{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "9MRhjbpm6IK2"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!pip install datasets --upgrade --user\n",
    "!pip install transformers --upgrade --user\n",
    "#!pip install torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n",
    "!pip install librosa\n",
    "!pip install jiwer\n",
    "#!pip install kaggle\n",
    "!pip install huggingface_hub==0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "wwJJ57AI6IK3",
    "outputId": "425ea36a-e43a-485d-bf75-ffedb422ac6f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.17.0.dev0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import transformers\n",
    "transformers.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "RimCN6er6IK3",
    "outputId": "76ed5db9-1e1f-4419-a688-898fe28277b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.10.2+cu102'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torchaudio\n",
    "torchaudio.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fZ7KBQ2H6IK3",
    "outputId": "5ff14194-90b3-47b9-fb21-830940d436c3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.18.3\n"
     ]
    }
   ],
   "source": [
    "import datasets\n",
    "from datasets import load_dataset, load_metric, Audio\n",
    "datasets.set_caching_enabled(False)\n",
    "print(datasets.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "aE6HSloZ6IK4"
   },
   "outputs": [],
   "source": [
    "# Environment settings: \n",
    "import pandas as pd\n",
    "pd.set_option('display.max_column', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_seq_items', None)\n",
    "pd.set_option('display.max_colwidth', 500)\n",
    "pd.set_option('expand_frame_repr', True)\n",
    "\n",
    "from datasets import concatenate_datasets, load_dataset, Audio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 239,
     "referenced_widgets": [
      "788cdcbedc8a4991a3b534419a0ccd6a",
      "7bab74822a7b44d8ada58e748d77fb2d",
      "4d2580b0ba464e0b84da40f315eeda49",
      "a89d970858d64e8f83877877297cac95",
      "1e7bd26d7af84d579ce27b134b5c0ad4",
      "3b0c46e5462a42f6bc6165a42d2764ae",
      "9d95fb669a084746a1d0a2d1501592d4",
      "392df3b560104792b77c93a6eedf803b",
      "89227add654e4d519c7b3064765cb629",
      "9d2413cd1db84267becfa867a1a38e60",
      "c6462f9ceb234ec5a0704d6a134060a7",
      "6190087ca74b412e84aa4e2412430e4a",
      "3361dea70d5644fe898a3f95e6ad02d0",
      "f8b4af22c7ca446998791962c80171c4",
      "0c07d443d4fa4843bb1a70f28a29fa08",
      "afa4f545b4974675ba5ac137a10b6adb"
     ]
    },
    "id": "yiK2-jAZ6IK4",
    "outputId": "6b901e8f-37b4-45a9-d27e-3691cd868f3a"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e104ff0fceb249b7b91b384bb2655a18",
       "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": 6,
   "metadata": {
    "id": "PyTHH7ML6IK5"
   },
   "outputs": [],
   "source": [
    "!git config --global credential.helper store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "gd1Qta2j6IK5"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!apt install git-lfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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      "473f93fbb2b84d1885edf7d24de4ccdd",
      "4f80dd77268c46e5ab7c3f1e802a0bd6",
      "36dc65cafc3f48c084af3c84ef61f772",
      "303edca2db32467e92f0b611d01705ed",
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      "76fb3ff4fbcb42c9a8a236e12267c5c2",
      "257c5791a156488ab7d7ba06310327fe",
      "24df39263d1548149d150439d1c76d1f",
      "0d9202a504bb46a09639b8926f11ab8f",
      "876aa02a4ad34a218df5fe7f09fbdf91"
     ]
    },
    "id": "fvU2Vllp6IK5",
    "outputId": "f37a379e-a909-455b-deec-1881647f9314"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset common_voice/ru to /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/ru/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2dde94b53704ef2af6007909106f145",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/4.97G [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": [
       "0 examples [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": [
       "0 examples [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": [
       "0 examples [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": [
       "0 examples [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": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset common_voice downloaded and prepared to /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/ru/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8. Subsequent calls will reuse this data.\n"
     ]
    }
   ],
   "source": [
    "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"ru\", split=\"train+validation+other\", use_auth_token=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iE32WLNn6IK6",
    "outputId": "39a8e725-645e-409b-9f15-e18659bd3226"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/ru/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)\n"
     ]
    }
   ],
   "source": [
    "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"ru\", split=\"test\", use_auth_token=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4CfD6ByJ6IK6",
    "outputId": "a8ef9cec-9940-4c2e-fe09-138f39add8e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],\n",
       "    num_rows: 46329\n",
       "})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_voice_train[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8wTWTsEY6IK6",
    "outputId": "09c05a29-235e-4451-b9d7-23fc5d7cb9fa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'client_id': 'c397ae16cd3952894b97d7df8935f75221977cddae67a8f69c54bc51b8b6ffd85aac58a7c6b1b8ef1d29e61a7ca7fe92c9d8a28ef1d2ecc080aa006f59680aa3',\n",
       " 'path': 'cv-corpus-8.0-2022-01-19/ru/clips/common_voice_ru_18849869.mp3',\n",
       " 'audio': {'path': 'cv-corpus-8.0-2022-01-19/ru/clips/common_voice_ru_18849869.mp3',\n",
       "  'array': array([0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.3053417e-05,\n",
       "         5.9902668e-05, 8.5949898e-05], dtype=float32),\n",
       "  'sampling_rate': 48000},\n",
       " 'sentence': 'Я беру маленький кусочек бумажки.',\n",
       " 'up_votes': 2,\n",
       " 'down_votes': 0,\n",
       " 'age': 'twenties',\n",
       " 'gender': 'male',\n",
       " 'accent': '',\n",
       " 'locale': 'ru',\n",
       " 'segment': ''}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_voice_test[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "a9Xj0hKk6IK6"
   },
   "outputs": [],
   "source": [
    "common_voice_train = common_voice_train.remove_columns(['client_id','up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'])\n",
    "common_voice_test = common_voice_test.remove_columns(['client_id','up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "NYrXnTcW6IK7"
   },
   "outputs": [],
   "source": [
    "from datasets import ClassLabel\n",
    "import random\n",
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "def show_random_elements(dataset, num_examples=10):\n",
    "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
    "    picks = []\n",
    "    for _ in range(num_examples):\n",
    "        pick = random.randint(0, len(dataset)-1)\n",
    "        while pick in picks:\n",
    "            pick = random.randint(0, len(dataset)-1)\n",
    "        picks.append(pick)\n",
    "    \n",
