{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "YP3vVkqYUpLx"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AWly9SmkgSwE",
    "outputId": "8af190ed-5037-4e3b-b91b-b5286d8e0888"
   },
   "outputs": [],
   "source": [
    "!sudo apt-get install git-lfs tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OWGc_zfyq5_T",
    "outputId": "35ea3459-6f2d-449c-e717-74e7a27c41bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "Requirement already satisfied: datasets in /workspace/.local/lib/python3.8/site-packages (2.2.2)\n",
      "Requirement already satisfied: transformers in /opt/conda/lib/python3.8/site-packages (4.17.0.dev0)\n",
      "Requirement already satisfied: aiohttp in /opt/conda/lib/python3.8/site-packages (from datasets) (3.8.1)\n",
      "Requirement already satisfied: dill<0.3.5 in /opt/conda/lib/python3.8/site-packages (from datasets) (0.3.4)\n",
      "Requirement already satisfied: tqdm>=4.62.1 in /opt/conda/lib/python3.8/site-packages (from datasets) (4.62.3)\n",
      "Requirement already satisfied: xxhash in /opt/conda/lib/python3.8/site-packages (from datasets) (2.0.2)\n",
      "Requirement already satisfied: responses<0.19 in /opt/conda/lib/python3.8/site-packages (from datasets) (0.18.0)\n",
      "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.8/site-packages (from datasets) (1.19.2)\n",
      "Requirement already satisfied: huggingface-hub<1.0.0,>=0.1.0 in /opt/conda/lib/python3.8/site-packages (from datasets) (0.4.0)\n",
      "Requirement already satisfied: packaging in /opt/conda/lib/python3.8/site-packages (from datasets) (21.3)\n",
      "Requirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.8/site-packages (from datasets) (2.24.0)\n",
      "Requirement already satisfied: fsspec[http]>=2021.05.0 in /opt/conda/lib/python3.8/site-packages (from datasets) (2022.1.0)\n",
      "Requirement already satisfied: multiprocess in /opt/conda/lib/python3.8/site-packages (from datasets) (0.70.12.2)\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.8/site-packages (from datasets) (1.4.0)\n",
      "Requirement already satisfied: pyarrow>=6.0.0 in /opt/conda/lib/python3.8/site-packages (from datasets) (6.0.1)\n",
      "Requirement already satisfied: sacremoses in /opt/conda/lib/python3.8/site-packages (from transformers) (0.0.47)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.8/site-packages (from transformers) (3.0.12)\n",
      "Requirement already satisfied: tokenizers!=0.11.3,>=0.10.1 in /opt/conda/lib/python3.8/site-packages (from transformers) (0.11.4)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.8/site-packages (from transformers) (2022.1.18)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.8/site-packages (from transformers) (5.4.1)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.8/site-packages (from huggingface-hub<1.0.0,>=0.1.0->datasets) (4.0.1)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.8/site-packages (from packaging->datasets) (3.0.7)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (1.25.11)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests>=2.19.0->datasets) (2020.12.5)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (4.0.2)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (21.4.0)\n",
      "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (2.0.10)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (1.7.2)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (1.3.0)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (6.0.2)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.8/site-packages (from aiohttp->datasets) (1.2.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.8/site-packages (from pandas->datasets) (2021.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.8/site-packages (from pandas->datasets) (2.8.2)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from sacremoses->transformers) (1.15.0)\n",
