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            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ktnAD1ruEEKY",
        "outputId": "9660457e-20fc-43c3-d907-3d79099740ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Jan 24 03:58:42 2022       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 495.46       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 T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   56C    P8    10W /  70W |      0MiB / 15109MiB |      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",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install -q datasets transformers\n",
        "!pip install huggingface_hub\n",
        "!pip install torchaudio\n",
        "!pip install librosa\n",
        "!pip install jiwer"
      ],
      "metadata": {
        "id": "DtlJ3Ey1Bafu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!apt install git-lfs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OFHZExbKD82m",
        "outputId": "83a32fd0-363e-40e4-9ad6-f4b3113fe476"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "The following NEW packages will be installed:\n",
            "  git-lfs\n",
            "0 upgraded, 1 newly installed, 0 to remove and 37 not upgraded.\n",
            "Need to get 2,129 kB of archives.\n",
            "After this operation, 7,662 kB of additional disk space will be used.\n",
            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 git-lfs amd64 2.3.4-1 [2,129 kB]\n",
            "Fetched 2,129 kB in 1s (2,377 kB/s)\n",
            "Selecting previously unselected package git-lfs.\n",
            "(Reading database ... 155229 files and directories currently installed.)\n",
            "Preparing to unpack .../git-lfs_2.3.4-1_amd64.deb ...\n",
            "Unpacking git-lfs (2.3.4-1) ...\n",
            "Setting up git-lfs (2.3.4-1) ...\n",
            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359,
          "referenced_widgets": [
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          ]
        },
        "id": "GsGvBYj-aWkk",
        "outputId": "63292a34-7d53-4a26-bdec-58f6acf2317e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Login successful\n",
            "Your token has been saved to /root/.huggingface/token\n",
            "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
            "\n",
            "git config --global credential.helper store\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset, load_metric, Audio"
      ],
      "metadata": {
        "id": "Zo8s-AlYDyCj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"pa-IN\", split=\"train+validation\", use_auth_token=True)\n",
        "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"pa-IN\", split=\"test\", use_auth_token=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197,
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        "id": "0R2fwMfAD_oh",
        "outputId": "cebdfb7b-b097-4ca7-a8d9-b37223159e10"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4bdcddb6b60f4bb9bffc01ead12ab011",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/9.79k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7008109b38be4703ade3baf07218c2ac",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/2.92k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "Downloading:   0%|          | 0.00/49.7k [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset common_voice/hi to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/hi/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba...\n"
          ]
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        {
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              "model_id": "e8860b5c46a5429d8fb7dbd2ac6b23bb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/253M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "1842990a86c4465c9a3e8bc35b8be6a0",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f7efe021ab9b468885914b0af8f6e2ae",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {}
        },
        {
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            "text/plain": [
              "0 examples [00:00, ? examples/s]"
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          },
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        },
        {
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            "text/plain": [
              "0 examples [00:00, ? examples/s]"
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        {
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              "model_id": "6eae98c905d345f5a3b2ea54bb3b64da",
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            "text/plain": [
              "0 examples [00:00, ? examples/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset common_voice downloaded and prepared to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/hi/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/hi/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "common_voice_train = common_voice_train.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])\n",
        "common_voice_test = common_voice_test.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])"
      ],
      "metadata": {
        "id": "k8SSDpswEXSA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "chars_to_remove_regex = '''[\\।\\!\\\"\\,\\-\\.\\?\\:\\|\\“\\”\\–\\;\\'\\’\\…\\¤]'''\n",
        "\n",
        "def remove_special_characters(batch):\n",
        "    batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n",
        "    return batch"
      ],
      "metadata": {
        "id": "9MpryC5hEoEN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "common_voice_train = common_voice_train.map(remove_special_characters)\n",
        "common_voice_test = common_voice_test.map(remove_special_characters)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        {
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    {
      "cell_type": "code",
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        "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]}"
      ],
      "metadata": {