    "    df = pd.DataFrame(dataset[picks])\n",
    "    display(HTML(df.to_html()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "5OHAiP236IK7",
    "outputId": "76ca5027-7ba8-44d5-99fd-b5d2fc5896e1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Позвольте мне добавить несколько слов в своем национальном качестве.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>«Но подумай хорошенько, – прибавила она, – со стороны твоих родных не будет ли препятствия?»</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Надеюсь, вы, в отличие от него, не злоупотребили радушием моей жены в своих целях.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Одновременно угрозы безопасности в информационно-компьютерной области создают серьезный вызов международному сообществу.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Слово имеет посол Швейцарии Фазель.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Давайте же мы единодушно потребуем, чтобы он прекратил свои злодеяния.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Только начав, мы можем определить, как далеко мы можем продвинуться.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Поэтому мы не обязаны выполнять ее решения.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>И как я полагаю, этот запрос должен оставаться на рассмотрении в рамках председательской шестерки.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Обедать, барышни!</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "NdLHxhRd6IK7"
   },
   "outputs": [],
   "source": [
    "import re\n",
    "CHARS_TO_IGNORE = [\",\", \"?\", \"¿\", \".\", \"!\", \"¡\", \";\", \"；\", \":\", '\"\"', \"%\", '\"', \"�\", \"ʿ\", \"·\", \"჻\", \"~\", \"՞\",\n",
    "                   \"؟\", \"،\", \"।\", \"॥\", \"«\", \"»\", \"„\", \"“\", \"”\", \"「\", \"」\", \"‘\", \"’\", \"《\", \"》\", \"(\", \")\", \"[\", \"]\",\n",
    "                   \"{\", \"}\", \"=\", \"`\", \"_\", \"+\", \"<\", \">\", \"…\", \"–\", \"°\", \"´\", \"ʾ\", \"‹\", \"›\", \"©\", \"®\", \"—\", \"→\", \"。\",\n",
    "                   \"、\", \"﹂\", \"﹁\", \"‧\", \"～\", \"﹏\", \"，\", \"｛\", \"｝\", \"（\", \"）\", \"［\", \"］\", \"【\", \"】\", \"‥\", \"〽\",\n",
    "                   \"『\", \"』\", \"〝\", \"〟\", \"⟨\", \"⟩\", \"〜\", \"：\", \"！\", \"？\", \"♪\", \"؛\", \"/\", \"\\\\\", \"º\", \"−\", \"^\", \"ʻ\", \"ˆ\"]\n",
    "\n",
    "\n",
    "chars_to_remove_regex = f\"[{re.escape(''.join(CHARS_TO_IGNORE))}]\"\n",
    "\n",
    "def remove_special_characters(batch):\n",
    "    batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n",
    "    batch[\"sentence\"] = re.sub('[-]', ' ', batch[\"sentence\"]).lower()\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "e3f4dd7cb7814691b99a98e00e42b413",
      "07356cbb5c644aed8eb9de437e8e4691",
      "24f1bfd43ca34ef9933d19e2b2ee93d6",
      "d0ea9b81b1254e00a3f407d4632ebe50",
      "92089a2dfb794fbe8a6a4b553f281d85",
      "add1cdf7f5904571ba09b696819e6724",
      "b083339d05a645718b9c697c0d3ce2a0",
      "a5ec641870f44cbe9d5c4f2a6caf9e8b",
      "8e3c6d7df70d47c5bf1ccfc5ce6b490c",
      "699c71d9472b4a64a993e19bbe6dd735",
      "2aaa74b624de44a78a55cea202430754",
      "37c68e11a6d748018e1478f583a03051",
      "990fd8d131e640e6ba0f6ed3613cfc8b",
      "156189bcb95b4a2893d58e4dae6335d7",
      "a4c60066b2ce4c78acb09269cf3e2894",
      "515e18bdede14b8f86c3d2b41e4cef00",
      "b8af84200dd44930970a304cc8fbeb8c",
      "b269c11828d8431198f55a0f1825a58c",
      "ba794f1f60eb4bd99c5a22a20acce667",
      "60493e52db344e60afc883249d71c170",
      "17e6b65ba76f4b2eb83148730734bddf",
      "430b2d8471f74285b910f96e44f62924"
     ]
    },
    "id": "q7YFJgON6IK7",
    "outputId": "f9c0c89c-08bb-4e06-e29d-40cf7da492c3"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "812eae255443470aa0440b7fb100656a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0ex [00:00, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "03f9d0c65a5c44fdaecda41030ab8c6f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0ex [00:00, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "common_voice_train = common_voice_train.map(remove_special_characters)\n",
    "common_voice_test = common_voice_test.map(remove_special_characters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "NkGbORLu6IK8"
   },
   "outputs": [],
   "source": [
    "def extract_all_chars(batch):\n",
    "  all_text = \" \".join(batch[\"sentence\"])\n",
    "  vocab = list(set(all_text))\n",
    "  return {\"vocab\": [vocab], \"all_text\": [all_text]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "6825d36977104f0facdc9216b62ffeb7",
      "85b98111a8f4486d865d5e9213f28ebf",
      "0f745c9c99b44616a7dc0b41d63d800c",
      "ed001b4d74c24bda8a75e05f1c4cebff",
      "c854815e32334e88b18b3fc380b9dd01",
      "c8c624a55eeb4398aeb78978f1534073",
      "3da34632f24e40f18fc0d78691e58ba6",
      "d3a824f5974941beb35aa9d718db3476",
      "1cb79ffb0385495ba396e69add87a5fc",
      "15ed138efd55476da386e8f5ed48d583",
      "a1dfee9c96ab446cb1807d6dafadfab5",
      "a12d06dcf0a64868a18634f1f6b6bb54",
      "79a24cbc91f64305bc63e438f3b6be2a",
      "d378de92cbe24153b561cb416219d3ff",
      "51ae1caebc9a40b99e3889f79c01db9a",
      "ad09e4d8e90c4537a592a51f55af3858",
      "313a4ec6c0c247279c0520aaf1536f47",
      "cb22cfc078494aebab308a9daef8729b",
      "f9ae0de86e4244dd88d97fe64df30d3c",
      "3fbdad25464044029ebddaa7c4503cb6",
      "f63362b3cbad406891125013c655ab92",
      "954014a8b8a84c80a365609dfc2181b7"
     ]
    },
    "id": "Ju5Q4mA-6IK8",
    "outputId": "80cdeca0-05ed-414d-f779-78e05671cbc4"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d58fa712ef794540b45a3d18ed93acb9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "84af2cdf2384419780bba52c6ba7bf08",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n",
    "vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "gsolpWsJ6IK8"
   },
   "outputs": [],
   "source": [
    "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0q1NTOFJ6IK8",
    "outputId": "60753b19-43a0-49e5-b370-82fa1c573952"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "46329\n",
      "9419\n"
     ]
    }
   ],
   "source": [
    "print(len(common_voice_train))\n",
    "print(len(common_voice_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Inc3TW7j6IK8",
    "outputId": "9ea274ae-0d4a-48aa-d883-2f3b245aab57"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(vocab_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SPiQbdO66IK8",
    "outputId": "8eac4eab-33b5-4469-f28b-1413ef728054"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['f',\n",
       " 'n',\n",
       " 'e',\n",
       " 'й',\n",
       " 'g',\n",
       " 'х',\n",
       " 'г',\n",
       " 'ч',\n",
       " 'р',\n",
       " 'ы',\n",
       " 'ё',\n",
       " 'c',\n",
       " 'm',\n",
       " 'l',\n",
       " 'x',\n",
       " 'ш',\n",
       " '‑',\n",
       " 'а',\n",
       " 'k',\n",
       " 'я',\n",
       " 'r',\n",
       " 'i',\n",
       " 'б',\n",
       " 'е',\n",
       " 'ц',\n",
       " 'h',\n",
       " 'z',\n",
       " 'ф',\n",
       " 'к',\n",
       " \"'\",\n",
       " 'a',\n",
       " 't',\n",
       " 'э',\n",
       " 'з',\n",
       " 'у',\n",
       " 'л',\n",
       " 'ю',\n",
       " ' ',\n",
       " 'т',\n",
       " 'ь',\n",
       " 'д',\n",
       " 'o',\n",
       " 'п',\n",
       " 's',\n",
       " 'ъ',\n",
       " 'и',\n",
