mynemo.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJz6FDU1lRzc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "bcf76018-0512-4b7c-9609-b1c74c0bb4a7"
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      "source": [
        "# If you're using Google Colab and not running locally, run this cell.\n",
        "\n",
        "## Install dependencies\n",
        "!pip install wget\n",
        "!apt-get install sox libsndfile1 ffmpeg\n",
        "!pip install unidecode\n",
        "\n",
        "\n",
        "## Install NeMo\n",
        "!python -m pip install nemo_toolkit[all]\n",
        "\n",
        "# !pip install torchtext==0.10.1\n",
        "# !pip install matplotlib==3.1.3\n",
        "\"\"\"\n",
        "Remember to restart the runtime for the kernel to pick up any upgraded packages (e.g. matplotlib)!\n",
        "Alternatively, you can uncomment the exit() below to crash and restart the kernel, in the case\n",
        "that you want to use the \"Run All Cells\" (or similar) option.\n",
        "\"\"\"\n",
        "#exit()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting wget\n",
            "  Downloading wget-3.2.zip (10 kB)\n",
            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-py3-none-any.whl size=9672 sha256=fffe45d82dcdb41834179224a2a842390e6569d12a515d1322589506e83225b6\n",
            "  Stored in directory: /root/.cache/pip/wheels/a1/b6/7c/0e63e34eb06634181c63adacca38b79ff8f35c37e3c13e3c02\n",
            "Successfully built wget\n",
            "Installing collected packages: wget\n",
            "Successfully installed wget-3.2\n",
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "libsndfile1 is already the newest version (1.0.28-4ubuntu0.18.04.2).\n",
            "ffmpeg is already the newest version (7:3.4.8-0ubuntu0.2).\n",
            "The following additional packages will be installed:\n",
            "  libmagic-mgc libmagic1 libopencore-amrnb0 libopencore-amrwb0 libsox-fmt-alsa\n",
            "  libsox-fmt-base libsox3\n",
            "Suggested packages:\n",
            "  file libsox-fmt-all\n",
            "The following NEW packages will be installed:\n",
            "  libmagic-mgc libmagic1 libopencore-amrnb0 libopencore-amrwb0 libsox-fmt-alsa\n",
            "  libsox-fmt-base libsox3 sox\n",
            "0 upgraded, 8 newly installed, 0 to remove and 37 not upgraded.\n",
            "Need to get 760 kB of archives.\n",
            "After this operation, 6,717 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libopencore-amrnb0 amd64 0.1.3-2.1 [92.0 kB]\n",
            "Get:2 http://archive.ubuntu.com/ubuntu bionic/universe amd64 libopencore-amrwb0 amd64 0.1.3-2.1 [45.8 kB]\n",
            "Get:3 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libmagic-mgc amd64 1:5.32-2ubuntu0.4 [184 kB]\n",
            "Get:4 http://archive.ubuntu.com/ubuntu bionic-updates/main amd64 libmagic1 amd64 1:5.32-2ubuntu0.4 [68.6 kB]\n",
            "Get:5 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libsox3 amd64 14.4.2-3ubuntu0.18.04.1 [226 kB]\n",
            "Get:6 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libsox-fmt-alsa amd64 14.4.2-3ubuntu0.18.04.1 [10.6 kB]\n",
            "Get:7 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 libsox-fmt-base amd64 14.4.2-3ubuntu0.18.04.1 [32.1 kB]\n",
            "Get:8 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 sox amd64 14.4.2-3ubuntu0.18.04.1 [101 kB]\n",
            "Fetched 760 kB in 1s (663 kB/s)\n",
            "Selecting previously unselected package libopencore-amrnb0:amd64.\n",
            "(Reading database ... 155222 files and directories currently installed.)\n",
            "Preparing to unpack .../0-libopencore-amrnb0_0.1.3-2.1_amd64.deb ...\n",
            "Unpacking libopencore-amrnb0:amd64 (0.1.3-2.1) ...\n",
            "Selecting previously unselected package libopencore-amrwb0:amd64.\n",
            "Preparing to unpack .../1-libopencore-amrwb0_0.1.3-2.1_amd64.deb ...\n",
            "Unpacking libopencore-amrwb0:amd64 (0.1.3-2.1) ...\n",
            "Selecting previously unselected package libmagic-mgc.\n",
            "Preparing to unpack .../2-libmagic-mgc_1%3a5.32-2ubuntu0.4_amd64.deb ...\n",
            "Unpacking libmagic-mgc (1:5.32-2ubuntu0.4) ...\n",
            "Selecting previously unselected package libmagic1:amd64.\n",
            "Preparing to unpack .../3-libmagic1_1%3a5.32-2ubuntu0.4_amd64.deb ...\n",
            "Unpacking libmagic1:amd64 (1:5.32-2ubuntu0.4) ...\n",
            "Selecting previously unselected package libsox3:amd64.\n",
            "Preparing to unpack .../4-libsox3_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking libsox3:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package libsox-fmt-alsa:amd64.\n",
            "Preparing to unpack .../5-libsox-fmt-alsa_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking libsox-fmt-alsa:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package libsox-fmt-base:amd64.\n",
            "Preparing to unpack .../6-libsox-fmt-base_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking libsox-fmt-base:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Selecting previously unselected package sox.\n",
            "Preparing to unpack .../7-sox_14.4.2-3ubuntu0.18.04.1_amd64.deb ...\n",
            "Unpacking sox (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Setting up libmagic-mgc (1:5.32-2ubuntu0.4) ...\n",
            "Setting up libmagic1:amd64 (1:5.32-2ubuntu0.4) ...\n",
            "Setting up libopencore-amrnb0:amd64 (0.1.3-2.1) ...\n",
            "Setting up libopencore-amrwb0:amd64 (0.1.3-2.1) ...\n",
            "Setting up libsox3:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Setting up libsox-fmt-base:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Setting up libsox-fmt-alsa:amd64 (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Setting up sox (14.4.2-3ubuntu0.18.04.1) ...\n",
            "Processing triggers for libc-bin (2.27-3ubuntu1.3) ...\n",
            "/sbin/ldconfig.real: /usr/local/lib/python3.7/dist-packages/ideep4py/lib/libmkldnn.so.0 is not a symbolic link\n",
            "\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
            "Processing triggers for mime-support (3.60ubuntu1) ...\n",
            "Collecting unidecode\n",
            "  Downloading Unidecode-1.3.2-py3-none-any.whl (235 kB)\n",
            "\u001b[K     |████████████████████████████████| 235 kB 5.2 MB/s \n",
            "\u001b[?25hInstalling collected packages: unidecode\n",
            "Successfully installed unidecode-1.3.2\n",
            "Collecting nemo_toolkit[all]\n",
            "  Downloading nemo_toolkit-1.5.0-py3-none-any.whl (2.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.7 MB 5.4 MB/s \n",
            "\u001b[?25hCollecting ruamel.yaml\n",
            "  Downloading ruamel.yaml-0.17.17-py3-none-any.whl (109 kB)\n",
            "\u001b[K     |████████████████████████████████| 109 kB 45.7 MB/s \n",
            "\u001b[?25hCollecting sentencepiece<1.0.0\n",
            "  Downloading sentencepiece-0.1.96-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2 MB 37.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (4.62.3)\n",
            "Requirement already satisfied: wrapt in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.13.3)\n",
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            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.8-py3-none-any.whl size=141230 sha256=538c0dd03f55e9bfb187236ae6ff7bd81df648f57b1a77816bbd0072fc22d405\n",
            "  Stored in directory: /root/.cache/pip/wheels/ca/33/b7/336836125fc9bb4ceaa4376d8abca10ca8bc84ddc824baea6c\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for future: filename=future-0.18.2-py3-none-any.whl size=491070 sha256=917bff98ff7ca1d13dfbcd73246014ca8e5f7bb68a97df114794c77bcf25a9a0\n",
            "  Stored in directory: /root/.cache/pip/wheels/56/b0/fe/4410d17b32f1f0c3cf54cdfb2bc04d7b4b8f4ae377e2229ba0\n",
            "  Building wheel for fasttext (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fasttext: filename=fasttext-0.9.2-cp37-cp37m-linux_x86_64.whl size=3122640 sha256=84bd076a388a21747fe94f3ce325ef44c325ead24ea385722508b67c7d0b986f\n",
            "  Stored in directory: /root/.cache/pip/wheels/4e/ca/bf/b020d2be95f7641801a6597a29c8f4f19e38f9c02a345bab9b\n",
            "  Building wheel for distance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for distance: filename=Distance-0.1.3-py3-none-any.whl size=16275 sha256=4476627788b014671624b80867e19d396c3843f9a35d5a1d6d57b001eb26cbc7\n",
            "  Stored in directory: /root/.cache/pip/wheels/b2/10/1b/96fca621a1be378e2fe104cfb0d160bb6cdf3d04a3d35266cc\n",
            "  Building wheel for kaldi-python-io (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kaldi-python-io: filename=kaldi_python_io-1.2.2-py3-none-any.whl size=8968 sha256=1767002a990c1a213952d0201450795da78326ef6453c0d122e0f16f46ac154a\n",
            "  Stored in directory: /root/.cache/pip/wheels/a9/26/38/7678d1ff6cd1bbcbfc0d80b0a29d94d917dfa9ad790b4a85a9\n",
            "  Building wheel for kaldiio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kaldiio: filename=kaldiio-2.17.2-py3-none-any.whl size=24472 sha256=082dede1c06bffa90c73552306ab411e3ef2722a389fd8cdb142099d2dbc2f34\n",
            "  Stored in directory: /root/.cache/pip/wheels/04/07/e8/45641287c59bf6ce41e22259f8680b521c31e6306cb88392ac\n",
            "  Building wheel for pesq (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pesq: filename=pesq-0.0.3-cp37-cp37m-linux_x86_64.whl size=209965 sha256=db31ef6991658e5028b33d4bea3ca274b5ae9083e2c069af6f652ce0f721c5bf\n",
            "  Stored in directory: /root/.cache/pip/wheels/4f/67/5b/aa7cf31fe0c7199e35c604bb7bc91c629a13726bf221fedba0\n",
            "  Building wheel for pystoi (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pystoi: filename=pystoi-0.3.3-py2.py3-none-any.whl size=7793 sha256=4d2f75d990104df79be258daf8394fe67143a4100f5638bdffb824a751a194cc\n",
            "  Stored in directory: /root/.cache/pip/wheels/46/4a/ad/3ab460193ed0535430b4b1575f255aa6bae69df17453628e86\n",
            "  Building wheel for ipadic (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ipadic: filename=ipadic-1.0.0-py3-none-any.whl size=13556723 sha256=8cf8388c3c7210b6be9dfd25f956f649d7b54d1f186b56bbc4d649929cffda97\n",
            "  Stored in directory: /root/.cache/pip/wheels/33/8b/99/cf0d27191876637cd3639a560f93aa982d7855ce826c94348b\n",
