR_DIR+'test/test/'\n",
        "#TRAIN_IMG_PATH = CUR_DIR+'train/train-img/'\n",
        "#TEST_IMG_PATH = CUR_DIR+'test/test-img/'\n",
        "TRAIN_MAT_PATH = MAT_DIR + 'train/'\n",
        "TEST_MAT_PATH = MAT_DIR + 'test/'\n",
        "MOZILLA_PATH= CUR_DIR+'mozilla/'\n",
        "MOZILLA_VALID_TRAIN = CUR_DIR+'mozilla/cv-valid-train/'\n",
        "MOZILLA_VALID_DEV = CUR_DIR+'mozilla/cv-valid-dev/'\n",
        "MOZILLA_VALID_TEST = CUR_DIR+'mozilla/cv-valid-test/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "elHdKK-PBVe3",
        "outputId": "f966dbc0-26d0-44af-dc4a-c24fa69b333f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘/content/mat/’: File exists\n",
            "mkdir: cannot create directory ‘/content/voice/’: File exists\n",
            "mkdir: cannot create directory ‘/content/mat/train/’: File exists\n",
            "mkdir: cannot create directory ‘/content/mat/test/’: File exists\n"
          ]
        }
      ],
      "source": [
        "! mkdir $MAT_DIR\n",
        "! mkdir $VOICE_DIR\n",
        "! mkdir $TRAIN_MAT_PATH\n",
        "! mkdir $TEST_MAT_PATH"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9S7dMYTCVJyS"
      },
      "source": [
        "## mount google drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "wIC__YLQ4a52",
        "outputId": "05f25ec1-c07e-4bee-ff25-aa71de7b0a3c"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(CUR_DIR+'drive/', force_remount=True)\n",
        "#%cd '{CUR_DIR}drive/MyDrive/Bachelor_Project/'\n",
        "#%ls"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4JYKCq_iWqkk"
      },
      "source": [
        "## import libraries\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tKaCosVCH3to"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "from glob import glob\n",
        "import librosa\n",
        "import librosa.display\n",
        "import soundfile as sf\n",
        "import scipy.signal as signal\n",
        "import gc\n",
        "import shutil"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "56womG9d7qKe"
      },
      "source": [
        "## Load Dataset from kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ih3NUPBw7k2P",
        "outputId": "b76b5da1-0b5a-46ba-9ca4-25f2c1666a9c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.13)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.5.7)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.27.1)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.65.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.26.16)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.4)\n"
          ]
        }
      ],
      "source": [
        "!pip install kaggle"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NbJFwuDG7vOH"
      },
      "outputs": [],
      "source": [
        "#! mkdir ~/.kaggle\n",
        "#! cp kaggle.json ~/.kaggle/\n",
        "#! chmod 600 ~/.kaggle/kaggle.json\n",
        "#! kaggle datasets download mozillaorg/common-voice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NJJTzhHd71rO"
      },
      "outputs": [],
      "source": [
        "#!unzip {CUR_DIR}common-voice.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mWz-0PAmBGu0"
      },
      "source": [
        "# spoken-language-identification dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IiDJhi0oBGcV",
        "outputId": "16205602-883a-486e-8bd8-29c20a3ee54a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading spoken-language-identification.zip to /content\n",
            "100% 14.9G/14.9G [03:16<00:00, 103MB/s] \n",
            "100% 14.9G/14.9G [03:16<00:00, 81.3MB/s]\n"
          ]
        }
      ],
      "source": [
        "! mkdir ~/.kaggle\n",
        "! cp $DRIVE_PATH/kaggle.json ~/.kaggle/\n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "! kaggle datasets download toponowicz/spoken-language-identification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SLKtPwKI-oZv"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!unzip {CUR_DIR}spoken-language-identification.zip -d $VOICE_DIR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iqV7sP6ligzP"
      },
      "outputs": [],
      "source": [
        "! mkdir $CUR_DIR/train\n",
        "! mkdir $CUR_DIR/test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3-XIBcY_hllf"
      },
      "outputs": [],
      "source": [
        "! mv $TRAIN_VOICE_PATH $TRAIN_VOICE_OLD_PATH\n",
        "! mv $TEST_VOICE_PATH $TEST_VOICE_OLD_PATH"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsqFx6eL_n8H",
        "outputId": "2e81c425-3a0d-4d77-a6ed-c787731edadd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mkdir: missing operand\n",
            "Try 'mkdir --help' for more information.\n",
            "mkdir: missing operand\n",
            "Try 'mkdir --help' for more information.\n"
          ]
        }
      ],
      "source": [
        "!mkdir $TRAIN_IMG_PATH\n",
        "!mkdir $TEST_IMG_PATH"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0UurKaezGDn7"
      },
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "my5nSPwcGE3a"
      },
      "outputs": [],
      "source": [
        "! kaggle datasets download mozillaorg/common-voice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qfkd966-IT4n"
      },
      "outputs": [],
      "source": [
        "!mkdir mozilla"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f2RS9C4tGP5j"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!unzip {CUR_DIR}common-voice.zip -d $MOZILLA_PATH"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3PGxfNHYI4Ev"
      },
      "source": [
        "#### remove invalid and other datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b3CWtQwQI3ag"
      },
      "outputs": [],
      "source": [
        "!rm $MOZILLA_PATH/LICENSE.txt\n",
        "!rm $MOZILLA_PATH/README.txt\n",
        "!rm -r $MOZILLA_PATH/cv-other*\n",
        "!rm -r $MOZILLA_PATH/cv-invalid*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eKNLQb_HLZZN"
      },
      "outputs": [],
      "source": [
        "valid_train_df = pd.read_csv(MOZILLA_PATH+\"cv-valid-train.csv\")\n",
        "valid_train_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jkrbXBPrXH8X"
      },
      "outputs": [],
      "source": [
        "valid_dev_df = pd.read_csv(MOZILLA_PATH+\"cv-valid-dev.csv\")\n",
        "valid_dev_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l2t5U5n1XHxp"
      },
      "outputs": [],
      "source": [
        "valid_test_df = pd.read_csv(MOZILLA_PATH+\"cv-valid-test.csv\")\n",
        "valid_test_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vHQ0uqZAXW94"
      },
      "outputs": [],
      "source": [
        "def process_mozilla_df(df, path_type):\n",
        "    #df = df[pd.notnull(df['accent'])]\n",
        "    df.dropna(subset=['accent'], inplace=True)\n",
        "    df.rename(columns={'filename': 'file', 'accent': 'lang'}, inplace=True)\n",
        "    df['ID'] = df['file'].str.split('/', expand=True)[1] + '.npy'\n",
        "    df['file'] = path_type+df['file']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o1Y-m4L_Xwno"
      },
      "outputs": [],
      "source": [
        "process_mozilla_df(valid_train_df, MOZILLA_VALID_TRAIN)\n",
        "process_mozilla_df(valid_dev_df, MOZILLA_VALID_DEV)\n",
        "process_mozilla_df(valid_test_df, MOZILLA_VALID_TEST)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "5KajzVirY77m",
        "outputId": "322df90b-2810-4941-d429-9438d2cb7768"
      },
      "outputs": [
        {
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              "      <th></th>\n",
              "      <th>file</th>\n",
              "      <th>text</th>\n",
              "      <th>up_votes</th>\n",
              "      <th>down_votes</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>lang</th>\n",
              "      <th>duration</th>\n",
              "      <th>ID</th>\n",
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              "      <th>5</th>\n",
              "      <td>/content/mozilla/cv-valid-train/cv-valid-train...</td>\n",
              "      <td>a shepherd may like to travel but he should ne...</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>/content/mozilla/cv-valid-train/cv-valid-train...</td>\n",
              "      <td>put jackie right on the staff</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
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              "      <td>sample-000008.mp3.jpg</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>/content/mozilla/cv-valid-train/cv-valid-train...</td>\n",
              "      <td>but he had found a guide and didn't want to mi...</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>/content/mozilla/cv-valid-train/cv-valid-train...</td>\n",
              "      <td>as they began to decorate the hallway a silhou...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>male</td>\n",
              "      <td>england</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sample-000014.mp3.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>/content/mozilla/cv-valid-train/cv-valid-train...</td>\n",
              "      <td>then they got ahold of some dough and went goofy</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "      <td>male</td>\n",
              "      <td>australia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sample-000019.mp3.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195766</th>\n",
              "      <td>/content/mozilla/cv-valid-train/cv-valid-train...</td>\n",
              "      <td>but before i go i want to tell you a little story</td>\n",
              "      <td>1</td>\n",
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              "      <th>195767</th>\n",
              "      <td>/content/mozilla/cv-valid-train/cv-valid-train...</td>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>195770</th>\n",
              "      <td>/content/mozilla/cv-valid-train/cv-valid-train...</td>\n",
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              "      <td>4</td>\n",
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              "      <th>195771</th>\n",
              "      <td>/content/mozilla/cv-valid-train/cv-valid-train...</td>\n",
              "      <td>the englishman said nothing</td>\n",
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              "      <th>195774</th>\n",
              "      <td>/content/mozilla/cv-valid-train/cv-valid-train...</td>\n",
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              "      <td>2</td>\n",
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            "text/plain": [
              "                                                     file  \\\n",
              "5       /content/mozilla/cv-valid-train/cv-valid-train...   \n",
              "8       /content/mozilla/cv-valid-train/cv-valid-train...   \n",
              "13      /content/mozilla/cv-valid-train/cv-valid-train...   \n",
              "14      /content/mozilla/cv-valid-train/cv-valid-train...   \n",
              "19      /content/mozilla/cv-valid-train/cv-valid-train...   \n",
              "...                                                   ...   \n",
              "195766  /content/mozilla/cv-valid-train/cv-valid-train...   \n",
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              "\n",
              "                                                     text  up_votes  \\\n",
              "5       a shepherd may like to travel but he should ne...         1   \n",
              "8                           put jackie right on the staff         3   \n",
              "13      but he had found a guide and didn't want to mi...         1   \n",
              "14      as they began to decorate the hallway a silhou...         1   \n",
              "19       then they got ahold of some dough and went goofy         1   \n",
              "...                                                   ...       ...   \n",
              "195766  but before i go i want to tell you a little story         1   \n",
              "195767  down below in the darkness were hundreds of pe...         3   \n",
              "195770  he heard a muffled grating sound and saw the b...         4   \n",
              "195771                        the englishman said nothing         1   \n",
              "195774                 the phone rang while she was awake         2   \n",
              "\n",
              "        down_votes        age  gender       lang  duration  \\\n",
              "5                0   twenties  female         us       NaN   \n",
              "8                0  seventies    male         us       NaN   \n",
              "13               0   thirties  female         us       NaN   \n",
              "14               0    sixties    male    england       NaN   \n",
              "19               0    fifties    male  australia       NaN   \n",
              "...            ...        ...     ...        ...       ...   \n",
              "195766           0   fourties    male    england       NaN   \n",
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              "195771           0   thirties    male    england       NaN   \n",
              "195774           0   twenties    male         us       NaN   \n",
              "\n",
              "                           ID  \n",
              "5       sample-000005.mp3.jpg  \n",
              "8       sample-000008.mp3.jpg  \n",
              "13      sample-000013.mp3.jpg  \n",
              "14      sample-000014.mp3.jpg  \n",
              "19      sample-000019.mp3.jpg  \n",
              "...                       ...  \n",
              "195766  sample-195766.mp3.jpg  \n",
              "195767  sample-195767.mp3.jpg  \n",
              "195770  sample-195770.mp3.jpg  \n",
              "195771  sample-195771.mp3.jpg  \n",
