Tormashova_task_2.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJz6FDU1lRzc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e0e3a07a-3b24-4dfe-f052-d41330eab7d4"
      },
      "source": [
        "## 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": null,
      "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=297b7e31439c4ae90e28314012e63787460efab647970acaa87dd03ae493ce5d\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 2s (350 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 4.2 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 4.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.0.1)\n",
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            "Requirement already satisfied: unidecode in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (1.3.2)\n",
            "Collecting onnx>=1.7.0\n",
            "  Downloading onnx-1.10.2-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (12.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 12.7 MB 33.5 MB/s \n",
            "\u001b[?25hRequirement already satisfied: wget in /usr/local/lib/python3.7/dist-packages (from nemo_toolkit[all]) (3.2)\n",
            "Collecting frozendict\n",
            "  Downloading frozendict-2.1.1-py3-none-any.whl (12 kB)\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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            "Collecting 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",
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            "  Downloading ruamel.yaml-0.17.17-py3-none-any.whl (109 kB)\n",
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            "Collecting pydub\n",
            "  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
            "Collecting matplotlib>=3.3.2\n",
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            "\u001b[K     |████████████████████████████████| 11.2 MB 39.9 MB/s \n",
            "\u001b[?25hCollecting isort[requirements]<5\n",
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            "  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, 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, 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: 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: 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 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 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": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gAhsmi6HlRzh"
      },
      "source": [
        "# Папка, где будет размещаться датасет Golos\n",
        "data_dir = '/content'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIWkld7od6-o",
        "outputId": "e6698ca9-c802-41a0-d046-b71b5368de9c"
      },
      "source": [
        "! ls /content"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yb4fuUvWlRzk",
        "scrolled": true
      },
      "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"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ObnBPQ08qHav",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fed09578-0030-45ac-86c9-54bbe6d749e9"
      },
      "source": [
        "# Download train dataset\n",
        "train_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(train_path)\n",
        "    tar.extractall(path=data_dir)\n",
        "\n",
        "# Download test dataset\n",
        "test_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(test_path)\n",
        "    tar.extractall(path=data_dir)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset downloaded at: /content/train.tar\n",
            "Dataset downloaded at: /content/test.tar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UKVIhN6POXFU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7acf6ea1-6d22-4c72-8208-683d83e46445"
      },
      "source": [
        "# Download manifest\n",
        "!wget https://github.com/LeraTormashova/nis_speech/raw/master/train_manifest.jsonl.zip\n",
        "!unzip /content/train_manifest.jsonl.zip\n",
        "!mv /content/train_manifest.jsonl /content/train/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2021-11-30 14:38:20--  https://github.com/LeraTormashova/nis_speech/raw/master/train_manifest.jsonl.zip\n",
            "Resolving github.com (github.com)... 13.114.40.48\n",
            "Connecting to github.com (github.com)|13.114.40.48|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/LeraTormashova/nis_speech/master/train_manifest.jsonl.zip [following]\n",
            "--2021-11-30 14:38:21--  https://raw.githubusercontent.com/LeraTormashova/nis_speech/master/train_manifest.jsonl.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 52145788 (50M) [application/zip]\n",
            "Saving to: ‘train_manifest.jsonl.zip.2’\n",
            "\n",
            "train_manifest.json 100%[===================>]  49.73M   132MB/s    in 0.4s    \n",
            "\n",
            "2021-11-30 14:38:21 (132 MB/s) - ‘train_manifest.jsonl.zip.2’ saved [52145788/52145788]\n",
            "\n",
            "Archive:  /content/train_manifest.jsonl.zip\n",
            "replace train_manifest.jsonl? [y]es, [n]o, [A]ll, [N]one, [r]ename: y\n",
            "  inflating: train_manifest.jsonl    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Zy44BImExKv"