            "  Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for subprocess32: filename=subprocess32-3.5.4-py3-none-any.whl size=6502 sha256=a345504cb6707842ec620a02f3c2a1ffefc9232a34c450bd410b863f9ec8b7cc\n",
            "  Stored in directory: /root/.cache/pip/wheels/50/ca/fa/8fca8d246e64f19488d07567547ddec8eb084e8c0d7a59226a\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8807 sha256=a3ce186375d5ed4b9c10e369e972b12374920525fda371c19bbd7ff6228e9bb5\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/31/09/fa59cef12cdcfecc627b3d24273699f390e71828921b2cbba2\n",
            "Successfully built wordninja antlr4-python3-runtime future fasttext distance kaldi-python-io kaldiio pesq pystoi ipadic subprocess32 pathtools\n",
            "Installing collected packages: urllib3, multidict, frozenlist, yarl, typer, smmap, simplejson, shellingham, pyyaml, latexcodec, jmespath, colorama, asynctest, async-timeout, aiosignal, yarg, sphinxcontrib-qthelp, sphinxcontrib-jsmath, sphinxcontrib-htmlhelp, sphinxcontrib-devhelp, sphinxcontrib-applehelp, ruamel.yaml.clib, regex, pybtex, pyannote.core, portalocker, gitdb, fsspec, botocore, antlr4-python3-runtime, aiohttp, yaspin, typed-ast, torchmetrics, tokenizers, subprocess32, sphinx, shortuuid, sentry-sdk, sentencepiece, sacremoses, sacrebleu, s3transfer, ruamel.yaml, pyDeprecate, pybtex-docutils, pybind11, pyannote.database, pipreqs, pip-api, pathtools, pathspec, onnx, omegaconf, nltk, mecab-python3, isort, ipadic, huggingface-hub, GitPython, future, frozendict, docker-pycreds, distance, configparser, braceexpand, youtokentome, wordninja, webdataset, wandb, transformers, torch-stft, sphinxcontrib-bibtex, sox, rapidfuzz, pytorch-lightning, pytest-runner, pystoi, pypinyin, pydub, pyannote.metrics, pesq, parameterized, pangu, opencc, nemo-toolkit, marshmallow, kaldiio, kaldi-python-io, kaldi-io, hydra-core, g2p-en, fasttext, boto3, black, attrdict\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Attempting uninstall: regex\n",
            "    Found existing installation: regex 2019.12.20\n",
            "    Uninstalling regex-2019.12.20:\n",
            "      Successfully uninstalled regex-2019.12.20\n",
            "  Attempting uninstall: sphinx\n",
            "    Found existing installation: Sphinx 1.8.6\n",
            "    Uninstalling Sphinx-1.8.6:\n",
            "      Successfully uninstalled Sphinx-1.8.6\n",
            "  Attempting uninstall: nltk\n",
            "    Found existing installation: nltk 3.2.5\n",
            "    Uninstalling nltk-3.2.5:\n",
            "      Successfully uninstalled nltk-3.2.5\n",
            "  Attempting uninstall: future\n",
            "    Found existing installation: future 0.16.0\n",
            "    Uninstalling future-0.16.0:\n",
            "      Successfully uninstalled future-0.16.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed GitPython-3.1.24 aiohttp-3.8.1 aiosignal-1.2.0 antlr4-python3-runtime-4.8 async-timeout-4.0.1 asynctest-0.13.0 attrdict-2.0.1 black-19.10b0 boto3-1.20.16 botocore-1.23.16 braceexpand-0.1.7 colorama-0.4.4 configparser-5.1.0 distance-0.1.3 docker-pycreds-0.4.0 fasttext-0.9.2 frozendict-2.1.1 frozenlist-1.2.0 fsspec-2021.11.1 future-0.18.2 g2p-en-2.1.0 gitdb-4.0.9 huggingface-hub-0.1.2 hydra-core-1.1.1 ipadic-1.0.0 isort-4.3.21 jmespath-0.10.0 kaldi-io-0.9.4 kaldi-python-io-1.2.2 kaldiio-2.17.2 latexcodec-2.0.1 marshmallow-3.14.1 mecab-python3-1.0.3 multidict-5.2.0 nemo-toolkit-1.5.0 nltk-3.6.5 omegaconf-2.1.1 onnx-1.10.2 opencc-1.1.3 pangu-4.0.6.1 parameterized-0.8.1 pathspec-0.9.0 pathtools-0.1.2 pesq-0.0.3 pip-api-0.0.23 pipreqs-0.4.11 portalocker-2.3.2 pyDeprecate-0.3.1 pyannote.core-4.3 pyannote.database-4.1.1 pyannote.metrics-3.1 pybind11-2.8.1 pybtex-0.24.0 pybtex-docutils-1.0.1 pydub-0.25.1 pypinyin-0.44.0 pystoi-0.3.3 pytest-runner-5.3.1 pytorch-lightning-1.5.3 pyyaml-5.4.1 rapidfuzz-1.8.3 regex-2021.11.10 ruamel.yaml-0.17.17 ruamel.yaml.clib-0.2.6 s3transfer-0.5.0 sacrebleu-2.0.0 sacremoses-0.0.46 sentencepiece-0.1.96 sentry-sdk-1.5.0 shellingham-1.4.0 shortuuid-1.0.8 simplejson-3.17.6 smmap-5.0.0 sox-1.4.1 sphinx-4.3.1 sphinxcontrib-applehelp-1.0.2 sphinxcontrib-bibtex-2.4.1 sphinxcontrib-devhelp-1.0.2 sphinxcontrib-htmlhelp-2.0.0 sphinxcontrib-jsmath-1.0.1 sphinxcontrib-qthelp-1.0.3 subprocess32-3.5.4 tokenizers-0.10.3 torch-stft-0.1.4 torchmetrics-0.6.0 transformers-4.12.5 typed-ast-1.5.0 typer-0.4.0 urllib3-1.25.11 wandb-0.12.7 webdataset-0.1.62 wordninja-2.0.0 yarg-0.1.9 yarl-1.7.2 yaspin-2.1.0 youtokentome-1.0.6\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'\\nRemember to restart the runtime for the kernel to pick up any upgraded packages (e.g. matplotlib)!\\nAlternatively, you can uncomment the exit() below to crash and restart the kernel, in the case\\nthat you want to use the \"Run All Cells\" (or similar) option.\\n'"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gAhsmi6HlRzh"
      },
      "source": [
        "# Папка, где будет размещаться датасет Golos\n",
        "data_dir = '/content'"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIWkld7od6-o",
        "outputId": "e38d6fc5-214e-4846-c757-ac69143dc2b0"
      },
      "source": [
        "! ls /content"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yb4fuUvWlRzk",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f82efd1-2863-4d7a-be97-4066f77e8c93"
      },
      "source": [
        "import glob\n",
        "import os\n",
        "import subprocess\n",
        "import tarfile\n",
        "import wget\n",
        "\n",
        "# Загрузка датасета Golos\n",
        "def load(golos_url, fname):\n",
        "  if not os.path.exists(os.path.join(data_dir, fname)):\n",
        "      golos_path = wget.download(golos_url, os.path.join(data_dir, fname))\n",
        "      print(f\"Dataset downloaded at: {golos_path}\")\n",
        "  else:\n",
        "      print(\"Tarfile already exists.\")\n",
        "      golos_path = os.path.join(data_dir, fname)\n",
        "  return golos_path\n",
        "\n",
        "golos_path = load(\"https://sc.link/1Z3\", \"train.tar\")\n",
        "\n",
        "if not os.path.exists(os.path.join(data_dir, '/train/')):\n",
        "    tar = tarfile.open(golos_path)\n",
        "    tar.extractall(path=data_dir)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset downloaded at: /content/train.tar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6V6kExB_fW9R",
        "outputId": "954d1a54-8ef7-4d05-8f28-1e913606f23b"
      },
      "source": [
        "golos_path = load(\"https://sc.link/Kqr\", \"test.tar\")\n",
        "\n",
        "if not os.path.exists(os.path.join(data_dir, '/test/')):\n",
        "    tar = tarfile.open(golos_path)\n",
        "    tar.extractall(path=data_dir)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset downloaded at: /content/test.tar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tIlLEymnNZho",
        "outputId": "5de0f403-19bd-4ff9-e26a-d1aa1f3bda45"
      },
      "source": [
        "! head /content/train/farfield/manifest.jsonl"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\"id\": \"855e01d6ba9a4aa59950d62037b87709\", \"audio_filepath\": \"farfield/855e01d6ba9a4aa59950d62037b87709.wav\", \"text\": \"\\u0430\\u0444\\u0438\\u043d\\u0430 \\u0445\\u043e\\u0442\\u0435\\u043b\\u0430 \\u043d\\u043e\\u043c\\u0435\\u0440 \\u0442\\u0435\\u043b\\u0435\\u0444\\u043e\\u043d\\u0430 \\u043f\\u043e\\u043c\\u0435\\u043d\\u044f\\u0442\\u044c \\u043a\\u0430\\u0440\\u0442\\u044b\", \"duration\": 5.4596875}\r\n",
            "{\"id\": \"f6075fae0c592183f107cf781117d9b5\", \"audio_filepath\": \"farfield/f6075fae0c592183f107cf781117d9b5.wav\", \"text\": \"\\u0441\\u0430\\u043b\\u044e\\u0442 \\u0445\\u043e\\u0447\\u0443 \\u043f\\u043e\\u0433\\u043e\\u0432\\u043e\\u0440\\u0438\\u0442\\u044c \\u0441 \\u0434\\u0436\\u043e\\u0439\", \"duration\": 4.15125}\r\n",
            "{\"id\": \"2599a6cb55795b38ddd80b849d8670bc\", \"audio_filepath\": \"farfield/2599a6cb55795b38ddd80b849d8670bc.wav\", \"text\": \"\\u0434\\u0436\\u043e\\u0439 \\u043a\\u0430\\u043a\\u0430\\u044f \\u0432\\u0430\\u043b\\u044e\\u0442\\u0430 \\u0432 \\u043a\\u0430\\u043c\\u0431\\u043e\\u0434\\u0436\\u0435\", \"duration\": 3.9185625}\r\n",
            "{\"id\": \"5f7483035b2f26b773b0d3fb125b779d\", \"audio_filepath\": \"farfield/5f7483035b2f26b773b0d3fb125b779d.wav\", \"text\": \"\\u0441\\u0430\\u043b\\u044e\\u0442 \\u044f \\u043c\\u043e\\u0433\\u0443 \\u0447\\u0442\\u043e \\u043d\\u0438\\u0431\\u0443\\u0434\\u044c \\u0441\\u0434\\u0435\\u043b\\u0430\\u0442\\u044c\", \"duration\": 5.063625}\r\n",
            "{\"id\": \"4c95a12b71e1262ac937d2fe6a803906\", \"audio_filepath\": \"farfield/4c95a12b71e1262ac937d2fe6a803906.wav\", \"text\": \"\\u0434\\u0436\\u043e\\u0439 \\u0441\\u043c\\u043e\\u0442\\u0440\\u0435\\u0442\\u044c \\u043e\\u043f\\u0435\\u0440\\u0430\\u0446\\u0438\\u044e \\u044b \\u0438 \\u0434\\u0440\\u0443\\u0433\\u0438\\u0435 \\u043f\\u0440\\u0438\\u043a\\u043b\\u044e\\u0447\\u0435\\u043d\\u0438\\u044f \\u0448\\u0443\\u0440\\u0438\\u043a\\u0430 \\u043e\\u043d\\u043b\\u0430\\u0439\\u043d\", \"duration\": 7.3826875}\r\n",
            "{\"id\": \"90655e9a042c4e79c424cd7dd82b37a6\", \"audio_filepath\": \"farfield/90655e9a042c4e79c424cd7dd82b37a6.wav\", \"text\": \"\\u0441\\u0430\\u043b\\u044e\\u0442 \\u043d\\u0430 \\u043a\\u0430\\u043a\\u043e\\u043c \\u044f\\u0437\\u044b\\u043a\\u0435 \\u0441\\u043e\\u0437\\u0434\\u0430\\u043d\\u0430 \\u043a\\u0430\\u0432\\u043a\\u0430 \\u043d\\u0430 \\u043f\\u043b\\u044f\\u0436\\u0435\", \"duration\": 5.232625}\r\n",
            "{\"id\": \"14b5e99616d446c3887c8927403632a0\", \"audio_filepath\": \"farfield/14b5e99616d446c3887c8927403632a0.wav\", \"text\": \"\\u0441\\u0431\\u0435\\u0440 \\u0447\\u0435\\u043c \\u043f\\u043b\\u0430\\u0442\\u0438\\u0442\\u044c \\u043d\\u0430 \\u043a\\u0430\\u043d\\u0430\\u0440\\u0430\\u0445\", \"duration\": 4.524}\r\n",
            "{\"id\": \"d71107319855ef21249be2ab0edea9c2\", \"audio_filepath\": \"farfield/d71107319855ef21249be2ab0edea9c2.wav\", \"text\": \"\\u0430\\u0444\\u0438\\u043d\\u0430 \\u043d\\u0430 \\u043f\\u0430\\u0440\\u0443 \\u043c\\u0438\\u043d\\u0443\\u0442 \\u043f\\u0435\\u0440\\u0435\\u043c\\u043e\\u0442\\u0430\\u0439 \\u0432\\u043f\\u0435\\u0440\\u0435\\u0434\", \"duration\": 4.4630625}\r\n",
            "{\"id\": \"566e4f7d788d755e2fa76ab57837d7dd\", \"audio_filepath\": \"farfield/566e4f7d788d755e2fa76ab57837d7dd.wav\", \"text\": \"\\u0430\\u0444\\u0438\\u043d\\u0430 \\u043f\\u043b\\u043e\\u0449\\u0430\\u0434\\u044c \\u043a\\u043e\\u0440\\u043e\\u043b\\u0435\\u0432\\u0441\\u0442\\u0432\\u0430 \\u0442\\u043e\\u043d\\u0433\\u0430\", \"duration\": 4.75025}\r\n",