              "195774  sample-195774.mp3.jpg  \n",
              "\n",
              "[64711 rows x 9 columns]"
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              "                                                   file  \\\n",
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              "8     /content/mozilla/cv-valid-dev/cv-valid-dev/sam...   \n",
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              "\n",
              "                                                   text  up_votes  down_votes  \\\n",
              "4     he moved about invisible but everyone could he...         1           0   \n",
              "5                            but everything had changed         3           0   \n",
              "8     the shop folks were taking down their shutters...         1           0   \n",
              "9     the teacher thought that he'd taught himself a...         1           0   \n",
              "13    in those days very few of the people had any i...         5           0   \n",
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              "4053  if you start your emails with greetings let me...         1           0   \n",
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              "4069                    no sense messing up the streets         2           0   \n",
              "4073                that's what i'm not supposed to say         2           0   \n",
              "\n",
              "           age  gender       lang  duration                     ID  \n",
              "4     fourties  female    england       NaN  sample-000004.mp3.jpg  \n",
              "5        teens    male         us       NaN  sample-000005.mp3.jpg  \n",
              "8     twenties  female     canada       NaN  sample-000008.mp3.jpg  \n",
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              "...        ...     ...        ...       ...                    ...  \n",
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              "4073  thirties    male         us       NaN  sample-004073.mp3.jpg  \n",
              "\n",
              "[1350 rows x 9 columns]"
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              "    <tr>\n",
              "      <th>3971</th>\n",
              "      <td>/content/mozilla/cv-valid-test/cv-valid-test/s...</td>\n",
              "      <td>he's on my phone</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>teens</td>\n",
              "      <td>male</td>\n",
              "      <td>england</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sample-003971.mp3.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3975</th>\n",
              "      <td>/content/mozilla/cv-valid-test/cv-valid-test/s...</td>\n",
              "      <td>the planet's core is made of solid gold but ev...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>twenties</td>\n",
              "      <td>male</td>\n",
              "      <td>us</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sample-003975.mp3.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3976</th>\n",
              "      <td>/content/mozilla/cv-valid-test/cv-valid-test/s...</td>\n",
              "      <td>we've got her located</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>thirties</td>\n",
              "      <td>male</td>\n",
              "      <td>england</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sample-003976.mp3.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3980</th>\n",
              "      <td>/content/mozilla/cv-valid-test/cv-valid-test/s...</td>\n",
              "      <td>i've got to see nicole right away</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>fifties</td>\n",
              "      <td>male</td>\n",
              "      <td>australia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sample-003980.mp3.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3989</th>\n",
              "      <td>/content/mozilla/cv-valid-test/cv-valid-test/s...</td>\n",
              "      <td>i'm playing for keeps</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>fifties</td>\n",
              "      <td>male</td>\n",
              "      <td>australia</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sample-003989.mp3.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1338 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-82e00292-9d80-4749-a9bd-489594c6e33e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-82e00292-9d80-4749-a9bd-489594c6e33e 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-82e00292-9d80-4749-a9bd-489594c6e33e');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                   file  \\\n",
              "3     /content/mozilla/cv-valid-test/cv-valid-test/s...   \n",
              "5     /content/mozilla/cv-valid-test/cv-valid-test/s...   \n",
              "8     /content/mozilla/cv-valid-test/cv-valid-test/s...   \n",
              "9     /content/mozilla/cv-valid-test/cv-valid-test/s...   \n",
              "14    /content/mozilla/cv-valid-test/cv-valid-test/s...   \n",
              "...                                                 ...   \n",
              "3971  /content/mozilla/cv-valid-test/cv-valid-test/s...   \n",
              "3975  /content/mozilla/cv-valid-test/cv-valid-test/s...   \n",
              "3976  /content/mozilla/cv-valid-test/cv-valid-test/s...   \n",
              "3980  /content/mozilla/cv-valid-test/cv-valid-test/s...   \n",
              "3989  /content/mozilla/cv-valid-test/cv-valid-test/s...   \n",
              "\n",
              "                                                   text  up_votes  down_votes  \\\n",
              "3     down below in the darkness were hundreds of pe...         4           0   \n",
              "5     down below in the darkness were hundreds of pe...         4           1   \n",
              "8     this was the strangest of all things that ever...         1           0   \n",
              "9     it was glaringly hot not a cloud in the sky no...         3           0   \n",
              "14                         follow the instructions here         1           0   \n",
              "...                                                 ...       ...         ...   \n",
              "3971                                   he's on my phone         1           0   \n",
              "3975  the planet's core is made of solid gold but ev...         1           0   \n",
              "3976                              we've got her located         1           0   \n",
              "3980                  i've got to see nicole right away         1           0   \n",
              "3989                              i'm playing for keeps         2           0   \n",
              "\n",
              "           age gender       lang  duration                     ID  \n",
              "3     twenties   male         us       NaN  sample-000003.mp3.jpg  \n",
              "5     twenties   male         us       NaN  sample-000005.mp3.jpg  \n",
              "8     thirties   male    england       NaN  sample-000008.mp3.jpg  \n",
              "9      fifties   male         us       NaN  sample-000009.mp3.jpg  \n",
              "14    twenties   male   scotland       NaN  sample-000014.mp3.jpg  \n",
              "...        ...    ...        ...       ...                    ...  \n",
              "3971     teens   male    england       NaN  sample-003971.mp3.jpg  \n",
              "3975  twenties   male         us       NaN  sample-003975.mp3.jpg  \n",
              "3976  thirties   male    england       NaN  sample-003976.mp3.jpg  \n",
              "3980   fifties   male  australia       NaN  sample-003980.mp3.jpg  \n",
              "3989   fifties   male  australia       NaN  sample-003989.mp3.jpg  \n",
              "\n",
              "[1338 rows x 9 columns]"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "valid_test_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVpP1-wL5bCG"
      },
      "source": [
        "# Move to label directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TYPdn2T57hKP"
      },
      "outputs": [],
      "source": [
        "labels = ['en','es','de']\n",
        "for x in labels:\n",
        "    !mkdir $TRAIN_IMG_PATH/$x\n",
        "    !mkdir $TEST_IMG_PATH/$x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "44EIDFSN5ezC"
      },
      "outputs": [],
      "source": [
        "def move_to_label(name,lang, is_train=True):\n",
        "    prefix= TRAIN_IMG_PATH\n",
        "    if not is_train:\n",
        "        prefix= TEST_IMG_PATH\n",
        "    shutil.move(prefix+name,prefix+lang+\"/\"+name)\n",
        "    #print(prefix+name, \"to\", prefix+lang+\"/\"+name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e82Oa-y_Dt6H"
      },
      "outputs": [],
      "source": [
        "train_data_dir=np.array(glob(TRAIN_VOICE_PATH+\"*\"))\n",
        "test_data_dir=np.array(glob(TEST_VOICE_PATH+\"*\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "db65db8ek_jz",
        "outputId": "b79b8bd8-bd54-43cc-d006-ddc637d53bb1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['/content/voice/train/train/de_f_8e8ed685b0cf9f5bbd13a04629ee64c5.fragment21.pitch1.flac',\n",
              "       '/content/voice/train/train/es_m_60f8a38a1112aed756fd2bc91b0b52e0.fragment9.speed5.flac',\n",
              "       '/content/voice/train/train/es_m_3068613fb44c320f7318311a86929dc7.fragment23.noise2.flac',\n",
              "       ...,\n",
              "       '/content/voice/train/train/es_f_bf4285930fa46f2052e5bdbc37a8a4df.fragment20.noise6.flac',\n",
              "       '/content/voice/train/train/en_f_25836f5c38f5151611da3e2be4cfd20f.fragment16.pitch5.flac',\n",
              "       '/content/voice/train/train/de_m_df90bffac026f705f4a99871d71684f1.fragment7.speed2.flac'],\n",
              "      dtype='<U88')"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_data_dir"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5YW5vRAXH1fN",
        "outputId": "bd7d5561-fe3e-4033-f5ec-167ab4ac6b16"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "73080\n"
          ]
        }
      ],
      "source": [
        "print(len(train_data_dir))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cAI2IvaJK2BL"
      },
      "source": [
        "## Create spectogram"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2LJocrPwLMEF",
        "outputId": "46c2da3a-de2a-4015-d985-98906b41a293"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "before:  [0.72, 0.72]\n",
            "after:  [0.72, 0.72]\n"
          ]
        }
      ],
      "source": [
        "train_size= 10000\n",
        "test_size= 100\n",
        "print(\"before: \", plt.rcParams['figure.figsize'])\n",
        "plt.rcParams['figure.figsize'] = (0.72, 0.72)\n",
        "print(\"after: \", plt.rcParams['figure.figsize'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ejk1OpwAJaCv"
      },
      "outputs": [],
      "source": [
        "def create_spectrogram(filename,name, is_train=True):\n",
        "    plt.interactive(False)\n",
        "    clip, sample_rate = librosa.load(filename, sr=None)\n",
        "    fig = plt.figure(figsize=[0.72,0.72])\n",
        "    ax = fig.add_subplot(111)\n",
        "    ax.axes.get_xaxis().set_visible(False)\n",
        "    ax.axes.get_yaxis().set_visible(False)\n",
        "    ax.set_frame_on(False)\n",
        "    S = librosa.feature.melspectrogram(y=clip, sr=sample_rate)\n",
        "    #S = librosa.feature.mfcc(y=clip, sr=sample_rate)\n",
        "    librosa.display.specshow(librosa.power_to_db(S, ref=np.max))\n",
        "    prefix= TRAIN_IMG_PATH\n",
        "    if not is_train:\n",
        "        prefix= TEST_IMG_PATH\n",
        "    filename  = prefix + name\n",
        "    plt.savefig(filename, dpi=400, bbox_inches='tight',pad_inches=0)\n",
        "    plt.close()\n",
        "    fig.clf()\n",
        "    plt.close(fig)\n",
        "    plt.close('all')\n",
        "    del filename,name,clip,sample_rate,fig,ax,S"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iZtKCDrD_L47"
      },
      "outputs": [],
      "source": [
        "def just_spec(filename, name, is_train=True):\n",
        "    plt.interactive(False)\n",
        "    matplotlib.use('Agg')\n",
        "    data, samplerate = sf.read(filename)\n",
        "    Pxx, freqs, bins, im = plt.specgram(data, Fs=samplerate)\n",
        "    ax = plt.gca()\n",
        "    ax.axis(\"off\")\n",
        "    prefix= TRAIN_IMG_PATH\n",
        "    if not is_train:\n",
        "        prefix= TEST_IMG_PATH\n",
        "    filename  = prefix + name\n",
        "    plt.savefig(filename, dpi=400, bbox_inches='tight',pad_inches=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "14O2ae7LxtXE"
      },
      "outputs": [],
      "source": [
        "def spectrogram_mat(filename, file, is_train=True):\n",
        "    #plt.interactive(False)\n",
        "    clip, sample_rate = librosa.load(filename, sr=None)\n",
        "    #fig = plt.figure(figsize=[0.72,0.72])\n",
        "    #ax = fig.add_subplot(111)\n",
        "    #ax.axes.get_xaxis().set_visible(False)\n",
        "    #ax.axes.get_yaxis().set_visible(False)\n",
        "    #ax.set_frame_on(False)\n",
        "    S = librosa.feature.melspectrogram(y=clip, sr=sample_rate)\n",
        "    SP = librosa.power_to_db(S, ref=np.max)\n",
        "    #S = librosa.feature.mfcc(y=clip, sr=sample_rate)\n",
        "    #librosa.display.specshow(librosa.power_to_db(S, ref=np.max))\n",
        "    prefix= TRAIN_MAT_PATH\n",
        "    if not is_train:\n",
        "        prefix= TEST_MAT_PATH\n",
        "    name  = prefix + file[:-4] + \".npy\"\n",
        "    #name2  = prefix + file[:-4] + \"2.npy\"\n",
        "    #np.save(name, S)\n",
        "    np.save(name, SP)\n",