      "Requirement already satisfied: joblib in /opt/conda/lib/python3.8/site-packages (from sacremoses->transformers) (1.1.0)\n",
      "Requirement already satisfied: click in /opt/conda/lib/python3.8/site-packages (from sacremoses->transformers) (8.0.3)\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.1.1 is available.\n",
      "You should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TvDJ7CYpzSJQ",
    "outputId": "f58b6c87-1c32-4aa6-9945-8fe3b1eb4a66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "Collecting https://github.com/kpu/kenlm/archive/master.zip\n",
      "  Using cached https://github.com/kpu/kenlm/archive/master.zip (542 kB)\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hRequirement already satisfied: pyctcdecode in /opt/conda/lib/python3.8/site-packages (0.3.0)\n",
      "Requirement already satisfied: numpy<2.0.0,>=1.15.0 in /opt/conda/lib/python3.8/site-packages (from pyctcdecode) (1.19.2)\n",
      "Requirement already satisfied: hypothesis<7,>=6.14 in /opt/conda/lib/python3.8/site-packages (from pyctcdecode) (6.46.9)\n",
      "Requirement already satisfied: pygtrie<3.0,>=2.1 in /opt/conda/lib/python3.8/site-packages (from pyctcdecode) (2.4.2)\n",
      "Requirement already satisfied: attrs>=19.2.0 in /opt/conda/lib/python3.8/site-packages (from hypothesis<7,>=6.14->pyctcdecode) (21.4.0)\n",
      "Requirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /opt/conda/lib/python3.8/site-packages (from hypothesis<7,>=6.14->pyctcdecode) (2.4.0)\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -atasets (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.1.1 is available.\n",
      "You should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 415,
     "referenced_widgets": [
      "edc436f12376423798af31da019eb50b",
      "0705b2e7a85c4b4aaebc6cc3494af44b",
      "8a978848e55a481a94a96b36c30a5076",
      "acafad3a99b0403486ceac05b768bbbc",
      "5e07b1951a8d4478b3e38f1e81020d92",
      "858ba1c848f24b8491d022b277914e84",
      "3d8e921c0e854bdebb17108b4cabc9f6",
      "4743028b12014476a634153931a26702",
      "ef61af4612034388ac9a97125375c2b2",
      "a45572c8a5714f358bdf6733e7754be3",
      "0ad2427a4ecc4636a10f295d9178d5c2",
      "7740c0f39b704684bf0c51a0f2f437af",
      "48ad04d04f6e4adaa33d635b577a8018",
      "fd3e054fad4047daae45266d480bcf6a",
      "ab27dee9582a4f3d9b4010a31558d5fb",
      "a2a21e4c9deb4a34bffd835a7cb3495b",
      "244473d30bdb4aa7a08a22e752763da6"
     ]
    },
    "id": "JHTeonOGXiGq",
    "outputId": "e4a93331-f896-4d7f-db9a-6f537ee9ad34"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb1fe87003eb4d6b936693d8dce9066e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center>\\n<img src=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "fsrpUSEBYH7g"
   },
   "outputs": [],
   "source": [
    "!git config --global credential.helper store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FKMMWfVQp_gP",
    "outputId": "9055c090-311f-4df9-bfc7-bd8bb3568e48"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "E: Could not open lock file /var/lib/dpkg/lock-frontend - open (13: Permission denied)\n",
      "E: Unable to acquire the dpkg frontend lock (/var/lib/dpkg/lock-frontend), are you root?\n"
     ]
    }
   ],
   "source": [
    "!sudo apt install build-essential cmake libboost-system-dev libboost-thread-dev libboost-program-options-dev libboost-test-dev libeigen3-dev zlib1g-dev libbz2-dev liblzma-dev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "J8mm4ExzqIaZ",
    "outputId": "bcadfcea-48dd-4fa2-9ac8-9d0cfe4b8668"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-05-26 13:39:11--  https://kheafield.com/code/kenlm.tar.gz\n",
      "Resolving kheafield.com (kheafield.com)... 35.196.63.85\n",
      "Connecting to kheafield.com (kheafield.com)|35.196.63.85|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 491888 (480K) [application/x-gzip]\n",
      "Saving to: ‘STDOUT’\n",