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5M28Mwhxfml0",
        "outputId": "f4fb51ef-ba69-45e2-8e1f-c0e948b94cbc"
      },
      "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tarfile already exists.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 15:52:32 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 15:52:32 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 15:52:32 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 15:52:32 features:265] PADDING: 16\n",
            "[NeMo I 2021-11-30 15:52:32 features:282] STFT using torch\n",
            "[NeMo I 2021-11-30 15:52:34 save_restore_connector:149] Model EncDecCTCModel was successfully restored from /content/QuartzNet15x5_golos.nemo.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWtStF94z46m",
        "outputId": "5c3db4e8-abfa-4335-cc8a-2b388eac21a8"
      },
      "source": [
        "# --- Building Manifest Files --- #\n",
        "import json\n",
        "\n",
        "# Function to build a manifest\n",
        "def build_manifest(manifest_rel, manifest_abs):\n",
        "    n = 0\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",
        "              # print(metadata['audio_filepath'])\n",
        "              if metadata['audio_filepath'].startswith('farfield'):\n",
        "                n += 1\n",
        "                if n > 10000:\n",
        "                  break\n",
        "                metadata[\"audio_filepath\"]=os.path.join(manifest_path, metadata[\"audio_filepath\"])\n",
        "                json.dump(metadata, fout)\n",
        "                fout.write('\\n')\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/train_manifest.jsonl')\n",
        "train_abs = os.path.join(data_dir, 'train/manifest.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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "******\n",
            "test_manifest /content/test/farfield/farfield.jsonl\n",
            "train_manifest /content/train/manifest.jsonl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XuQmoSDSFEf1",
        "outputId": "f9d56073-d65b-44f6-9fa3-d237870c0091"
      },
      "source": [
        "! wc -l /content/train/manifest.jsonl"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10000 /content/train/manifest.jsonl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tIfxH8pjXTsk",
        "outputId": "7936f612-1f9b-4de9-d9f8-dfb2c2a05550"
      },
      "source": [
        "import pytorch_lightning as pl\n",
        "trainer = pl.Trainer(gpus=1, max_epochs=1, precision=16)"
      ],
      "execution_count": null,
      "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": "-5jfdUXjiJob"
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNJAKtKUJvCx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d8ed29dd-0b48-45e9-c515-ec1da51bcd86"
      },
      "source": [
        "params_model"
      ],
      "execution_count": null,
      "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/manifest.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": 207
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nsqMhCWAXzw2"
      },
      "source": [
        "params_model['optim']['lr'] = 0.005"
      ],
      "execution_count": null,
      "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": "fbff93c0-99b2-4a2b-e787-4a9b23fa26f8"
      },
      "source": [
        "from omegaconf import DictConfig\n",
        "\n",
        "\n",
        "first_asr_model = nemo_asr.models.EncDecCTCModel(cfg=DictConfig(params_model), trainer=trainer)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 15:56:03 collections:173] Dataset loaded with 10000 files totalling 14.63 hours\n",
            "[NeMo I 2021-11-30 15:56:03 collections:174] 0 files were filtered totalling 0.00 hours\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 15:56:03 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 15:56:03 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-30 15:56:03 collections:174] 0 files were filtered totalling 0.00 hours\n",
            "[NeMo I 2021-11-30 15:56:03 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-30 15:56:03 collections:174] 0 files were filtered totalling 0.00 hours\n",
            "[NeMo I 2021-11-30 15:56:03 features:265] PADDING: 16\n",
            "[NeMo I 2021-11-30 15:56:03 features:282] STFT using torch\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "inRJsnrz1psq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847,
          "referenced_widgets": [
            "2b1a930dc23c4d9690a99412aa2e47b4",
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            "f769d7c88f6944d49c4bd96b9134bc79"
          ]
        },
        "outputId": "b758c6c2-3268-42a2-fcd9-cd2a6ed51ebf"
      },
      "source": [
        "# Start training!!!\n",
        "trainer.fit(first_asr_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "[NeMo W 2021-11-30 15:56:08 modelPT:471] The lightning trainer received accelerator: <pytorch_lightning.accelerators.gpu.GPUAccelerator object at 0x7f55604632d0>. We recommend to use 'ddp' instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 15:56:08 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: 0.005\n",