        "    #plt.savefig(filename, dpi=400, bbox_inches='tight',pad_inches=0)\n",
        "    #plt.close()\n",
        "    #fig.clf()\n",
        "    #plt.close(fig)\n",
        "    #plt.close('all')\n",
        "    #del filename,name,clip,sample_rate,fig,ax,S"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yik9qPtO4Q_n"
      },
      "outputs": [],
      "source": [
        "def show_spectrogram_mat(filename, file, is_train=True):\n",
        "    plt.interactive(False)\n",
        "    ###clip, sample_rate = librosa.load(filename, sr=None)\n",
        "    fig = plt.figure(figsize=[0.72,0.72])\n",
        "    ax = fig.add_subplot(111)\n",
        "    ax.axes.get_xaxis().set_visible(False)\n",
        "    ax.axes.get_yaxis().set_visible(False)\n",
        "    ax.set_frame_on(False)\n",
        "    ###S = librosa.feature.melspectrogram(y=clip, sr=sample_rate)\n",
        "    ###SP = librosa.power_to_db(S, ref=np.max)\n",
        "    #S = librosa.feature.mfcc(y=clip, sr=sample_rate)\n",
        "    S= np.load(filename)\n",
        "    #librosa.display.specshow(librosa.power_to_db(S, ref=np.max))\n",
        "    librosa.display.specshow(S)\n",
        "    prefix= TRAIN_IMG_PATH\n",
        "    if not is_train:\n",
        "        prefix= TEST_IMG_PATH\n",
        "    name  = prefix + file[:-4] + \".jpg\"\n",
        "    #name2  = prefix + file[:-4] + \"2.jpg\"\n",
        "    #np.save(name, S)\n",
        "    #np.save(name2, SP)\n",
        "    plt.savefig(name, dpi=400, bbox_inches='tight',pad_inches=0)\n",
        "    plt.close()\n",
        "    fig.clf()\n",
        "    plt.close(fig)\n",
        "    plt.close('all')\n",
        "    #del filename,name,clip,sample_rate,fig,ax,S"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ab3_vsvoIIWT",
        "outputId": "68bc75a8-f03e-4bab-bda8-3c3da127265e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 32.8 s, sys: 11.3 s, total: 44.1 s\n",
            "Wall time: 36.9 s\n"
          ]
        }
      ],
      "source": [
        "#%%time\n",
        "#i=0\n",
        "#train_id = []\n",
        "#for file in train_data_dir[i:i+train_size]:\n",
        "#    name = file.split('/')[-1] + '.jpg'\n",
        "#    train_id.append(name+\".jpg\")\n",
        "#    create_spectrogram(file,name)\n",
        "#\n",
        "#traindf = pd.DataFrame(train_data_dir[i:i+train_size],columns = ['ID'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "jJRHP5EUIHia",
        "outputId": "c00add36-b1b7-4176-9093-148ebd947a60"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 9min 29s, sys: 6min 49s, total: 16min 19s\n",
            "Wall time: 10min 54s\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ecc4dcb6-6278-4526-b69b-f9fe78f5af15\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file</th>\n",
              "      <th>ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/voice/train/train/de_f_8e8ed685b0cf9f...</td>\n",
              "      <td>de_f_8e8ed685b0cf9f5bbd13a04629ee64c5.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/voice/train/train/es_m_60f8a38a1112ae...</td>\n",
              "      <td>es_m_60f8a38a1112aed756fd2bc91b0b52e0.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/voice/train/train/es_m_3068613fb44c32...</td>\n",
              "      <td>es_m_3068613fb44c320f7318311a86929dc7.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/voice/train/train/de_m_3aa01c55ba7ca8...</td>\n",
              "      <td>de_m_3aa01c55ba7ca85cad2977c848eb826f.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/voice/train/train/de_f_26ddc4e756f612...</td>\n",
              "      <td>de_f_26ddc4e756f61233d74d42cc000c9dae.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>/content/voice/train/train/es_f_47bd2e6178465c...</td>\n",
              "      <td>es_f_47bd2e6178465cd745c86c9db5ffe447.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>/content/voice/train/train/en_m_6a5535d7512259...</td>\n",
              "      <td>en_m_6a5535d7512259e7bef76ff997750873.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>/content/voice/train/train/es_m_3068613fb44c32...</td>\n",
              "      <td>es_m_3068613fb44c320f7318311a86929dc7.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>/content/voice/train/train/en_m_1e512792ebab2b...</td>\n",
              "      <td>en_m_1e512792ebab2bd93b7aacac3d521390.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>/content/voice/train/train/de_f_0809fd0642232f...</td>\n",
              "      <td>de_f_0809fd0642232f8c85b0b3d545dc2b5a.fragment...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 2 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ecc4dcb6-6278-4526-b69b-f9fe78f5af15')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ecc4dcb6-6278-4526-b69b-f9fe78f5af15 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-ecc4dcb6-6278-4526-b69b-f9fe78f5af15');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                   file  \\\n",
              "0     /content/voice/train/train/de_f_8e8ed685b0cf9f...   \n",
              "1     /content/voice/train/train/es_m_60f8a38a1112ae...   \n",
              "2     /content/voice/train/train/es_m_3068613fb44c32...   \n",
              "3     /content/voice/train/train/de_m_3aa01c55ba7ca8...   \n",
              "4     /content/voice/train/train/de_f_26ddc4e756f612...   \n",
              "...                                                 ...   \n",
              "9995  /content/voice/train/train/es_f_47bd2e6178465c...   \n",
              "9996  /content/voice/train/train/en_m_6a5535d7512259...   \n",
              "9997  /content/voice/train/train/es_m_3068613fb44c32...   \n",
              "9998  /content/voice/train/train/en_m_1e512792ebab2b...   \n",
              "9999  /content/voice/train/train/de_f_0809fd0642232f...   \n",
              "\n",
              "                                                     ID  \n",
              "0     de_f_8e8ed685b0cf9f5bbd13a04629ee64c5.fragment...  \n",
              "1     es_m_60f8a38a1112aed756fd2bc91b0b52e0.fragment...  \n",
              "2     es_m_3068613fb44c320f7318311a86929dc7.fragment...  \n",
              "3     de_m_3aa01c55ba7ca85cad2977c848eb826f.fragment...  \n",
              "4     de_f_26ddc4e756f61233d74d42cc000c9dae.fragment...  \n",
              "...                                                 ...  \n",
              "9995  es_f_47bd2e6178465cd745c86c9db5ffe447.fragment...  \n",
              "9996  en_m_6a5535d7512259e7bef76ff997750873.fragment...  \n",
              "9997  es_m_3068613fb44c320f7318311a86929dc7.fragment...  \n",
              "9998  en_m_1e512792ebab2bd93b7aacac3d521390.fragment...  \n",
              "9999  de_f_0809fd0642232f8c85b0b3d545dc2b5a.fragment...  \n",
              "\n",
              "[10000 rows x 2 columns]"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%%time\n",
        "traindf = pd.DataFrame(train_data_dir[0:train_size],columns = ['file'])\n",
        "traindf['ID']= traindf['file'].apply(lambda x: x.split('/')[-1]+'.npy')\n",
        "np.vectorize(spectrogram_mat)(traindf['file'], traindf['ID'])\n",
        "traindf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8AqKncPl5J_G"
      },
      "outputs": [],
      "source": [
        "show_spectrogram_mat('/content/train/train-img/de_f_26ddc4e756f61233d74d42cc000c9dae.fragment23.pitch8.flac2.npy','de_f_26ddc4e756f61233d74d42cc000c9dae.fragment23.pitch8.flac2.npy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "uJnS8weTz3oE",
        "outputId": "f149e11a-af57-40ed-9885-65f02c93044e"
      },
      "outputs": [
        {
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file</th>\n",
              "      <th>ID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/train/train/de_f_8e8ed685b0cf9f5bbd13...</td>\n",
              "      <td>de_f_8e8ed685b0cf9f5bbd13a04629ee64c5.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/train/train/es_m_60f8a38a1112aed756fd...</td>\n",
              "      <td>es_m_60f8a38a1112aed756fd2bc91b0b52e0.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/train/train/es_m_3068613fb44c320f7318...</td>\n",
              "      <td>es_m_3068613fb44c320f7318311a86929dc7.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/train/train/de_m_3aa01c55ba7ca85cad29...</td>\n",
              "      <td>de_m_3aa01c55ba7ca85cad2977c848eb826f.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/train/train/de_f_26ddc4e756f61233d74d...</td>\n",
              "      <td>de_f_26ddc4e756f61233d74d42cc000c9dae.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>/content/train/train/es_f_e27d922d06d483e4df13...</td>\n",
              "      <td>es_f_e27d922d06d483e4df1305235a6cd87b.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>/content/train/train/es_m_4d7e5bf6deb997d71eb4...</td>\n",
              "      <td>es_m_4d7e5bf6deb997d71eb46ae46043c3db.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>/content/train/train/es_f_bf4285930fa46f2052e5...</td>\n",
              "      <td>es_f_bf4285930fa46f2052e5bdbc37a8a4df.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>/content/train/train/de_m_502293f229751563d176...</td>\n",
              "      <td>de_m_502293f229751563d1768dd2367a6b29.fragment...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>/content/train/train/es_f_db16e07d9bfc39deb179...</td>\n",
              "      <td>es_f_db16e07d9bfc39deb1792ec84d9e61fe.fragment...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c0a6a3e8-798e-4696-ba45-abc87129b7fc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-c0a6a3e8-798e-4696-ba45-abc87129b7fc 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-c0a6a3e8-798e-4696-ba45-abc87129b7fc');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
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            "text/plain": [
              "                                                file  \\\n",
              "0  /content/train/train/de_f_8e8ed685b0cf9f5bbd13...   \n",
              "1  /content/train/train/es_m_60f8a38a1112aed756fd...   \n",
              "2  /content/train/train/es_m_3068613fb44c320f7318...   \n",
              "3  /content/train/train/de_m_3aa01c55ba7ca85cad29...   \n",
              "4  /content/train/train/de_f_26ddc4e756f61233d74d...   \n",
              "5  /content/train/train/es_f_e27d922d06d483e4df13...   \n",
              "6  /content/train/train/es_m_4d7e5bf6deb997d71eb4...   \n",
              "7  /content/train/train/es_f_bf4285930fa46f2052e5...   \n",
              "8  /content/train/train/de_m_502293f229751563d176...   \n",
              "9  /content/train/train/es_f_db16e07d9bfc39deb179...   \n",
              "\n",
              "                                                  ID  \n",
              "0  de_f_8e8ed685b0cf9f5bbd13a04629ee64c5.fragment...  \n",
              "1  es_m_60f8a38a1112aed756fd2bc91b0b52e0.fragment...  \n",
              "2  es_m_3068613fb44c320f7318311a86929dc7.fragment...  \n",
              "3  de_m_3aa01c55ba7ca85cad2977c848eb826f.fragment...  \n",
              "4  de_f_26ddc4e756f61233d74d42cc000c9dae.fragment...  \n",
              "5  es_f_e27d922d06d483e4df1305235a6cd87b.fragment...  \n",
              "6  es_m_4d7e5bf6deb997d71eb46ae46043c3db.fragment...  \n",
              "7  es_f_bf4285930fa46f2052e5bdbc37a8a4df.fragment...  \n",
              "8  de_m_502293f229751563d1768dd2367a6b29.fragment...  \n",
              "9  es_f_db16e07d9bfc39deb1792ec84d9e61fe.fragment...  "
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "traindf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RsAW6GObyiLo",
        "outputId": "9ded9942-9ed6-41ce-dbf7-e7614d1bdacd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                                                file  \\\n",
            "0  /content/train/train/de_f_8e8ed685b0cf9f5bbd13...   \n",
            "1  /content/train/train/es_m_60f8a38a1112aed756fd...   \n",
            "2  /content/train/train/es_m_3068613fb44c320f7318...   \n",
            "3  /content/train/train/de_m_3aa01c55ba7ca85cad29...   \n",
            "4  /content/train/train/de_f_26ddc4e756f61233d74d...   \n",
            "5  /content/train/train/es_f_e27d922d06d483e4df13...   \n",
            "6  /content/train/train/es_m_4d7e5bf6deb997d71eb4...   \n",
            "7  /content/train/train/es_f_bf4285930fa46f2052e5...   \n",
            "8  /content/train/train/de_m_502293f229751563d176...   \n",
            "9  /content/train/train/es_f_db16e07d9bfc39deb179...   \n",
            "\n",
            "                                                  ID  \n",
            "0  de_f_8e8ed685b0cf9f5bbd13a04629ee64c5.fragment...  \n",
            "1  es_m_60f8a38a1112aed756fd2bc91b0b52e0.fragment...  \n",
            "2  es_m_3068613fb44c320f7318311a86929dc7.fragment...  \n",
            "3  de_m_3aa01c55ba7ca85cad2977c848eb826f.fragment...  \n",
            "4  de_f_26ddc4e756f61233d74d42cc000c9dae.fragment...  \n",
            "5  es_f_e27d922d06d483e4df1305235a6cd87b.fragment...  \n",
            "6  es_m_4d7e5bf6deb997d71eb46ae46043c3db.fragment...  \n",
            "7  es_f_bf4285930fa46f2052e5bdbc37a8a4df.fragment...  \n",
            "8  de_m_502293f229751563d1768dd2367a6b29.fragment...  \n",
            "9  es_f_db16e07d9bfc39deb1792ec84d9e61fe.fragment...  \n"
          ]
        }
      ],
      "source": [
        "print(traindf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "izW38vKhQqWz"
      },
      "outputs": [],
      "source": [
        "traindf['lang'] = traindf['ID'].apply(lambda x: x.split('_')[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "OiwIDVj2FVXF",
        "outputId": "9a771a3b-d4a0-40cc-b8e1-564e7de94182"
      },
      "outputs": [
        {