       " 'в',\n",
       " 'щ',\n",
       " 'ж',\n",
       " 'о',\n",
       " 'н',\n",
       " 'м',\n",
       " 'p',\n",
       " 'b',\n",
       " 'с']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "eDCcZFFI6IK9"
   },
   "outputs": [],
   "source": [
    "vocab_dict = {v: k for k, v in enumerate(sorted(vocab_list))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "wjywVk9I6IK9"
   },
   "outputs": [],
   "source": [
    "vocab_dict[\"|\"] = vocab_dict[\" \"]\n",
    "del vocab_dict[\" \"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tgmet03p6IK9",
    "outputId": "b95dddea-49db-41f7-f710-9452bf897495"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{\"'\": 1,\n",
       " 'a': 2,\n",
       " 'b': 3,\n",
       " 'c': 4,\n",
       " 'e': 5,\n",
       " 'f': 6,\n",
       " 'g': 7,\n",
       " 'h': 8,\n",
       " 'i': 9,\n",
       " 'k': 10,\n",
       " 'l': 11,\n",
       " 'm': 12,\n",
       " 'n': 13,\n",
       " 'o': 14,\n",
       " 'p': 15,\n",
       " 'r': 16,\n",
       " 's': 17,\n",
       " 't': 18,\n",
       " 'x': 19,\n",
       " 'z': 20,\n",
       " 'а': 21,\n",
       " 'б': 22,\n",
       " 'в': 23,\n",
       " 'г': 24,\n",
       " 'д': 25,\n",
       " 'е': 26,\n",
       " 'ж': 27,\n",
       " 'з': 28,\n",
       " 'и': 29,\n",
       " 'й': 30,\n",
       " 'к': 31,\n",
       " 'л': 32,\n",
       " 'м': 33,\n",
       " 'н': 34,\n",
       " 'о': 35,\n",
       " 'п': 36,\n",
       " 'р': 37,\n",
       " 'с': 38,\n",
       " 'т': 39,\n",
       " 'у': 40,\n",
       " 'ф': 41,\n",
       " 'х': 42,\n",
       " 'ц': 43,\n",
       " 'ч': 44,\n",
       " 'ш': 45,\n",
       " 'щ': 46,\n",
       " 'ъ': 47,\n",
       " 'ы': 48,\n",
       " 'ь': 49,\n",
       " 'э': 50,\n",
       " 'ю': 51,\n",
       " 'я': 52,\n",
       " 'ё': 53,\n",
       " '‑': 54,\n",
       " '|': 0}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZLcIQUV-6IK9",
    "outputId": "9d909643-8981-4b4b-cf35-216533a61486"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "57"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_dict[\"[UNK]\"] = len(vocab_dict)\n",
    "vocab_dict[\"[PAD]\"] = len(vocab_dict)\n",
    "len(vocab_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "OlE9Fm916IK9"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "with open('vocab.json', 'w') as vocab_file:\n",
    "    json.dump(vocab_dict, vocab_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZnwWceMF6IK9",
    "outputId": "c55f12b5-d417-4363-92f8-75cd1a5d6677"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "file ./config.json not found\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "from transformers import Wav2Vec2CTCTokenizer\n",
    "\n",
    "tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(\"./\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "ziVOWgep6IK9"
   },
   "outputs": [],
   "source": [
    "repo_name = \"wav2vec2-xlsr-1b-ru\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "biccWGbt6IK9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py:1001: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n",
      "  warnings.warn(\n",
      "Cloning https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-ru into local empty directory.\n",
      "To https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-ru\n",
      "   903c4df..2f7425d  main -> main\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-ru/commit/2f7425d996ea36bb7c47215cea4cecc882eaadaf'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.push_to_hub(repo_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "4GDqmpau6IK9"
   },
   "outputs": [],
   "source": [
    "from transformers import Wav2Vec2FeatureExtractor\n",
    "\n",
    "feature_extractor = Wav2Vec2FeatureExtractor(feature_size=1, sampling_rate=16000, padding_value=0.0, do_normalize=True, return_attention_mask=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "3ydgeSof6IK-"
   },
   "outputs": [],
   "source": [
    "from transformers import Wav2Vec2Processor\n",
    "\n",
    "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "6RGWMIrk6IK-"
   },
   "outputs": [],
   "source": [
    "import torchaudio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "2oXxiiMx6IK-"
   },
   "outputs": [],
   "source": [
    "common_voice_train = common_voice_train.cast_column(\"audio\", Audio(sampling_rate=16_000))\n",
    "common_voice_test = common_voice_test.cast_column(\"audio\", Audio(sampling_rate=16_000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ljOF6qF86IK-",
    "outputId": "531347dd-66dd-4e28-9ff4-ee52b7222398"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'path': 'cv-corpus-8.0-2022-01-19/ru/clips/common_voice_ru_18849051.mp3',\n",
       " 'array': array([ 0.0000000e+00,  0.0000000e+00,  0.0000000e+00, ...,\n",
       "         5.1862571e-05, -7.1976043e-05, -7.0710674e-05], dtype=float32),\n",
       " 'sampling_rate': 16000}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_voice_train[0][\"audio\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "2Lt4U5wL6IK-"
   },
   "outputs": [],
   "source": [
    "def prepare_dataset(batch):\n",
    "    audio = batch[\"audio\"]\n",
    "\n",
    "    # batched output is \"un-batched\"\n",
    "    batch[\"input_values\"] = processor(audio[\"array\"], sampling_rate=audio[\"sampling_rate\"]).input_values[0]\n",
    "    batch[\"input_length\"] = len(batch[\"input_values\"])\n",
    "    \n",
    "    with processor.as_target_processor():\n",
    "        batch[\"labels\"] = processor(batch[\"sentence\"]).input_ids\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 98,
     "referenced_widgets": [
      "5ce68963158f43c6afd697112ae91b86",
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     ]
    },
    "id": "ldXFrogk6IK-",
    "outputId": "839b1d78-4017-4b1e-c259-c1fec5641de0"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4b538c91f6f9471c99421a2ff3f7de02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0ex [00:00, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done train\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d22be2b5e0541de91f9f87a4cdd891e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0ex [00:00, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names)\n",
    "print(\"done train\")\n",
    "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "JP1OWPs36IK-"
   },
   "outputs": [],
   "source": [
    "max_input_length = 20.0 * feature_extractor.sampling_rate\n",
    "min_input_length = 0.0 * feature_extractor.sampling_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "1gHoa7lL6IK-"
   },
   "outputs": [],
   "source": [
    "def is_audio_in_length_range(length):\n",
    "    return length > min_input_length and length < max_input_length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
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    },
    "id": "TjEkpBpJ6IK_",
    "outputId": "0b74024a-66b5-444c-a230-9f119eab3824"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a27d4c29eaa41b38e525c0122bf1bb3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/47 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "common_voice_train = common_voice_train.filter(\n",
    "            is_audio_in_length_range,\n",
    "            input_columns=[\"input_length\"],\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "c9K5jjV96IK_"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "from dataclasses import dataclass, field\n",
