ASR_NIS.ipynb

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        "# 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=b9d18d4e457c087f16aa9589aee25e78a760db346753c3f1baefdb03947eda76\n",
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            "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",
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            "Get:8 http://archive.ubuntu.com/ubuntu bionic-updates/universe amd64 sox amd64 14.4.2-3ubuntu0.18.04.1 [101 kB]\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",
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            "    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-1.5.4 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "och6mxlR37IO",
        "outputId": "748da157-079c-4f19-de05-a9185b7b70f9"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ms-KfYf49aps"
      },
      "source": [
        "from omegaconf import OmegaConf\n",
        "import copy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gi_tCodJWwmd",
        "outputId": "90e98b9a-0da2-4049-a89c-827b13219367"
      },
      "source": [
        "! ls drive/MyDrive"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " animals10\t    farfield_ft2.nemo   farfield_ft.nemo     Практика\n",
            " checkpoints\t    farfield_ft3.nemo   train_crowd9.tar\n",
            "'Colab Notebooks'   farfield_ft4.nemo   train_farfield.tar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PAjKHMM98Gj"
      },
      "source": [
        "! cp drive/MyDrive/manifest.jsonl /content/"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_9eYjAW-JSA",
        "outputId": "b2b8cdf6-420d-4ed1-cea0-0a9c58206e89"
      },
      "source": [
        "! ls"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "drive  manifest.jsonl  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-nOv2W507Al"
      },
      "source": [
        "# Папка, где будет размещаться датасет Golos\n",
        "data_dir = '/content'"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iZR5aN2PSE4x"
      },
      "source": [
        "import glob\n",
        "import os\n",
        "import subprocess\n",
        "import tarfile\n",
        "import wget"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2f6wP2whSdDT"
      },
      "source": [
        "# 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": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kw1bviSj09kt",
        "outputId": "dd038fd9-34db-47c8-d8ee-1adb56ebd7b2"
      },
      "source": [
        "test_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(test_golos_path)\n",
        "    tar.extractall(path=data_dir)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset downloaded at: /content/test.tar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pI8BJzletD10"
      },
      "source": [
        "train_golos_path = '/content/drive/MyDrive/train_crowd9.tar'\n",
        "tar = tarfile.open(train_golos_path)\n",
        "tar.extractall(path=data_dir)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYMrlnd78TPT"
      },
      "source": [
        "train_golos_path = '/content/drive/MyDrive/train_farfield.tar'\n",
        "tar = tarfile.open(train_golos_path)\n",
        "tar.extractall(path=data_dir)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92TOrcd5SybD",
        "outputId": "66984eb0-35f7-4bc6-9508-e68378670be5"
      },
      "source": [
        "! ls /content/train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "farfield\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QoKn85xhexE-"
      },
      "source": [
        "! mv /content/drive/MyDrive/manifest.jsonl /content/train/"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQqtFRYoC2Hr"
      },
      "source": [
        "! rm -r /content/train/farfield.jsonl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlrAZALbTcpS"
      },
      "source": [
        "! head /content/train/10hours.jsonl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "strGXhY53Jhe"
      },
      "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",
        "def build_train_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",
        "                if metadata['audio_filepath'][6] == \"9\":\n",
        "\n",
        "                    metadata[\"audio_filepath\"]=os.path.join(manifest_path, metadata[\"audio_filepath\"])\n",
        "                    json.dump(metadata, fout)\n",
        "                    fout.write('\\n')\n",
        "\n",
        "def build_train_farfield_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",
        "                # print(metadata['audio_filepath'][:8])\n",
        "                if metadata['audio_filepath'][:8] == \"farfield\":\n",
        "                    metadata[\"audio_filepath\"]=os.path.join(manifest_path, metadata[\"audio_filepath\"])\n",
        "                    json.dump(metadata, fout)\n",
        "                    fout.write('\\n')\n",
        "                \n"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P0s1JUpbT7K5",
        "outputId": "74e886c4-c439-4195-9154-af1eb146b84c"
      },
      "source": [
        "# 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)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "******\n",
            "test_manifest /content/test/farfield/farfield.jsonl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fu4DusRDVaZ9",
        "outputId": "9b3fbae4-c685-4002-eb40-9d4ae8553c35"
      },
      "source": [
        "# Building Manifests\n",
        "print(\"******\")\n",