      "\n",
      "-                   100%[===================>] 480.36K   845KB/s    in 0.6s    \n",
      "\n",
      "2022-05-26 13:39:12 (845 KB/s) - written to stdout [491888/491888]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O - https://kheafield.com/code/kenlm.tar.gz | tar xz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MS4mqMyZqVAI",
    "outputId": "864b730f-b466-412d-9c0a-756baa86aed3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: cmake: command not found\n",
      "ls: cannot access 'kenlm/build/bin': No such file or directory\n"
     ]
    }
   ],
   "source": [
    "!mkdir kenlm/build && cd kenlm/build && cmake .. && make -j2\n",
    "!ls kenlm/build/bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 511,
     "referenced_widgets": [
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      "962d48469bf04e97b5af13ecdba2f0c8",
      "5f092f839d7e40c68c6252f338b99180",
      "da9f8456e08046758276c8f08b9866a5",
      "bca9be36ed354e31bd13c5d8220f8ce0",
      "d617e7d92ca547aab65ecb5178514377",
      "05d1ae830f75487b8da76df724172e8b",
      "af5bea550f644e57ae2261bc25d2166f"
     ]
    },
    "id": "VIgErMqApENm",
    "outputId": "a1ff4e21-4b06-48f0-be96-9464f732dbe5"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c57c8653f4214621b67e3587c228e22f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.16k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using custom data configuration hf-test--swedish_corpora_parliament_processed-93bdb791339208fb\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset europarl_bilingual/en-sv (download: 151.40 MiB, generated: 278.82 MiB, post-processed: Unknown size, total: 430.21 MiB) to /workspace/.cache/huggingface/datasets/hf-test___parquet/hf-test--swedish_corpora_parliament_processed-93bdb791339208fb/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3b81e8bc233451989b4c98cfa1d39c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a20b514305b241818b9f18c46a7bedce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/159M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f788a26e46214168871dba27890eb424",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset parquet downloaded and prepared to /workspace/.cache/huggingface/datasets/hf-test___parquet/hf-test--swedish_corpora_parliament_processed-93bdb791339208fb/0.0.0/0b6d5799bb726b24ad7fc7be720c170d8e497f575d02d47537de9a5bac074901. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/sv-SE/9.0.0/26f54721b57ee2f31a333b315ed9151fbd8e693a3983c295fef63c67a12b9bf7)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac607dbc6721410084c7ad043ca50895",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading builder script:   0%|          | 0.00/1.24k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "958bc9db5fa74b4e90d1a39f7c334bdc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading metadata:   0%|          | 0.00/630 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset swedish_reviews/plain_text (download: 11.29 MiB, generated: 30.04 MiB, post-processed: Unknown size, total: 41.33 MiB) to /workspace/.cache/huggingface/datasets/swedish_reviews/plain_text/1.0.0/b5ca53a6d7b4004000f565882a5294e9d7c9f4b341a2aa226bcf08b8762f68f5...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c82e7a667d8848a49e576f6d0e7013df",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/11.8M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test split:   0%|          | 0/20697 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating validation split:   0%|          | 0/20696 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/62089 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset swedish_reviews downloaded and prepared to /workspace/.cache/huggingface/datasets/swedish_reviews/plain_text/1.0.0/b5ca53a6d7b4004000f565882a5294e9d7c9f4b341a2aa226bcf08b8762f68f5. Subsequent calls will reuse this data.\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "from datasets import load_dataset\n",