    "from typing import Any, Dict, List, Optional, Union\n",
    "from transformers import AutoProcessor\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class DataCollatorCTCWithPadding:\n",
    "    \"\"\"\n",
    "    Data collator that will dynamically pad the inputs received.\n",
    "    Args:\n",
    "        processor (:class:`~transformers.AutoProcessor`)\n",
    "            The processor used for proccessing the data.\n",
    "        padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):\n",
    "            Select a strategy to pad the returned sequences (according to the model's padding side and padding index)\n",
    "            among:\n",
    "            * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a single\n",
    "              sequence if provided).\n",
    "            * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the\n",
    "              maximum acceptable input length for the model if that argument is not provided.\n",
    "            * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of\n",
    "              different lengths).\n",
    "        max_length (:obj:`int`, `optional`):\n",
    "            Maximum length of the ``input_values`` of the returned list and optionally padding length (see above).\n",
    "        max_length_labels (:obj:`int`, `optional`):\n",
    "            Maximum length of the ``labels`` returned list and optionally padding length (see above).\n",
    "        pad_to_multiple_of (:obj:`int`, `optional`):\n",
    "            If set will pad the sequence to a multiple of the provided value.\n",
    "            This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >=\n",
    "            7.5 (Volta).\n",
    "    \"\"\"\n",
    "\n",
    "    processor: AutoProcessor\n",
    "    padding: Union[bool, str] = \"longest\"\n",
    "    pad_to_multiple_of: Optional[int] = None\n",
    "    pad_to_multiple_of_labels: Optional[int] = None\n",
    "\n",
    "    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:\n",
    "        # split inputs and labels since they have to be of different lenghts and need\n",
    "        # different padding methods\n",
    "        input_features = [{\"input_values\": feature[\"input_values\"]} for feature in features]\n",
    "        label_features = [{\"input_ids\": feature[\"labels\"]} for feature in features]\n",
    "\n",
    "        batch = self.processor.pad(\n",
    "            input_features,\n",
    "            padding=self.padding,\n",
    "            pad_to_multiple_of=self.pad_to_multiple_of,\n",
    "            return_tensors=\"pt\",\n",
    "        )\n",
    "\n",
    "        with self.processor.as_target_processor():\n",
    "            labels_batch = self.processor.pad(\n",
    "                label_features,\n",
    "                padding=self.padding,\n",
    "                pad_to_multiple_of=self.pad_to_multiple_of_labels,\n",
    "                return_tensors=\"pt\",\n",
    "            )\n",
    "\n",
    "        # replace padding with -100 to ignore loss correctly\n",
    "        labels = labels_batch[\"input_ids\"].masked_fill(labels_batch.attention_mask.ne(1), -100)\n",
    "\n",
    "        batch[\"labels\"] = labels\n",
    "\n",
    "        return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "WcQgSGyj6IK_"
   },
   "outputs": [],
   "source": [
    "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
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      "e210bd8662c04b0b96f8736b71463994"
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    },
    "id": "wSSJacE46IK_",
    "outputId": "3bbde635-9e7b-458d-c89c-ef3986c8f5f2"
   },
   "outputs": [],
   "source": [
    "wer_metric = load_metric(\"wer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "AWh2K1ia6IK_"
   },
   "outputs": [],
   "source": [
    "def compute_metrics(pred):\n",
    "    pred_logits = pred.predictions\n",
    "    pred_ids = np.argmax(pred_logits, axis=-1)\n",
    "\n",
    "    pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id\n",
    "\n",
    "    pred_str = processor.batch_decode(pred_ids)\n",
    "    # we do not want to group tokens when computing the metrics\n",
    "    label_str = processor.batch_decode(pred.label_ids, group_tokens=False)\n",
    "\n",
    "    wer = wer_metric.compute(predictions=pred_str, references=label_str)\n",
    "\n",
    "    return {\"wer\": wer}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
     "referenced_widgets": [
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    "collapsed": true,
    "id": "XCnGR0uB6ILA",
    "jupyter": {
     "outputs_hidden": true
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   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-1b were not used when initializing Wav2Vec2ForCTC: ['project_hid.weight', 'quantizer.codevectors', 'project_q.bias', 'quantizer.weight_proj.weight', 'quantizer.weight_proj.bias', 'project_q.weight', 'project_hid.bias']\n",
      "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-1b and are newly initialized: ['lm_head.weight', 'lm_head.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Wav2Vec2ForCTC(\n",
       "  (wav2vec2): Wav2Vec2Model(\n",
       "    (feature_extractor): Wav2Vec2FeatureEncoder(\n",
       "      (conv_layers): ModuleList(\n",
       "        (0): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(1, 512, kernel_size=(10,), stride=(5,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (1): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (2): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (3): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (4): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(3,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (5): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (6): Wav2Vec2LayerNormConvLayer(\n",
       "          (conv): Conv1d(512, 512, kernel_size=(2,), stride=(2,))\n",
       "          (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (feature_projection): Wav2Vec2FeatureProjection(\n",
       "      (layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
       "      (projection): Linear(in_features=512, out_features=1280, bias=True)\n",
       "      (dropout): Dropout(p=0.04, inplace=False)\n",
       "    )\n",
       "    (encoder): Wav2Vec2EncoderStableLayerNorm(\n",
       "      (pos_conv_embed): Wav2Vec2PositionalConvEmbedding(\n",
       "        (conv): Conv1d(1280, 1280, kernel_size=(128,), stride=(1,), padding=(64,), groups=16)\n",
       "        (padding): Wav2Vec2SamePadLayer()\n",
       "      )\n",
       "      (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.047, inplace=False)\n",
       "      (layers): ModuleList(\n",