        "train_rel = os.path.join(data_dir, 'test/crowd/manifest.jsonl')\n",
        "train_abs = os.path.join(data_dir, 'test/crowd/crowd.jsonl')\n",
        "if not os.path.isfile(train_abs):\n",
        "  build_manifest(train_rel, train_abs)\n",
        "test_crowd_manifest = train_abs\n",
        "print(\"test_crowd_manifest\", test_crowd_manifest)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "******\n",
            "test_crowd_manifest /content/test/crowd/crowd.jsonl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0MQHwV--ViVI",
        "outputId": "ac5c9f99-8a2d-4a33-80b0-82cabd76d5f0"
      },
      "source": [
        "! head /content/test/crowd/crowd.jsonl"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\"id\": \"e632f7d39c15e7edfc665b91e6f2071f\", \"audio_filepath\": \"/content/test/crowd/files/e632f7d39c15e7edfc665b91e6f2071f.wav\", \"text\": \"\\u0430\\u0444\\u0438\\u043d\\u0430 \\u0432\\u043e\\u0441\\u043f\\u0440\\u043e\\u0438\\u0437\\u0432\\u0435\\u0434\\u0438 \\u043c\\u0443\\u0437\\u044b\\u043a\\u0443 \\u0432\\u043f\\u0435\\u0440\\u0435\\u043c\\u0435\\u0448\\u043a\\u0443\", \"duration\": 4.9}\n",
            "{\"id\": \"5db5df8bb9e3b6660b2a04b34d4a355d\", \"audio_filepath\": \"/content/test/crowd/files/5db5df8bb9e3b6660b2a04b34d4a355d.wav\", \"text\": \"\\u043d\\u0430\\u0439\\u0442\\u0438 \\u0441\\u0435\\u0440\\u0438\\u0430\\u043b \\u0433\\u0440\\u0438\\u0433\\u043e\\u0440\\u0438\\u0439 \\u0440\", \"duration\": 3.652}\n",
            "{\"id\": \"2c471aedc6979109f28cd53c58f8c4fb\", \"audio_filepath\": \"/content/test/crowd/files/2c471aedc6979109f28cd53c58f8c4fb.wav\", \"text\": \"\\u043f\\u0440\\u044f\\u043c\\u043e\\u0439 \\u044d\\u0444\\u0438\\u0440 \\u0430\\u043f\\u043b \\u043c\\u0430\\u043d\\u0447\\u0435\\u0441\\u0442\\u0435\\u0440 \\u044e\\u043d\\u0430\\u0439\\u0442\\u0435\\u0434 \\u0442\\u043e\\u0442\\u0442\\u0435\\u043d\\u0445\\u044d\\u043c\", \"duration\": 4.34175}\n",
            "{\"id\": \"756a137ee9debde4a008adc4a4121dc7\", \"audio_filepath\": \"/content/test/crowd/files/756a137ee9debde4a008adc4a4121dc7.wav\", \"text\": \"\\u0438\\u043b\\u044c\\u0432\\u0438\\u0440\\u043e\\u043c \\u0438\\u0432\\u0430\\u043d\\u043e\\u0432\\u0438\\u0447\\u0435\\u043c \\u0432\\u043e\\u0440\\u043e\\u043d\\u0447\\u0438\\u0445\\u0438\\u043d\\u044b\\u043c\", \"duration\": 3.9}\n",
            "{\"id\": \"1ee3b00170123a6723a40e129b2f6bce\", \"audio_filepath\": \"/content/test/crowd/files/1ee3b00170123a6723a40e129b2f6bce.wav\", \"text\": \"\\u043c\\u043e\\u0436\\u0435\\u0448\\u044c \\u043f\\u043e\\u043a\\u0430\\u0437\\u0430\\u0442\\u044c \\u043a\\u0438\\u043d\\u043e\\u0448\\u043a\\u0443 \\u0438\\u0441\\u0445\\u043e\\u0434\\u043d\\u044b\\u0439 \\u043a\\u043e\\u0434\", \"duration\": 3.32}\n",
            "{\"id\": \"35e8b07c1109b98209aded328fa6da0e\", \"audio_filepath\": \"/content/test/crowd/files/35e8b07c1109b98209aded328fa6da0e.wav\", \"text\": \"\\u0438\\u0437\\u043c\\u0435\\u043d\\u0438\\u043b\\u0441\\u044f \\u043b\\u0438 \\u043a\\u0443\\u0440\\u0441 \\u0440\\u0443\\u0431\\u043b\\u044f \\u043a \\u0430\\u043c\\u0435\\u0440\\u0438\\u043a\\u0430\\u043d\\u0441\\u043a\\u043e\\u043c\\u0443 \\u0434\\u043e\\u043b\\u043b\\u0430\\u0440\\u0443 \\u0441\\u043e \\u0432\\u0447\\u0435\\u0440\\u0430\\u0448\\u043d\\u0435\\u0433\\u043e \\u0434\\u043d\\u044f\", \"duration\": 4.66}\n",
            "{\"id\": \"c35deaf94921ca67be23c19580a13397\", \"audio_filepath\": \"/content/test/crowd/files/c35deaf94921ca67be23c19580a13397.wav\", \"text\": \"\\u043d\\u0430\\u0439\\u0442\\u0438 \\u0444\\u0438\\u043b\\u044c\\u043c \\u043f\\u043b\\u043e\\u0445\\u0438\\u0435 \\u043f\\u0430\\u0440\\u043d\\u0438 \\u043f\\u0435\\u0440\\u0432\\u0443\\u044e \\u0447\\u0430\\u0441\\u0442\\u044c\", \"duration\": 6.56}\n",
            "{\"id\": \"769fc2582dd82d1a3d4b7de9154d43b4\", \"audio_filepath\": \"/content/test/crowd/files/769fc2582dd82d1a3d4b7de9154d43b4.wav\", \"text\": \"\\u044d\\u043f\\u0438\\u0437\\u043e\\u0434 \\u0442\\u0440\\u0438\\u0434\\u0446\\u0430\\u0442\\u044c \\u043e\\u0434\\u0438\\u043d \\u0441\\u0435\\u0437\\u043e\\u043d\\u0430 \\u0442\\u0440\\u0438\\u043d\\u0430\\u0434\\u0446\\u0430\\u0442\\u044c \\u0444\\u0438\\u043b\\u044c\\u043c\\u0430 \\u043b\\u044e\\u0434\\u0438\", \"duration\": 5.88}\n",
            "{\"id\": \"6a5c6ed155b43cdc77d2225084f6bd0a\", \"audio_filepath\": \"/content/test/crowd/files/6a5c6ed155b43cdc77d2225084f6bd0a.wav\", \"text\": \"\\u043a\\u0443\\u043f\\u0438\\u0442\\u044c \\u043c\\u043e\\u044e\\u0449\\u0435\\u0435 \\u0441\\u0440\\u0435\\u0434\\u0441\\u0442\\u0432\\u043e\", \"duration\": 5.1}\n",
            "{\"id\": \"38bf4f3c89e0507ee921a669fec12e9d\", \"audio_filepath\": \"/content/test/crowd/files/38bf4f3c89e0507ee921a669fec12e9d.wav\", \"text\": \"\\u043d\\u043e\\u043b\\u044c \\u0442\\u0440\\u0438\\u0441\\u0442\\u0430 \\u0432\\u043e\\u0441\\u0435\\u043c\\u044c \\u0442\\u0440\\u0438\\u0441\\u0442\\u0430 \\u0432\\u043e\\u0441\\u0435\\u043c\\u043d\\u0430\\u0434\\u0446\\u0430\\u0442\\u044c \\u0442\\u0440\\u0438\\u0441\\u0442\\u0430 \\u0447\\u0435\\u0442\\u044b\\u0440\\u0435 \\u0448\\u0435\\u0441\\u0442\\u044c\\u0434\\u0435\\u0441\\u044f\\u0442 \\u0434\\u0435\\u0432\\u044f\\u0442\\u044c\", \"duration\": 8.8231875}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HEsZhwrMT9Uh"
      },
      "source": [
        "train_rel = os.path.join(data_dir, 'train/10hours.jsonl')\n",
        "train_abs = os.path.join(data_dir, 'train/crowd.jsonl')\n",
        "if not os.path.isfile(train_abs):\n",
        "  build_train_manifest(train_rel, train_abs)\n",
