week02/seminar02_2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ksetdekov/apdl/blob/master/week02/seminar02_2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "wired-brick",
      "metadata": {
        "id": "wired-brick"
      },
      "source": [
        "# CTC based ASR\n",
        "\n",
        "In this seminar we will do the following things:\n",
        "\n",
        "    - Write data processing to train a speech recognition model\n",
        "    - Write a simple model\n",
        "    - Train the model using the CTC loss function \n",
        "    - Measure the quality using metrics like CER"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "favorite-withdrawal",
      "metadata": {
        "id": "favorite-withdrawal"
      },
      "outputs": [],
      "source": [
        "from IPython import display\n",
        "from matplotlib import pyplot as plt\n",
        "from collections import defaultdict\n",
        "\n",
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import Dataset, DataLoader, Subset\n",
        "\n",
        "import torchaudio\n",
        "from torchaudio.transforms import MelSpectrogram\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "negative-anderson",
      "metadata": {
        "id": "negative-anderson"
      },
      "outputs": [],
      "source": [
        "def visualize_audio(wav: torch.Tensor, sr: int = 22050):\n",
        "    if wav.dim() == 2:\n",
        "        wav = wav.mean(dim=0)\n",
        "    \n",
        "    plt.figure(figsize=(20, 5))\n",
        "    plt.plot(wav, alpha=.7, c='green')\n",
        "    plt.grid()\n",
        "    plt.xlabel('Time', size=20)\n",
        "    plt.ylabel('Amplitude', size=20)\n",
        "    plt.show()\n",
        "    \n",
        "    display.display(display.Audio(wav, rate=sr))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "quality-bread",
      "metadata": {
        "id": "quality-bread"
      },
      "source": [
        "# Data\n",
        "\n",
        "This is a dataset with numbers from 0 to 999_999 pronounced in Russian.\n",
        "There are different speakers of different ages and genders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "7YVP87uwkFAK",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7YVP87uwkFAK",
        "outputId": "36c11d7a-b1a7-41e1-d229-f9bf68f530f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-12-11 14:37:08--  https://raw.githubusercontent.com/markovka17/apdl/master/gfile.py\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3423 (3.3K) [text/plain]\n",
            "Saving to: ‘gfile.py’\n",
            "\n",
            "\rgfile.py              0%[                    ]       0  --.-KB/s               \rgfile.py            100%[===================>]   3.34K  --.-KB/s    in 0s      \n",
            "\n",
            "2021-12-11 14:37:08 (45.6 MB/s) - ‘gfile.py’ saved [3423/3423]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://raw.githubusercontent.com/markovka17/apdl/master/gfile.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "respective-morris",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "respective-morris",
        "outputId": "a655373a-7817-4062-ea4f-c4bf53f3b665"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mВыходные данные были обрезаны до нескольких последних строк (5000).\u001b[0m\n",
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          ]
        }
      ],
      "source": [
        "!mkdir data\n",
        "!python gfile.py -u https://drive.google.com/file/d/1HKtLLbiEk0c3l1mKz9LUXRAmKd3DvD0P \\\n",
        "    -f \"numbers.zip\" \\\n",
        "    -d data/\n",
        "\n",
        "!unzip data/numbers.zip -d data/"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aBgPcMNMAsxP",
      "metadata": {
        "id": "aBgPcMNMAsxP"
      },
      "source": [
        "Convert all audio to the same sample_rate=16000, and convert the answers to the form we want. For example, if the number 123 is pronounced, then the answer is: y = [1,2,3].\n",
        "\n",
        "Let's implement all these stuff in `SpeechDataset` class. This class should have constructor (\\_\\_init\\_\\_ method), \\_\\_getitem\\_\\_ and \\_\\_len\\_\\_."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "pressed-expert",
      "metadata": {
        "id": "pressed-expert"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "class SpeechDataset(Dataset):\n",
        "    \n",
        "    SAMPLE_RATE = 16000\n",
        "    _ROOT_PREFIX = 'data/numbers'\n",