            "{\"id\": \"40bf19adace8d787cd3c63784ed0b27b\", \"audio_filepath\": \"farfield/40bf19adace8d787cd3c63784ed0b27b.wav\", \"text\": \"\\u0430\\u0444\\u0438\\u043d\\u0430 \\u0447\\u0442\\u043e \\u0442\\u0430\\u043a\\u043e\\u0435 \\u0434\\u0435\\u043b\\u0444\\u0442\", \"duration\": 3.74225}\r\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nYzGDKl2H_5p",
        "outputId": "06ca7c48-5b4b-4395-e98d-4b795eeca41e"
      },
      "source": [
        "! head /content/test/farfield/manifest.jsonl"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\"id\": \"58b586f67f5e634506e215df5996b82e\", \"audio_filepath\": \"files/58b586f67f5e634506e215df5996b82e.wav\", \"text\": \"\\u0434\\u0436\\u043e\\u0439 \\u0445\\u0432\\u0430\\u0442\\u0438\\u0442\", \"duration\": 1.696625}\n",
            "{\"id\": \"26093ef7a8c5ec6a3c586a6a929c1bd3\", \"audio_filepath\": \"files/26093ef7a8c5ec6a3c586a6a929c1bd3.wav\", \"text\": \"\\u0441\\u0430\\u043b\\u044e\\u0442 \\u0432\\u044b\\u0437\\u043e\\u0432 \\u0441\\u0432\\u0435\\u0442\\u043b\\u0430\\u043d\\u0435 \\u0432\\u0430\\u0441\\u0438\\u043b\\u044c\\u0435\\u0432\\u043d\\u0435 \\u043d\\u0438\\u043a\\u043e\\u043b\\u0435\\u043d\\u043a\\u043e\", \"duration\": 3.1720625}\n",
            "{\"id\": \"b42d13ab3dc159fac39a1120e8011f66\", \"audio_filepath\": \"files/b42d13ab3dc159fac39a1120e8011f66.wav\", \"text\": \"\\u0441\\u0430\\u043b\\u044e\\u0442 \\u0445\\u0432\\u0430\\u0442\\u0438\\u0442\", \"duration\": 1.5226875}\n",
            "{\"id\": \"7e55843e745a239b35212e2caba77239\", \"audio_filepath\": \"files/7e55843e745a239b35212e2caba77239.wav\", \"text\": \"\\u0434\\u0436\\u043e\\u0439 \\u0437\\u0432\\u043e\\u043d\\u043e\\u043a \\u044e\\u0440\\u0438\\u044e \\u0438\\u0432\\u0430\\u043d\\u043e\\u0432\\u0438\\u0447\\u0443 \\u0446\\u0430\\u0440\\u044c\\u043a\\u043e\\u0432\\u0443\", \"duration\": 3.0640625}\n",
            "{\"id\": \"a0c621e2819a3747e42d05940aab0446\", \"audio_filepath\": \"files/a0c621e2819a3747e42d05940aab0446.wav\", \"text\": \"\\u0434\\u0436\\u043e\\u0439 \\u0432\\u044b\\u0439\\u0442\\u0438 \\u0438\\u0437 \\u0441\\u0446\\u0435\\u043d\\u0430\\u0440\\u0438\\u044f\", \"duration\": 2.31475}\n",
            "{\"id\": \"f293e3548bd266460c8ea6ac1fb76478\", \"audio_filepath\": \"files/f293e3548bd266460c8ea6ac1fb76478.wav\", \"text\": \"\\u0441\\u0430\\u043b\\u044e\\u0442 \\u0432\\u044b\\u0439\\u0442\\u0438\", \"duration\": 1.310125}\n",
            "{\"id\": \"b3fc5f249351a9087ffedc0a34385463\", \"audio_filepath\": \"files/b3fc5f249351a9087ffedc0a34385463.wav\", \"text\": \"\\u0441\\u0430\\u043b\\u044e\\u0442 \\u0437\\u0430\\u043a\\u0440\\u043e\\u0439\\u0441\\u044f\", \"duration\": 1.9009375}\n",
            "{\"id\": \"ee2144d4bc00e96131a9bef5c74edd97\", \"audio_filepath\": \"files/ee2144d4bc00e96131a9bef5c74edd97.wav\", \"text\": \"\\u0441\\u0430\\u043b\\u044e\\u0442 \\u043d\\u0430\\u0431\\u0435\\u0440\\u0438 \\u0434\\u0430\\u043d\\u0438\\u043b\\u043e\\u0432\\u0430\", \"duration\": 1.8129375}\n",
            "{\"id\": \"94f4ad577967f9f61d5d88cd92b1339c\", \"audio_filepath\": \"files/94f4ad577967f9f61d5d88cd92b1339c.wav\", \"text\": \"\\u0441\\u0431\\u0435\\u0440 \\u043c\\u043d\\u0435 \\u043d\\u0440\\u0430\\u0432\\u0438\\u0442\\u0441\\u044f\", \"duration\": 1.9945}\n",
            "{\"id\": \"a97958419827c57069bc3550c4353eb9\", \"audio_filepath\": \"files/a97958419827c57069bc3550c4353eb9.wav\", \"text\": \"\\u0441\\u0430\\u043b\\u044e\\u0442 \\u043f\\u0440\\u0435\\u043a\\u0440\\u0430\\u0442\\u0438\", \"duration\": 1.4965}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_LFeM0elRzm"
      },
      "source": [
        "Теперь в папке `/content` должна размещаться папка `test` с тестовыми данных датасета Golos. Она содержит файлы манифесты `./test/crowd/manifest.jsonl`, `./test/farfield/manifest.jsonl` и аудио файлы в подпапках `./test/crowd/files/` и `./test/farfield/files/`.\n",
        "\n",
        "Мы можем загрузить и посмотреть данные. Например файл `./test/crowd/files/e632f7d39c15e7edfc665b91e6f2071f.wav` это четырехсекундная запись мужчины, который произносит фразу `Купить моющее средство`. Чтобы убедиться в этом давайте послушаем запись:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_M_bSs3MjQlz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "outputId": "3dc74c15-89a2-4859-cd95-a367832e4e76"
      },
      "source": [
        "import librosa\n",
        "import IPython.display as ipd\n",
        "\n",
        "# Load and listen to the audio file\n",
        "example_file = data_dir + '/train/farfield/farfield/000140f4107fabaf6e5b58520b13a6f2.wav'\n",
        "\n",
        "audio, sample_rate = librosa.load(example_file)\n",
        "\n",
        "ipd.Audio(example_file, rate=sample_rate)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "                <audio controls=\"controls\" >\n",
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type=\"audio/x-wav\" />\n",
              "                    Your browser does not support the audio element.\n",
              "                </audio>\n",
              "              "
            ],
            "text/plain": [
              "<IPython.lib.display.Audio object>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qZyElgPVjQl5"
      },
      "source": [
        "В задаче распознавания речи, если этот WAV файлявляется нашим входом в систему, то фраза `Купить моющее средство` будет желаемым выходом из системы.\n",
        "\n",
        "Давайте изобразим форму волны по точкам, которые содержатся в файлах. \n",
        "Этот формат отображения аудио, который скорее всего вам знаком, така как используется во многих аудио радакторах и плеерах:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqIAKkqelRzm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "outputId": "d431e788-abfc-4287-956d-5e7d14d12632"
      },
      "source": [
        "%matplotlib inline\n",
        "import librosa.display\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot our example audio file's waveform\n",
        "plt.rcParams['figure.figsize'] = (15,7)\n",
        "plt.title('Waveform of Audio Example')\n",
        "plt.ylabel('Amplitude')\n",
        "\n",
        "_ = librosa.display.waveplot(audio)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\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--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\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    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\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/python3.7/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    242\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/python3.7/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\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--> 125\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\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    126\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\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    127\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\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/python3.7/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2092\u001b[0m         \u001b[0mbool\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2093\u001b[0m             \u001b[0mWhether\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mratio\u001b[0m \u001b[0mhas\u001b[0m \u001b[0mchanged\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mBackends\u001b[0m \u001b[0mmay\u001b[0m \u001b[0minterpret\u001b[0m \u001b[0mthis\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2094\u001b[0;31m             \u001b[0msignal\u001b[0m \u001b[0mto\u001b[0m \u001b[0mresize\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mwindow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepaint\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcanvas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mchange\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2095\u001b[0m             \u001b[0mother\u001b[0m \u001b[0mrelevant\u001b[0m \u001b[0mproperties\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2096\u001b[0m         \"\"\"\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[0;34m(figure, print_method)\u001b[0m\n\u001b[1;32m   1558\u001b[0m     \u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_without_rendering\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   1559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1560\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1561\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_non_interactive_terminal_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mip\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   1562\u001b[0m     \"\"\"\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    503\u001b[0m             \u001b[0mMetadata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mPNG\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0mpairs\u001b[0m \u001b[0mof\u001b[0m \u001b[0mbytes\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlatin\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m             \u001b[0mencodable\u001b[0m \u001b[0mstrings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m             \u001b[0mAccording\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mPNG\u001b[0m \u001b[0mspecification\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0mmust\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mshorter\u001b[0m \u001b[0mthan\u001b[0m \u001b[0;36m79\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    506\u001b[0m             \u001b[0mchars\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: cannot import name '_png' from 'matplotlib' (/usr/local/lib/python3.7/dist-packages/matplotlib/__init__.py)"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gg6RR_yolRzo"
      },
      "source": [
        "### Спектрограмма и мел-спектрограмма\n",
        "\n",
        "Давайте применим [Фурье преобразование](https://en.wikipedia.org/wiki/Fourier_transform) к аудио сигналу, чтобы получить что то более полезное: **спектрограмму**, которая представляет собой величину энергии (амплитуда или громкость) для каждой частоты (гармоники) сигнала на каждом промежутке времени.\n",
        "\n",