       "        (0): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (1): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (2): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (3): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (4): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (5): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (6): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (7): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (8): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (9): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (10): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (11): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (12): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (13): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (14): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (15): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (16): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (17): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (18): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (19): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (20): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (21): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (22): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (23): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (24): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (25): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (26): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (27): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (28): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (29): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (30): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (31): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (32): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (33): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (34): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (35): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (36): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (37): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (38): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (39): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (40): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (41): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (42): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (43): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (44): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (45): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (46): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "        (47): Wav2Vec2EncoderLayerStableLayerNorm(\n",
       "          (attention): Wav2Vec2Attention(\n",
       "            (k_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (v_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (q_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "            (out_proj): Linear(in_features=1280, out_features=1280, bias=True)\n",
       "          )\n",
       "          (dropout): Dropout(p=0.047, inplace=False)\n",
       "          (layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "          (feed_forward): Wav2Vec2FeedForward(\n",
       "            (intermediate_dropout): Dropout(p=0.055, inplace=False)\n",
       "            (intermediate_dense): Linear(in_features=1280, out_features=5120, bias=True)\n",
       "            (output_dense): Linear(in_features=5120, out_features=1280, bias=True)\n",
       "            (output_dropout): Dropout(p=0.047, inplace=False)\n",
       "          )\n",
       "          (final_layer_norm): LayerNorm((1280,), eps=1e-05, elementwise_affine=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.0, inplace=False)\n",
       "  (lm_head): Linear(in_features=1280, out_features=59, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import Wav2Vec2ForCTC\n",
    "\n",
    "model = Wav2Vec2ForCTC.from_pretrained(\n",
    "    \"facebook/wav2vec2-xls-r-1b\", \n",
    "    attention_dropout=0.094,\n",
    "    hidden_dropout=0.047,\n",
    "    feat_proj_dropout=0.04,\n",
    "    mask_time_prob=0.082,\n",
    "    layerdrop=0.041,\n",
    "    activation_dropout=0.055,\n",
    "    ctc_loss_reduction=\"mean\", \n",
    "    pad_token_id=processor.tokenizer.pad_token_id,\n",
    "    vocab_size=len(processor.tokenizer),\n",
    ")\n",
    "model.to('cuda')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vJ4YmBLK6ILA",
    "outputId": "5ff744c2-0a83-41fa-8b5b-aa0e430ff1e9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1700: FutureWarning: The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5.Please use the equivalent `freeze_feature_encoder` method instead.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "model.freeze_feature_extractor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "Wu3bwi8i6ILA"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"WANDB_DISABLED\"] = \"true\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "0NXLfMI56ILA"
   },
   "outputs": [],
   "source": [
    "from transformers import TrainingArguments\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "  output_dir=repo_name,\n",
    "  group_by_length=True,\n",
    "  per_device_train_batch_size=32,\n",
    "  gradient_accumulation_steps=1,\n",
    "  evaluation_strategy=\"steps\",\n",
    "  num_train_epochs=10,\n",
    "  gradient_checkpointing=True,\n",
    "  fp16=True,\n",
    "  save_steps=500,\n",
    "  eval_steps=500,\n",
    "  logging_steps=50,\n",
    "  learning_rate=5e-5,\n",
    "  warmup_steps=500,\n",
    "  save_total_limit=3,\n",
    "  push_to_hub=True,\n",
    "  load_best_model_at_end=True,\n",
    "  greater_is_better=False,\n",
    "  report_to=\"tensorboard\",\n",
    "  metric_for_best_model='eval_wer',\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MTdz79SyGz0y",
    "outputId": "867f5a41-e82e-44be-aaf0-3fc6c0698a7e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sat Feb  5 08:58:36 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   73C    P0    72W / 149W |   1806MiB / 11441MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "collapsed": true,
    "id": "eTNIJiY66ILA",
    "jupyter": {
     "outputs_hidden": true
    },
    "outputId": "4c18731e-6766-439b-de44-5bb70c3040ac"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "Collecting bitsandbytes-cuda111\n",
      "  Downloading bitsandbytes_cuda111-0.26.0-py3-none-any.whl (4.0 MB)\n",
      "     |████████████████████████████████| 4.0 MB 24.2 MB/s            \n",
      "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: bitsandbytes-cuda111\n",
      "Successfully installed bitsandbytes-cuda111-0.26.0\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 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 bitsandbytes-cuda111 --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "Collecting tensorboard\n",
      "  Downloading tensorboard-2.8.0-py3-none-any.whl (5.8 MB)\n",
      "     |████████████████████████████████| 5.8 MB 4.8 MB/s            \n",
      "\u001b[?25hCollecting absl-py>=0.4\n",
      "  Downloading absl_py-1.0.0-py3-none-any.whl (126 kB)\n",
      "     |████████████████████████████████| 126 kB 99.3 MB/s            \n",
      "\u001b[?25hCollecting google-auth<3,>=1.6.3\n",
      "  Downloading google_auth-2.6.0-py2.py3-none-any.whl (156 kB)\n",
      "     |████████████████████████████████| 156 kB 92.1 MB/s            \n",
      "\u001b[?25hCollecting werkzeug>=0.11.15\n",
      "  Downloading Werkzeug-2.0.2-py3-none-any.whl (288 kB)\n",
      "     |████████████████████████████████| 288 kB 80.4 MB/s            \n",
      "\u001b[?25hCollecting markdown>=2.6.8\n",
      "  Downloading Markdown-3.3.6-py3-none-any.whl (97 kB)\n",
      "     |████████████████████████████████| 97 kB 27.2 MB/s            \n",