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m/usr/lib/python3.10/shutil.py\u001b[0m in \u001b[0;36mmove\u001b[0;34m(src, dst, copy_function)\u001b[0m\n\u001b[1;32m    815\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 816\u001b[0;31m         \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_dst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    817\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/train/train-img/es_m_b8e0e6f56f02e6f8f79cc360958e5982.fragment25.speed6.flac.jpg' -> '/content/train/train-img/es/es_m_b8e0e6f56f02e6f8f79cc360958e5982.fragment25.speed6.flac.jpg'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-b3c884ce2c9e>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmove_to_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraindf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ID'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraindf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'lang'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-30-3fffe55f01ab>\u001b[0m in \u001b[0;36mmove_to_label\u001b[0;34m(name, lang, is_train)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_train\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mprefix\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mTEST_IMG_PATH\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mshutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefix\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprefix\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mlang\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefix\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"to\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mlang\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"/\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/shutil.py\u001b[0m in \u001b[0;36mmove\u001b[0;34m(src, dst, copy_function)\u001b[0m\n\u001b[1;32m    834\u001b[0m             \u001b[0mrmtree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    835\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 836\u001b[0;31m             \u001b[0mcopy_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreal_dst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    837\u001b[0m             \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munlink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    838\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mreal_dst\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/shutil.py\u001b[0m in \u001b[0;36mcopy2\u001b[0;34m(src, dst, follow_symlinks)\u001b[0m\n\u001b[1;32m    432\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m         \u001b[0mdst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 434\u001b[0;31m     \u001b[0mcopyfile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfollow_symlinks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfollow_symlinks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    435\u001b[0m     \u001b[0mcopystat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfollow_symlinks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfollow_symlinks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    436\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/shutil.py\u001b[0m in \u001b[0;36mcopyfile\u001b[0;34m(src, dst, follow_symlinks)\u001b[0m\n\u001b[1;32m    252\u001b[0m         \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymlink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdst\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 254\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'rb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfsrc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    255\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfdst\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/train/train-img/es_m_b8e0e6f56f02e6f8f79cc360958e5982.fragment25.speed6.flac.jpg'"
          ]
        }
      ],
      "source": [
        "move_to_label(traindf['ID'][0], traindf['lang'][0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bBgN5An38tm3",
        "outputId": "1ca254d2-2973-4b10-82de-86c2768947b2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/train/train-img/es_m_b8e0e6f56f02e6f8f79cc360958e5982.fragment25.speed6.flac.jpg to /content/train/train-img/es/es_m_b8e0e6f56f02e6f8f79cc360958e5982.fragment25.speed6.flac.jpg\n",
            "/content/train/train-img/en_f_25836f5c38f5151611da3e2be4cfd20f.fragment13.speed8.flac.jpg to /content/train/train-img/en/en_f_25836f5c38f5151611da3e2be4cfd20f.fragment13.speed8.flac.jpg\n",
            "/content/train/train-img/en_f_058b70233667e1b64506dddf9f9d6b46.fragment20.speed1.flac.jpg to /content/train/train-img/en/en_f_058b70233667e1b64506dddf9f9d6b46.fragment20.speed1.flac.jpg\n",
            "/content/train/train-img/es_f_cc86b9208b120f1f147a9246ab39f5e4.fragment12.flac.jpg to /content/train/train-img/es/es_f_cc86b9208b120f1f147a9246ab39f5e4.fragment12.flac.jpg\n",
            "/content/train/train-img/de_m_0345d503166fbcb125b6e10e2e804443.fragment21.pitch7.flac.jpg to /content/train/train-img/de/de_m_0345d503166fbcb125b6e10e2e804443.fragment21.pitch7.flac.jpg\n",
            "/content/train/train-img/de_f_f8c37c21aabee92394e3ebd45f39a2d5.fragment9.pitch4.flac.jpg to /content/train/train-img/de/de_f_f8c37c21aabee92394e3ebd45f39a2d5.fragment9.pitch4.flac.jpg\n",
            "/content/train/train-img/es_m_9d8242673cefba8a44695b3d4f4ddd3f.fragment27.pitch6.flac.jpg to /content/train/train-img/es/es_m_9d8242673cefba8a44695b3d4f4ddd3f.fragment27.pitch6.flac.jpg\n",
            "/content/train/train-img/en_m_d630900a88e2681c9d5d1026d7025d67.fragment14.flac.jpg to /content/train/train-img/en/en_m_d630900a88e2681c9d5d1026d7025d67.fragment14.flac.jpg\n",
            "/content/train/train-img/es_m_d5b91a4ffb1ead826b7968ec19cbfa1c.fragment30.pitch2.flac.jpg to /content/train/train-img/es/es_m_d5b91a4ffb1ead826b7968ec19cbfa1c.fragment30.pitch2.flac.jpg\n",
            "/content/train/train-img/en_m_17b5d4995191976232356345dfbb82cb.fragment30.pitch8.flac.jpg to /content/train/train-img/en/en_m_17b5d4995191976232356345dfbb82cb.fragment30.pitch8.flac.jpg\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([None, None, None, None, None, None, None, None, None, None],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.vectorize(move_to_label, cache=True)(traindf['ID'], traindf['lang'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "--b-gMPxTCWx"
      },
      "source": [
        "# save data to drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Nmvg5WsfTBdR"
      },
      "outputs": [],
      "source": [
        "traindf.to_csv(DRIVE_PATH+'traindf.csv', index = False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5DzHJs3sRJ5h"
      },
      "outputs": [],
      "source": [
        "#!cp traindf.csv $DRIVE_PATH\n",
        "##!cp $TRAIN_IMG_PATH $DRIVE_PATH -r\n",
        "!cp $TRAIN_MAT_PATH $DRIVE_PATH -r\n",
        "#!echo $DRIVE_PATH"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mvANCgj-TGOf"
      },
      "source": [
        "# read data from drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qmyH3T2jTI5y"
      },
      "outputs": [],
      "source": [
        "traindf=pd.read_csv(DRIVE_PATH+'traindf.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HKKPBPIgVw0S"
      },
      "outputs": [],
      "source": [
        "#import shutil\n",
        "#shutil.rmtree(TRAIN_IMG_PATH, ignore_errors=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_0MSVeavTVsI"
      },
      "outputs": [],
      "source": [
        "#!cp $DRIVE_PATH/train-img/ $TRAIN_IMG_PATH -r\n",
        "!cp {DRIVE_PATH}train/ $TRAIN_MAT_PATH -r"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FNiuDK1aWySP"
      },
      "outputs": [],
      "source": [
        "!rm {CUR_DIR}spoken-language-identification.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8XWhZ55jTWWI"
      },
      "source": [
        "# Parallelism"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "km3auTtyBl2H"
      },
      "outputs": [],
      "source": [
        "!pip install swifter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        },
        "id": "3Ptq65gzBwCS",
        "outputId": "11d4ea10-3405-4c3d-b6c3-c86b825bc001"
      },
      "outputs": [],
      "source": [
        "import swifter\n",
        "traindf.swifter.apply(lambda x: create_spectrogram(x.file, x.ID), axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jhuqq6nFOlTZ"
      },
      "outputs": [],
      "source": [
        "    #! rm {CUR_DIR}train/*.jpg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TSUhsMzeRuGH",
        "outputId": "be6ee3b5-5614-4c5b-d5fc-9dbc39f9e4d7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ewQysI9ARw6j"
      },
      "outputs": [],
      "source": [
        "#print(train_id)\n",
        "#train_lang = []\n",
        "#for i in range(len(train_id)):\n",
        "#    train_lang.append(train_id[i].split('_')[0])\n",
        "#traindf=pd.DataFrame(train_id,columns = ['ID'])\n",
        "#\n",
        "#traindf['lang'] = traindf['ID'].apply(lambda x: x.split('_')[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zBos31WMUObZ"
      },
      "outputs": [],
      "source": [
        "#traindf['lang'] = train_lang\n",
        "#traindf.drop(columns='Class', inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 505
        },
        "id": "s-t0FVrIUQpo",
        "outputId": "7533c1f7-d276-4cc2-cbfb-13eec5534818"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-d62fe23b-eb4a-456e-b371-9815fbecb2c5\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Unnamed: 0</th>\n",
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              "      <th>0</th>\n",
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              "      <td>es</td>\n",
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              "      <th>3</th>\n",
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              "      <td>/content/voice/train/train/de_m_3aa01c55ba7ca8...</td>\n",
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              "      <td>es</td>\n",
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              "      <th>9996</th>\n",
              "      <td>9996</td>\n",
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              "      <td>es</td>\n",
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              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>9998</td>\n",
              "      <td>/content/voice/train/train/en_m_1e512792ebab2b...</td>\n",
              "      <td>en_m_1e512792ebab2bd93b7aacac3d521390.fragment...</td>\n",
              "      <td>en</td>\n",
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              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>9999</td>\n",
              "      <td>/content/voice/train/train/de_f_0809fd0642232f...</td>\n",
              "      <td>de_f_0809fd0642232f8c85b0b3d545dc2b5a.fragment...</td>\n",
              "      <td>de</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d62fe23b-eb4a-456e-b371-9815fbecb2c5')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d62fe23b-eb4a-456e-b371-9815fbecb2c5 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-d62fe23b-eb4a-456e-b371-9815fbecb2c5');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Unnamed: 0                                               file  \\\n",
              "0              0  /content/voice/train/train/de_f_8e8ed685b0cf9f...   \n",
              "1              1  /content/voice/train/train/es_m_60f8a38a1112ae...   \n",
              "2              2  /content/voice/train/train/es_m_3068613fb44c32...   \n",
              "3              3  /content/voice/train/train/de_m_3aa01c55ba7ca8...   \n",
              "4              4  /content/voice/train/train/de_f_26ddc4e756f612...   \n",
              "...          ...                                                ...   \n",
              "9995        9995  /content/voice/train/train/es_f_47bd2e6178465c...   \n",
              "9996        9996  /content/voice/train/train/en_m_6a5535d7512259...   \n",
              "9997        9997  /content/voice/train/train/es_m_3068613fb44c32...   \n",
              "9998        9998  /content/voice/train/train/en_m_1e512792ebab2b...   \n",
              "9999        9999  /content/voice/train/train/de_f_0809fd0642232f...   \n",
              "\n",
              "                                                     ID lang  \n",
              "0     de_f_8e8ed685b0cf9f5bbd13a04629ee64c5.fragment...   de  \n",
              "1     es_m_60f8a38a1112aed756fd2bc91b0b52e0.fragment...   es  \n",
              "2     es_m_3068613fb44c320f7318311a86929dc7.fragment...   es  \n",
              "3     de_m_3aa01c55ba7ca85cad2977c848eb826f.fragment...   de  \n",
              "4     de_f_26ddc4e756f61233d74d42cc000c9dae.fragment...   de  \n",
              "...                                                 ...  ...  \n",
              "9995  es_f_47bd2e6178465cd745c86c9db5ffe447.fragment...   es  \n",
              "9996  en_m_6a5535d7512259e7bef76ff997750873.fragment...   en  \n",
              "9997  es_m_3068613fb44c320f7318311a86929dc7.fragment...   es  \n",
              "9998  en_m_1e512792ebab2bd93b7aacac3d521390.fragment...   en  \n",
              "9999  de_f_0809fd0642232f8c85b0b3d545dc2b5a.fragment...   de  \n",
              "\n",
              "[10000 rows x 4 columns]"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "traindf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6G7LSW0j1Q4S",
        "outputId": "cda27891-6a12-4672-bb62-b03d17660bdd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['Unnamed: 0', 'file', 'ID', 'lang'], dtype='object')\n"
          ]
        }
      ],
      "source": [
        "#print(traindf.columns)\n",
        "#traindf.drop(['Unnamed: 0'], axis=1,inplace=True)\n",
        "##print(traindf[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "MjiOWPVZ11i9",