        "\n",
        "    def __init__(self, path_to_csv: str):\n",
        "        self.path_to_csv = path_to_csv\n",
        "\n",
        "        self.csv: pd.DataFrame = pd.read_csv(path_to_csv)\n",
        "\n",
        "    @staticmethod\n",
        "    def number2digits(number: np.ndarray) -> torch.Tensor:\n",
        "        digits = list(map(int, list(str(number))))\n",
        "        return torch.LongTensor(digits)\n",
        "\n",
        "    def __getitem__(self, index: int):\n",
        "        output = self.csv.iloc[index]\n",
        "        \n",
        "        if len(output) == 3:\n",
        "            path, gender, number = output\n",
        "            y = self.number2digits(number)\n",
        "        else:\n",
        "            path, = output\n",
        "            y = torch.LongTensor([-1])\n",
        "\n",
        "        path = os.path.join(self._ROOT_PREFIX, path)\n",
        "\n",
        "        wav, sr = torchaudio.load(path)\n",
        "        if sr != self.SAMPLE_RATE:\n",
        "            resampler = torchaudio.transforms.Resample(sr, self.SAMPLE_RATE)\n",
        "            wav = resampler(wav)\n",
        "        \n",
        "        wav = wav.squeeze()\n",
        "\n",
        "        instance = {\n",
        "            'path': path,\n",
        "            'x': wav,\n",
        "            'y': y\n",
        "        }\n",
        "\n",
        "        return instance\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.csv.shape[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "authentic-animation",
      "metadata": {
        "id": "authentic-animation"
      },
      "source": [
        "Randomly split dataset on `train` and `validation`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "virtual-swiss",
      "metadata": {
        "id": "virtual-swiss"
      },
      "outputs": [],
      "source": [
        "dataset = SpeechDataset('data/numbers/train.csv')\n",
        "\n",
        "train_ratio = 0.9\n",
        "train_size = int(len(dataset) * train_ratio)\n",
        "random_indexes = torch.randperm(len(dataset)).tolist()\n",
        "\n",
        "train_dataset = Subset(dataset, random_indexes[:train_size])\n",
        "val_dataset = Subset(dataset, random_indexes[train_size:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "neither-microwave",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "neither-microwave",
        "outputId": "075ec60e-17e5-4be0-db76-af01aca9e9d7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8100, 900)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "len(train_dataset), len(val_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "activated-religious",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "id": "activated-religious",
        "outputId": "0cc42058-0c25-4a43-83d0-de375a68f07d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
              "                    <source 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type=\"audio/wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "instance = train_dataset[0]\n",
        "visualize_audio(instance['x'], dataset.SAMPLE_RATE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "gjNA_uCVzueP",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gjNA_uCVzueP",
        "outputId": "7e14c691-d0e2-413f-e3d4-42b3f2f146db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([9, 1, 2, 0, 9, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "instance['y']"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Vrr9eofUBmZ8",
      "metadata": {
        "id": "Vrr9eofUBmZ8"
      },
      "source": [
        "Let's introduce padding. Dataset contains numbers from 0 to 999999. To work with the dataset, it is convenient for us that all the answers y be of the same length. If the number is shorter than six digits, we fill the remaining cells with '10':\n",
        "\n",
        "    123456 -> y = [1, 2, 3, 4, 5, 6]\n",
        "\n",
        "    123    -> y = [1, 2, 3, 10, 10, 10]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "interesting-argument",
      "metadata": {
        "id": "interesting-argument"
      },
      "outputs": [],
      "source": [
        "from tqdm import tqdm\n",
        "from itertools import islice\n",
        "\n",
        "\n",
        "class PaddingCollator:\n",
        "\n",
        "    def __call__(self, instances):\n",
        "        \n",
        "        batch_size = len(instances)\n",