      "\u001b[?25hRequirement already satisfied: wheel>=0.26 in /opt/conda/lib/python3.8/site-packages (from tensorboard) (0.35.1)\n",
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      "  Downloading tensorboard_plugin_wit-1.8.1-py3-none-any.whl (781 kB)\n",
      "     |████████████████████████████████| 781 kB 97.0 MB/s            \n",
      "\u001b[?25hRequirement already satisfied: setuptools>=41.0.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard) (50.3.1.post20201107)\n",
      "Requirement already satisfied: numpy>=1.12.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard) (1.19.2)\n",
      "Collecting grpcio>=1.24.3\n",
      "  Downloading grpcio-1.43.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.1 MB)\n",
      "     |████████████████████████████████| 4.1 MB 76.3 MB/s            \n",
      "\u001b[?25hCollecting protobuf>=3.6.0\n",
      "  Downloading protobuf-3.19.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB)\n",
      "     |████████████████████████████████| 1.1 MB 76.3 MB/s            \n",
      "\u001b[?25hCollecting google-auth-oauthlib<0.5,>=0.4.1\n",
      "  Downloading google_auth_oauthlib-0.4.6-py2.py3-none-any.whl (18 kB)\n",
      "Requirement already satisfied: requests<3,>=2.21.0 in /opt/conda/lib/python3.8/site-packages (from tensorboard) (2.24.0)\n",
      "Collecting tensorboard-data-server<0.7.0,>=0.6.0\n",
      "  Downloading tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl (4.9 MB)\n",
      "     |████████████████████████████████| 4.9 MB 39.7 MB/s            \n",
      "\u001b[?25hRequirement already satisfied: six in /opt/conda/lib/python3.8/site-packages (from absl-py>=0.4->tensorboard) (1.15.0)\n",
      "Collecting rsa<5,>=3.1.4\n",
      "  Downloading rsa-4.8-py3-none-any.whl (39 kB)\n",
      "Collecting pyasn1-modules>=0.2.1\n",
      "  Downloading pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB)\n",
      "     |████████████████████████████████| 155 kB 99.3 MB/s            \n",
      "\u001b[?25hCollecting cachetools<6.0,>=2.0.0\n",
      "  Downloading cachetools-5.0.0-py3-none-any.whl (9.1 kB)\n",
      "Collecting requests-oauthlib>=0.7.0\n",
      "  Downloading requests_oauthlib-1.3.1-py2.py3-none-any.whl (23 kB)\n",
      "Collecting importlib-metadata>=4.4\n",
      "  Downloading importlib_metadata-4.10.1-py3-none-any.whl (17 kB)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /opt/conda/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard) (3.0.4)\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests<3,>=2.21.0->tensorboard) (2020.12.5)\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<3,>=2.21.0->tensorboard) (1.25.11)\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.8/site-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard) (3.7.0)\n",
      "Collecting pyasn1<0.5.0,>=0.4.6\n",
      "  Downloading pyasn1-0.4.8-py2.py3-none-any.whl (77 kB)\n",
      "     |████████████████████████████████| 77 kB 26.5 MB/s            \n",
      "\u001b[?25hCollecting oauthlib>=3.0.0\n",
      "  Downloading oauthlib-3.2.0-py3-none-any.whl (151 kB)\n",
      "     |████████████████████████████████| 151 kB 104.7 MB/s            \n",
      "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: pyasn1, rsa, pyasn1-modules, oauthlib, cachetools, requests-oauthlib, importlib-metadata, google-auth, werkzeug, tensorboard-plugin-wit, tensorboard-data-server, protobuf, markdown, grpcio, google-auth-oauthlib, absl-py, tensorboard\n",
      "\u001b[33m  WARNING: The scripts pyrsa-decrypt, pyrsa-encrypt, pyrsa-keygen, pyrsa-priv2pub, pyrsa-sign and pyrsa-verify are installed in '/workspace/.local/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
      "\u001b[33m  WARNING: The script markdown_py is installed in '/workspace/.local/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
      "\u001b[33m  WARNING: The script google-oauthlib-tool is installed in '/workspace/.local/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
      "\u001b[33m  WARNING: The script tensorboard is installed in '/workspace/.local/bin' which is not on PATH.\n",
      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\n",
      "Successfully installed absl-py-1.0.0 cachetools-5.0.0 google-auth-2.6.0 google-auth-oauthlib-0.4.6 grpcio-1.43.0 importlib-metadata-4.10.1 markdown-3.3.6 oauthlib-3.2.0 protobuf-3.19.4 pyasn1-0.4.8 pyasn1-modules-0.2.8 requests-oauthlib-1.3.1 rsa-4.8 tensorboard-2.8.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.1 werkzeug-2.0.2\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: Ignoring invalid distribution -uggingface-hub (/opt/conda/lib/python3.8/site-packages)\u001b[0m\n",
      "\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.3 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 tensorboard --user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 732,
     "referenced_widgets": [
      "dcc0badbab394367a5639c05dac0d33d",
      "16d1947231f340d89822b4b71b1928e8",
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     ]
    },
    "id": "9jie-V0n6ILA",
    "outputId": "831edb46-1b75-4c69-9694-14be0dadfd00"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/workspace/wav2vec2-xlsr-1b-ru is already a clone of https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-ru. Make sure you pull the latest changes with `repo.git_pull()`.\n",
      "Using amp half precision backend\n"
     ]
    }
   ],
   "source": [
    "import bitsandbytes as bnb\n",
    "from transformers import Trainer\n",
    "from transformers.trainer_pt_utils import get_parameter_names\n",
    "\n",
    "decay_parameters = get_parameter_names(model, [torch.nn.LayerNorm])\n",
    "decay_parameters = [name for name in decay_parameters if \"bias\" not in name]\n",
    "optimizer_grouped_parameters = [\n",
    "    {\n",
    "        \"params\": [p for n, p in model.named_parameters() if n in decay_parameters],\n",
    "        \"weight_decay\": training_args.weight_decay,\n",
    "    },\n",
    "    {\n",
    "        \"params\": [p for n, p in model.named_parameters() if n not in decay_parameters],\n",
    "        \"weight_decay\": 0.0,\n",
    "    },\n",
    "]\n",
    "optimizer = bnb.optim.Adam8bit(\n",
    "    params=optimizer_grouped_parameters,\n",
    "    lr=training_args.learning_rate,\n",
    "    betas=(training_args.adam_beta1, training_args.adam_beta2),\n",
    "    eps=training_args.adam_epsilon,\n",
    ")\n",
    "\n",
    "optimizers = (optimizer, None)\n",
    "\n",
    "# Initialize Trainer\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    data_collator=data_collator,\n",
    "    args=training_args,\n",
    "    compute_metrics=compute_metrics,\n",
    "    train_dataset=common_voice_train,\n",
    "    eval_dataset=common_voice_test,\n",
    "    tokenizer=processor.feature_extractor,\n",
    "    optimizers=optimizers,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "955VTrNd6ILB"
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 529
    },
    "id": "tMtJagfc6ILB",
    "outputId": "9022f51c-29ca-4f66-8f46-8eeeba41bc60"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the training set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running training *****\n",
      "  Num examples = 46329\n",
      "  Num Epochs = 10\n",
