week02/seminar02_2.ipynb

{
 "cells": [
  {
   "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": 48,
   "id": "favorite-withdrawal",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "favorite-withdrawal",
    "outputId": "668791de-b997-439e-d37c-30ba482e0e02"
   },
   "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",
    "\n",
    "assert torch.__version__.startswith('1.8')\n",
    "assert torchaudio.__version__.startswith('0.8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "negative-anderson",
   "metadata": {},
   "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, normalize=False))"
   ]
  },
  {
   "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": 7,
   "id": "7YVP87uwkFAK",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7YVP87uwkFAK",
    "outputId": "0ecdd5b8-539d-4ff3-898c-3fa7b4456e7f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-04-20 16:22:59--  https://raw.githubusercontent.com/markovka17/apdl/master/gfile.py\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...\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",
      "gfile.py            100%[===================>]   3,34K  --.-KB/s    in 0s      \n",
      "\n",
      "2021-04-20 16:23:00 (19,2 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": 9,
   "id": "respective-morris",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "respective-morris",
    "outputId": "dea0cb78-5c2a-46ae-bae0-e55ccad7d3ed"
   },
   "outputs": [],
   "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": 13,
   "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": {},
   "source": [
    "Randomly split dataset on `train` and `validation`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": 18,
   "id": "neither-microwave",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "neither-microwave",
    "outputId": "901a3069-8aeb-42e8-c066-50369e69d1e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8100, 900)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataset), len(val_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "activated-religious",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 477
    },
    "id": "activated-religious",
    "outputId": "071d6015-c5fb-4ba4-82a5-d0e9547e37ff"
   },
   "outputs": [
    {
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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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "instance = train_dataset[0]\n",
    "visualize_audio(instance['x'], dataset.SAMPLE_RATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "gjNA_uCVzueP",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gjNA_uCVzueP",
    "outputId": "92348089-5e1f-49db-dd57-56ad4610b4b8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 8, 4, 6, 2, 7])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": 25,
   "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": 27,
   "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": {},
   "source": [
    "Let's see what we have in our batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "unexpected-settlement",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "unexpected-settlement",
    "outputId": "09d4e7b8-1980-4297-bddd-454e3b53077b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[0., 0., 0.,  ..., 0., 0., 0.],\n",
       "        [0., 0., 0.,  ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch = list(islice(val_dataloader, 1))[0]\n",
    "batch['x'][:2, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "joint-consensus",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[2, 7, 3, 1, 8, 9],\n",
       "        [1, 4, 2, 0, 2, 3]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch['y'][:2, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "worldwide-beast",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['x', 'y', 'x_len', 'y_len', 'path'])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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 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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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": 40,
   "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": 41,
   "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": 42,
   "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": 45,
   "id": "wh7T5us5AxvE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "wh7T5us5AxvE",
    "outputId": "203ee419-3b93-45f5-f57f-325f617cd875"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: Levenshtein in /Users/markaa/miniconda3/envs/common/lib/python3.8/site-packages (0.12.0)\r\n",
      "Requirement already satisfied: setuptools in /Users/markaa/miniconda3/envs/common/lib/python3.8/site-packages (from Levenshtein) (52.0.0.post20210125)\r\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": 46,
   "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": 47,
   "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": null,
   "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": null,
   "id": "Ks9zYRxpQZVh",
   "metadata": {
    "id": "Ks9zYRxpQZVh"
   },
   "outputs": [],
   "source": [
    "history = defaultdict(list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "experienced-edward",
   "metadata": {
    "colab": {
     "background_save": true,
     "base_uri": "https://localhost:8080/",
     "height": 528
    },
    "id": "experienced-edward",
    "outputId": "b82f23b6-2ad1-43f8-ce61-57c1daed0d82"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 19\n",
      "Example of prediction:  421001 -> 421001959198131\n",
      "Example of prediction:  378376 -> 3783765\n",
      "Example of prediction:  528450 -> 528450895481\n",
      "Example of prediction:  893448 -> 8934483549\n",
      "Example of prediction:  653402 -> 653402543651\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "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 :)"
   ]
  }
 ],
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