        "        x_len = torch.LongTensor([instance['x'].size(0) for instance in instances])\n",
        "        y_len = torch.LongTensor([instance['y'].size(0) for instance in instances])\n",
        "        path = [instance['path'] for instance in instances]\n",
        "\n",
        "        x = torch.zeros(batch_size, max(x_len), dtype=torch.float32)\n",
        "        y = torch.zeros(batch_size, max(y_len), dtype=torch.long)\n",
        "        y.fill_(10)  # 10 -- padding\n",
        "\n",
        "        for i, instance in enumerate(instances):\n",
        "            x[i, :x_len[i]] = instance['x']\n",
        "            y[i, :y_len[i]] = instance['y']\n",
        "\n",
        "        instance = {\n",
        "            'x': x,\n",
        "            'y': y,\n",
        "            'x_len': x_len,\n",
        "            'y_len': y_len,\n",
        "            'path': path\n",
        "        }\n",
        "        \n",
        "        return instance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "employed-hughes",
      "metadata": {
        "id": "employed-hughes"
      },
      "outputs": [],
      "source": [
        "train_dataloader = DataLoader(\n",
        "    train_dataset, 64,\n",
        "    collate_fn=PaddingCollator(), shuffle=True,\n",
        "#     pin_memory=True, num_workers=2\n",
        ")\n",
        "val_dataloader = DataLoader(\n",
        "    val_dataset, 32, collate_fn=PaddingCollator(),\n",
        "#     pin_memory=True, num_workers=2\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fifty-tours",
      "metadata": {
        "id": "fifty-tours"
      },
      "source": [
        "Let's see what we have in our batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "unexpected-settlement",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "unexpected-settlement",
        "outputId": "e0b569c2-13a1-4b3e-ea3b-531d6b391968"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0., 0., 0.,  ..., 0., 0., 0.],\n",
              "        [0., 0., 0.,  ..., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "batch = list(islice(val_dataloader, 1))[0]\n",
        "batch['x'][:2, :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "joint-consensus",
      "metadata": {
        "id": "joint-consensus",
        "outputId": "51b003f7-cf6e-4aa3-daac-81874edcf2e0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 5, 3, 2, 6, 5],\n",
              "        [7, 5, 4, 3, 5, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "batch['y'][:2, :]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "worldwide-beast",
      "metadata": {
        "id": "worldwide-beast",
        "outputId": "5f4fc753-c481-4978-9496-4d4595297453",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['x', 'y', 'x_len', 'y_len', 'path'])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "batch.keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "personal-quality",
      "metadata": {
        "id": "personal-quality"
      },
      "source": [
        "# Feature Extraction"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4cV9h6BG8fwl",
      "metadata": {
        "id": "4cV9h6BG8fwl"
      },
      "source": [
        "Example of obtaining a (mel) spectrogram from a signal:![FFT.jpg](data:image/jpeg;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "early-staff",
      "metadata": {
        "id": "early-staff"
      },
      "outputs": [],
      "source": [
        "def apply_compression(melspec):\n",
        "    # Add eps=1e-7 to avoid log(0)\n",
        "    return torch.log(melspec.clamp(1e-5))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "former-consultancy",
      "metadata": {
        "id": "former-consultancy"
      },
      "outputs": [],
      "source": [
        "class Featurizer(nn.Module):\n",
        "    \n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        \n",
        "        self.featurizer = MelSpectrogram(\n",
        "            sample_rate=train_dataset.dataset.SAMPLE_RATE,\n",
        "            n_fft=1024,\n",
        "            win_length=1024,\n",
        "            hop_length=256,\n",
        "            n_mels=64,\n",
        "            power=1\n",
        "        )\n",
        "    \n",
        "    def forward(self, x, x_len=None):\n",
        "        x = self.featurizer(x)\n",
        "        x = apply_compression(x)\n",
        "        \n",
        "        # Convert the length from wav dimension to spectrogram dimension\n",
        "        if x_len is not None:\n",