        "id": "6J_JKsIhEsqy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "27e5289020704e2ea220aaf4b1c84c6f",
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      "execution_count": null,
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        {
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    },
    {
      "cell_type": "code",
      "source": [
        "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))"
      ],
      "metadata": {
        "id": "-7INalVeEuxS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_dict = {v: k for k, v in enumerate(sorted(vocab_list))}\n",
        "vocab_dict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IM-VpPKoEv97",
        "outputId": "39f666b4-c44f-40e4-e26e-7a6588a0ad64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{' ': 0,\n",
              " 'ਂ': 1,\n",
              " 'ਅ': 2,\n",
              " 'ਆ': 3,\n",
              " 'ਇ': 4,\n",
              " 'ਈ': 5,\n",
              " 'ਉ': 6,\n",
              " 'ਊ': 7,\n",
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              " 'ਐ': 9,\n",
              " 'ਓ': 10,\n",
              " 'ਔ': 11,\n",
              " 'ਕ': 12,\n",
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              " 'ਸ਼': 40,\n",
              " 'ਸ': 41,\n",
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              " 'ਫ਼': 58,\n",
              " 'ੰ': 59,\n",
              " 'ੱ': 60}"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_dict[\"|\"] = vocab_dict[\" \"]\n",
        "del vocab_dict[\" \"]"
      ],
      "metadata": {
        "id": "7b961JX1E1X3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vocab_dict[\"[UNK]\"] = len(vocab_dict)\n",
        "vocab_dict[\"[PAD]\"] = len(vocab_dict)\n",
        "len(vocab_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k45hegDKFoFW",
        "outputId": "c3d74bec-18f2-4e3e-e42c-362de7cd70b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "63"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "with open('vocab.json', 'w') as vocab_file:\n",
        "    json.dump(vocab_dict, vocab_file)"
      ],
      "metadata": {
        "id": "TC7QqjmTFoI2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Wav2Vec2CTCTokenizer\n",
        "\n",
        "tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(\"./\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4wMI4q8FoLF",
        "outputId": "1114c379-2d16-46d0-b404-3b996d420741"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "repo_name = \"wav2vec2-large-xls-r-300m-pa-in\""
      ],
      "metadata": {
        "id": "oQm9jF7WFoNc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.push_to_hub(repo_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "id": "jOzuCg9bFoPj",
        "outputId": "5f393d82-8793-46d0-d065-5afc540a9b0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/hf_api.py:1004: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n",
            "  FutureWarning,\n",
            "Cloning https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-pa-in into local empty directory.\n",
            "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-pa-in\n",
            "   aa975ff..50e2ea6  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-pa-in/commit/50e2ea63d2998b4c2877a0b6cda18c59a518a357'"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "pZMN1_9VFoR4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Wav2Vec2Processor\n",
        "\n",
        "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "OdqVLzfpFyak"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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))"
      ],
      "metadata": {
        "id": "__QcVbmkF0ZD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "XH83kfcIF2HI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "common_voice_train = common_voice_train.map(prepare_dataset, remove_columns=common_voice_train.column_names)\n",
        "common_voice_test = common_voice_test.map(prepare_dataset, remove_columns=common_voice_test.column_names)"
      ],
      "metadata": {
        "colab": {
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    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "from dataclasses import dataclass, field\n",
        "from typing import Any, Dict, List, Optional, Union\n",
        "\n",
        "@dataclass\n",
        "class DataCollatorCTCWithPadding:\n",
        "    \"\"\"\n",
        "    Data collator that will dynamically pad the inputs received.\n",
        "    Args:\n",
        "        processor (:class:`~transformers.Wav2Vec2Processor`)\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",
        "    \"\"\"\n",
        "\n",
        "    processor: Wav2Vec2Processor\n",
        "    padding: Union[bool, str] = True\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",
        "            return_tensors=\"pt\",\n",
        "        )\n",
        "        with self.processor.as_target_processor():\n",
        "            labels_batch = self.processor.pad(\n",
        "                label_features,\n",
        "                padding=self.padding,\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"
      ],
      "metadata": {
        "id": "bsKE5G8EF6nS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"
      ],
      "metadata": {
        "id": "pCAvRcS0F8qm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "wer_metric = load_metric(\"wer\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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      },
      "execution_count": null,
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        {
          "output_type": "display_data",
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    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "zxSG-t66GAqu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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}"
      ],
      "metadata": {
        "id": "yjnMNMdrGBK_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Wav2Vec2ForCTC\n",
        "\n",
        "model = Wav2Vec2ForCTC.from_pretrained(\n",
        "    \"facebook/wav2vec2-xls-r-300m\", \n",