        "train_manifest = train_abs\n",
        "print(\"train_manifest\", train_manifest)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rq9Uwm8P8IHY",
        "outputId": "ee1fd713-760c-4f81-908f-7024b5e9bb48"
      },
      "source": [
        "train_rel = os.path.join(data_dir, 'train/manifest.jsonl')\n",
        "train_abs = os.path.join(data_dir, 'train/farfield.jsonl')\n",
        "if not os.path.isfile(train_abs):\n",
        "  build_train_farfield_manifest(train_rel, train_abs)\n",
        "train_manifest_farfield = train_abs\n",
        "print(\"train_manifest\", train_manifest_farfield)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_manifest /content/train/farfield.jsonl\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cdENEI8aUFqT",
        "outputId": "ddf65c28-a795-409d-921f-566394cff815"
      },
      "source": [
        "! head /content/train/farfield.jsonl"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\"id\": \"855e01d6ba9a4aa59950d62037b87709\", \"audio_filepath\": \"/content/train/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/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/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/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/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/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/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/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/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/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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cM35gTu_1Go_",
        "outputId": "865b8d64-32af-477b-d78a-f7c0097765cc"
      },
      "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": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 16:46:42 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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "WH0z5AUl1J2R",
        "outputId": "39c57188-9400-40ec-e3e5-4aba35858df7"
      },
      "source": [
        "load(\"https://sc.link/ZMv\", \"QuartzNet15x5_golos.nemo\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset downloaded at: /content/QuartzNet15x5_golos.nemo\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/QuartzNet15x5_golos.nemo'"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qwSGR4eh1NHl",
        "outputId": "32d5ba6c-dbe2-498f-e579-2afe750e0151"
      },
      "source": [
        "pretrained_asr = nemo_asr.models.EncDecCTCModel.restore_from(os.path.join(data_dir, \"QuartzNet15x5_golos.nemo\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-28 20:42:33 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 2021-11-28 20:42:33 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 2021-11-28 20:42:33 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 2021-11-28 20:42:33 features:265] PADDING: 16\n",
            "[NeMo I 2021-11-28 20:42:33 features:282] STFT using torch\n",
            "[NeMo I 2021-11-28 20:42:47 save_restore_connector:149] Model EncDecCTCModel was successfully restored from /content/QuartzNet15x5_golos.nemo.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-UuAR5sG3YcR"
      },
      "source": [
        "params_model = pretrained_asr._cfg"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "StG8I5RQARw2",
        "outputId": "b8990832-b30c-4904-8927-9ca36665f006"
      },
      "source": [
        "params_model['train_ds']['manifest_filepath'] = train_manifest\n",
        "params_model['train_ds']['batch_size']=16\n",
        "params_model['train_ds']['parser']='base'\n",
        "params_model['train_ds']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'manifest_filepath': '/content/train/crowd.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'}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JyVvOlPx8Yt7",
        "outputId": "a1a1102b-eb8b-4661-c511-b119fc5c3b04"
      },
      "source": [
        "params_model['validation_ds']['manifest_filepath'] = test_manifest\n",
        "params_model['validation_ds']['batch_size']=16\n",
        "params_model['validation_ds']['parser']='base'\n",
        "params_model['validation_ds']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'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'}"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WB6Qk6Dg3Rni",
        "outputId": "7f259b41-ab5d-4d1c-901b-f33eff05cd1f"
      },
      "source": [
        "params_model['test_ds']['manifest_filepath'] = test_manifest\n",
        "params_model['test_ds']['batch_size']=16\n",
        "params_model['test_ds']['parser']='base'\n",
        "params_model['test_ds']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'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'}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y-WmdLUH3B1c",
        "outputId": "33d37cd4-517a-45c3-b722-2f270d1a89e9"
      },
      "source": [
        "# Bigger batch-size = bigger throughput\n",
        "# params['validation_ds']['batch_size'] = 8\n",
        "# Setup the test data loader and make sure the model is on GPU\n",
        "pretrained_asr.setup_test_data(test_data_config=params_model['test_ds'])\n",
        "pretrained_asr.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 pretrained_asr.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 = pretrained_asr(\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",
        "        pretrained_asr._wer.update(greedy_predictions, targets, targets_lengths)\n",
        "        _, wer_num, wer_denom = pretrained_asr._wer.compute()\n",
