Audio_Denoising_RNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DLAssignment3part2",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "IAwSnfJ9CoMx",
        "colab_type": "code",
        "outputId": "dde43dce-8b38-4dac-b7bd-72bba392ecfc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        }
      },
      "source": [
        "!pip install librosa\n",
        "from IPython import display\n",
        "\n",
        "from os.path import join\n",
        "\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from torch import optim\n",
        "\n",
        "import librosa\n",
        "import pickle\n",
        "import numpy as np"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: librosa in /usr/local/lib/python3.6/dist-packages (0.6.3)\n",
            "Requirement already satisfied: resampy>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.2.2)\n",
            "Requirement already satisfied: scikit-learn!=0.19.0,>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.21.3)\n",
            "Requirement already satisfied: numba>=0.38.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.40.1)\n",
            "Requirement already satisfied: audioread>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (2.1.8)\n",
            "Requirement already satisfied: scipy>=1.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.3.2)\n",
            "Requirement already satisfied: decorator>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (4.4.1)\n",
            "Requirement already satisfied: numpy>=1.8.0 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.17.4)\n",
            "Requirement already satisfied: joblib>=0.12 in /usr/local/lib/python3.6/dist-packages (from librosa) (0.14.0)\n",
            "Requirement already satisfied: six>=1.3 in /usr/local/lib/python3.6/dist-packages (from librosa) (1.12.0)\n",
            "Requirement already satisfied: llvmlite>=0.25.0dev0 in /usr/local/lib/python3.6/dist-packages (from numba>=0.38.0->librosa) (0.30.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2yg-IbsDHe9u",
        "colab_type": "code",
        "outputId": "764a2725-f2a1-4ae2-d898-d9046a3cb8e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IGRyRoHZRt8w",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "source_file_pickle = \"/content/drive/My Drive/Deep Learning Systems/Assignment3Part2Data/train/source_all.pickle\"\n",
        "noise_file_pickle = \"/content/drive/My Drive/Deep Learning Systems/Assignment3Part2Data/train/noise_all.pickle\"\n",
        "mixed_file_pickle = \"/content/drive/My Drive/Deep Learning Systems/Assignment3Part2Data/train/mixed_all.pickle\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ig3A8ZUSsI8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "source_files = []\n",
        "noise_files = []\n",
        "mixed_files = []\n",
        "ibm_list = []\n",
        "\n",
        "def get_ibm(noise_mat, source_mat):\n",
        "    #This function calculates ibm mask\n",
        "    return 1 * (source_mat > noise_mat) \n",
        "\n",
        "def pad_or_truncate(tensr):\n",
        "    shape_zero_dim = tensr.shape[0] \n",
        "    if not isinstance(tensr, torch.Tensor):\n",
        "        tensr = torch.from_numpy(tensr)\n",
        "    if shape_zero_dim < 100:\n",
        "        return F.pad(input=tensr, pad=(0,0,0, 100 - shape_zero_dim))\n",
        "    elif shape_zero_dim > 100:\n",
        "        return tensr[:100,:]\n",
        "    else:\n",
        "        return tensr\n",
        "\n",
        "def get_val_from_pickle(filename, ftype, append_to_arr):\n",
        "    with open(filename, 'rb') as f:\n",
        "        dict_file = pickle.load(f)\n",
        "        for i in range(len(dict_file)):\n",
        "            if i<10:\n",
        "                wav = dict_file[ftype+\"000\"+str(i)+\".wav\"]['stft_abs']\n",
        "                append_to_arr.append(pad_or_truncate(wav.T))\n",
        "            elif i>9 and i<100:\n",
        "                wav = dict_file[ftype+\"00\"+str(i)+\".wav\"]['stft_abs']\n",
        "                append_to_arr.append(pad_or_truncate(wav.T))\n",
        "            elif i>99 and i<1000:\n",
        "                wav = dict_file[ftype+\"0\"+str(i)+\".wav\"]['stft_abs']\n",