        "            x_len = ((x_len + 1).float() / self.featurizer.hop_length).ceil().long()\n",
        "            return x, x_len\n",
        "        \n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "center-double",
      "metadata": {
        "id": "center-double"
      },
      "source": [
        "## Model\n",
        "\n",
        "Let's write a simple and small model based on QuartzNet https://arxiv.org/pdf/1910.10261.pdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "satisfactory-newton",
      "metadata": {
        "id": "satisfactory-newton"
      },
      "outputs": [],
      "source": [
        "from torch import nn\n",
        "\n",
        "\n",
        "class QuartzBlock(nn.Module):\n",
        "    \"\"\"\n",
        "    Basic block of QuartzNet consisting of Separable Convolution, BatchNorm and ReLU repeating R times\n",
        "    \n",
        "    :param in_channels: number of input channels\n",
        "    :param hidden_channels: number of hidden and output channels\n",
        "    :param kernel_size: size of kernels\n",
        "    :param repeat: number of repetitions of block constituents\n",
        "    \"\"\"\n",
        "    \n",
        "    def __init__(\n",
        "        self,\n",
        "        in_channels: int,\n",
        "        hidden_channels: int,\n",
        "        kernel_size: int = 33,\n",
        "        repeat: int = 4,\n",
        "    ):\n",
        "        super().__init__()\n",
        "        \n",
        "        self.repeat = repeat\n",
        "        self.blocks = nn.ModuleList([\n",
        "            nn.ModuleList([\n",
        "                nn.Conv1d(\n",
        "                    in_channels=in_channels if i == 0 else hidden_channels,\n",
        "                    out_channels=hidden_channels,\n",
        "                    kernel_size=kernel_size,\n",
        "                    groups=in_channels if i == 0 else hidden_channels,\n",
        "                    padding=kernel_size // 2\n",
        "                ),\n",
        "                nn.Conv1d(hidden_channels, hidden_channels, kernel_size=1),\n",
        "                nn.BatchNorm1d(hidden_channels),\n",
        "                nn.ReLU(),\n",
        "            ])\n",
        "            for i in range(repeat)\n",
        "        ])\n",
        "        self.res_conv = nn.Sequential(  # convolution for residual\n",
        "            nn.Conv1d(in_channels, hidden_channels, kernel_size=1),\n",
        "            nn.BatchNorm1d(hidden_channels),\n",
        "        )\n",
        "\n",
        "    def forward(self, x):\n",
        "        x_initial = x\n",
        "        \n",
        "        for i, block in enumerate(self.blocks):\n",
        "            for j, layer in enumerate(block):\n",
        "                # If not last ReLU\n",
        "                if not (i == (len(self.blocks) - 1) and j ==( len(block) - 1)):\n",
        "                    x = layer(x)\n",
        "                else:\n",
        "                    # Pass residual\n",
        "                    x = x + self.res_conv(x_initial)\n",
        "                    x = layer(x)\n",
        "        return x\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "id": "universal-amateur",
      "metadata": {
        "id": "universal-amateur",
        "scrolled": true
      },
      "outputs": [],
      "source": [
        "class DummyModel(nn.Module):\n",
        "\n",
        "    def __init__(self, in_channels, hidden_channels, out_channels):\n",
        "        super().__init__()\n",
        "        \n",
        "        self.prenet = nn.Sequential(\n",
        "            nn.Conv1d(in_channels, hidden_channels, 33, padding=33 // 2, stride=2),\n",
        "            nn.Conv1d(hidden_channels, hidden_channels, kernel_size=1),\n",
        "            nn.BatchNorm1d(hidden_channels),\n",
        "            nn.ReLU()\n",
        "        )\n",
        "        \n",
        "        self.net = nn.Sequential(\n",
        "            QuartzBlock(hidden_channels, hidden_channels),\n",
        "            QuartzBlock(hidden_channels, hidden_channels * 2),\n",
        "\n",
        "            nn.Conv1d(hidden_channels * 2, hidden_channels * 2, 33,\n",
        "                      padding=33 // 2, stride=2),\n",
        "            nn.Conv1d(hidden_channels * 2, hidden_channels * 2, kernel_size=1),\n",
        "            nn.BatchNorm1d(hidden_channels * 2),\n",