        "        pretrained_asr._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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-28 20:43:16 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-28 20:43:16 collections:174] 0 files were filtered totalling 0.00 hours\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-28 20:43:16 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 2021-11-28 20:43:17 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 = 0.6670245764733954\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WldSYG2xCTBF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58b024ff-f53a-46c4-f532-9fa166d30b46"
      },
      "source": [
        "import pytorch_lightning as pl\n",
        "trainer = pl.Trainer(gpus=1, max_epochs=50, precision=16)\n",
        "pretrained_asr = nemo_asr.models.EncDecCTCModel.restore_from(os.path.join(data_dir, \"QuartzNet15x5_golos.nemo\"), trainer=trainer)"
      ],
      "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",
            "[NeMo W 2021-11-28 20:48:59 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 2021-11-28 20:48:59 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 2021-11-28 20:48:59 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 2021-11-28 20:48:59 features:265] PADDING: 16\n",
            "[NeMo I 2021-11-28 20:48:59 features:282] STFT using torch\n",
            "[NeMo I 2021-11-28 20:49:00 save_restore_connector:149] Model EncDecCTCModel was successfully restored from /content/QuartzNet15x5_golos.nemo.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYvqQpQY_IH0"
      },
      "source": [
        "params_model = pretrained_asr._cfg\n",
        "params_model['train_ds']['manifest_filepath'] = train_manifest\n",
        "params_model['train_ds']['batch_size']=16\n",
        "params_model['train_ds']['parser']='base'\n",
        "params_model['validation_ds']['manifest_filepath'] = test_manifest\n",
        "params_model['validation_ds']['batch_size']=16\n",
        "params_model['validation_ds']['parser']='base'\n",
        "params_model['test_ds']['manifest_filepath'] = test_manifest\n",
        "params_model['test_ds']['batch_size']=16\n",
        "params_model['test_ds']['parser']='base'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m-RCNERNCeWD"
      },
      "source": [
        "params_model['optim']['lr'] = 0.0001"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jlfn1i5HEsE-"
      },
      "source": [
        "import copy\n",
        "new_opt = copy.deepcopy(params_model['optim'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EFEpG6BCEIg7",
        "outputId": "a01bb105-cefa-4d01-acb5-8310d73b0093"
      },
      "source": [
        "from omegaconf import DictConfig\n",
        "# Use the smaller learning rate we set before\n",
        "pretrained_asr.setup_optimization(optim_config=DictConfig(new_opt))\n",
        "\n",
        "# Point to the data we'll use for fine-tuning as the training set\n",
        "pretrained_asr.setup_training_data(train_data_config=params_model['train_ds'])\n",
        "\n",
        "# Point to the new validation data for fine-tuning\n",
        "pretrained_asr.setup_validation_data(val_data_config=params_model['validation_ds'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-28 20:49:11 modelPT:471] The lightning trainer received accelerator: <pytorch_lightning.accelerators.gpu.GPUAccelerator object at 0x7efc0dd233d0>. We recommend to use 'ddp' instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-28 20:49:11 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: 0.0001\n",
            "        weight_decay: 0.001\n",
            "    )\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-28 20:49:12 lr_scheduler:683] As `t_max_epochs` is provided/computed, it is required to pass the train dataloader in order\n",
            "    to compute effective maximum number of steps.\n",
            "    Scheduler will not be instantiated !\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-28 20:49:12 collections:173] Dataset loaded with 657 files totalling 0.54 hours\n",
            "[NeMo I 2021-11-28 20:49:12 collections:174] 0 files were filtered totalling 0.00 hours\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-28 20:49:12 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 2021-11-28 20:49:12 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-28 20:49:12 collections:174] 0 files were filtered totalling 0.00 hours\n"
          ]
        }
      ]
    },
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        },
        "id": "vZcpgsg8C5ch",
        "outputId": "24e5643f-ec7e-4ea5-fadd-07a17b2e987d"
      },
      "source": [
        "trainer.fit(pretrained_asr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "[NeMo W 2021-11-28 21:13:08 modelPT:471] The lightning trainer received accelerator: <pytorch_lightning.accelerators.gpu.GPUAccelerator object at 0x7efc0dd233d0>. We recommend to use 'ddp' instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-28 21:13:08 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: 0.0001\n",
            "        weight_decay: 0.001\n",
            "    )\n",
            "[NeMo I 2021-11-28 21:13:08 lr_scheduler:752] Scheduler \"<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7efc0dc672d0>\" \n",
            "    will be used during training (effective maximum steps = 2100) - \n",
            "    Parameters : \n",
            "    (warmup_steps: 500\n",
            "    warmup_ratio: null\n",
            "    min_lr: 0.0\n",