            "        weight_decay: 0.001\n",
            "    )\n",
            "[NeMo I 2021-11-30 15:56:08 lr_scheduler:752] Scheduler \"<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7f55618654d0>\" \n",
            "    will be used during training (effective maximum steps = 625) - \n",
            "    Parameters : \n",
            "    (warmup_steps: 500\n",
            "    warmup_ratio: null\n",
            "    min_lr: 0.0\n",
            "    last_epoch: -1\n",
            "    max_steps: 625\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": "2b1a930dc23c4d9690a99412aa2e47b4",
              "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 15:56:08 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 15:56:10 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": "e43894c93c6c4495aaf1ffe6b6c38295",
              "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 15:56:16 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": "6838adc1881f4f2496cc5eb4be00c8c7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_PZJIso-eDl-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4188df17-3732-4fea-817c-8ff0b51e186c"
      },
      "source": [
        "# Point to the data we'll use for fine-tuning as the training set\n",
        "first_asr_model.setup_training_data(train_data_config=params_model['train_ds'])\n",
        "first_asr_model.setup_test_data(test_data_config=params_model['test_ds'])\n",
        "# Point to the new validation data for fine-tuning\n",
        "first_asr_model.setup_validation_data(val_data_config=params_model['validation_ds'])"
      ],
      "execution_count": 212,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 16:24:23 collections:173] Dataset loaded with 10000 files totalling 14.63 hours\n",
            "[NeMo I 2021-11-30 16:24:23 collections:174] 0 files were filtered totalling 0.00 hours\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 16:24:23 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 16:24:23 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-30 16:24:23 collections:174] 0 files were filtered totalling 0.00 hours\n",
            "[NeMo I 2021-11-30 16:24:23 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-30 16:24:23 collections:174] 0 files were filtered totalling 0.00 hours\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7mP4r1Gx_Ilt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d582bd78-eb9a-48f4-d432-a128b9106519"
      },
      "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": 214,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 16:24:42 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 = 0.9998806967310905\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847,
          "referenced_widgets": [
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          ]
        },
        "id": "fliTSMfCelqe",
        "outputId": "ed694946-e646-46f7-b5e4-21c82319d9ac"
      },
      "source": [
        "# Start training!!!\n",
        "trainer.fit(first_asr_model)"
      ],
      "execution_count": 216,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "[NeMo W 2021-11-30 16:25:54 modelPT:471] The lightning trainer received accelerator: <pytorch_lightning.accelerators.gpu.GPUAccelerator object at 0x7f55604632d0>. We recommend to use 'ddp' instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 16:25:54 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: 0.005\n",
            "        weight_decay: 0.001\n",
            "    )\n",
            "[NeMo I 2021-11-30 16:25:54 lr_scheduler:752] Scheduler \"<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7f5541e21690>\" \n",
            "    will be used during training (effective maximum steps = 625) - \n",
            "    Parameters : \n",
            "    (warmup_steps: 500\n",
            "    warmup_ratio: null\n",
            "    min_lr: 0.0\n",
            "    last_epoch: -1\n",
            "    max_steps: 625\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",
            "[NeMo W 2021-11-30 16:25:54 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py:623: UserWarning: Checkpoint directory /content/lightning_logs/version_11/checkpoints exists and is not empty.\n",
            "      rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
            "    \n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0405fc6fdd884c5ba7ba70b63d417f8e",
              "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 16:25:54 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 16:25:56 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": "aba08f3d0b3e4c5999dc5e20fefa622a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "399f8de535064d20b913bc64da833d44",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Validating: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lhndtnPKelxj",
        "outputId": "58f6b3bf-0d00-4d38-a11b-f2897dc7cbcb"
      },
      "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": 218,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 16:48:04 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 = 0.9998806967310905\n"
          ]
        }
      ]
    }
  ]
}