        "            nn.ReLU(),\n",
        "\n",
        "            QuartzBlock(hidden_channels * 2, hidden_channels * 4)\n",
        "        )\n",
        "        self.classifier = nn.Sequential(\n",
        "            nn.Conv1d(hidden_channels * 4, out_channels, kernel_size=1),\n",
        "            nn.LogSoftmax(dim=1)\n",
        "        )\n",
        "    \n",
        "    def forward(self, x, x_len):\n",
        "        x = self.prenet(x)\n",
        "        x = self.net(x)\n",
        "        return self.classifier(x), x_len // 4\n",
        "\n",
        "# DummyModel(64, 128, 10 + 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "limited-intensity",
      "metadata": {
        "id": "limited-intensity"
      },
      "source": [
        "## Calculate CER (character error rate)\n",
        "\n",
        "An example of calculating a similar metric, but by words (WER, word error rate): https://www.rev.ai/blog/how-to-calculate-word-error-rate/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "id": "wh7T5us5AxvE",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wh7T5us5AxvE",
        "outputId": "23acb2fc-0ce6-4e49-bc0c-66a35d6aa013"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting Levenshtein\n",
            "  Downloading Levenshtein-0.16.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (110 kB)\n",
            "\u001b[K     |████████████████████████████████| 110 kB 9.2 MB/s \n",
            "\u001b[?25hCollecting rapidfuzz<1.9,>=1.8.2\n",
            "  Downloading rapidfuzz-1.8.3-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (854 kB)\n",
            "\u001b[K     |████████████████████████████████| 854 kB 59.6 MB/s \n",
            "\u001b[?25hInstalling collected packages: rapidfuzz, Levenshtein\n",
            "Successfully installed Levenshtein-0.16.0 rapidfuzz-1.8.3\n"
          ]
        }
      ],
      "source": [
        "!pip install Levenshtein"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "Xc8Qwo5Wty66",
      "metadata": {
        "id": "Xc8Qwo5Wty66"
      },
      "source": [
        "Just a way to decode the predictions when we train model with `CTC` criterion is to collapse the repeating characters and remove the `blank` characters:\n",
        "\n",
        "![CTC.jfif](data:image/jpeg;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "id": "lesser-wyoming",
      "metadata": {
        "id": "lesser-wyoming"
      },
      "outputs": [],
      "source": [
        "import Levenshtein\n",
        "\n",
        "\n",
        "def ctc_decode(sequence, black_symbol=10):\n",
        "    \"\"\"\n",
        "    Decode hypotheses\n",
        "        1) Remove repetitive letters\n",
        "        2) Remove blank characters\n",
        "    \"\"\"\n",
        "    \n",
        "    current = []\n",
        "    for i in range(0, len(sequence)):\n",
        "        symbol = sequence[i]\n",
        "\n",
        "        if i > 0 and symbol == sequence[i - 1]:\n",
        "            continue\n",
        "\n",
        "        # skip blank symbol\n",
        "        if symbol == black_symbol:\n",
        "            continue\n",
        "\n",
        "        current.append(str(int(symbol)))\n",
        "\n",
        "    text = ''.join(current)\n",
        "    return text\n",
        "\n",
        "\n",
        "def calculate_cer(targets, decodings, padding_value=10):\n",
        "    \"\"\"\n",
        "    Calculate the Levenshtein distance between predictions and GT\n",
        "    \"\"\"\n",
        "    \n",
        "    cer = 0.0\n",
        "    targets = targets.detach().cpu().tolist()\n",
        "\n",
        "    pairs = []\n",
        "    for target, d in zip(targets, decodings):\n",
        "        target = [c for c in target if c != padding_value]\n",
        "        target = \"\".join(map(str, target))\n",
        "\n",
        "        decoding = ctc_decode(d.tolist())\n",
        "        cer += min(1, Levenshtein.distance(target, decoding) / (len(decoding) + 1e-7))\n",
        "\n",
        "        pairs.append(\n",
        "            target + '\\t\\t' + decoding\n",
        "        )\n",
        "\n",
        "    return cer / len(target), pairs"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "manufactured-twenty",
      "metadata": {
        "id": "manufactured-twenty"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "id": "gCI8MZqgLoI3",
      "metadata": {
        "id": "gCI8MZqgLoI3"
      },
      "outputs": [],