            "    last_epoch: -1\n",
            "    max_steps: 2100\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 2021-11-28 21:13:09 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/pytorch_lightning/callbacks/model_checkpoint.py:623: UserWarning: Checkpoint directory /content/lightning_logs/version_1/checkpoints exists and is not empty.\n",
            "      rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
            "    \n"
          ]
        },
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              "version_major": 2
            },
            "text/plain": [
              "Validation sanity check: 0it [00:00, ?it/s]"
            ]
          },
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-28 21:13:09 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 2021-11-28 21:13:11 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",
            "[NeMo W 2021-11-28 21:13:12 nemo_logging:349] /usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/data_loading.py:408: UserWarning: The number of training samples (42) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n",
            "      f\"The number of training samples ({self.num_training_batches}) is smaller than the logging interval\"\n",
            "    \n"
          ]
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        {
          "output_type": "display_data",
          "data": {
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "UIv_9D0IDAbw",
        "outputId": "af0df9bc-03a8-475f-dfb2-8aad4c34ebb4"
      },
      "source": [
        "# Bigger batch-size = bigger throughput\n",
        "# params['validation_ds']['batch_size'] = 8\n",
        "# Setup the test data loader and make sure the model is on GPU\n",
        "pretrained_asr.setup_test_data(test_data_config=params_model['test_ds'])\n",
        "pretrained_asr.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 pretrained_asr.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 = pretrained_asr(\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",
        "        pretrained_asr._wer.update(greedy_predictions, targets, targets_lengths)\n",
        "        _, wer_num, wer_denom = pretrained_asr._wer.compute()\n",
        "        pretrained_asr._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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-28 22:41:06 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-28 22:41:06 collections:174] 0 files were filtered totalling 0.00 hours\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-52-1fade1aa5deb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mtargets_lengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         log_probs, encoded_len, greedy_predictions = pretrained_asr(\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0minput_signal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_signal_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\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     23\u001b[0m         )\n\u001b[1;32m     24\u001b[0m         \u001b[0;31m# Notice the model has a helper object to compute WER\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\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[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\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/nemo/core/classes/common.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m    844\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m         \u001b[0;31m# Call the method - this can be forward, or any other callable method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 846\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m    847\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    848\u001b[0m         instance._attach_and_validate_output_types(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/models/ctc_models.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_signal, input_signal_length, processed_signal, processed_signal_length)\u001b[0m\n\u001b[1;32m    551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_processed_signal\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             processed_signal, processed_signal_length = self.preprocessor(\n\u001b[0;32m--> 553\u001b[0;31m                 \u001b[0minput_signal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_signal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlength\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_signal_length\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    554\u001b[0m             )\n\u001b[1;32m    555\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\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[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\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/nemo/core/classes/common.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, wrapped, instance, args, kwargs)\u001b[0m\n\u001b[1;32m    844\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    845\u001b[0m         \u001b[0;31m# Call the method - this can be forward, or any other callable method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 846\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwrapped\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m    847\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    848\u001b[0m         instance._attach_and_validate_output_types(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/autograd/grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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[1;32m     27\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\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---> 28\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/modules/audio_preprocessing.