      "  Instantaneous batch size per device = 32\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 32\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 14480\n"
     ]
    },
    {
     "data": {
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       "      <progress value='14480' max='14480' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [14480/14480 18:27:11, Epoch 10/10]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Step</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
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       "      <td>0.226491</td>\n",
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       "      <td>0.210009</td>\n",
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       "      <td>0.224100</td>\n",
       "      <td>0.197974</td>\n",
       "      <td>0.176719</td>\n",
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       "    <tr>\n",
       "      <td>3500</td>\n",
       "      <td>0.205600</td>\n",
       "      <td>0.202031</td>\n",
       "      <td>0.168287</td>\n",
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       "    <tr>\n",
       "      <td>4000</td>\n",
       "      <td>0.342300</td>\n",
       "      <td>0.186168</td>\n",
       "      <td>0.160555</td>\n",
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       "    <tr>\n",
       "      <td>4500</td>\n",
       "      <td>0.247800</td>\n",
       "      <td>0.178670</td>\n",
       "      <td>0.156309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5000</td>\n",
       "      <td>0.307900</td>\n",
       "      <td>0.175857</td>\n",
       "      <td>0.155537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5500</td>\n",
       "      <td>0.247700</td>\n",
       "      <td>0.171286</td>\n",
       "      <td>0.142268</td>\n",
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       "      <td>6000</td>\n",
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       "      <td>0.169515</td>\n",
       "      <td>0.139095</td>\n",
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       "      <td>6500</td>\n",
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       "      <td>0.167683</td>\n",
       "      <td>0.137201</td>\n",
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       "    <tr>\n",
       "      <td>7000</td>\n",
       "      <td>0.163100</td>\n",
       "      <td>0.165151</td>\n",
       "      <td>0.133293</td>\n",
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       "    <tr>\n",
       "      <td>7500</td>\n",
       "      <td>0.142900</td>\n",
       "      <td>0.160473</td>\n",
       "      <td>0.130832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8000</td>\n",
       "      <td>0.150500</td>\n",
       "      <td>0.161166</td>\n",
       "      <td>0.124511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8500</td>\n",
       "      <td>0.138500</td>\n",
       "      <td>0.148741</td>\n",
       "      <td>0.122497</td>\n",
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       "    <tr>\n",
       "      <td>9000</td>\n",
       "      <td>0.128500</td>\n",
       "      <td>0.152599</td>\n",
       "      <td>0.120072</td>\n",
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       "      <td>9500</td>\n",
       "      <td>0.115300</td>\n",
       "      <td>0.146372</td>\n",
       "      <td>0.117177</td>\n",
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       "    <tr>\n",
       "      <td>10000</td>\n",
       "      <td>0.115900</td>\n",
       "      <td>0.150462</td>\n",
       "      <td>0.114270</td>\n",
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       "    <tr>\n",
       "      <td>10500</td>\n",
       "      <td>0.106100</td>\n",
       "      <td>0.144378</td>\n",
       "      <td>0.110567</td>\n",
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       "    <tr>\n",
       "      <td>11000</td>\n",
       "      <td>0.101600</td>\n",
       "      <td>0.142675</td>\n",
       "      <td>0.107491</td>\n",
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       "    <tr>\n",
       "      <td>11500</td>\n",
       "      <td>0.112500</td>\n",
       "      <td>0.138553</td>\n",
       "      <td>0.104511</td>\n",
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       "    <tr>\n",
       "      <td>12000</td>\n",
       "      <td>0.093700</td>\n",
       "      <td>0.140342</td>\n",
       "      <td>0.102183</td>\n",
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       "      <td>0.140615</td>\n",
       "      <td>0.102220</td>\n",
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       "      <td>0.137990</td>\n",
       "      <td>0.097672</td>\n",
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       "      <td>0.091300</td>\n",
       "      <td>0.135158</td>\n",
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     "text": [
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-500/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-1000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-1000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-1000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-1000/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-1500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-1500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-1500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-1500/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-2000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-2000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-2000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-2000/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-500] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-2500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-2500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-2500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-2500/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-1000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-3000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-3000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-3000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-3000/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-1500] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-3500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-3500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-3500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-3500/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-2000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-4000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-4000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-4000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-4000/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-2500] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-4500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-4500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-4500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-4500/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-3000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-5000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-5000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-5000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-5000/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