      "source": [
        "class AverageMeter:\n",
        "    \n",
        "    def __init__(self):\n",
        "        self.reset()\n",
        "\n",
        "    def reset(self):\n",
        "        self.val = 0\n",
        "        self.avg = 0\n",
        "        self.sum = 0\n",
        "        self.count = 0\n",
        "\n",
        "    def update(self, val, n=1):\n",
        "        self.val = val\n",
        "        self.sum += val * n\n",
        "        self.count += n\n",
        "        self.avg = self.sum / self.count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "id": "beautiful-teach",
      "metadata": {
        "id": "beautiful-teach"
      },
      "outputs": [],
      "source": [
        "NUM_EPOCH = 20\n",
        "DEVICE = torch.device('cuda:0')\n",
        "\n",
        "model = DummyModel(64, 128, 10 + 1).to(DEVICE)\n",
        "mel_featurizer = Featurizer().to(DEVICE)\n",
        "criterion = nn.CTCLoss(blank=10)\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "id": "Ks9zYRxpQZVh",
      "metadata": {
        "id": "Ks9zYRxpQZVh"
      },
      "outputs": [],
      "source": [
        "history = defaultdict(list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "id": "experienced-edward",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "experienced-edward",
        "outputId": "108cc1f5-6a1e-44f5-fc47-1aace84a2b9a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 19\n",
            "Example of prediction:  302019 -> 30209190909708\n",
            "Example of prediction:  950539 -> 950539080\n",
            "Example of prediction:  265114 -> 26511408\n",
            "Example of prediction:  524403 -> 52440370908\n",
            "Example of prediction:  730627 -> 730627906080\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "for epoch in range(NUM_EPOCH):\n",
        "\n",
        "    average_cer = AverageMeter()\n",
        "    average_loss = AverageMeter()\n",
        "    \n",
        "    for batch in train_dataloader:\n",
        "        x = batch['x'].to(DEVICE)\n",
        "        y = batch['y'].to(DEVICE)\n",
        "        x_len = batch['x_len'].to(DEVICE)\n",
        "        y_len = batch['y_len'].to(DEVICE)\n",
        "        \n",
        "        x, x_len = mel_featurizer(x, x_len)\n",
        "        \n",
        "        log_probs, x_len = model(x, x_len)\n",
        "        \n",
        "        # B, C, T -> T, B, C\n",
        "        log_probs = log_probs.permute(2, 0, 1)\n",
        "        loss = criterion(log_probs, y, x_len, y_len)\n",
        "        \n",
        "        optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        \n",
        "        # T, B, C -> T, B -> B, T\n",
        "        argmax_decoding = log_probs.detach().cpu() \\\n",
        "            .argmax(dim=-1).transpose(0, 1)\n",
        "\n",
        "        cer, pairs = calculate_cer(y, argmax_decoding)\n",
        "\n",
        "        average_cer.update(cer)\n",
        "        average_loss.update(loss.item())\n",
        "\n",
        "        history['loss'].append(average_loss.avg)\n",
        "        history['cer'].append(average_cer.avg)\n",
        "    \n",
        "    display.clear_output()\n",
        "    print(f'Epoch: {epoch}')\n",
        "    for i in range(5):\n",
        "        print(f'Example of prediction: ', pairs[i].replace(\"\\t\\t\", \" -> \"))\n",
        "    \n",
        "    fig, axes = plt.subplots(1, 2, figsize=(16, 8))\n",
        "    axes[0].plot(history['loss'], label='Loss')\n",
        "    axes[1].plot(history['cer'], label='CER')\n",
        "    axes[0].legend(); axes[0].grid()\n",
        "    axes[1].legend(); axes[1].grid()\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "YvGTIuhOOYCO",
      "metadata": {
        "id": "YvGTIuhOOYCO"
      },
      "source": [
        "## What's next?\n",
        "\n",
        "    1) Add some augmentations from previous seminar\n",
        "\n",
        "    2) Improve the model. For example, reduce the time space even more. Now we reduce it 4 times, but it is still not enough since the length of GT string is 4-6.\n",
        "    3) Translate numbers into letters and train letter recognition model (overkill :))\n",
        "    4) Add a special symbol to separate the digits -- thousands/hundreds\n",
        "    5) Stack more layers :)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Seminar QuarzNet_comments.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}