        "Спектрограмма (которую можно рассматривать как тепловую карту) является хорошим способом увидеть, как *амплитуды различных частот в звуке меняются с течением времени*. Спектрограмма получается путем разбиения сигнала на короткие фреймы, обычно перекрывающиеся и выполнения кратковременного преобразования Фурье (STFT) для каждого из фреймов.\n",
        "\n",
        "Двайте посмотрим как выглядит спекторграмма для нашего звука. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oCFneEs1lRzp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "outputId": "f0ebfa17-ef54-44b8-ef94-3c3e460f29dc"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "# Get spectrogram using Librosa's Short-Time Fourier Transform (stft)\n",
        "spec = np.abs(librosa.stft(audio))\n",
        "spec_db = librosa.amplitude_to_db(spec, ref=np.max)  # Decibels\n",
        "\n",
        "# Use log scale to view frequencies\n",
        "librosa.display.specshow(spec_db, y_axis='log', x_axis='time')\n",
        "plt.colorbar()\n",
        "plt.title('Audio Spectrogram');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/IPython/core/formatters.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    332\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\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--> 334\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mprinter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\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    335\u001b[0m             \u001b[0;31m# Finally look for special method names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m             \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_real_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_method\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/python3.7/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(fig)\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'png'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'png'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    242\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;34m'retina'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m'png2x'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mformats\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[0mpng_formatter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfor_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFigure\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mretina_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/python3.7/dist-packages/IPython/core/pylabtools.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    124\u001b[0m     \u001b[0mbytes_io\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBytesIO\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--> 125\u001b[0;31m     \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanvas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprint_figure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytes_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\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    126\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\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    127\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfmt\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'svg'\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/python3.7/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36mprint_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)\u001b[0m\n\u001b[1;32m   2092\u001b[0m         \u001b[0mbool\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2093\u001b[0m             \u001b[0mWhether\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mratio\u001b[0m \u001b[0mhas\u001b[0m \u001b[0mchanged\u001b[0m\u001b[0;34m.\u001b[0m \u001b[0mBackends\u001b[0m \u001b[0mmay\u001b[0m \u001b[0minterpret\u001b[0m \u001b[0mthis\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2094\u001b[0;31m             \u001b[0msignal\u001b[0m \u001b[0mto\u001b[0m \u001b[0mresize\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mwindow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrepaint\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcanvas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mchange\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2095\u001b[0m             \u001b[0mother\u001b[0m \u001b[0mrelevant\u001b[0m \u001b[0mproperties\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2096\u001b[0m         \"\"\"\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backend_bases.py\u001b[0m in \u001b[0;36m_get_renderer\u001b[0;34m(figure, print_method)\u001b[0m\n\u001b[1;32m   1558\u001b[0m     \u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_without_rendering\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   1559\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1560\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1561\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_is_non_interactive_terminal_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mip\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   1562\u001b[0m     \"\"\"\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/matplotlib/backends/backend_agg.py\u001b[0m in \u001b[0;36mprint_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    503\u001b[0m             \u001b[0mMetadata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mPNG\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mvalue\u001b[0m \u001b[0mpairs\u001b[0m \u001b[0mof\u001b[0m \u001b[0mbytes\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mlatin\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m             \u001b[0mencodable\u001b[0m \u001b[0mstrings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m             \u001b[0mAccording\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mPNG\u001b[0m \u001b[0mspecification\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0mmust\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mshorter\u001b[0m \u001b[0mthan\u001b[0m \u001b[0;36m79\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    506\u001b[0m             \u001b[0mchars\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: cannot import name '_png' from 'matplotlib' (/usr/local/lib/python3.7/dist-packages/matplotlib/__init__.py)"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9OPc4tcalRzs"
      },
      "source": [
        "Можем сделать еще одну потенциально полезную настройку: использовать **Mel** спектрограмму вместо обычной спектрограммы. Это просто изменение шкалы частот, которую мы используем, с линейной (или логарифмической) на шкалу mel, которая представляет собой \"шкалу восприятия тонов, которые слушатели оценивают как равные на расстоянии друг от друга\" (из [Wikipedia](https://en.wikipedia.org/wiki/Mel_scale)).\n",
        "\n",
        "Другими словами, это преобразование частот, чтобы быть более согласованным с тем, что воспринимают люди; изменение +1000 Гц с 2000 Гц->3000 Гц звучит для нас как большая разница, чем 9000 Гц->10000 Гц, поэтому шкала mel нормализует это так, что равные расстояния звучат как равные различия для человеческого уха. Интуитивно мы используем спектрограмму mel, потому что в этом случае мы обрабатываем и расшифровываем человеческую речь, так что преобразование шкалы для лучшего соответствия тому, что мы слышим, является полезной процедурой."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7yQXVn-TlRzt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "395a0a63-04d2-4df0-d951-578f2b0eb786"
      },
      "source": [
        "# Plot the mel spectrogram of our sample\n",
        "mel_spec = librosa.feature.melspectrogram(audio, sr=sample_rate)\n",
        "mel_spec_db = librosa.power_to_db(mel_spec, ref=np.max)\n",
        "\n",
        "librosa.display.specshow(\n",
        "    mel_spec_db, x_axis='time', y_axis='mel')\n",
        "plt.colorbar()\n",
        "plt.title('Mel Spectrogram');"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RSCyVizDlRz1"
      },
      "source": [
        "## Сверточная модель распознавания речи\n",
        "### Jasper модель\n",
        "\n",
        "Мы будем тренировать небольшую модель [Jasper (Just Another SPeech Recognizer) model](https://arxiv.org/abs/1904.03288) с нуля (инициализируется случайным образом).\n",
        "Архитектуры Jasper состоят из повторяющейся блочной структуры, которая использует 1D-свертки.\n",
        "В модели Jasper_KxR подблоки `R` (состоящие из 1D свертки, batchnorm, ReLU и dropout) группируются в один блок, который затем повторяется `K` раз.\n",
        "Также есть один дополнительный блок в начале и еще несколько в конце, которые инвариантны к `K` и `R`, идалее используем CTC loss.\n",
        "\n",
        "### Модель QuartzNet\n",
        "\n",
        "QuartzNet является улучшенным вариантом Jasper с ключевым отличием в том, что она использует 1D-свертки, разделяемые по временному каналу. Это позволяет ему значительно сократить количество весов при сохранении аналогичной точности.\n",
        "\n",
        "Модели Jasper/QuartzNet выглядят так (на фото изображена модель QuartzNet):\n",
        "\n",
        "![QuartzNet with CTC](https://developer.nvidia.com/blog/wp-content/uploads/2020/05/quartznet-model-architecture-1-625x742.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gEpNci7slRzw"
      },
      "source": [
        "# Использование NeMo для распонавания речи\n",
        "\n",
        "Теперь мы знаем что такое задача автоматичского распознавания речи и речевые данные, давайте использовать NeMo для распознавания речи.\n",
        "\n",
        "Мы будем использовать **Neural Modules (NeMo) toolkit**, поэтому нужно скачать и установить все ее зависимости. Для этого следуйте инструкции в репозитории [GitHub page](https://github.com/NVIDIA/NeMo), или документации [documentation](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/).\n",
        "\n",
        "NeMo позволяет нам легко использовать все необходимые компоненты для нашей модели: dataloader,  промежуточные сверточные или рекурентные слои, разные loss  функции без необходимости разбираться в деталях реализации разных моедлей. В NeMo содержатся готовые реализованные модели в которых достаточно подать свои данные и задать гиперпараметры для обучения."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_W0lhaQlRzx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6740953-be1d-44d7-8bca-33e3a27a5be8"
      },
      "source": [
        "# NeMo's \"core\" package\n",
        "import nemo\n",
        "# NeMo's ASR collection - this collections contains complete ASR models and\n",
        "# building blocks (modules) for ASR\n",