        "outputId": "d41fc114-d280-49b8-f043-b6aa13963ded"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>file</th>\n",
              "      <th>ID</th>\n",
              "      <th>lang</th>\n",
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              "  <tbody>\n",
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              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>/content/voice/train/train/en_m_1e512792ebab2b...</td>\n",
              "      <td>en_m_1e512792ebab2bd93b7aacac3d521390.fragment...</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>/content/voice/train/train/de_f_0809fd0642232f...</td>\n",
              "      <td>de_f_0809fd0642232f8c85b0b3d545dc2b5a.fragment...</td>\n",
              "      <td>de</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3293c062-d6ba-471b-8adc-d4bc7b104ef6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3293c062-d6ba-471b-8adc-d4bc7b104ef6 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-3293c062-d6ba-471b-8adc-d4bc7b104ef6');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                   file  \\\n",
              "0     /content/voice/train/train/de_f_8e8ed685b0cf9f...   \n",
              "1     /content/voice/train/train/es_m_60f8a38a1112ae...   \n",
              "2     /content/voice/train/train/es_m_3068613fb44c32...   \n",
              "3     /content/voice/train/train/de_m_3aa01c55ba7ca8...   \n",
              "4     /content/voice/train/train/de_f_26ddc4e756f612...   \n",
              "...                                                 ...   \n",
              "9995  /content/voice/train/train/es_f_47bd2e6178465c...   \n",
              "9996  /content/voice/train/train/en_m_6a5535d7512259...   \n",
              "9997  /content/voice/train/train/es_m_3068613fb44c32...   \n",
              "9998  /content/voice/train/train/en_m_1e512792ebab2b...   \n",
              "9999  /content/voice/train/train/de_f_0809fd0642232f...   \n",
              "\n",
              "                                                     ID lang  \n",
              "0     de_f_8e8ed685b0cf9f5bbd13a04629ee64c5.fragment...   de  \n",
              "1     es_m_60f8a38a1112aed756fd2bc91b0b52e0.fragment...   es  \n",
              "2     es_m_3068613fb44c320f7318311a86929dc7.fragment...   es  \n",
              "3     de_m_3aa01c55ba7ca85cad2977c848eb826f.fragment...   de  \n",
              "4     de_f_26ddc4e756f61233d74d42cc000c9dae.fragment...   de  \n",
              "...                                                 ...  ...  \n",
              "9995  es_f_47bd2e6178465cd745c86c9db5ffe447.fragment...   es  \n",
              "9996  en_m_6a5535d7512259e7bef76ff997750873.fragment...   en  \n",
              "9997  es_m_3068613fb44c320f7318311a86929dc7.fragment...   es  \n",
              "9998  en_m_1e512792ebab2bd93b7aacac3d521390.fragment...   en  \n",
              "9999  de_f_0809fd0642232f8c85b0b3d545dc2b5a.fragment...   de  \n",
              "\n",
              "[10000 rows x 3 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "traindf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "nHgZeTlh57MF",
        "outputId": "7e6f62df-a46c-475a-baaa-0b91b3f130f6"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-da1ce915-6fda-411b-af40-f65d521e672f\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file</th>\n",
              "      <th>ID</th>\n",
              "      <th>lang</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/voice/train/train/de_f_8e8ed685b0cf9f...</td>\n",
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              "      <td>es_m_60f8a38a1112aed756fd2bc91b0b52e0.fragment...</td>\n",
              "      <td>es</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/voice/train/train/es_m_3068613fb44c32...</td>\n",
              "      <td>es_m_3068613fb44c320f7318311a86929dc7.fragment...</td>\n",
              "      <td>es</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/voice/train/train/de_m_3aa01c55ba7ca8...</td>\n",
              "      <td>de_m_3aa01c55ba7ca85cad2977c848eb826f.fragment...</td>\n",
              "      <td>de</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/voice/train/train/de_f_26ddc4e756f612...</td>\n",
              "      <td>de_f_26ddc4e756f61233d74d42cc000c9dae.fragment...</td>\n",
              "      <td>de</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>/content/voice/train/train/es_f_47bd2e6178465c...</td>\n",
              "      <td>es_f_47bd2e6178465cd745c86c9db5ffe447.fragment...</td>\n",
              "      <td>es</td>\n",
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              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>/content/voice/train/train/en_m_6a5535d7512259...</td>\n",
              "      <td>en_m_6a5535d7512259e7bef76ff997750873.fragment...</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>/content/voice/train/train/es_m_3068613fb44c32...</td>\n",
              "      <td>es_m_3068613fb44c320f7318311a86929dc7.fragment...</td>\n",
              "      <td>es</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>/content/voice/train/train/en_m_1e512792ebab2b...</td>\n",
              "      <td>en_m_1e512792ebab2bd93b7aacac3d521390.fragment...</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>/content/voice/train/train/de_f_0809fd0642232f...</td>\n",
              "      <td>de_f_0809fd0642232f8c85b0b3d545dc2b5a.fragment...</td>\n",
              "      <td>de</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-da1ce915-6fda-411b-af40-f65d521e672f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-da1ce915-6fda-411b-af40-f65d521e672f 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-da1ce915-6fda-411b-af40-f65d521e672f');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                                   file  \\\n",
              "0     /content/voice/train/train/de_f_8e8ed685b0cf9f...   \n",
              "1     /content/voice/train/train/es_m_60f8a38a1112ae...   \n",
              "2     /content/voice/train/train/es_m_3068613fb44c32...   \n",
              "3     /content/voice/train/train/de_m_3aa01c55ba7ca8...   \n",
              "4     /content/voice/train/train/de_f_26ddc4e756f612...   \n",
              "...                                                 ...   \n",
              "9995  /content/voice/train/train/es_f_47bd2e6178465c...   \n",
              "9996  /content/voice/train/train/en_m_6a5535d7512259...   \n",
              "9997  /content/voice/train/train/es_m_3068613fb44c32...   \n",
              "9998  /content/voice/train/train/en_m_1e512792ebab2b...   \n",
              "9999  /content/voice/train/train/de_f_0809fd0642232f...   \n",
              "\n",
              "                                                     ID lang  \n",
              "0     de_f_8e8ed685b0cf9f5bbd13a04629ee64c5.fragment...   de  \n",
              "1     es_m_60f8a38a1112aed756fd2bc91b0b52e0.fragment...   es  \n",
              "2     es_m_3068613fb44c320f7318311a86929dc7.fragment...   es  \n",
              "3     de_m_3aa01c55ba7ca85cad2977c848eb826f.fragment...   de  \n",
              "4     de_f_26ddc4e756f61233d74d42cc000c9dae.fragment...   de  \n",
              "...                                                 ...  ...  \n",
              "9995  es_f_47bd2e6178465cd745c86c9db5ffe447.fragment...   es  \n",
              "9996  en_m_6a5535d7512259e7bef76ff997750873.fragment...   en  \n",
              "9997  es_m_3068613fb44c320f7318311a86929dc7.fragment...   es  \n",
              "9998  en_m_1e512792ebab2bd93b7aacac3d521390.fragment...   en  \n",
              "9999  de_f_0809fd0642232f8c85b0b3d545dc2b5a.fragment...   de  \n",
              "\n",
              "[10000 rows x 3 columns]"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "traindf['ID']=  traindf['ID'].str.split('.jpg', expand=True)[0] + '.npy'\n",
        "traindf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ujbxLx5udcYh"
      },
      "outputs": [],
      "source": [
        "#!ls $TEST_IMG_PATH"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bApG_yILdjX5"
      },
      "outputs": [],
      "source": [
        "#!ls {CUR_DIR}train/train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "A1P9Y8O9XDHY",
        "outputId": "fc958cb2-7cd1-4e3e-89a7-0a10580cacf1"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6fabb3f1-bf43-43d3-ad98-b4eb0ba2d815\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file</th>\n",
              "      <th>ID</th>\n",
              "      <th>lang</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>/content/test/test/en_f_67a0cba10d171b24039a79...</td>\n",
              "      <td>en_f_67a0cba10d171b24039a79faa1d4d603.fragment...</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>/content/test/test/de_f_63f5b79c76cf5a1a4bbd1c...</td>\n",
              "      <td>de_f_63f5b79c76cf5a1a4bbd1c40f54b166e.fragment...</td>\n",
              "      <td>de</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>/content/test/test/es_m_08111ce8d6a7ebc6cd2c27...</td>\n",
              "      <td>es_m_08111ce8d6a7ebc6cd2c27e62a3d98f0.fragment...</td>\n",
              "      <td>es</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>/content/test/test/de_f_63f5b79c76cf5a1a4bbd1c...</td>\n",
              "      <td>de_f_63f5b79c76cf5a1a4bbd1c40f54b166e.fragment...</td>\n",
              "      <td>de</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>/content/test/test/de_f_63f5b79c76cf5a1a4bbd1c...</td>\n",
              "      <td>de_f_63f5b79c76cf5a1a4bbd1c40f54b166e.fragment...</td>\n",
              "      <td>de</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>/content/test/test/en_f_67a0cba10d171b24039a79...</td>\n",
              "      <td>en_f_67a0cba10d171b24039a79faa1d4d603.fragment...</td>\n",
              "      <td>en</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>/content/test/test/de_m_923551d571cc437382d029...</td>\n",
              "      <td>de_m_923551d571cc437382d0294dda2dd0aa.fragment...</td>\n",
              "      <td>de</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>/content/test/test/es_m_08111ce8d6a7ebc6cd2c27...</td>\n",
              "      <td>es_m_08111ce8d6a7ebc6cd2c27e62a3d98f0.fragment...</td>\n",
              "      <td>es</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>/content/test/test/es_f_50298ab71aaba8508ebeef...</td>\n",
              "      <td>es_f_50298ab71aaba8508ebeef49d853df11.fragment...</td>\n",
              "      <td>es</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>/content/test/test/de_m_923551d571cc437382d029...</td>\n",
              "      <td>de_m_923551d571cc437382d0294dda2dd0aa.fragment...</td>\n",
              "      <td>de</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6fabb3f1-bf43-43d3-ad98-b4eb0ba2d815')\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          document.querySelector('#df-6fabb3f1-bf43-43d3-ad98-b4eb0ba2d815 button.colab-df-convert');\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6fabb3f1-bf43-43d3-ad98-b4eb0ba2d815');\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",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
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              "          element.appendChild(docLink);\n",
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              "  "
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              "96  de_m_923551d571cc437382d0294dda2dd0aa.fragment...   de  \n",
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              "99  de_m_923551d571cc437382d0294dda2dd0aa.fragment...   de  \n",
              "\n",
              "[100 rows x 3 columns]"
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#%%time\n",
        "#i= train_size #start i after train_size\n",
        "#test_id = []\n",
        "#for file in test_data_dir[i:i+test_size]:\n",
        "#    name = file.split('/')[-1]\n",
        "#    test_id.append(name+'.jpg')\n",
        "#    create_spectrogram(file,name, is_train=False)\n",
        "\n",
        "testdf = pd.DataFrame(test_data_dir[:test_size],columns = ['file'])\n",
        "testdf['ID']= testdf['file'].apply(lambda x: x.split('/')[-1]+'.jpg')\n",
        "#testdf.apply(lambda x: create_spectrogram(x.file, x.ID), axis=1)\n",
        "np.vectorize(create_spectrogram)(testdf['file'], testdf['ID'], is_train=False)\n",
        "testdf['lang'] = testdf['ID'].apply(lambda x: x.split('_')[0])\n",
        "testdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6UvbSb45XSuP"
      },
      "outputs": [],
      "source": [
        "#testdf=pd.DataFrame(test_id,columns = ['ID'])\n",
        "#\n",
        "#test_lang = []\n",
        "#for i in range(len(test_id)):\n",
        "#    test_lang.append(test_id[i].split('_')[0])\n",
        "#\n",
        "#testdf['lang'] = test_lang\n",
        "#"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "ZjdswInagYuR",
        "outputId": "40b74901-2cb4-47a4-fff0-74b98829032c"
      },
      "outputs": [
        {
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              "\n",
              "    .colab-df-convert {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "4   /content/test/test/de_f_63f5b79c76cf5a1a4bbd1c...   \n",
              "..                                                ...   \n",
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              "..                                                ...  ...  \n",
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              "96  de_m_923551d571cc437382d0294dda2dd0aa.fragment...   de  \n",
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              "99  de_m_923551d571cc437382d0294dda2dd0aa.fragment...   de  \n",
              "\n",