    "\n",
    "username = \"hf-test\"  # change to your username\n",
    "target_lang = \"sv\"\n",
    "\n",
    "regex_chars_to_ignore = '[,?.!\\-\\;\\:\\\"“%‘”�—’…–]'\n",
    "\n",
    "dataset_1 = load_dataset(\"hf-test/sv_corpora_parliament_processed\", split=\"train\")\n",
    "dataset_2 = load_dataset(\"mozilla-foundation/common_voice_9_0\", \"sv-SE\", split=\"train+validation\", use_auth_token=True)\n",
    "dataset_3 = load_dataset(\"swedish_reviews\", split=\"train+validation+test\")\n",
    "\n",
    "def clean_text(text):\n",
    "  return re.sub(regex_chars_to_ignore, \"\", text.lower()) \\\n",
    "      .replace(\"\\\\\\\\punkt\", \"\") \\\n",
    "      .replace(\"\\\\\\\\komma\", \"\") \\\n",
    "      .replace(\"è\", \"e\") \\\n",
    "      .replace(\"é\", \"e\") \\\n",
    "      .replace(\"î\", \"i\") \\\n",
    "      .replace(\"ü\", \"u\") \\\n",
    "      .replace(\"ÿ\", \"y\") \\\n",
    "      .replace(\"ô\", \"o\") \\\n",
    "      .replace(\"\\\\\", \"\") \\\n",
    "      .replace(\"/\", \"\") \\\n",
    "      .replace(\"|\", \"\")\n",
    "\n",
    "dataset_1_text_clean = clean_text(\" \".join(dataset_1[\"text\"]))\n",
    "dataset_2_text_clean =  clean_text(\" \".join(dataset_2[\"sentence\"]))\n",
    "dataset_3_text_clean =  clean_text(\" \".join(dataset_3[\"text\"]))\n",
    "\n",
    "with open(\"text.txt\", \"w\") as file:\n",
    "  file.write(dataset_1_text_clean + \" \" + dataset_2_text_clean + \" \" + dataset_3_text_clean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "_MdDNBlZrPOm",
    "outputId": "08cf7acc-8c73-43a0-9c9a-c0b355e4a217"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/bin/bash: kenlm/build/bin/lmplz: No such file or directory\n"
     ]
    }
   ],
   "source": [
    "!kenlm/build/bin/lmplz -o 5 <\"text.txt\" > \"5gram.arpa\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TRnV8Miusl--",
    "outputId": "ae772021-8cf5-43ba-fb8b-471cafd1a3d8"
   },
   "outputs": [],
   "source": [
    "!head -20 5gram.arpa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "_7u7dVPkvyRZ"
   },
   "outputs": [],
   "source": [
    "with open(\"5gram.arpa\", \"r\") as read_file, open(\"5gram_correct.arpa\", \"w\") as write_file:\n",
    "  has_added_eos = False\n",
    "  for line in read_file:\n",
    "    if not has_added_eos and \"ngram 1=\" in line:\n",
    "      count=line.strip().split(\"=\")[-1]\n",
    "      write_file.write(line.replace(f\"{count}\", f\"{int(count)+1}\"))\n",
    "    elif not has_added_eos and \"<s>\" in line:\n",
    "      write_file.write(line)\n",
    "      write_file.write(line.replace(\"<s>\", \"</s>\"))\n",
    "      has_added_eos = True\n",
    "    else:\n",
    "      write_file.write(line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YF1RSm-Pxst5",
    "outputId": "45046169-0a2e-4170-82b5-ad96a7876d75"
   },
   "outputs": [],
   "source": [
    "!head -20 5gram_correct.arpa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 209,
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     ]
    },
    "id": "paV71gdAtkDC",
    "outputId": "c2df6859-db57-4d4a-92b0-41b54a4215bf"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoProcessor\n",
    "\n",
    "processor = AutoProcessor.from_pretrained(\"marinone94/xls-r-300m-sv-robust\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 162,
     "referenced_widgets": [
      "324a5803fe4f4277854ff940667ac33b",
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      "15a403c93bb44b8e8317af79f0c4c99f",
      "2a5b956c8c674662ac45f8a607a8c0db",
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      "342381cde8c440e28a8b02b5f8b784da",
      "405bcd0971094e0583429fee5a322f93",
      "dd3b325ae4fc49b9bcf50bd42a4fc6b1",
      "c0cae41fed0149f291247e5d4eed81f3",
      "1a02cd59d48e454da6b9d1f45afd465f",