        "                append_to_arr.append(pad_or_truncate(wav.T))\n",
        "            else:\n",
        "                wav = dict_file[ftype+\"\"+str(i)+\".wav\"]['stft_abs']\n",
        "                append_to_arr.append(pad_or_truncate(wav.T))\n",
        "    return append_to_arr\n",
        "\n",
        "def get_sftf_from_pickle(filename, ftype, append_to_arr):\n",
        "    with open(filename, 'rb') as f:\n",
        "        dict_file = pickle.load(f)\n",
        "        for i in range(len(dict_file)):\n",
        "            if i<10:\n",
        "                wav = dict_file[ftype+\"000\"+str(i)+\".wav\"]['stft']\n",
        "                append_to_arr.append(wav.T)\n",
        "            elif i>9 and i<100:\n",
        "                wav = dict_file[ftype+\"00\"+str(i)+\".wav\"]['stft']\n",
        "                append_to_arr.append(wav.T)\n",
        "            elif i>99 and i<1000:\n",
        "                wav = dict_file[ftype+\"0\"+str(i)+\".wav\"]['stft']\n",
        "                append_to_arr.append(wav.T)\n",
        "            else:\n",
        "                wav = dict_file[ftype+\"\"+str(i)+\".wav\"]['stft']\n",
        "                append_to_arr.append(wav.T)\n",
        "    return append_to_arr\n",
        "\n",
        "source_files = get_val_from_pickle(source_file_pickle, 'trs', source_files)\n",
        "noise_files = get_val_from_pickle(noise_file_pickle, 'trn', noise_files)\n",
        "mixed_files = get_val_from_pickle(mixed_file_pickle, 'trx', mixed_files)\n",
        "\n",
        "for i in range(len(noise_files)):\n",
        "    ibm_list.append(\n",
        "        get_ibm(noise_files[i], source_files[i])\n",
        "    )\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1scydGPJ9n8S",
        "colab_type": "text"
      },
      "source": [
        "I have created the stft and their abs value of those. Stored them in a dictionary and pickled the dictonary and uploaded it on my drive.\n",
        "\n",
        "This part of code takes care of preprocessing which involves, padding or truncating. Calculations of ibm masks etc."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q7VS8trZz-aM",
        "colab_type": "code",
        "outputId": "586f2e2b-b071-4697-f13f-a3f7e0c8d39f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "class Net(nn.Module):\n",
        "\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.lstm1  = nn.LSTM(513, 513, batch_first=True)\n",
        "        self.lstm2  = nn.LSTM(513, 513, batch_first=True)\n",
        "        self.fc = nn.Linear(513,513)\n",
        "\n",
        "    def forward(self, x):\n",
        "        op, _ = self.lstm1(x)\n",
        "        op, _ = self.lstm2(op)\n",
        "        op2 = F.sigmoid(self.fc(op))\n",
        "        return op2\n",
        "\n",
        "net = Net()\n",
        "print(net)\n",
        "learning_rate = 1e-3\n",
        "criterion = nn.MSELoss()\n",
        "optimizer = optim.Adam(net.parameters(), lr=learning_rate)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Net(\n",
            "  (lstm1): LSTM(513, 513, batch_first=True)\n",
            "  (lstm2): LSTM(513, 513, batch_first=True)\n",
            "  (fc): Linear(in_features=513, out_features=513, bias=True)\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_EU4Y_a4nhA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class AudioDataset(Dataset):\n",
        "    def __init__(self, data, label):\n",
        "        self.sample = torch.stack(data).float()\n",
        "        self.label = torch.stack(label).float()\n",
        "        \n",
        "    def __len__(self):\n",
        "        if len(self.sample) == len(self.label):\n",
        "            return len(self.sample)\n",
        "    \n",
        "    def __getitem__(self, idx):\n",
        "        return self.sample[idx], self.label[idx]\n",
        "\n",
        "ds = AudioDataset(mixed_files, ibm_list)\n",
        "train_loader = DataLoader(ds, batch_size=400, shuffle=False, num_workers=2)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFJ-vfuA4-gZ",
        "colab_type": "code",
        "outputId": "f35a8afa-9892-4e7a-aa77-8f0f8620fd7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 937