              "[100 rows x 3 columns]"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "testdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K7RQ0D7R2hHu",
        "outputId": "a355a88b-3e3e-4934-9712-3b9534348e9c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "! pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0DufTgrvePZZ",
        "outputId": "859bc681-775a-4561-88ef-d043485bc237"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "%cd $CUR_DIR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "id": "RkJTZpXM2j4U",
        "outputId": "90327fc0-57fb-4f02-c463-9dc90e03277f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/mat/train/train\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/mat/train/train'"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%cd mat/train/train\n",
        "%pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "z34b7eyD8CVW",
        "outputId": "458d35bf-e8f9-42c6-83b7-3afbf8b6d5b5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/mat/train/train\n"
          ]
        }
      ],
      "source": [
        "! pwd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQRW7sAC8Epn",
        "outputId": "50f8c016-d393-4eef-a5ce-4e6df86c71d8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "en_f_fff98d05640df27726dcd6183642ffaa.fragment3.noise8.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment3.noise9.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment3.speed6.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment4.speed2.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment4.speed4.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment4.speed5.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment5.noise11.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment5.noise4.flac.npy\n",
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            "en_f_fff98d05640df27726dcd6183642ffaa.fragment6.noise10.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment6.noise12.flac.npy\n",
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            "en_f_fff98d05640df27726dcd6183642ffaa.fragment6.speed4.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment7.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment7.noise10.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment7.noise3.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment7.pitch7.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment8.noise2.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment8.noise8.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment8.speed1.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment8.speed7.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment9.noise1.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment9.noise4.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment9.pitch1.flac.npy\n",
            "en_f_fff98d05640df27726dcd6183642ffaa.fragment9.pitch4.flac.npy\n",
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            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment14.noise11.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment14.noise6.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment14.pitch1.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment14.pitch3.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment15.noise10.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment15.noise3.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment15.noise9.flac.npy\n",
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            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment15.speed2.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment15.speed3.flac.npy\n",
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            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment17.noise12.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment17.pitch7.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment18.noise7.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment18.pitch4.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment18.pitch7.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment19.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment19.noise6.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment19.pitch3.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment19.pitch8.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment19.speed1.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment19.speed3.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment1.noise11.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment1.noise6.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment1.pitch7.flac.npy\n",
            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment20.noise10.flac.npy\n",
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            "en_m_011f3a2d0aa2880305c08b76873c3e10.fragment20.speed4.flac.npy\n",
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            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment30.noise11.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment30.noise2.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment30.noise5.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment30.speed2.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment3.noise11.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment3.noise4.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment3.noise8.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment3.pitch5.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment3.speed5.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment4.noise10.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment4.pitch1.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment4.speed7.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment5.noise1.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment5.noise4.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment5.noise5.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment5.pitch6.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment5.pitch7.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment6.noise11.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment6.noise3.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment6.speed2.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment6.speed5.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment7.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment7.noise10.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment7.pitch1.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment7.pitch3.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment7.pitch7.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment8.pitch3.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment8.speed7.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment8.speed8.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment9.noise5.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment9.noise6.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment9.pitch7.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment9.speed2.flac.npy\n",
            "es_m_f7d959494477e5e7e33d4666f15311c9.fragment9.speed8.flac.npy\n"
          ]
        }
      ],
      "source": [
        "! ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d64w7Jdz_2ZU",
        "outputId": "e420be8d-9e22-4aa9-869b-942e98eab7fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10000\n"
          ]
        }
      ],
      "source": [
        "! ls -1 | wc -l"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jOtyGbwP_4JD",
        "outputId": "28b3faf6-e014-4918-91eb-0f45ec1bbbef"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(traindf)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O_sUzyqS2HAm"
      },
      "outputs": [],
      "source": [
        "file_paths = traindf['ID'].values\n",
        "lang = np.array(traindf['lang'].values)\n",
        "\n",
        "data = [np.load(file_path) for file_path in file_paths]\n",
        "data = np.stack(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zfh66l0Ll7CF"
      },
      "outputs": [],
      "source": [
        "cur_shape = data.shape\n",
        "data.reshape((cur_shape[0], cur_shape[1],))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WZHb4IfgAEEE",
        "outputId": "03067a56-33bd-4782-b2aa-ac6c6fd1d7af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "%cd $CUR_DIR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gx4Sg3UxAL5F",
        "outputId": "857b60c7-90e3-4223-b73f-5c67a9f791f3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10000\n"
          ]
        }
      ],
      "source": [
        "! ls {DRIVE_PATH}/train -1 | wc -l"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fAgFwxJRgbNK",
        "outputId": "3d50d505-9ca0-4bbd-c59c-a7950a6f34c6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting Keras-Preprocessing\n",
            "  Downloading Keras_Preprocessing-1.1.2-py2.py3-none-any.whl (42 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/42.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.6/42.6 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.10/dist-packages (from Keras-Preprocessing) (1.22.4)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from Keras-Preprocessing) (1.16.0)\n",
            "Installing collected packages: Keras-Preprocessing\n",
            "Successfully installed Keras-Preprocessing-1.1.2\n"
          ]
        }
      ],
      "source": [
        "!pip install Keras-Preprocessing\n",
        "from keras_preprocessing.image import ImageDataGenerator"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jegheRISg6IK",
        "outputId": "b8a2b8bb-2580-43f9-af48-16c134544108"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 0 validated image filenames belonging to 0 classes.\n",
            "Found 0 validated image filenames belonging to 0 classes.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras_preprocessing/image/dataframe_iterator.py:279: UserWarning: Found 10000 invalid image filename(s) in x_col=\"ID\". These filename(s) will be ignored.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/keras_preprocessing/image/dataframe_iterator.py:279: UserWarning: Found 10000 invalid image filename(s) in x_col=\"ID\". These filename(s) will be ignored.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "datagen=ImageDataGenerator(rescale=1./255.,validation_split=0.2) #.flow_from_\n",
        "\n",
        "\n",
        "train_generator=datagen.flow_from_dataframe(\n",
        "    dataframe=traindf,\n",
        "    directory=TRAIN_MAT_PATH+\"train/\",\n",
        "    x_col=\"ID\",\n",
        "    y_col=\"lang\",\n",
        "    subset=\"training\",\n",
        "    batch_size=32,\n",
        "    seed=42,\n",
        "    shuffle=True,\n",
        "    class_mode=\"categorical\",\n",
        "    target_size=(64,64))\n",
        "\n",
        "valid_generator=datagen.flow_from_dataframe(\n",
        "    dataframe=traindf,\n",
        "    directory=TRAIN_MAT_PATH+\"train/\",\n",
        "    x_col=\"ID\",\n",
        "    y_col=\"lang\",\n",
        "    subset=\"validation\",\n",
        "    batch_size=32,\n",
        "    seed=42,\n",
        "    shuffle=True,\n",
        "    class_mode=\"categorical\",\n",
        "    target_size=(64,64))\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "h7S7EmNpB7vC",
        "outputId": "25d94f2d-cc63-447d-d1e5-e1412e17e799"
      },
      "outputs": [],
      "source": [
        "# npy\n",
        "datagen=ImageDataGenerator(rescale=1./255.,validation_split=0.2) #.flow_from_\n",
        "\n",
        "\n",
        "train_generator=datagen.flow(\n",
        "    #dataframe=traindf,\n",
        "    #directory=TRAIN_MAT_PATH+\"train/\",\n",
        "    x=data,\n",
        "    y=lang,\n",
        "    subset=\"training\",\n",
        "    batch_size=32,\n",
        "    seed=42,\n",
        "    shuffle=True,\n",
        "    #class_mode=\"categorical\",\n",
        "    #target_size=(64,64)\n",
        "    )\n",
        "\n",
        "valid_generator=datagen.flow(\n",
        "    #dataframe=traindf,\n",
        "    #directory=TRAIN_MAT_PATH+\"train/\",\n",
        "    x=data,\n",
        "    y=lang,\n",
        "    subset=\"validation\",\n",
        "    batch_size=32,\n",
        "    seed=42,\n",
        "    shuffle=True,\n",
        "    #class_mode=\"categorical\",\n",
        "    #target_size=(64,64)\n",
        "    )\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rmOlMsCfliAd",
        "outputId": "27ec7bd4-8ba3-42fb-a5c0-61375c098e16"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(128, 431)"
            ]
          },
          "execution_count": 39,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "data[0].shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AMV31YTFlKm0"
      },
      "source": [
        "### new method"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vqFOJc5ek37j"
      },
      "outputs": [],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(data, lang, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "id": "z22D1nMVlaSy",
        "outputId": "dcd24159-8c1d-4a28-82ed-baf722450bf9"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-37-88c798951205>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSequential\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mConv2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_channels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMaxPooling2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mConv2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'relu'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMaxPooling2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'width' is not defined"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(width, height, num_channels)))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