        "import nemo.collections.asr as nemo_asr"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 14:41:45 optimizers:50] Apex was not found. Using the lamb or fused_adam optimizer will error out.\n",
            "################################################################################\n",
            "### WARNING, path does not exist: KALDI_ROOT=/mnt/matylda5/iveselyk/Tools/kaldi-trunk\n",
            "###          (please add 'export KALDI_ROOT=<your_path>' in your $HOME/.profile)\n",
            "###          (or run as: KALDI_ROOT=<your_path> python <your_script>.py)\n",
            "################################################################################\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_W8EbYktZE3"
      },
      "source": [
        "## Исползование предобученной модели\n",
        "\n",
        "\n",
        "Коллекция для распознавания речи в NeMo содержит готовые блоки, которые можно использовать чтобы тренировать и использовать свою модель. Кроме этого существет ряд предобученных моделей, которые можно просто скачать и исползовать. Давайте скачаем и инициализируем готовую модель QuartzNet15x5, обученную на открытом датасете Golos.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFZZpYult96G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5cac52c-a98c-42e5-bfda-74251d00f708"
      },
      "source": [
        "# asr_model = nemo_asr.models.EncDecCTCModel.from_pretrained(model_name=\"stt_ru_quartznet15x5\")\n",
        "\n",
        "load(\"https://sc.link/ZMv\", \"QuartzNet15x5_golos.nemo\")\n",
        "asr_model = nemo_asr.models.EncDecCTCModel.restore_from(os.path.join(data_dir, \"QuartzNet15x5_golos.nemo\"))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset downloaded at: /content/QuartzNet15x5_golos.nemo\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 14:41:59 modelPT:131] If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() method and provide a valid configuration file to setup the train data loader.\n",
            "    Train config : \n",
            "    manifest_filepath: train/golos_and_mcv.jsonl\n",
            "    sample_rate: 16000\n",
            "    labels:\n",
            "    - ' '\n",
            "    - а\n",
            "    - б\n",
            "    - в\n",
            "    - г\n",
            "    - д\n",
            "    - е\n",
            "    - ж\n",
            "    - з\n",
            "    - и\n",
            "    - й\n",
            "    - к\n",
            "    - л\n",
            "    - м\n",
            "    - н\n",
            "    - о\n",
            "    - п\n",
            "    - р\n",
            "    - с\n",
            "    - т\n",
            "    - у\n",
            "    - ф\n",
            "    - х\n",
            "    - ц\n",
            "    - ч\n",
            "    - ш\n",
            "    - щ\n",
            "    - ъ\n",
            "    - ы\n",
            "    - ь\n",
            "    - э\n",
            "    - ю\n",
            "    - я\n",
            "    batch_size: 64\n",
            "    trim_silence: false\n",
            "    max_duration: 20.0\n",
            "    min_duration: 0.1\n",
            "    num_workers: 20\n",
            "    shuffle: true\n",
            "    is_tarred: false\n",
            "    tarred_audio_filepaths: null\n",
            "    tarred_shard_strategy: scatter\n",
            "    parser: ru\n",
            "    \n",
            "[NeMo W 2021-11-30 14:41:59 modelPT:138] If you intend to do validation, please call the ModelPT.setup_validation_data() or ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to setup the validation data loader(s). \n",
            "    Validation config : \n",
            "    manifest_filepath:\n",
            "    - test/mcv/dev_ru.jsonl\n",
            "    - test/mcv/test_ru.jsonl\n",
            "    - test/crowd/crowd.jsonl\n",
            "    - test/farfield/farfield.jsonl\n",
            "    sample_rate: 16000\n",
            "    labels:\n",
            "    - ' '\n",
            "    - а\n",
            "    - б\n",
            "    - в\n",
            "    - г\n",
            "    - д\n",
            "    - е\n",
            "    - ж\n",
            "    - з\n",
            "    - и\n",
            "    - й\n",
            "    - к\n",
            "    - л\n",
            "    - м\n",
            "    - н\n",
            "    - о\n",
            "    - п\n",
            "    - р\n",
            "    - с\n",
            "    - т\n",
            "    - у\n",
            "    - ф\n",
            "    - х\n",
            "    - ц\n",
            "    - ч\n",
            "    - ш\n",
            "    - щ\n",
            "    - ъ\n",
            "    - ы\n",
            "    - ь\n",
            "    - э\n",
            "    - ю\n",
            "    - я\n",
            "    batch_size: 64\n",
            "    num_workers: 20\n",
            "    shuffle: false\n",
            "    parser: ru\n",
            "    \n",
            "[NeMo W 2021-11-30 14:41:59 modelPT:144] Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method and provide a valid configuration file to setup the test data loader(s).\n",
            "    Test config : \n",
            "    manifest_filepath: test/mcv/test_ru.jsonl\n",
            "    sample_rate: 16000\n",
            "    labels:\n",
            "    - ' '\n",
            "    - а\n",
            "    - б\n",
            "    - в\n",
            "    - г\n",
            "    - д\n",
            "    - е\n",
            "    - ж\n",
            "    - з\n",
            "    - и\n",
            "    - й\n",
            "    - к\n",
            "    - л\n",
            "    - м\n",
            "    - н\n",
            "    - о\n",
            "    - п\n",
            "    - р\n",
            "    - с\n",
            "    - т\n",
            "    - у\n",
            "    - ф\n",
            "    - х\n",
            "    - ц\n",
            "    - ч\n",
            "    - ш\n",
            "    - щ\n",
            "    - ъ\n",
            "    - ы\n",
            "    - ь\n",
            "    - э\n",
            "    - ю\n",
            "    - я\n",
            "    batch_size: 64\n",
            "    shuffle: false\n",
            "    parser: ru\n",
            "    \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 14:41:59 features:265] PADDING: 16\n",
            "[NeMo I 2021-11-30 14:41:59 features:282] STFT using torch\n",
            "[NeMo I 2021-11-30 14:42:14 save_restore_connector:149] Model EncDecCTCModel was successfully restored from /content/QuartzNet15x5_golos.nemo.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KucxoFJhum0i"
      },
      "source": [
        "Теперь указваем список фалов которые мы хотим транскрибировать и передаем в нашу модель. Это будет работать для относительно коротких аудио (<25 секунд) файлов. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3QCpR_93u1hp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173,
          "referenced_widgets": [
            "e2867e13c09f45ffb149d55fdba068f6",
            "b27889bcdda34b32924faf8319d3cc3c",
            "262cfe85615b4b7aba1a3449463bc740",
            "e2b76b3bea364c83845005c3d0c652af",
            "34e3182ee4d74e8c9fd7f3281053ec55",
            "35870271d71042ae855be988055f6d71",
            "57b652a311674cda96e5debf23a54b1b",
            "92a248a8b2144d62b7b1a52f3fe2541b",
            "5530980921c4464caf3ce628ab338cb7",
            "843b5129a3cd4fe8a6017ee2e605a684",
            "aa0379ff38ab414ca3b54e7dff66ca65"
          ]
        },
        "outputId": "a6ddb555-e6d2-4bcd-c657-89a25f0820ce"
      },
      "source": [
        "files = [data_dir + \"/train/farfield/farfield/855e01d6ba9a4aa59950d62037b87709.wav\"]\n",
        "transcriptions = asr_model.transcribe(paths2audio_files=files)\n",
        "\n",
        "for fname, transcription in zip(files, transcriptions):\n",
        "  print(f\"Audio in {fname} was recognized as: {transcription}\")"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e2867e13c09f45ffb149d55fdba068f6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Transcribing:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 14:42:24 patch_utils:50] torch.stft() signature has been updated for PyTorch 1.7+\n",
            "    Please update PyTorch to remain compatible with later versions of NeMo.\n",
            "[NeMo W 2021-11-30 14:42:24 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/nemo/collections/asr/parts/submodules/jasper.py:280: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "      ) // self.conv.stride[0] + 1\n",
            "    \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Audio in /content/train/farfield/farfield/855e01d6ba9a4aa59950d62037b87709.wav was recognized as: афина хотела номер телефона поменять карты\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ppUm_kuavm_f"
      },
      "source": [
        "Это было просто! Но существует масса сценариев, когда мы хотим дообучить модель на наших данных или обучить заново. Например это модель не сможет распознать украинский язык и скоре всего будет плохо распознавать речь в телефонных разговорах. Поэтому, если вы применяете модель на своих данных, то без сомнения, вам нужно дообучать модель на них!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABUDaC5Js7AW"
      },
      "source": [
        "## Обучение \n",
        "\n",
        "\n",
        "Для обучения нужно подготовить данные в нужном формате. Для этого добавим абсолютные пути к относительным в наших манифестах и используем их для обучения."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWtStF94z46m",
        "outputId": "e59ead19-1da7-492d-9510-960c865415d9"
      },
      "source": [
        "# --- Building Manifest Files --- #\n",
        "import json\n",
        "\n",
        "# Function to build a manifest\n",
        "def build_manifest(manifest_rel, manifest_abs):\n",
        "    manifest_path = os.path.split(os.path.abspath(manifest_rel))[0]\n",
        "    with open(manifest_rel, 'r') as fin:\n",
        "        with open(manifest_abs, 'w') as fout:\n",
        "            for line in fin:\n",
        "                metadata = json.loads(line)\n",
        "                metadata[\"audio_filepath\"]=os.path.join(manifest_path, metadata[\"audio_filepath\"])\n",
        "                json.dump(metadata, fout)\n",
        "                fout.write('\\n')\n",
        "                \n",
        "# Building Manifests\n",
        "print(\"******\")\n",