        }
      },
      "source": [
        "epoch_count = 50\n",
        "for epoch in range(epoch_count):\n",
        "    running_loss = 0.0\n",
        "    for index, data in enumerate(train_loader):\n",
        "        inputs, labels = data\n",
        "        optimizer.zero_grad() \n",
        "\n",
        "        outputs = net(inputs)\n",
        "        \n",
        "        loss = criterion(outputs, labels)\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        running_loss += loss.item()\n",
        "        if index % 3 == 2:\n",
        "            print('[%d, %5d] loss: %.6f' %\n",
        "                  (epoch + 1, index + 1, running_loss / 3))\n",
        "            running_loss = 0.0\n",
        "\n",
        "print(\"Done training\")"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1351: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
            "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1,     3] loss: 0.248992\n",
            "[2,     3] loss: 0.247087\n",
            "[3,     3] loss: 0.239145\n",
            "[4,     3] loss: 0.238331\n",
            "[5,     3] loss: 0.234305\n",
            "[6,     3] loss: 0.231520\n",
            "[7,     3] loss: 0.225339\n",
            "[8,     3] loss: 0.222450\n",
            "[9,     3] loss: 0.215089\n",
            "[10,     3] loss: 0.210606\n",
            "[11,     3] loss: 0.205771\n",
            "[12,     3] loss: 0.198151\n",
            "[13,     3] loss: 0.194126\n",
            "[14,     3] loss: 0.190219\n",
            "[15,     3] loss: 0.186370\n",
            "[16,     3] loss: 0.182570\n",
            "[17,     3] loss: 0.179893\n",
            "[18,     3] loss: 0.176964\n",
            "[19,     3] loss: 0.174926\n",
            "[20,     3] loss: 0.170423\n",
            "[21,     3] loss: 0.167864\n",
            "[22,     3] loss: 0.165541\n",
            "[23,     3] loss: 0.162556\n",
            "[24,     3] loss: 0.160261\n",
            "[25,     3] loss: 0.160502\n",
            "[26,     3] loss: 0.158927\n",
            "[27,     3] loss: 0.157614\n",
            "[28,     3] loss: 0.153914\n",
            "[29,     3] loss: 0.153137\n",
            "[30,     3] loss: 0.151212\n",
            "[31,     3] loss: 0.148266\n",
            "[32,     3] loss: 0.146727\n",
            "[33,     3] loss: 0.146079\n",
            "[34,     3] loss: 0.146575\n",
            "[35,     3] loss: 0.144845\n",
            "[36,     3] loss: 0.142155\n",
            "[37,     3] loss: 0.141583\n",
            "[38,     3] loss: 0.141890\n",
            "[39,     3] loss: 0.142315\n",
            "[40,     3] loss: 0.140084\n",
            "[41,     3] loss: 0.138937\n",
            "[42,     3] loss: 0.138373\n",
            "[43,     3] loss: 0.137670\n",
            "[44,     3] loss: 0.135748\n",
            "[45,     3] loss: 0.134435\n",
            "[46,     3] loss: 0.133690\n",
            "[47,     3] loss: 0.132395\n",
            "[48,     3] loss: 0.131469\n",
            "[49,     3] loss: 0.131615\n",
            "[50,     3] loss: 0.130704\n",
            "Done training\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPCieNlj-_WL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "valnoise_pickle = \"/content/drive/My Drive/Deep Learning Systems/Assignment3Part2Data/validation/noise_val.pickle\"\n",
        "valsource_pickle = \"/content/drive/My Drive/Deep Learning Systems/Assignment3Part2Data/validation/source_val.pickle\"\n",
        "valmixed_pickle = \"/content/drive/My Drive/Deep Learning Systems/Assignment3Part2Data/validation/mix_val.pickle\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BmzLqcHx_2jU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a5499002-0966-4e64-9d14-b790439d5af2"
      },
      "source": [
        "#abs files\n",
        "source_val_files = []\n",
        "noise_val_files = []\n",
        "mixed_val_files = []\n",
        "#complex files\n",
        "source_stft_files = []\n",
        "noise_stft_files = []\n",
        "mixed_stft_files = []\n",
        "source_val_files = get_val_from_pickle(valsource_pickle, 'vs', source_val_files)\n",
        "noise_val_files = get_val_from_pickle(valnoise_pickle, 'vn', noise_val_files)\n",