        "model.add(MaxPooling2D((2, 2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(num_classes, activation='softmax'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSuTRn_7haXr",
        "outputId": "00421648-5060-45e3-f737-295d5853d3e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 64, 64, 32)        896       \n",
            "                                                                 \n",
            " activation (Activation)     (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 62, 62, 64)        18496     \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 62, 62, 64)        0         \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 31, 31, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 31, 31, 64)        0         \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 31, 31, 64)        36928     \n",
            "                                                                 \n",
            " activation_2 (Activation)   (None, 31, 31, 64)        0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 29, 29, 64)        36928     \n",
            "                                                                 \n",
            " activation_3 (Activation)   (None, 29, 29, 64)        0         \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 14, 14, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 14, 14, 64)        0         \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 14, 14, 128)       73856     \n",
            "                                                                 \n",
            " activation_4 (Activation)   (None, 14, 14, 128)       0         \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 12, 12, 128)       147584    \n",
            "                                                                 \n",
            " activation_5 (Activation)   (None, 12, 12, 128)       0         \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 6, 6, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 6, 6, 128)         0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 4608)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               2359808   \n",
            "                                                                 \n",
            " activation_6 (Activation)   (None, 512)               0         \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 512)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3)                 1539      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,676,035\n",
            "Trainable params: 2,676,035\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/optimizers/legacy/rmsprop.py:143: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super().__init__(name, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "from keras.layers import Dense, Activation, Flatten, Dropout, BatchNormalization\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Conv2D, MaxPooling2D\n",
        "from keras import regularizers, optimizers\n",
        "import keras\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Conv2D(32, (3, 3), padding='same',\n",
        "                 input_shape=(64,64,3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Conv2D(64, (3, 3), padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(64, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Conv2D(128, (3, 3), padding='same'))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Conv2D(128, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(512))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(3, activation='softmax'))\n",
        "model.compile(optimizer=keras.optimizers.RMSprop(lr=0.0005, decay=1e-6),loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5M5kVoXiXwW",
        "outputId": "c9db0580-164f-4a61-b674-c67585a32f20"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-40-f86d2eeb155e>:3: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  model.fit_generator(generator=train_generator,\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/150\n",
            "2/2 [==============================] - 4s 902ms/step - loss: 1.1243 - accuracy: 0.3750\n",
            "Epoch 2/150\n",
            "2/2 [==============================] - 2s 659ms/step - loss: 1.0985 - accuracy: 0.3333\n",
            "Epoch 3/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 1.0858 - accuracy: 0.3333\n",
            "Epoch 4/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 1.2371 - accuracy: 0.3750\n",
            "Epoch 5/150\n",
            "2/2 [==============================] - 2s 550ms/step - loss: 1.0915 - accuracy: 0.3542\n",
            "Epoch 6/150\n",
            "2/2 [==============================] - 2s 980ms/step - loss: 1.0920 - accuracy: 0.2969\n",
            "Epoch 7/150\n",
            "2/2 [==============================] - 2s 532ms/step - loss: 1.0849 - accuracy: 0.3958\n",
            "Epoch 8/150\n",
            "2/2 [==============================] - 3s 1s/step - loss: 1.0868 - accuracy: 0.3542\n",
            "Epoch 9/150\n",
            "2/2 [==============================] - 2s 563ms/step - loss: 1.0995 - accuracy: 0.3750\n",
            "Epoch 10/150\n",
            "2/2 [==============================] - 2s 986ms/step - loss: 1.0830 - accuracy: 0.3750\n",
            "Epoch 11/150\n",
            "2/2 [==============================] - 2s 552ms/step - loss: 1.0957 - accuracy: 0.3333\n",
            "Epoch 12/150\n",
            "2/2 [==============================] - 2s 987ms/step - loss: 1.1087 - accuracy: 0.2344\n",
            "Epoch 13/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 1.0838 - accuracy: 0.3750\n",
            "Epoch 14/150\n",
            "2/2 [==============================] - 3s 959ms/step - loss: 1.0861 - accuracy: 0.3750\n",
            "Epoch 15/150\n",
            "2/2 [==============================] - 2s 548ms/step - loss: 1.0941 - accuracy: 0.3125\n",
            "Epoch 16/150\n",
            "2/2 [==============================] - 2s 526ms/step - loss: 1.0960 - accuracy: 0.3542\n",
            "Epoch 17/150\n",
            "2/2 [==============================] - 2s 972ms/step - loss: 1.0925 - accuracy: 0.3125\n",
            "Epoch 18/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 1.1023 - accuracy: 0.3125\n",
            "Epoch 19/150\n",
            "2/2 [==============================] - 3s 1s/step - loss: 1.0915 - accuracy: 0.4167\n",
            "Epoch 20/150\n",
            "2/2 [==============================] - 2s 990ms/step - loss: 1.1038 - accuracy: 0.3750\n",
            "Epoch 21/150\n",
            "2/2 [==============================] - 2s 534ms/step - loss: 1.0863 - accuracy: 0.3958\n",
            "Epoch 22/150\n",
            "2/2 [==============================] - 2s 544ms/step - loss: 1.1000 - accuracy: 0.2083\n",
            "Epoch 23/150\n",
            "2/2 [==============================] - 2s 535ms/step - loss: 1.1102 - accuracy: 0.3125\n",
            "Epoch 24/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 1.0860 - accuracy: 0.4375\n",
            "Epoch 25/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 1.0713 - accuracy: 0.3750\n",
            "Epoch 26/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 1.1086 - accuracy: 0.3542\n",
            "Epoch 27/150\n",
            "2/2 [==============================] - 2s 976ms/step - loss: 1.0889 - accuracy: 0.4062\n",
            "Epoch 28/150\n",
            "2/2 [==============================] - 2s 971ms/step - loss: 1.0709 - accuracy: 0.4219\n",
            "Epoch 29/150\n",
            "2/2 [==============================] - 2s 525ms/step - loss: 1.0946 - accuracy: 0.3750\n",
            "Epoch 30/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 1.0578 - accuracy: 0.4583\n",
            "Epoch 31/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 1.0871 - accuracy: 0.4167\n",
            "Epoch 32/150\n",
            "2/2 [==============================] - 2s 994ms/step - loss: 1.1061 - accuracy: 0.2708\n",
            "Epoch 33/150\n",
            "2/2 [==============================] - 2s 990ms/step - loss: 1.0885 - accuracy: 0.3542\n",
            "Epoch 34/150\n",
            "2/2 [==============================] - 5s 4s/step - loss: 1.0789 - accuracy: 0.4219\n",
            "Epoch 35/150\n",
            "2/2 [==============================] - 3s 960ms/step - loss: 1.2428 - accuracy: 0.4375\n",
            "Epoch 36/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 1.0854 - accuracy: 0.3906\n",
            "Epoch 37/150\n",
            "2/2 [==============================] - 2s 549ms/step - loss: 1.0864 - accuracy: 0.3750\n",
            "Epoch 38/150\n",
            "2/2 [==============================] - 2s 968ms/step - loss: 1.0846 - accuracy: 0.3542\n",
            "Epoch 39/150\n",
            "2/2 [==============================] - 3s 903ms/step - loss: 1.0978 - accuracy: 0.3125\n",
            "Epoch 40/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 1.0800 - accuracy: 0.4792\n",
            "Epoch 41/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 1.1043 - accuracy: 0.3750\n",
            "Epoch 42/150\n",
            "2/2 [==============================] - 2s 547ms/step - loss: 1.0799 - accuracy: 0.3750\n",
            "Epoch 43/150\n",
            "2/2 [==============================] - 2s 524ms/step - loss: 1.0371 - accuracy: 0.5417\n",
            "Epoch 44/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 1.0596 - accuracy: 0.4219\n",
            "Epoch 45/150\n",
            "2/2 [==============================] - 2s 550ms/step - loss: 1.1024 - accuracy: 0.3542\n",
            "Epoch 46/150\n",
            "2/2 [==============================] - 2s 958ms/step - loss: 1.0317 - accuracy: 0.5000\n",
            "Epoch 47/150\n",
            "2/2 [==============================] - 2s 945ms/step - loss: 1.1179 - accuracy: 0.3594\n",
            "Epoch 48/150\n",
            "2/2 [==============================] - 2s 977ms/step - loss: 1.0816 - accuracy: 0.3958\n",
            "Epoch 49/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 1.0779 - accuracy: 0.3958\n",
            "Epoch 50/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 1.0717 - accuracy: 0.3750\n",
            "Epoch 51/150\n",
            "2/2 [==============================] - 3s 940ms/step - loss: 1.0170 - accuracy: 0.4844\n",
            "Epoch 52/150\n",
            "2/2 [==============================] - 2s 999ms/step - loss: 1.0794 - accuracy: 0.4792\n",
            "Epoch 53/150\n",
            "2/2 [==============================] - 2s 534ms/step - loss: 1.0006 - accuracy: 0.4583\n",
            "Epoch 54/150\n",
            "2/2 [==============================] - 2s 540ms/step - loss: 0.9550 - accuracy: 0.6042\n",
            "Epoch 55/150\n",
            "2/2 [==============================] - 2s 957ms/step - loss: 1.0845 - accuracy: 0.4219\n",
            "Epoch 56/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 1.0020 - accuracy: 0.5417\n",
            "Epoch 57/150\n",
            "2/2 [==============================] - 3s 911ms/step - loss: 0.9938 - accuracy: 0.4792\n",
            "Epoch 58/150\n",
            "2/2 [==============================] - 2s 547ms/step - loss: 0.9799 - accuracy: 0.5000\n",
            "Epoch 59/150\n",
            "2/2 [==============================] - 2s 962ms/step - loss: 0.9741 - accuracy: 0.5156\n",
            "Epoch 60/150\n",
            "2/2 [==============================] - 2s 994ms/step - loss: 1.0564 - accuracy: 0.4792\n",
            "Epoch 61/150\n",
            "2/2 [==============================] - 2s 969ms/step - loss: 1.0355 - accuracy: 0.5000\n",
            "Epoch 62/150\n",
            "2/2 [==============================] - 2s 710ms/step - loss: 0.9684 - accuracy: 0.6250\n",
            "Epoch 63/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.8514 - accuracy: 0.5625\n",
            "Epoch 64/150\n",
            "2/2 [==============================] - 3s 990ms/step - loss: 1.0901 - accuracy: 0.4219\n",
            "Epoch 65/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.9255 - accuracy: 0.6042\n",
            "Epoch 66/150\n",
            "2/2 [==============================] - 2s 536ms/step - loss: 0.8278 - accuracy: 0.6875\n",
            "Epoch 67/150\n",
            "2/2 [==============================] - 2s 694ms/step - loss: 0.7904 - accuracy: 0.6667\n",
            "Epoch 68/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.7674 - accuracy: 0.6458\n",
            "Epoch 69/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 1.0730 - accuracy: 0.4531\n",
            "Epoch 70/150\n",
            "2/2 [==============================] - 2s 541ms/step - loss: 0.9286 - accuracy: 0.6250\n",
            "Epoch 71/150\n",
            "2/2 [==============================] - 2s 540ms/step - loss: 0.7465 - accuracy: 0.6875\n",
            "Epoch 72/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.8337 - accuracy: 0.6250\n",
            "Epoch 73/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.7053 - accuracy: 0.7083\n",
            "Epoch 74/150\n",
            "2/2 [==============================] - 2s 989ms/step - loss: 0.8131 - accuracy: 0.6042\n",
            "Epoch 75/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.9504 - accuracy: 0.5833\n",
            "Epoch 76/150\n",
            "2/2 [==============================] - 2s 992ms/step - loss: 0.7832 - accuracy: 0.6250\n",
            "Epoch 77/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.5998 - accuracy: 0.7292\n",
            "Epoch 78/150\n",
            "2/2 [==============================] - 3s 1s/step - loss: 0.9646 - accuracy: 0.5417\n",
            "Epoch 79/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.7628 - accuracy: 0.5938\n",
            "Epoch 80/150\n",