        "train_rel = os.path.join(data_dir, 'test/farfield/manifest.jsonl')\n",
        "train_abs = os.path.join(data_dir, 'test/farfield/farfield.jsonl')\n",
        "if not os.path.isfile(train_abs):\n",
        "  build_manifest(train_rel, train_abs)\n",
        "test_manifest = train_abs\n",
        "print(\"test_manifest\", test_manifest)\n",
        "\n",
        "train_rel = os.path.join(data_dir, 'train/farfield/manifest.jsonl')\n",
        "train_abs = os.path.join(data_dir, 'train/farfield/farfield.jsonl')\n",
        "if not os.path.isfile(train_abs):\n",
        "  build_manifest(train_rel, train_abs)\n",
        "train_manifest = train_abs\n",
        "print(\"train_manifest\", train_manifest)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "******\n",
            "test_manifest /content/test/farfield/farfield.jsonl\n",
            "train_manifest /content/train/farfield/farfield.jsonl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uctl3t5qrY2c",
        "outputId": "244b7256-d65c-4646-ad7e-8c7a6b4c7ac8"
      },
      "source": [
        "# ! head /content/test/farfield/manifest.jsonl\n",
        "! head /content/train/farfield/farfield.jsonl"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\"id\": \"855e01d6ba9a4aa59950d62037b87709\", \"audio_filepath\": \"/content/train/farfield/farfield/855e01d6ba9a4aa59950d62037b87709.wav\", \"text\": \"\\u0430\\u0444\\u0438\\u043d\\u0430 \\u0445\\u043e\\u0442\\u0435\\u043b\\u0430 \\u043d\\u043e\\u043c\\u0435\\u0440 \\u0442\\u0435\\u043b\\u0435\\u0444\\u043e\\u043d\\u0430 \\u043f\\u043e\\u043c\\u0435\\u043d\\u044f\\u0442\\u044c \\u043a\\u0430\\u0440\\u0442\\u044b\", \"duration\": 5.4596875}\n",
            "{\"id\": \"f6075fae0c592183f107cf781117d9b5\", \"audio_filepath\": \"/content/train/farfield/farfield/f6075fae0c592183f107cf781117d9b5.wav\", \"text\": \"\\u0441\\u0430\\u043b\\u044e\\u0442 \\u0445\\u043e\\u0447\\u0443 \\u043f\\u043e\\u0433\\u043e\\u0432\\u043e\\u0440\\u0438\\u0442\\u044c \\u0441 \\u0434\\u0436\\u043e\\u0439\", \"duration\": 4.15125}\n",
            "{\"id\": \"2599a6cb55795b38ddd80b849d8670bc\", \"audio_filepath\": \"/content/train/farfield/farfield/2599a6cb55795b38ddd80b849d8670bc.wav\", \"text\": \"\\u0434\\u0436\\u043e\\u0439 \\u043a\\u0430\\u043a\\u0430\\u044f \\u0432\\u0430\\u043b\\u044e\\u0442\\u0430 \\u0432 \\u043a\\u0430\\u043c\\u0431\\u043e\\u0434\\u0436\\u0435\", \"duration\": 3.9185625}\n",
            "{\"id\": \"5f7483035b2f26b773b0d3fb125b779d\", \"audio_filepath\": \"/content/train/farfield/farfield/5f7483035b2f26b773b0d3fb125b779d.wav\", \"text\": \"\\u0441\\u0430\\u043b\\u044e\\u0442 \\u044f \\u043c\\u043e\\u0433\\u0443 \\u0447\\u0442\\u043e \\u043d\\u0438\\u0431\\u0443\\u0434\\u044c \\u0441\\u0434\\u0435\\u043b\\u0430\\u0442\\u044c\", \"duration\": 5.063625}\n",
            "{\"id\": \"4c95a12b71e1262ac937d2fe6a803906\", \"audio_filepath\": \"/content/train/farfield/farfield/4c95a12b71e1262ac937d2fe6a803906.wav\", \"text\": \"\\u0434\\u0436\\u043e\\u0439 \\u0441\\u043c\\u043e\\u0442\\u0440\\u0435\\u0442\\u044c \\u043e\\u043f\\u0435\\u0440\\u0430\\u0446\\u0438\\u044e \\u044b \\u0438 \\u0434\\u0440\\u0443\\u0433\\u0438\\u0435 \\u043f\\u0440\\u0438\\u043a\\u043b\\u044e\\u0447\\u0435\\u043d\\u0438\\u044f \\u0448\\u0443\\u0440\\u0438\\u043a\\u0430 \\u043e\\u043d\\u043b\\u0430\\u0439\\u043d\", \"duration\": 7.3826875}\n",
            "{\"id\": \"90655e9a042c4e79c424cd7dd82b37a6\", \"audio_filepath\": \"/content/train/farfield/farfield/90655e9a042c4e79c424cd7dd82b37a6.wav\", \"text\": \"\\u0441\\u0430\\u043b\\u044e\\u0442 \\u043d\\u0430 \\u043a\\u0430\\u043a\\u043e\\u043c \\u044f\\u0437\\u044b\\u043a\\u0435 \\u0441\\u043e\\u0437\\u0434\\u0430\\u043d\\u0430 \\u043a\\u0430\\u0432\\u043a\\u0430 \\u043d\\u0430 \\u043f\\u043b\\u044f\\u0436\\u0435\", \"duration\": 5.232625}\n",
            "{\"id\": \"14b5e99616d446c3887c8927403632a0\", \"audio_filepath\": \"/content/train/farfield/farfield/14b5e99616d446c3887c8927403632a0.wav\", \"text\": \"\\u0441\\u0431\\u0435\\u0440 \\u0447\\u0435\\u043c \\u043f\\u043b\\u0430\\u0442\\u0438\\u0442\\u044c \\u043d\\u0430 \\u043a\\u0430\\u043d\\u0430\\u0440\\u0430\\u0445\", \"duration\": 4.524}\n",
            "{\"id\": \"d71107319855ef21249be2ab0edea9c2\", \"audio_filepath\": \"/content/train/farfield/farfield/d71107319855ef21249be2ab0edea9c2.wav\", \"text\": \"\\u0430\\u0444\\u0438\\u043d\\u0430 \\u043d\\u0430 \\u043f\\u0430\\u0440\\u0443 \\u043c\\u0438\\u043d\\u0443\\u0442 \\u043f\\u0435\\u0440\\u0435\\u043c\\u043e\\u0442\\u0430\\u0439 \\u0432\\u043f\\u0435\\u0440\\u0435\\u0434\", \"duration\": 4.4630625}\n",
            "{\"id\": \"566e4f7d788d755e2fa76ab57837d7dd\", \"audio_filepath\": \"/content/train/farfield/farfield/566e4f7d788d755e2fa76ab57837d7dd.wav\", \"text\": \"\\u0430\\u0444\\u0438\\u043d\\u0430 \\u043f\\u043b\\u043e\\u0449\\u0430\\u0434\\u044c \\u043a\\u043e\\u0440\\u043e\\u043b\\u0435\\u0432\\u0441\\u0442\\u0432\\u0430 \\u0442\\u043e\\u043d\\u0433\\u0430\", \"duration\": 4.75025}\n",
            "{\"id\": \"40bf19adace8d787cd3c63784ed0b27b\", \"audio_filepath\": \"/content/train/farfield/farfield/40bf19adace8d787cd3c63784ed0b27b.wav\", \"text\": \"\\u0430\\u0444\\u0438\\u043d\\u0430 \\u0447\\u0442\\u043e \\u0442\\u0430\\u043a\\u043e\\u0435 \\u0434\\u0435\\u043b\\u0444\\u0442\", \"duration\": 3.74225}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W2fShQzRzo-M"
      },
      "source": [
        "### Задаем модель при помощи YAML конфиг файла\n",
        "\n",
        "\n",
        "Ниже приведен конфиг файл, давайте рассмотрим его и найдем части описанной архитектуры Jasper. Модель (model) содержит поле под названием `encoder` с под-полем `jasper` который состоит из списка полей. Каждое поле в списке задает конфигурацию блока в нашей моделе. Каждый блок выглядит примерно так:\n",
        "\n",
        "```\n",
        "- filters: 128\n",
        "  repeat: 1\n",
        "  kernel: [11]\n",
        "  stride: [2]\n",
        "  dilation: [1]\n",
        "  dropout: 0.2\n",
        "  residual: false\n",
        "  separable: true\n",
        "  se: true\n",
        "  se_context_size: -1\n",
        "```\n",
        "\n",
        "\n",
        "Первый элемент в спике соответствует первому блоку в Jasper архитектуре.\n",
        "\n",
        "Параметры обучающего и тестового датасета в полях (`train_ds`) и (`validation_ds`)\n",
        "\n",
        "Конфиг в формате YAML позволяем легко и в читаемой форме читать и модифицировать модель без необходимости менять код пррограммы."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wUmq3p2Aw_5N"
      },
      "source": [
        "### Использование PyTorch Lightning\n",
        "\n",
        "NeMo модели и модули могуть исползоваться в любом PyTorch проекте где ожидается тип torch.nn.Module.\n",
        "\n",
        "Однако, NeMo модели созданы на основе [PytorchLightning's](https://github.com/PyTorchLightning/pytorch-lightning) LightningModule, поэтому рекомендуется использовать PytorchLightning для обучения и дообучения (fine-tuning) так как это позволяет легко применять mixed precision и распределенное обучение. Давайте создадим объект Trainer для обучения на GPU 5 эпох."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUfR6tAK0k2u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ec0ab95-66f3-4beb-9c58-a686f7db78ef"
      },
      "source": [
        "import pytorch_lightning as pl\n",
        "trainer = pl.Trainer(gpus=1, max_epochs=1, precision=16)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using 16bit native Automatic Mixed Precision (AMP)\n",
            "GPU available: True, used: True\n",
            "TPU available: False, using: 0 TPU cores\n",
            "IPU available: False, using: 0 IPUs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mRqwuhHAoCvo"
      },
      "source": [
        "params_model = asr_model._cfg\n",
        "\n",
        "# --- Config Information ---#\n",
        "# try:\n",
        "#     from ruamel.yaml import YAML\n",
        "# except ModuleNotFoundError:\n",
        "#     from ruamel_yaml import YAML\n",
        "# config_path = '/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/models/configs/'\n",
        "\n",
        "# yaml = YAML(typ='safe')\n",
        "# with open(config_path) as f:\n",
        "#     params = yaml.load(f)\n",
        "# print(params)\n",
        "params_model['train_ds']['parser']='base'\n",
        "params_model['validation_ds']['parser']='base'\n",
        "params_model['test_ds']['parser']='base'\n",
        "params_model['train_ds']['manifest_filepath'] = train_manifest\n",
        "params_model['train_ds']['batch_size']=16\n",
        "params_model['validation_ds']['manifest_filepath'] = test_manifest\n",
        "params_model['validation_ds']['batch_size']=16\n",
        "params_model['test_ds']['manifest_filepath'] = test_manifest\n",
        "params_model['test_ds']['batch_size']=16"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wNJAKtKUJvCx",
        "outputId": "c345ee95-ef57-4a79-c140-6ae34cf61093"
      },
      "source": [