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      "bb06252fb0d94914846a916c3461bd30",
      "3370865a6d694b83be4daa58d08354a3",
      "66a047337e784857bb16ead35166b862",
      "e551f313a97a4f178614393a70e1a400",
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      "659492fc13fe48cc855da71f3b9d8c9b",
      "0a32666f4c064f13b94b4031e0764e9f",
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      "5d13614c4f6649cf9c6295adbf21e159",
      "4f346a7d210e4794b523970b7decacd9",
      "fd5805d3dcb849cd9ed754ceb39ce3b4",
      "80245c57576545798077341a48fc5795",
      "4de995f6df1e44a6987fb628cbcfb54b"
     ]
    },
    "id": "dEPO1dwacf_3",
    "outputId": "8c5463a3-31e5-4ba8-ee20-dba1e79b0f05"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspace/xls-r-300m-sv-robust/xls-r-300m-sv-robust is already a clone of https://huggingface.co/marinone94/xls-r-300m-sv-robust. Make sure you pull the latest changes with `repo.git_pull()`.\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import Repository\n",
    "\n",
    "repo = Repository(local_dir=\"xls-r-300m-sv-robust\", clone_from=\"marinone94/xls-r-300m-sv-robust\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "ZKwKxMoitoGS"
   },
   "outputs": [],
   "source": [
    "vocab_dict = processor.tokenizer.get_vocab()\n",
    "sorted_vocab_dict = {k.lower(): v for k, v in sorted(vocab_dict.items(), key=lambda item: item[1])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zTLzCLB2tQP7",
    "outputId": "263ceecc-fc94-44d7-e1e0-3680a14691ad"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading the LM will be faster if you build a binary file.\n",
      "Reading /workspace/xls-r-300m-sv-robust/5gram_correct.arpa\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "Cannot read model '5gram_correct.arpa' (End of file Byte: 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "File \u001b[0;32mkenlm.pyx:139\u001b[0m, in \u001b[0;36mkenlm.Model.__init__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: End of file Byte: 0",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "Input \u001b[0;32mIn [20]\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpyctcdecode\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m build_ctcdecoder\n\u001b[0;32m----> 3\u001b[0m decoder \u001b[38;5;241m=\u001b[39m \u001b[43mbuild_ctcdecoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msorted_vocab_dict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkeys\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkenlm_model_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m5gram_correct.arpa\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43malpha\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbeta\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1.5\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.8/site-packages/pyctcdecode/decoder.py:790\u001b[0m, in \u001b[0;36mbuild_ctcdecoder\u001b[0;34m(labels, kenlm_model_path, unigrams, alpha, beta, unk_score_offset, lm_score_boundary)\u001b[0m\n\u001b[1;32m    767\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbuild_ctcdecoder\u001b[39m(\n\u001b[1;32m    768\u001b[0m     labels: List[\u001b[38;5;28mstr\u001b[39m],\n\u001b[1;32m    769\u001b[0m     kenlm_model_path: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    774\u001b[0m     lm_score_boundary: \u001b[38;5;28mbool\u001b[39m \u001b[38;5;241m=\u001b[39m DEFAULT_SCORE_LM_BOUNDARY,\n\u001b[1;32m    775\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m BeamSearchDecoderCTC:\n\u001b[1;32m    776\u001b[0m     \u001b[38;5;124;03m\"\"\"Build a BeamSearchDecoderCTC instance with main functionality.