        "mixed_val_files = get_val_from_pickle(valmixed_pickle, 'vx', mixed_val_files)\n",
        "\n",
        "source_stft_files = get_sftf_from_pickle(valsource_pickle, 'vs', source_stft_files)\n",
        "noise_stft_files = get_sftf_from_pickle(valnoise_pickle, 'vn', noise_stft_files)\n",
        "mixed_stft_files = get_sftf_from_pickle(valmixed_pickle, 'vx', mixed_stft_files)\n",
        "\n",
        "val_ibm = []\n",
        "\n",
        "for i in range(len(noise_val_files)):\n",
        "    val_ibm.append(\n",
        "        get_ibm(noise_val_files[i], source_val_files[i])\n",
        "    )\n",
        "print(\n",
        "    len(source_val_files), len(noise_val_files), len(mixed_val_files), len(val_ibm)\n",
        ")"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1200 1200 1200 1200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LpHTgpWLEPBA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ds = AudioDataset(mixed_val_files, val_ibm)\n",
        "val_loader = DataLoader(ds, batch_size=512, shuffle=False)\n",
        "\n",
        "def calculate_audio(mask, noise):\n",
        "    mask_size = 100\n",
        "    noise_size = 100\n",
        "    if mask.shape[0] < noise.shape[0]:\n",
        "        return mask.data.cpu().numpy()*noise[:mask.shape[0],:] \n",
        "    else:\n",
        "        return mask[:noise.shape[0],:].data.cpu().numpy()*noise\n",
        "\n",
        "def getsnr(t, t_bar):\n",
        "    snr = np.dot(t,t.T)/np.dot(\n",
        "            (t[:len(t_bar)]-t_bar),\n",
        "            (t[:len(t_bar)]-t_bar).T\n",
        "    )\n",
        "    print(\"SNR is {}\".format(snr))\n",
        "    return snr\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zftOD3o6EkC5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4c3aceb0-04ff-401b-d7ad-1718c962816e"
      },
      "source": [
        "cross_validation_snr_array = []\n",
        "with torch.no_grad():\n",
        "    for index, data in enumerate(val_loader):\n",
        "        inputs, labels = data\n",
        "        optimizer.zero_grad() \n",
        "        outputs = net(inputs)\n",
        "        loss = criterion(outputs, labels)\n",
        "\n",
        "        for index2, op in enumerate(outputs):\n",
        "            op_mask = 1 * (op > 0.5)\n",
        "            lsss = calculate_audio(op_mask, mixed_stft_files[index2])\n",
        "\n",
        "            pred_istft = librosa.core.istft(lsss, hop_length=513)\n",
        "            original_sound = librosa.core.istft(\n",
        "                source_stft_files[index2], hop_length=513\n",
        "            )\n",
        "            snr_stuff = getsnr(original_sound, pred_istft) \n",
        "            print(\"snr for item {} is {}\".format(index2, snr_stuff))\n",
        "            cross_validation_snr_array.append(snr_stuff)\n",
        "        print(\n",
        "            '[%d, %5d] loss: %.6f' %(\n",
        "                epoch + 1, index + 1, loss.item()\n",
        "            )\n",
        "        )\n"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1351: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
            "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "SNR is 63.20085525512695\n",
            "snr for item 0 is 63.20085525512695\n",
            "SNR is 6.36367130279541\n",
            "snr for item 1 is 6.36367130279541\n",
            "SNR is 7.17567253112793\n",
            "snr for item 2 is 7.17567253112793\n",
            "SNR is 1.4942082166671753\n",
            "snr for item 3 is 1.4942082166671753\n",
            "SNR is 1.4511268138885498\n",
            "snr for item 4 is 1.4511268138885498\n",
            "SNR is 4.968997478485107\n",
            "snr for item 5 is 4.968997478485107\n",
            "SNR is 2.3781862258911133\n",
            "snr for item 6 is 2.3781862258911133\n",
            "SNR is 6.061437129974365\n",
            "snr for item 7 is 6.061437129974365\n",
            "SNR is 2.4517412185668945\n",
            "snr for item 8 is 2.4517412185668945\n",
            "SNR is 1.4178576469421387\n",
            "snr for item 9 is 1.4178576469421387\n",
            "SNR is 0.6652364730834961\n",
            "snr for item 10 is 0.6652364730834961\n",