        "params_model"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'sample_rate': 16000, 'repeat': 5, 'dropout': 0.0, 'separable': True, 'labels': [' ', 'а', 'б', 'в', 'г', 'д', 'е', 'ж', 'з', 'и', 'й', 'к', 'л', 'м', 'н', 'о', 'п', 'р', 'с', 'т', 'у', 'ф', 'х', 'ц', 'ч', 'ш', 'щ', 'ъ', 'ы', 'ь', 'э', 'ю', 'я'], 'optim': {'name': 'novograd', 'lr': 0.05, 'betas': [0.9, 0.98], 'weight_decay': 0.001, 'sched': {'name': 'CosineAnnealing', 'warmup_steps': 500, 'warmup_ratio': None, 'min_lr': 0.0, 'last_epoch': -1}}, 'train_ds': {'manifest_filepath': '/content/train/farfield/farfield.jsonl', 'sample_rate': 16000, 'labels': [' ', 'а', 'б', 'в', 'г', 'д', 'е', 'ж', 'з', 'и', 'й', 'к', 'л', 'м', 'н', 'о', 'п', 'р', 'с', 'т', 'у', 'ф', 'х', 'ц', 'ч', 'ш', 'щ', 'ъ', 'ы', 'ь', 'э', 'ю', 'я'], 'batch_size': 16, 'trim_silence': False, 'max_duration': 20.0, 'min_duration': 0.1, 'num_workers': 20, 'shuffle': True, 'is_tarred': False, 'tarred_audio_filepaths': None, 'tarred_shard_strategy': 'scatter', 'parser': 'base'}, 'validation_ds': {'manifest_filepath': '/content/test/farfield/farfield.jsonl', 'sample_rate': 16000, 'labels': [' ', 'а', 'б', 'в', 'г', 'д', 'е', 'ж', 'з', 'и', 'й', 'к', 'л', 'м', 'н', 'о', 'п', 'р', 'с', 'т', 'у', 'ф', 'х', 'ц', 'ч', 'ш', 'щ', 'ъ', 'ы', 'ь', 'э', 'ю', 'я'], 'batch_size': 16, 'num_workers': 20, 'shuffle': False, 'parser': 'base'}, 'test_ds': {'manifest_filepath': '/content/test/farfield/farfield.jsonl', 'sample_rate': 16000, 'labels': [' ', 'а', 'б', 'в', 'г', 'д', 'е', 'ж', 'з', 'и', 'й', 'к', 'л', 'м', 'н', 'о', 'п', 'р', 'с', 'т', 'у', 'ф', 'х', 'ц', 'ч', 'ш', 'щ', 'ъ', 'ы', 'ь', 'э', 'ю', 'я'], 'batch_size': 16, 'shuffle': False, 'parser': 'base'}, 'preprocessor': {'_target_': 'nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor', 'normalize': 'per_feature', 'window_size': 0.02, 'sample_rate': 16000, 'window_stride': 0.01, 'window': 'hann', 'features': 64, 'n_fft': 512, 'frame_splicing': 1, 'dither': 1e-05, 'stft_conv': False}, 'spec_augment': {'_target_': 'nemo.collections.asr.modules.SpectrogramAugmentation', 'rect_freq': 50, 'rect_masks': 5, 'rect_time': 120}, 'encoder': {'_target_': 'nemo.collections.asr.modules.ConvASREncoder', 'feat_in': 64, 'activation': 'relu', 'conv_mask': True, 'jasper': [{'dilation': [1], 'dropout': 0.0, 'filters': 256, 'kernel': [33], 'repeat': 1, 'residual': False, 'separable': True, 'stride': [2]}, {'dilation': [1], 'dropout': 0.0, 'filters': 256, 'kernel': [33], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 256, 'kernel': [33], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 256, 'kernel': [33], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 256, 'kernel': [39], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 256, 'kernel': [39], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 256, 'kernel': [39], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 512, 'kernel': [51], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 512, 'kernel': [51], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 512, 'kernel': [51], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 512, 'kernel': [63], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 512, 'kernel': [63], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 512, 'kernel': [63], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 512, 'kernel': [75], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 512, 'kernel': [75], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 512, 'kernel': [75], 'repeat': 5, 'residual': True, 'separable': True, 'stride': [1]}, {'dilation': [2], 'dropout': 0.0, 'filters': 512, 'kernel': [87], 'repeat': 1, 'residual': False, 'separable': True, 'stride': [1]}, {'dilation': [1], 'dropout': 0.0, 'filters': 1024, 'kernel': [1], 'repeat': 1, 'residual': False, 'stride': [1]}]}, 'decoder': {'_target_': 'nemo.collections.asr.modules.ConvASRDecoder', 'feat_in': 1024, 'num_classes': 33, 'vocabulary': [' ', 'а', 'б', 'в', 'г', 'д', 'е', 'ж', 'з', 'и', 'й', 'к', 'л', 'м', 'н', 'о', 'п', 'р', 'с', 'т', 'у', 'ф', 'х', 'ц', 'ч', 'ш', 'щ', 'ъ', 'ы', 'ь', 'э', 'ю', 'я']}, 'target': 'nemo.collections.asr.models.ctc_models.EncDecCTCModel'}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsqMhCWAXzw2"
      },
      "source": [
        "params_model['optim']['lr'] = 0.001"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IEn2RyvgxxvO"
      },
      "source": [
        "Затем мы создадим экземпляр и модель ASR на основе нашего файла ``config.yaml`` из предыдущего раздела. \n",
        "Обратите внимание, что это этап, на котором мы также сообщаем модели, где находятся наши обучающие и тестовые манифесты."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cbf0fsMK09lk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e57ac26d-2f91-4266-bb11-618e3558a732"
      },
      "source": [
        "from omegaconf import DictConfig\n",
        "\n",
        "\n",
        "first_asr_model = nemo_asr.models.EncDecCTCModel(cfg=DictConfig(params_model), trainer=trainer)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 14:45:21 collections:173] Dataset loaded with 123995 files totalling 132.43 hours\n",
            "[NeMo I 2021-11-30 14:45:21 collections:174] 8 files were filtered totalling 0.03 hours\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 14:45:21 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 20 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "      cpuset_checked))\n",
            "    \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 14:45:21 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-30 14:45:21 collections:174] 0 files were filtered totalling 0.00 hours\n",
            "[NeMo I 2021-11-30 14:45:21 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-30 14:45:21 collections:174] 0 files were filtered totalling 0.00 hours\n",
            "[NeMo I 2021-11-30 14:45:21 features:265] PADDING: 16\n",
            "[NeMo I 2021-11-30 14:45:21 features:282] STFT using torch\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hWtzwL5qXTYq"
      },
      "source": [
        "Мы можем начать обучение всего одной строчкой!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inRJsnrz1psq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847,
          "referenced_widgets": [
            "ffdfb4255e2e4fd5845a8242fe71cf0c",
            "d75abdbc2381437786bad70c0d3d2c27",
            "aa0af6138fc84656a9e254072d46ffe0",
            "f928ed870227426baeb779274a6db25c",
            "f1efb2da07604c6a91877ceafb73b699",
            "a510311df3eb4620942340ee6df625a2",
            "72764862f079460c9964bcddfb315076",
            "36de6c59bc1c4614870f059bed6ba9c8",
            "f4750bc9ad964fc4b29b3770169ae26a",
            "b840d524d6504f7181b6cfb6da311fe8",
            "91b6c532fa0f455eb8876f9097cb6f2c",
            "fb50767c67b34410b1b1036842458d1f",
            "975a0ce7ed0a4efc810ac8cacfeb6bc6",
            "17454fcd8aa0437db83a43d2762af769",
            "e6cae573e04b4d61806b3e3a5bc50981",
            "8546501503ff402b800adc9f0adb6feb",
            "1a9f8285354d421984b9f1955c481ab6",
            "8dd936ce70144ca89a1cb735b2d3c608",
            "f3e223536d1640a3a177afe72b2cff2e",
            "665ed20aa90e4f8fbd7e450dc2e593b7",
            "d17e40f97a5a47ad93cffd3791e16003",
            "3a2c4a1401a04c1eb3b37ce7cbdd31c4",
            "dae1d7f182464522aba48ad40ce04810",
            "de72b91135554b99af6d0e4685aed6d1",
            "3b8db7076a1247e481e3d353ff5a0d16",
            "28c228a51f144b27a737e9cda7c875ed",
            "d1463a703f6a4cf290e7eb788d33ccf1",
            "84e37a58f7ec4da9bb47050b6fd6b8d9",
            "0bd1017307044a36bb63812f2d54f3a7",
            "2792a7c9baa3426cb8dac7ef982678f0",
            "593a92df381d459b9c9e9ecde7d4b746",
            "1a56553fc6754113af27cd6da0f8f97d",
            "8dff796b6f6e4437989ee2c63021b069"
          ]
        },
        "outputId": "abce9c0d-fac3-4380-dfd4-41ae785b63f2"
      },
      "source": [
        "# Start training!!!\n",
        "trainer.fit(first_asr_model)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "[NeMo W 2021-11-30 14:46:25 modelPT:471] The lightning trainer received accelerator: <pytorch_lightning.accelerators.gpu.GPUAccelerator object at 0x7f512c664c90>. We recommend to use 'ddp' instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 14:46:25 modelPT:561] Optimizer config = Novograd (\n",
            "    Parameter Group 0\n",
            "        amsgrad: False\n",
            "        betas: [0.9, 0.98]\n",
            "        eps: 1e-08\n",
            "        grad_averaging: False\n",
            "        lr: 0.001\n",
            "        weight_decay: 0.001\n",
            "    )\n",
            "[NeMo I 2021-11-30 14:46:25 lr_scheduler:752] Scheduler \"<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7f510a35ff10>\" \n",
            "    will be used during training (effective maximum steps = 7750) - \n",
            "    Parameters : \n",
            "    (warmup_steps: 500\n",
            "    warmup_ratio: null\n",
            "    min_lr: 0.0\n",
            "    last_epoch: -1\n",
            "    max_steps: 7750\n",
            "    )\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "  | Name              | Type                              | Params\n",
            "------------------------------------------------------------------------\n",
            "0 | preprocessor      | AudioToMelSpectrogramPreprocessor | 0     \n",
            "1 | encoder           | ConvASREncoder                    | 18.9 M\n",
            "2 | decoder           | ConvASRDecoder                    | 34.9 K\n",
            "3 | loss              | CTCLoss                           | 0     \n",
            "4 | spec_augmentation | SpectrogramAugmentation           | 0     \n",
            "5 | _wer              | WER                               | 0     \n",
            "------------------------------------------------------------------------\n",
            "18.9 M    Trainable params\n",
            "0         Non-trainable params\n",
            "18.9 M    Total params\n",
            "37.859    Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ffdfb4255e2e4fd5845a8242fe71cf0c",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Validation sanity check: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 14:46:25 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 20 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "      cpuset_checked))\n",
            "    \n",
            "[NeMo W 2021-11-30 14:46:28 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/nemo/collections/asr/parts/submodules/jasper.py:280: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "      ) // self.conv.stride[0] + 1\n",
            "    \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fb50767c67b34410b1b1036842458d1f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 14:46:33 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:134: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "      \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n",
            "    \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dae1d7f182464522aba48ad40ce04810",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "niC8XGtyDXQJ"
      },
      "source": [
        "first_asr_model.save_to('model_cp.nemo')"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpYXX-GslR0E"
      },
      "source": [
        "There we go! We've put together a full training pipeline for the model and trained it for 5 epochs.\n",
        "\n",
        "If you'd like to save this model checkpoint for loading later (e.g. for fine-tuning, or for continuing training), you can simply call `first_asr_model.save_to(<checkpoint_path>)`. Then, to restore your weights, you can rebuild the model using the config (let's say you call it `first_asr_model_continued` this time) and call `first_asr_model_continued.restore_from(<checkpoint_path>)`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D5Kwg8Cz-aaO"
      },
      "source": [