            "2/2 [==============================] - 2s 963ms/step - loss: 0.6851 - accuracy: 0.6875\n",
            "Epoch 81/150\n",
            "2/2 [==============================] - 2s 537ms/step - loss: 0.6156 - accuracy: 0.7708\n",
            "Epoch 82/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.8397 - accuracy: 0.5833\n",
            "Epoch 83/150\n",
            "2/2 [==============================] - 4s 2s/step - loss: 0.6908 - accuracy: 0.7969\n",
            "Epoch 84/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.5841 - accuracy: 0.7083\n",
            "Epoch 85/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.5438 - accuracy: 0.7292\n",
            "Epoch 86/150\n",
            "2/2 [==============================] - 2s 537ms/step - loss: 0.5056 - accuracy: 0.8333\n",
            "Epoch 87/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.6013 - accuracy: 0.8125\n",
            "Epoch 88/150\n",
            "2/2 [==============================] - 2s 529ms/step - loss: 0.5891 - accuracy: 0.7292\n",
            "Epoch 89/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.4468 - accuracy: 0.8281\n",
            "Epoch 90/150\n",
            "2/2 [==============================] - 2s 996ms/step - loss: 0.5668 - accuracy: 0.7708\n",
            "Epoch 91/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.5193 - accuracy: 0.7917\n",
            "Epoch 92/150\n",
            "2/2 [==============================] - 2s 558ms/step - loss: 0.3796 - accuracy: 0.8542\n",
            "Epoch 93/150\n",
            "2/2 [==============================] - 2s 1000ms/step - loss: 0.3461 - accuracy: 0.8958\n",
            "Epoch 94/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.4705 - accuracy: 0.7812\n",
            "Epoch 95/150\n",
            "2/2 [==============================] - 4s 2s/step - loss: 0.5953 - accuracy: 0.7500\n",
            "Epoch 96/150\n",
            "2/2 [==============================] - 2s 538ms/step - loss: 0.4976 - accuracy: 0.7708\n",
            "Epoch 97/150\n",
            "2/2 [==============================] - 2s 976ms/step - loss: 0.3545 - accuracy: 0.8906\n",
            "Epoch 98/150\n",
            "2/2 [==============================] - 2s 978ms/step - loss: 0.3776 - accuracy: 0.8750\n",
            "Epoch 99/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.4523 - accuracy: 0.8125\n",
            "Epoch 100/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.4276 - accuracy: 0.8125\n",
            "Epoch 101/150\n",
            "2/2 [==============================] - 2s 537ms/step - loss: 0.3099 - accuracy: 0.8750\n",
            "Epoch 102/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.3877 - accuracy: 0.8542\n",
            "Epoch 103/150\n",
            "2/2 [==============================] - 2s 999ms/step - loss: 0.3229 - accuracy: 0.8333\n",
            "Epoch 104/150\n",
            "2/2 [==============================] - 2s 978ms/step - loss: 0.2265 - accuracy: 0.9375\n",
            "Epoch 105/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.2371 - accuracy: 0.9375\n",
            "Epoch 106/150\n",
            "2/2 [==============================] - 2s 996ms/step - loss: 0.2211 - accuracy: 0.9375\n",
            "Epoch 107/150\n",
            "2/2 [==============================] - 2s 820ms/step - loss: 0.3149 - accuracy: 0.8958\n",
            "Epoch 108/150\n",
            "2/2 [==============================] - 3s 1s/step - loss: 0.3144 - accuracy: 0.8438\n",
            "Epoch 109/150\n",
            "2/2 [==============================] - 2s 570ms/step - loss: 0.5070 - accuracy: 0.7708\n",
            "Epoch 110/150\n",
            "2/2 [==============================] - 2s 967ms/step - loss: 0.3168 - accuracy: 0.9062\n",
            "Epoch 111/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1852 - accuracy: 0.9375\n",
            "Epoch 112/150\n",
            "2/2 [==============================] - 2s 938ms/step - loss: 0.2765 - accuracy: 0.8906\n",
            "Epoch 113/150\n",
            "2/2 [==============================] - 2s 568ms/step - loss: 0.2123 - accuracy: 0.8958\n",
            "Epoch 114/150\n",
            "2/2 [==============================] - 3s 1s/step - loss: 0.4536 - accuracy: 0.8750\n",
            "Epoch 115/150\n",
            "2/2 [==============================] - 2s 981ms/step - loss: 0.1477 - accuracy: 0.9792\n",
            "Epoch 116/150\n",
            "2/2 [==============================] - 2s 552ms/step - loss: 0.1611 - accuracy: 0.9375\n",
            "Epoch 117/150\n",
            "2/2 [==============================] - 2s 541ms/step - loss: 0.1882 - accuracy: 0.9375\n",
            "Epoch 118/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1110 - accuracy: 0.9583\n",
            "Epoch 119/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.5343 - accuracy: 0.7500\n",
            "Epoch 120/150\n",
            "2/2 [==============================] - 2s 549ms/step - loss: 0.3709 - accuracy: 0.8333\n",
            "Epoch 121/150\n",
            "2/2 [==============================] - 2s 987ms/step - loss: 0.3257 - accuracy: 0.9375\n",
            "Epoch 122/150\n",
            "2/2 [==============================] - 2s 562ms/step - loss: 0.2175 - accuracy: 0.9375\n",
            "Epoch 123/150\n",
            "2/2 [==============================] - 2s 633ms/step - loss: 0.2664 - accuracy: 0.8750\n",
            "Epoch 124/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.1018 - accuracy: 0.9792\n",
            "Epoch 125/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1854 - accuracy: 0.8750\n",
            "Epoch 126/150\n",
            "2/2 [==============================] - 2s 956ms/step - loss: 0.0994 - accuracy: 0.9844\n",
            "Epoch 127/150\n",
            "2/2 [==============================] - 2s 542ms/step - loss: 0.1337 - accuracy: 0.9375\n",
            "Epoch 128/150\n",
            "2/2 [==============================] - 3s 2s/step - loss: 0.1491 - accuracy: 0.9375\n",
            "Epoch 129/150\n",
            "2/2 [==============================] - 2s 544ms/step - loss: 0.0778 - accuracy: 1.0000\n",
            "Epoch 130/150\n",
            "2/2 [==============================] - 2s 541ms/step - loss: 0.1022 - accuracy: 0.9792\n",
            "Epoch 131/150\n",
            "2/2 [==============================] - 2s 565ms/step - loss: 0.1629 - accuracy: 0.9583\n",
            "Epoch 132/150\n",
            "2/2 [==============================] - 2s 988ms/step - loss: 0.0980 - accuracy: 0.9688\n",
            "Epoch 133/150\n",
            "2/2 [==============================] - 3s 1s/step - loss: 0.1840 - accuracy: 0.8958\n",
            "Epoch 134/150\n",
            "2/2 [==============================] - 3s 1s/step - loss: 0.1762 - accuracy: 0.9375\n",
            "Epoch 135/150\n",
            "2/2 [==============================] - 1s 973ms/step - loss: 0.1084 - accuracy: 0.9583\n",
            "Epoch 136/150\n",
            "2/2 [==============================] - 2s 983ms/step - loss: 0.0970 - accuracy: 0.9792\n",
            "Epoch 137/150\n",
            "2/2 [==============================] - 2s 543ms/step - loss: 0.0447 - accuracy: 1.0000\n",
            "Epoch 138/150\n",
            "2/2 [==============================] - 2s 533ms/step - loss: 0.2911 - accuracy: 0.8750\n",
            "Epoch 139/150\n",
            "2/2 [==============================] - 4s 2s/step - loss: 0.1845 - accuracy: 0.9375\n",
            "Epoch 140/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.2454 - accuracy: 0.9792\n",
            "Epoch 141/150\n",
            "2/2 [==============================] - 2s 998ms/step - loss: 0.1117 - accuracy: 0.9583\n",
            "Epoch 142/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.1081 - accuracy: 0.9583\n",
            "Epoch 143/150\n",
            "2/2 [==============================] - 2s 988ms/step - loss: 0.1139 - accuracy: 0.9583\n",
            "Epoch 144/150\n",
            "2/2 [==============================] - 2s 552ms/step - loss: 0.1016 - accuracy: 0.9583\n",
            "Epoch 145/150\n",
            "2/2 [==============================] - 3s 987ms/step - loss: 0.0251 - accuracy: 1.0000\n",
            "Epoch 146/150\n",
            "2/2 [==============================] - 3s 685ms/step - loss: 0.1272 - accuracy: 0.9167\n",
            "Epoch 147/150\n",
            "2/2 [==============================] - 2s 992ms/step - loss: 0.1101 - accuracy: 0.9583\n",
            "Epoch 148/150\n",
            "2/2 [==============================] - 2s 981ms/step - loss: 0.0991 - accuracy: 0.9531\n",
            "Epoch 149/150\n",
            "2/2 [==============================] - 2s 970ms/step - loss: 0.0610 - accuracy: 0.9792\n",
            "Epoch 150/150\n",
            "2/2 [==============================] - 2s 1s/step - loss: 0.0621 - accuracy: 0.9844\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-40-f86d2eeb155e>:9: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
            "  model.evaluate_generator(generator=valid_generator, steps=STEP_SIZE_VALID\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "STEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size\n",
        "STEP_SIZE_VALID=valid_generator.n//valid_generator.batch_size\n",
        "model.fit_generator(generator=train_generator,\n",
        "                    steps_per_epoch=STEP_SIZE_TRAIN,\n",
        "                    validation_data=valid_generator,\n",
        "                    validation_steps=STEP_SIZE_VALID,\n",
        "                    epochs=150\n",
        ")\n",
        "model.evaluate_generator(generator=valid_generator, steps=STEP_SIZE_VALID\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UbZ-fWJmAmDR",
        "outputId": "4f61f3ab-2e4f-4c73-8509-1bee95a90e1a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.save(CUR_DIR+'model/Model_CNN1000.h5')\n",
        "gc.collect()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kl7nrE8rB_tb",
        "outputId": "c88e0391-6f68-4316-9276-c1985b6a26fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 100 validated image filenames belonging to 3 classes.\n"
          ]
        }
      ],
      "source": [
        "test_datagen=ImageDataGenerator(rescale=1./255.)\n",
        "test_generator=test_datagen.flow_from_dataframe(\n",
        "    dataframe=testdf,\n",
        "    directory=TEST_IMG_PATH,\n",
        "    x_col=\"ID\",\n",
        "    y_col='lang',\n",
        "    batch_size=32,\n",
        "    seed=42,\n",
        "    shuffle=False,\n",
        "    class_mode='categorical',\n",
        "    target_size=(64,64))\n",
        "STEP_SIZE_TEST=test_generator.n//test_generator.batch_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jUDI50SCNV8",
        "outputId": "1f638999-a2d1-477a-d0e1-f743477c472b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-61-f904d4e6328a>:2: UserWarning: `Model.predict_generator` is deprecated and will be removed in a future version. Please use `Model.predict`, which supports generators.\n",
            "  pred=model.predict_generator(test_generator,\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3/3 [==============================] - 1s 275ms/step\n"
          ]
        }
      ],
      "source": [
        "test_generator.reset()\n",
        "pred=model.predict_generator(test_generator,\n",
        "steps=STEP_SIZE_TEST,\n",
        "verbose=1)\n",
        "predicted_class_indices=np.argmax(pred,axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IOSofAElCUbI",
        "outputId": "80c36db3-b442-4a9a-ceb5-f8d2e9291bc7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(96,)"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predicted_class_indices.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ciClLsBRCZFV",
        "outputId": "a76f0f3a-96b0-4ac2-949e-91eab929cb9d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 137ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([[0.00112203, 0.04741188, 0.951466  ]], dtype=float32)"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from PIL import Image\n",
        "import numpy as np\n",
        "from skimage import transform\n",
        "def load(filename):\n",
        "    np_image = Image.open(filename)\n",
        "    np_image = np.array(np_image).astype('float32')/255\n",
        "    np_image = transform.resize(np_image, (64, 64, 3))\n",
        "    np_image = np.expand_dims(np_image, axis=0)\n",
        "    return np_image\n",
        "\n",
        "image = load('/content/test/test-img/de_f_0809fd0642232f8c85b0b3d545dc2b5a.fragment18.pitch2.flac.jpg')\n",
        "model.predict(image)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PD4U_-tVCxuT",
        "outputId": "bb8c97ff-6cc7-49d8-86e7-d4babf5aaa63"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'de': 0, 'en': 1, 'es': 2}"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_generator.class_indices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFdNsjfncREZ",
        "outputId": "dc18b920-e5e6-4157-ff15-da7df6c60ed2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "4/4 [==============================] - 1s 171ms/step\n"
          ]
        }
      ],
      "source": [
        "prediction = model.predict(x=test_generator)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZaBwL9IjabED",
        "outputId": "67f8d351-a756-4d8a-98dd-abee88a78769"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "accuracy on test: 43.0\n"
          ]
        }
      ],
      "source": [
        "print(\"accuracy on test:\", np.sum(test_generator.classes == np.argmax(prediction, axis=-1)) / len(prediction) * 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rii1Noc7ioK-"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "BNt4eZDyB-GZ",
        "-zZ98U9wa2qn"
      ],
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}