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_signal, length)\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\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     82\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_signal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlength\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---> 83\u001b[0;31m         \u001b[0mprocessed_signal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocessed_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_signal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlength\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     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     85\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mprocessed_signal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocessed_length\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/nemo/collections/asr/modules/audio_preprocessing.py\u001b[0m in \u001b[0;36mget_features\u001b[0;34m(self, input_signal, length)\u001b[0m\n\u001b[1;32m    269\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    270\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_signal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlength\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--> 271\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfeaturizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_signal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlength\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    272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    273\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m   1100\u001b[0m         if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m   1101\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\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[0m\n\u001b[0m\u001b[1;32m   1103\u001b[0m         \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1104\u001b[0m         \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\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/torch/autograd/grad_mode.py\u001b[0m in \u001b[0;36mdecorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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[1;32m     27\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\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---> 28\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorate_context\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/nemo/collections/asr/parts/preprocessing/features.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, seq_len)\u001b[0m\n\u001b[1;32m    403\u001b[0m         \u001b[0;31m# disable autocast to get full range of stft values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    404\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mamp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menabled\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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--> 405\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstft\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\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    406\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m         \u001b[0;31m# torch returns real, imag; so convert to magnitude\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/nemo/collections/asr/parts/preprocessing/features.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    300\u001b[0m                 \u001b[0mcenter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mexact_pad\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m                 \u001b[0mwindow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwindow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\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--> 302\u001b[0;31m                 \u001b[0mreturn_complex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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    303\u001b[0m             )\n\u001b[1;32m    304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/nemo/collections/common/parts/patch_utils.py\u001b[0m in \u001b[0;36mstft_patch\u001b[0;34m(input, n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided, return_complex)\u001b[0m\n\u001b[1;32m     75\u001b[0m             \u001b[0mnormalized\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnormalized\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0monesided\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0monesided\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m             \u001b[0mreturn_complex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreturn_complex\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     78\u001b[0m         )\n\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/functional.py\u001b[0m in \u001b[0;36mstft\u001b[0;34m(input, n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided, return_complex)\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0msignal_dim\u001b[0m\u001b[0;34m:\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    572\u001b[0m     return _VF.stft(input, n_fft, hop_length, win_length, window,  # type: ignore[attr-defined]\n\u001b[0;32m--> 573\u001b[0;31m                     normalized, onesided, return_complex)\n\u001b[0m\u001b[1;32m    574\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m def istft(input: Tensor, n_fft: int, hop_length: Optional[int] = None,\n",