            "SNR is 0.781225323677063\n",
            "snr for item 11 is 0.781225323677063\n",
            "SNR is 0.6769945025444031\n",
            "snr for item 12 is 0.6769945025444031\n",
            "SNR is 0.6595921516418457\n",
            "snr for item 13 is 0.6595921516418457\n",
            "SNR is 0.8330897092819214\n",
            "snr for item 14 is 0.8330897092819214\n",
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            "snr for item 175 is 2.7167327404022217\n",
            "[50,     3] loss: 0.133171\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJdGAdgaRuG4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bffacea7-85e7-4e11-a4f4-f8d31dac9f72"
      },
      "source": [
        "print(np.average(cross_validation_snr_array))"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3.1535788\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XH8xrdsA-b2C",
        "colab_type": "text"
      },
      "source": [
        "Here we can see that the average snr over validation files is positive."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RqThicgPkZdf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "d78c99fc-863e-4ecf-a4b7-b638c1e18318"
      },
      "source": [
        "testmixed_pickle = \"/content/drive/My Drive/Deep Learning Systems/Assignment3Part2Data/test/test_mix.pickle\"\n",
        "\n",
        "#abs files\n",
        "mixed_test_abs = []\n",
        "mixed_test_stft = []\n",
        "mixed_test_abs = get_val_from_pickle(testmixed_pickle, 'tex', mixed_test_abs)\n",
        "mixed_test_stft = get_sftf_from_pickle(testmixed_pickle, 'tex', mixed_test_stft)\n",
        "\n",
        "test_ibm = []\n",
        "#generate this \n",
        "generated_test_istft = []\n",
        "with torch.no_grad():\n",
        "    for index, ip in enumerate(mixed_test_abs):\n",
        "        outputs = net(ip.unsqueeze(0))\n",
        "        lsss = calculate_audio(op_mask, mixed_test_stft[index])\n",
        "        pred_istft = librosa.core.istft(lsss, hop_length=513)\n",
        "        generated_test_istft.append(pred_istft)\n"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:1351: UserWarning: nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\n",
            "  warnings.warn(\"nn.functional.sigmoid is deprecated. Use torch.sigmoid instead.\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rr5GwSzZFJ6X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7dc66a12-39b3-40fb-fa2c-81ed059905d4"
      },
      "source": [
        "print(len(generated_test_istft))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "400\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIIeoSh2HxFh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sr_for_generated_test_istft=[]\n",
        "def get_sr_for_test_audio(filename, ftype, append_to_arr):\n",
        "    with open(filename, 'rb') as f:\n",
        "        dict_file = pickle.load(f)\n",
        "        for i in range(len(dict_file)):\n",
        "            if i<10:\n",
        "                append_to_arr.append(\n",
        "                    dict_file[ftype+\"000\"+str(i)+\".wav\"]['sr']\n",
        "                )\n",
        "                \n",
        "            elif i>9 and i<100:\n",
        "                append_to_arr.append(\n",
        "                    dict_file[ftype+\"00\"+str(i)+\".wav\"]['sr']\n",
        "                )\n",
        "            elif i>99 and i<1000:\n",
        "                append_to_arr.append(\n",
        "                    dict_file[ftype+\"0\"+str(i)+\".wav\"]['sr']\n",
        "                )\n",
        "            else:\n",
        "                append_to_arr.append(\n",
        "                    dict_file[ftype+\"\"+str(i)+\".wav\"]['sr']\n",
        "                )\n",
        "    return append_to_arr\n",
        "#We can use following line of code to generate test output\n",
        "# sr_for_generated_test_istft = get_sr_for_test_audio(testmixed_pickle, 'tex', sr_for_generated_test_istft)\n",
        "\n",
        "# display.display(display.Audio(\n",
        "#     data=generated_test_istft[id], rate=sr_for_generated_test_istft[id])\n",
        "# )"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}