\u001b[39;00m\n\u001b[1;32m    777\u001b[0m \n\u001b[1;32m    778\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    788\u001b[0m \u001b[38;5;124;03m        instance of BeamSearchDecoderCTC\u001b[39;00m\n\u001b[1;32m    789\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 790\u001b[0m     kenlm_model \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m kenlm_model_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[43mkenlm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mModel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkenlm_model_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    791\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m kenlm_model_path \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m kenlm_model_path\u001b[38;5;241m.\u001b[39mendswith(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.arpa\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    792\u001b[0m         logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing arpa instead of binary LM file, decoder instantiation might be slow.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32mkenlm.pyx:142\u001b[0m, in \u001b[0;36mkenlm.Model.__init__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: Cannot read model '5gram_correct.arpa' (End of file Byte: 0)"
     ]
    }
   ],
   "source": [
    "from pyctcdecode import build_ctcdecoder\n",
    "\n",
    "decoder = build_ctcdecoder(\n",
    "    labels=list(sorted_vocab_dict.keys()),\n",
    "    kenlm_model_path=\"5gram_correct.arpa\",\n",
    "    alpha=0.5,\n",
    "    beta=1.5,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "VBVf50EzZgAQ"
   },
   "outputs": [],
   "source": [
    "from transformers import Wav2Vec2ProcessorWithLM\n",
    "\n",
    "processor_with_lm = Wav2Vec2ProcessorWithLM(\n",
    "    feature_extractor=processor.feature_extractor,\n",
    "    tokenizer=processor.tokenizer,\n",
    "    decoder=decoder\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "UZ1sWfPH2oce"
   },
   "outputs": [],
   "source": [
    "lm_dir = \"xls-r-300m-sv-robust/language_model\"\n",
    "if os.path.exists(lm_dir):\n",
    "  shutil.rmtree(lm_dir)\n",
    "processor_with_lm.save_pretrained(\"xls-r-300m-sv-robust\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ClyENOYFcC_C",
    "outputId": "f70b816e-f308-4630-8863-d7c72f9ee667"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xls-r-300m-sv-robust/\n",
      "├── [  23]  added_tokens.json\n",
      "├── [ 404]  all_results.json\n",
      "├── [ 223]  alphabet.json\n",
      "├── [2.2K]  config.json\n",
      "├── [ 38K]  eda.ipynb\n",
      "├── [4.6K]  eval.py\n",
      "├── [ 228]  eval_results.json\n",
      "├── [ 38K]  join_datasets_asr_ctc.py\n",
      "├── [1.2K]  join_datasets_asr_ctc_run.sh\n",
      "├── [4.0K]  language_model\n",
      "│   ├── [4.5G]  5gram_correct.arpa\n",
      "│   ├── [  79]  attrs.json\n",
      "│   └── [5.8M]  unigrams.txt\n",
      "├── [221K]  log_mozilla-foundation_common_voice_8_0_sv-SE_test_predictions.txt\n",
      "├── [222K]  log_mozilla-foundation_common_voice_8_0_sv-SE_test_targets.txt\n",
      "├── [ 12K]  log_speech-recognition-community-v2_dev_data_sv_validation_predictions.txt\n",
      "├── [ 12K]  log_speech-recognition-community-v2_dev_data_sv_validation_targets.txt\n",
      "├── [  50]  mozilla-foundation_common_voice_8_0_sv-SE_test_eval_results.txt\n",
      "├── [ 285]  prepare_dataset_lm.py\n",
      "├── [ 260]  preprocessor_config.json\n",
      "├── [1.2G]  pytorch_model.bin\n",
      "├── [3.1K]  README.md\n",
      "├── [1.6K]  README_TEMPLATE.md\n",
      "├── [  74]  requirements.txt\n",
      "├── [ 207]  run_eval_cv.sh\n",
      "├── [ 217]  run_eval_real_world.sh\n",
      "├── [1.1K]  run.sh\n",
      "├── [ 38K]  run_speech_recognition_ctc.py\n",
      "├── [5.2K]  special_tokens_map.json\n",
      "├── [  48]  speech-recognition-community-v2_dev_data_sv_validation_eval_results.txt\n",
      "├── [ 335]  tokenizer_config.json\n",
      "├── [ 27K]  trainer_state.json\n",
      "├── [3.0K]  training_args.bin\n",
      "├── [7.4K]  train_n_gram_lm_with_KenLM.ipynb\n",
      "├── [ 198]  train_results.json\n",