-3500] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-5500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-5500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-5500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-5500/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-4000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-6000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-6000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-6000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-6000/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-4500] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-6500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-6500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-6500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-6500/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-5000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-7000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-7000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-7000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-7000/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-5500] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-7500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-7500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-7500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-7500/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-6000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-8000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-8000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-8000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-8000/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-6500] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-8500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-8500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-8500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-8500/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-7000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-9000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-9000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-9000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-9000/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-7500] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-9500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-9500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-9500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-9500/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-8000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-10000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-10000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-10000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-10000/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-8500] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-10500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-10500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-10500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-10500/preprocessor_config.json\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-9000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-11000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-11000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-11000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-11000/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-9500] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-11500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-11500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-11500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-11500/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-10000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-12000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-12000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-12000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-12000/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-10500] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-12500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-12500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-12500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-12500/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-11000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-13000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-13000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-13000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-13000/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-11500] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-13500\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-13500/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-13500/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-13500/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-12000] due to args.save_total_limit\n",
      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 9419\n",
      "  Batch size = 8\n",
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru/checkpoint-14000\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-14000/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/checkpoint-14000/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/checkpoint-14000/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-xlsr-1b-ru/checkpoint-12500] due to args.save_total_limit\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n",
      "Loading best model from wav2vec2-xlsr-1b-ru/checkpoint-14000 (score: 0.09712907117008444).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=14480, training_loss=0.30223861218157394, metrics={'train_runtime': 66440.375, 'train_samples_per_second': 6.973, 'train_steps_per_second': 0.218, 'total_flos': 2.2389266516763502e+20, 'train_loss': 0.30223861218157394, 'epoch': 10.0})"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "WpLhQDv26ILB"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to wav2vec2-xlsr-1b-ru\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/config.json\n",
      "Model weights saved in wav2vec2-xlsr-1b-ru/pytorch_model.bin\n",
      "Configuration saved in wav2vec2-xlsr-1b-ru/preprocessor_config.json\n"
     ]
    },
    {
     "data": {
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       "model_id": "4a5a3a310e364ff7a951fca976ba2487",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Upload file pytorch_model.bin:   0%|          | 3.38k/3.59G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "To https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-ru\n",
      "   ceae18d..2be1f44  main -> main\n",
      "\n",
      "Dropping the following result as it does not have all the necessary fields:\n",
      "{'dataset': {'name': 'common_voice', 'type': 'common_voice', 'args': 'ru'}}\n",
      "To https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-ru\n",
      "   2be1f44..dacc045  main -> main\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://huggingface.co/RASMUS/wav2vec2-xlsr-1b-ru/commit/2be1f447609b8e54c6d7d60460897541ca6fc5f8'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.push_to_hub()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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