            "\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 20.00 MiB (GPU 0; 11.17 GiB total capacity; 9.93 GiB already allocated; 15.81 MiB free; 10.54 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8prYIRjuKgfO"
      },
      "source": [
        "pretrained_asr.save_to('/content/drive/MyDrive/checkpoint.nemo')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_EgIjN5qLRN",
        "outputId": "a9fc8a3f-f75c-404c-e975-3bf1c5a0f70b"
      },
      "source": [
        "! ls /content/drive/MyDrive"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'Colab Notebooks'   train_crowd9.tar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zjKDtFIYqvQU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25792a16-e066-4b8d-dce9-7ff9afa6fedc"
      },
      "source": [
        "import pytorch_lightning as pl\n",
        "trainer = pl.Trainer(gpus=1, max_epochs=5, precision=16)\n",
        "pretrained_asr = nemo_asr.models.EncDecCTCModel.restore_from('/content/drive/MyDrive/farfield_ft4.nemo', trainer=trainer)"
      ],
      "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",
            "[NeMo W 2021-11-30 16:46:48 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: /content/train/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: 16\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: base\n",
            "    \n",
            "[NeMo W 2021-11-30 16:46:48 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: /content/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: 16\n",
            "    num_workers: 20\n",
            "    shuffle: false\n",
            "    parser: base\n",
            "    \n",
            "[NeMo W 2021-11-30 16:46:48 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: /content/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: 16\n",
            "    shuffle: false\n",
            "    parser: base\n",
            "    \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 16:46:48 features:265] PADDING: 16\n",
            "[NeMo I 2021-11-30 16:46:48 features:282] STFT using torch\n",
            "[NeMo I 2021-11-30 16:47:12 save_restore_connector:149] Model EncDecCTCModel was successfully restored from /content/drive/MyDrive/farfield_ft4.nemo.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hy7_Pf3ZYVzS"
      },
      "source": [
        "params_model = pretrained_asr._cfg"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P2gxv4tZ6Q1r"
      },
      "source": [
        "\n",
        "params_model['train_ds']['manifest_filepath'] = train_manifest_farfield \n",
        "params_model['train_ds']['batch_size']=16\n",
        "params_model['train_ds']['parser']='base'\n",
        "params_model['validation_ds']['manifest_filepath'] = test_manifest\n",
        "params_model['validation_ds']['batch_size']=16\n",
        "params_model['validation_ds']['parser']='base'\n",
        "params_model['test_ds']['manifest_filepath'] = test_manifest\n",
        "params_model['test_ds']['batch_size']=16\n",
        "params_model['test_ds']['parser']='base'"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "240mDSxmFTP6"
      },
      "source": [
        "params_model['optim']['lr'] = 0.00008"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "loe7SZieFWiD"
      },
      "source": [
        "import copy\n",
        "new_opt = copy.deepcopy(params_model['optim'])"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U3s8HdiIFZ8f",
        "outputId": "733cfd33-a028-42bb-e72a-7793a594103b"
      },
      "source": [
        "from omegaconf import DictConfig\n",
        "# Use the smaller learning rate we set before\n",
        "pretrained_asr.setup_optimization(optim_config=DictConfig(new_opt))\n",
        "\n",
        "# Point to the data we'll use for fine-tuning as the training set\n",
        "pretrained_asr.setup_training_data(train_data_config=params_model['train_ds'])\n",
        "\n",
        "# Point to the new validation data for fine-tuning\n",
        "pretrained_asr.setup_validation_data(val_data_config=params_model['validation_ds'])"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 18:12:41 modelPT:471] The lightning trainer received accelerator: <pytorch_lightning.accelerators.gpu.GPUAccelerator object at 0x7f80c856c950>. We recommend to use 'ddp' instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 18:12:41 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: 8e-05\n",
            "        weight_decay: 0.001\n",
            "    )\n",
            "[NeMo I 2021-11-30 18:12:41 lr_scheduler:752] Scheduler \"<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7f7f37fe1710>\" \n",
            "    will be used during training (effective maximum steps = 38750) - \n",
            "    Parameters : \n",
            "    (warmup_steps: 500\n",
            "    warmup_ratio: null\n",
            "    min_lr: 0.0\n",
            "    last_epoch: -1\n",
            "    max_steps: 38750\n",
            "    )\n",
            "[NeMo I 2021-11-30 18:12:45 collections:173] Dataset loaded with 123995 files totalling 132.43 hours\n",
            "[NeMo I 2021-11-30 18:12:45 collections:174] 8 files were filtered totalling 0.03 hours\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 18:12:45 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 18:12:45 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-30 18:12:45 collections:174] 0 files were filtered totalling 0.00 hours\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VJCEcCqb6VYY",