      "└── [ 289]  vocab.json\n",
      "\n",
      "1 directory, 35 files\n"
     ]
    }
   ],
   "source": [
    "!tree -h xls-r-300m-sv-robust/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X9qg4FPt2zi8",
    "outputId": "2f6e6de5-e47b-4e4f-bdaa-e57ae5b04368"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading xls-r-300m-sv-robust/language_model/5gram_correct.arpa\n",
      "----5---10---15---20---25---30---35---40---45---50---55---60---65---70---75---80---85---90---95--100\n",
      "****************************************************************************************************\n",
      "SUCCESS\n"
     ]
    }
   ],
   "source": [
    "!kenlm/build/bin/build_binary xls-r-300m-sv-robust/language_model/5gram_correct.arpa xls-r-300m-sv-robust/language_model/5gram.bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Zn4J-4OZdMPc",
    "outputId": "e983f1ac-08ed-4989-e7c7-6e084d6b118a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "xls-r-300m-sv-robust/\n",
      "├── [  23]  added_tokens.json\n",
      "├── [ 404]  all_results.json\n",
      "├── [ 223]  alphabet.json\n",
      "├── [2.2K]  config.json\n",
      "├── [ 38K]  eda.ipynb\n",
      "├── [4.6K]  eval.py\n",
      "├── [ 228]  eval_results.json\n",
      "├── [ 38K]  join_datasets_asr_ctc.py\n",
      "├── [1.2K]  join_datasets_asr_ctc_run.sh\n",
      "├── [4.0K]  language_model\n",
      "│   ├── [2.0G]  5gram.bin\n",
      "│   ├── [  79]  attrs.json\n",
      "│   └── [5.8M]  unigrams.txt\n",
      "├── [221K]  log_mozilla-foundation_common_voice_8_0_sv-SE_test_predictions.txt\n",
      "├── [222K]  log_mozilla-foundation_common_voice_8_0_sv-SE_test_targets.txt\n",
      "├── [ 12K]  log_speech-recognition-community-v2_dev_data_sv_validation_predictions.txt\n",
      "├── [ 12K]  log_speech-recognition-community-v2_dev_data_sv_validation_targets.txt\n",
      "├── [  50]  mozilla-foundation_common_voice_8_0_sv-SE_test_eval_results.txt\n",
      "├── [ 285]  prepare_dataset_lm.py\n",
      "├── [ 260]  preprocessor_config.json\n",
      "├── [1.2G]  pytorch_model.bin\n",
      "├── [3.1K]  README.md\n",
      "├── [1.6K]  README_TEMPLATE.md\n",
      "├── [  74]  requirements.txt\n",
      "├── [ 207]  run_eval_cv.sh\n",
      "├── [ 217]  run_eval_real_world.sh\n",
      "├── [1.1K]  run.sh\n",
      "├── [ 38K]  run_speech_recognition_ctc.py\n",
      "├── [5.2K]  special_tokens_map.json\n",
      "├── [  48]  speech-recognition-community-v2_dev_data_sv_validation_eval_results.txt\n",
      "├── [ 335]  tokenizer_config.json\n",
      "├── [ 27K]  trainer_state.json\n",
      "├── [3.0K]  training_args.bin\n",
      "├── [7.4K]  train_n_gram_lm_with_KenLM.ipynb\n",
      "├── [ 198]  train_results.json\n",
      "└── [ 289]  vocab.json\n",
      "\n",
      "1 directory, 35 files\n"
     ]
    }
   ],
   "source": [
    "!rm xls-r-300m-sv-robust/language_model/5gram_correct.arpa && tree -h xls-r-300m-sv-robust/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 87
    },
    "id": "WEV1sx6ee3aT",
    "outputId": "b0e8e2e6-ad6c-44b6-bb6a-efb4a3fa1dcb"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "To https://huggingface.co/marinone94/xls-r-300m-sv-robust\n",
      "   fc97a76..7f44855  main -> main\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'https://huggingface.co/marinone94/xls-r-300m-sv-robust/commit/7f448551468d2896a8605cc133b844d92149b0d9'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!git config --global user.email \"marinone94@gmail.com\"\n",
    "repo.push_to_hub(commit_message=\"Upload 5-gram lm-boosted decoder\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "cHv8hehT1wEM",
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      "On branch main\n",
      "Your branch is up to date with 'origin/main'.\n",
      "\n",
      "nothing to commit, working tree clean\n"
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