        "## Inference\n",
        "\n",
        "Let's have a quick look at how one could run inference with NeMo's ASR model.\n",
        "\n",
        "First, ``EncDecCTCModel`` and its subclasses contain a handy ``transcribe`` method which can be used to simply obtain audio files' transcriptions. It also has batch_size argument to improve performance."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3FT0klSV268p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 138,
          "referenced_widgets": [
            "973189c3820f46408c3e47a78d4e4acd",
            "f81ddff1b70c4027883c794902a38a22",
            "c0718826441649d582cf79f190e521a9",
            "5f2373ed7cdd48e7ad42addc51dc7598",
            "d1e307df0c6045d194733b343dbf0170",
            "7951b2c06cc94267a1c6c1c11f8cb063",
            "1b3788b89c154ca5a8348187e3b2f16e",
            "889483a50d9247bb94d924e008b7203b",
            "b0c901f084244f33b4a109636b074b00",
            "3732ae052dbe4c3a9037113489ddd9ec",
            "4f1925c7c82f402690e058b69eb89120"
          ]
        },
        "outputId": "838ee2c6-c1ab-40a7-f8e1-2789e54d8344"
      },
      "source": [
        "print(first_asr_model.transcribe(paths2audio_files=files,\n",
        "                                 batch_size=4))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "973189c3820f46408c3e47a78d4e4acd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Transcribing:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 18:39:24 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/nemo/collections/asr/parts/submodules/jasper.py:280: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "      ) // self.conv.stride[0] + 1\n",
            "    \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['афлн ка  а ']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FiCfLX0D7py"
      },
      "source": [
        "Below is an example of a simple inference loop in pure PyTorch. It also shows how one can compute Word Error Rate (WER) metric between predictions and references."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mP4r1Gx_Ilt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91286f5c-0ecf-4b1d-e56d-795afd7106b7"
      },
      "source": [
        "# Bigger batch-size = bigger throughput\n",
        "# params['model']['validation_ds']['batch_size'] = 16\n",
        "# Setup the test data loader and make sure the model is on GPU\n",
        "# first_asr_model.setup_test_data(test_data_config=params['model']['validation_ds'])\n",
        "first_asr_model.cuda()\n",
        "\n",
        "# We will be computing Word Error Rate (WER) metric between our hypothesis and predictions.\n",
        "# WER is computed as numerator/denominator.\n",
        "# We'll gather all the test batches' numerators and denominators.\n",
        "wer_nums = []\n",
        "wer_denoms = []\n",
        "\n",
        "# Loop over all test batches.\n",
        "# Iterating over the model's `test_dataloader` will give us:\n",
        "# (audio_signal, audio_signal_length, transcript_tokens, transcript_length)\n",
        "# See the AudioToCharDataset for more details.\n",
        "for test_batch in first_asr_model.test_dataloader():\n",
        "        test_batch = [x.cuda() for x in test_batch]\n",
        "        targets = test_batch[2]\n",
        "        targets_lengths = test_batch[3]        \n",
        "        log_probs, encoded_len, greedy_predictions = first_asr_model(\n",
        "            input_signal=test_batch[0], input_signal_length=test_batch[1]\n",
        "        )\n",
        "        # Notice the model has a helper object to compute WER\n",
        "        first_asr_model._wer.update(greedy_predictions, targets, targets_lengths)\n",
        "        _, wer_num, wer_denom = first_asr_model._wer.compute()\n",
        "        first_asr_model._wer.reset()\n",
        "        wer_nums.append(wer_num.detach().cpu().numpy())\n",
        "        wer_denoms.append(wer_denom.detach().cpu().numpy())\n",
        "\n",
        "        # Release tensors from GPU memory\n",
        "        del test_batch, log_probs, targets, targets_lengths, encoded_len, greedy_predictions\n",
        "\n",
        "# We need to sum all numerators and denominators first. Then divide.\n",
        "print(f\"WER = {sum(wer_nums)/sum(wer_denoms)}\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 18:41:27 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/nemo/collections/asr/parts/submodules/jasper.py:280: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "      ) // self.conv.stride[0] + 1\n",
            "    \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "WER = 0.9766165592937246\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0kM9kBNOCptf"
      },
      "source": [
        "This WER is not particularly impressive and could be significantly improved. You could train longer (try 100 epochs) to get a better number. Check out the next section on how to improve it further."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RBcJtg5ulR0H"
      },
      "source": [
        "## Model Improvements\n",
        "\n",
        "You already have all you need to create your own ASR model in NeMo, but there are a few more tricks that you can employ if you so desire. In this section, we'll briefly cover a few possibilities for improving an ASR model.\n",
        "\n",
        "### Data Augmentation\n",
        "\n",
        "There exist several ASR data augmentation methods that can increase the size of our training set.\n",
        "\n",
        "For example, we can perform augmentation on the spectrograms by zeroing out specific frequency segments (\"frequency masking\") or time segments (\"time masking\") as described by [SpecAugment](https://arxiv.org/abs/1904.08779), or zero out rectangles on the spectrogram as in [Cutout](https://arxiv.org/pdf/1708.04552.pdf). In NeMo, we can do all three of these by simply adding in a `SpectrogramAugmentation` neural module. (As of now, it does not perform the time warping from the SpecAugment paper.)\n",
        "\n",
        "Our toy model does not do spectrogram augmentation. But the real one we got from cloud does:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9glGogaPlR0H"
      },
      "source": [
        "print(first_asr_model._cfg['spec_augment'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LdwdcA_a640R"
      },
      "source": [
        "If you want to enable SpecAugment in your model, make sure your .yaml config file contains 'model/spec_augment' section which looks like the one above."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wteGqroafWg1"
      },
      "source": [
        "## Under the Hood\n",
        "\n",
        "NeMo is open-source and we do all our model development in the open, so you can inspect our code if you wish.\n",
        "\n",
        "In particular, ``nemo_asr.model.EncDecCTCModel`` is an encoder-decoder model which is constructed using several ``Neural Modules`` taken from ``nemo_asr.modules.`` Here is what its forward pass looks like:\n",
        "```python\n",
        "def forward(self, input_signal, input_signal_length):\n",
        "    processed_signal, processed_signal_len = self.preprocessor(\n",
        "        input_signal=input_signal, length=input_signal_length,\n",
        "    )\n",
        "    # Spec augment is not applied during evaluation/testing\n",
        "    if self.spec_augmentation is not None and self.training:\n",
        "        processed_signal = self.spec_augmentation(input_spec=processed_signal)\n",
        "    encoded, encoded_len = self.encoder(audio_signal=processed_signal, length=processed_signal_len)\n",
        "    log_probs = self.decoder(encoder_output=encoded)\n",
        "    greedy_predictions = log_probs.argmax(dim=-1, keepdim=False)\n",
        "    return log_probs, encoded_len, greedy_predictions\n",
        "```\n",
        "Here:\n",
        "\n",
        "* ``self.preprocessor`` is an instance of ``nemo_asr.modules.AudioToMelSpectrogramPreprocessor``, which is a neural module that takes audio signal and converts it into a Mel-Spectrogram\n",
        "* ``self.spec_augmentation`` - is a neural module of type ```nemo_asr.modules.SpectrogramAugmentation``, which implements data augmentation. \n",
        "* ``self.encoder`` - is a convolutional Jasper/QuartzNet-like encoder of type ``nemo_asr.modules.ConvASREncoder``\n",
        "* ``self.decoder`` - is a ``nemo_asr.modules.ConvASRDecoder`` which simply projects into the target alphabet (vocabulary).\n",
        "\n",
        "Also, ``EncDecCTCModel`` uses the audio dataset class ``nemo_asr.data.AudioToCharDataset`` and CTC loss implemented in ``nemo_asr.losses.CTCLoss``.\n",
        "\n",
        "You can use these and other neural modules (or create new ones yourself!) to construct new ASR models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "smzlvbhelR0U"
      },
      "source": [
        "# Further Reading/Watching:\n",
        "\n",
        "That's all for now! If you'd like to learn more about the topics covered in this tutorial, here are some resources that may interest you:\n",
        "- [Stanford Lecture on ASR](https://www.youtube.com/watch?v=3MjIkWxXigM)\n",
        "- [\"An Intuitive Explanation of Connectionist Temporal Classification\"](https://towardsdatascience.com/intuitively-understanding-connectionist-temporal-classification-3797e43a86c)\n",
        "- [Explanation of CTC with Prefix Beam Search](https://medium.com/corti-ai/ctc-networks-and-language-models-prefix-beam-search-explained-c11d1ee23306)\n",
        "- [Listen Attend and Spell Paper (seq2seq ASR model)](https://arxiv.org/abs/1508.01211)\n",
        "- [Explanation of the mel spectrogram in more depth](https://towardsdatascience.com/getting-to-know-the-mel-spectrogram-31bca3e2d9d0)\n",
        "- [Jasper Paper](https://arxiv.org/abs/1904.03288)\n",
        "- [QuartzNet paper](https://arxiv.org/abs/1910.10261)\n",
        "- [SpecAugment Paper](https://arxiv.org/abs/1904.08779)\n",
        "- [Explanation and visualization of SpecAugment](https://towardsdatascience.com/state-of-the-art-audio-data-augmentation-with-google-brains-specaugment-and-pytorch-d3d1a3ce291e)\n",
        "- [Cutout Paper](https://arxiv.org/pdf/1708.04552.pdf)\n",
        "- [Transfer Learning Blogpost](https://developer.nvidia.com/blog/jump-start-training-for-speech-recognition-models-with-nemo/)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3ERGX86lR0V"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}