        "outputId": "c4419b62-2f5e-4e63-f2e3-7f6caec6bf04"
      },
      "source": [
        "# Bigger batch-size = bigger throughput\n",
        "# params['validation_ds']['batch_size'] = 8\n",
        "# Setup the test data loader and make sure the model is on GPU\n",
        "pretrained_asr.setup_test_data(test_data_config=params_model['test_ds'])\n",
        "pretrained_asr.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 pretrained_asr.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 = pretrained_asr(\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",
        "        pretrained_asr._wer.update(greedy_predictions, targets, targets_lengths)\n",
        "        _, wer_num, wer_denom = pretrained_asr._wer.compute()\n",
        "        pretrained_asr._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": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-29 09:43:58 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-29 09:43:58 collections:174] 0 files were filtered totalling 0.00 hours\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-29 09:43:58 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 = 0.6301598663803388\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 795,
          "referenced_widgets": [
            "17fb0dc9996744ba82a62d42424e66d7",
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            "1945aa44ba0f4aa2aac13453866d0626",
            "d36cc887b99d43488540cd898aa96731",
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            "ad7e19609ca943c182dff13ead376cf5",
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            "9ae5a46ef2a9409aacb4ed53a7a0a048",
            "bdeada375dfe48f9a74df403cfcd2faf",
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            "fbdf083ee4974d10bddd6e3f36fdc2ae",
            "55bdb4c16fe14e9ab5e0f925cc7ce0c2",
            "ed9e8b3c69f44a74bddbc6139c55f13e",
            "e9d14efd54a44ab1865272d706e9c3f3",
            "6e1f54dede2643a69c1d4b1373d8e33e",
            "e5371ea3f35b45c4883b29601803c1e5"
          ]
        },
        "id": "yDwc4FqZFGY6",
        "outputId": "b1eef80b-9f12-4041-8fe5-d648ac5993ea"
      },
      "source": [
        "trainer.fit(pretrained_asr)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "[NeMo W 2021-11-30 18:12:45 modelPT:471] The lightning trainer received accelerator: <pytorch_lightning.accelerators.gpu.GPUAccelerator object at 0x7f80c856c950>. We recommend to use 'ddp' instead.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 18:12:45 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: 8e-05\n",
            "        weight_decay: 0.001\n",
            "    )\n",
            "[NeMo I 2021-11-30 18:12:45 lr_scheduler:752] Scheduler \"<nemo.core.optim.lr_scheduler.CosineAnnealing object at 0x7f7f3ee3f250>\" \n",
            "    will be used during training (effective maximum steps = 38750) - \n",
            "    Parameters : \n",
            "    (warmup_steps: 500\n",
            "    warmup_ratio: null\n",
            "    min_lr: 0.0\n",
            "    last_epoch: -1\n",
            "    max_steps: 38750\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": "17fb0dc9996744ba82a62d42424e66d7",
              "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 18:12:47 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 18:12:48 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": "ad7e19609ca943c182dff13ead376cf5",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fYoXPfesHc_X",
        "outputId": "f7674eaa-4c20-4dae-c7fa-170c568e13af"
      },
      "source": [
        "params_model['test_ds']['manifest_filepath'] = test_manifest\n",
        "params_model['test_ds']['batch_size']=16\n",
        "params_model['test_ds']['parser']='base'\n",
        "# Bigger batch-size = bigger throughput\n",
        "# params['validation_ds']['batch_size'] = 8\n",
        "# Setup the test data loader and make sure the model is on GPU\n",
        "pretrained_asr.setup_test_data(test_data_config=params_model['test_ds'])\n",
        "pretrained_asr.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 pretrained_asr.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 = pretrained_asr(\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",
        "        pretrained_asr._wer.update(greedy_predictions, targets, targets_lengths)\n",
        "        _, wer_num, wer_denom = pretrained_asr._wer.compute()\n",
        "        pretrained_asr._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": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[NeMo I 2021-11-30 16:10:32 collections:173] Dataset loaded with 1916 files totalling 1.41 hours\n",
            "[NeMo I 2021-11-30 16:10:32 collections:174] 0 files were filtered totalling 0.00 hours\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[NeMo W 2021-11-30 16:10:32 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 = 0.5275590551181102\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F5Iv2EJHHdpM"
      },
      "source": [
        "pretrained_asr.save_to('/content/drive/MyDrive/farfield_ft2.nemo')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "89NgwjxNLeg3"
      },
      "source": [
        "WER before: 0.667\n",
        "\n",
        "current WER: 0.5276"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsoI-RcjYaag"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}