        "    attention_dropout=0.2,\n",
        "    hidden_dropout=0.05,\n",
        "    feat_proj_dropout=0.05,\n",
        "    mask_time_prob=0.05,\n",
        "    layerdrop=0.04,\n",
        "    ctc_loss_reduction=\"mean\", \n",
        "    pad_token_id=processor.tokenizer.pad_token_id,\n",
        "    vocab_size=len(processor.tokenizer),\n",
        ")"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 188,
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            "ccfd5c96aab9498fbdc7d39eba3a9e04",
            "80b1c449181448ef80702e8813f247a4",
            "58b84e2831124734af25d53bedc1a94d",
            "a130b3771afd4c2bb99434bd8ccd9da7",
            "2b98c3c1ada943f898ce9188bbecf10d",
            "5cb2d2fb1c8349b7a28cd60197e47e45"
          ]
        },
        "id": "IrIo68UAGCqw",
        "outputId": "57b59f2c-274e-4657-aa21-e8cacd53f13c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "859998a6144b4aa6a40d6b186037aefc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.53k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0166eefd6d07437799914b69c9b4cf2b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.18G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['quantizer.weight_proj.bias', 'project_hid.weight', 'quantizer.codevectors', 'project_q.weight', 'project_hid.bias', 'quantizer.weight_proj.weight', 'project_q.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-300m 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.freeze_feature_extractor()"
      ],
      "metadata": {
        "id": "Jr5ybAlfGEN9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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=16,\n",
        "  gradient_accumulation_steps=2,\n",
        "  evaluation_strategy=\"steps\",\n",
        "  num_train_epochs=120,\n",
        "  gradient_checkpointing=True,\n",
        "  fp16=True,\n",
        "  save_steps=400,\n",
        "  eval_steps=400,\n",
        "  logging_steps=400,\n",
        "  learning_rate=3e-4,\n",
        "  warmup_ratio=0.12,\n",
        "  save_total_limit=1,\n",
        "  push_to_hub=True,\n",
        ")"
      ],
      "metadata": {
        "id": "p2MOKEf_GGa6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\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",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FoEFkVqAGHz0",
        "outputId": "af371c0a-c589-484d-c584-2ec2155d4143"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/wav2vec2-large-xls-r-300m-pa-in is already a clone of https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-pa-in. Make sure you pull the latest changes with `repo.git_pull()`.\n",
            "Using amp half precision backend\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BB0VjkhcGK2H",
        "outputId": "b06640ae-65f5-4c04-8584-0d72f9a62441"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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 = 781\n",
            "  Num Epochs = 120\n",
            "  Instantaneous batch size per device = 16\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 32\n",
            "  Gradient Accumulation steps = 2\n",
            "  Total optimization steps = 2880\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  return (input_length - kernel_size) // stride + 1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='2880' max='2880' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [2880/2880 3:13:48, Epoch 119/120]\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",
              "      <th>Wer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>400</td>\n",
              "      <td>6.480400</td>\n",
              "      <td>1.846061</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>800</td>\n",
              "      <td>0.474000</td>\n",
              "      <td>1.101765</td>\n",
              "      <td>0.662428</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>0.138900</td>\n",
              "      <td>1.191819</td>\n",
              "      <td>0.610336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1600</td>\n",
              "      <td>0.091900</td>\n",
              "      <td>1.188941</td>\n",
              "      <td>0.605824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2000</td>\n",
              "      <td>0.065700</td>\n",
              "      <td>1.226577</td>\n",
              "      <td>0.593109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>0.047900</td>\n",
              "      <td>1.251161</td>\n",
              "      <td>0.590238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2800</td>\n",
              "      <td>0.035500</td>\n",
              "      <td>1.254808</td>\n",
              "      <td>0.567678</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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 = 343\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-400\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-400/config.json\n",
            "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-400/pytorch_model.bin\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-400/preprocessor_config.json\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/preprocessor_config.json\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  return (input_length - kernel_size) // stride + 1\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 = 343\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-800\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-800/config.json\n",
            "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-800/pytorch_model.bin\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-800/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-large-xls-r-300m-pa-in/checkpoint-400] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  return (input_length - kernel_size) // stride + 1\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 = 343\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-1200\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-1200/config.json\n",
            "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-1200/pytorch_model.bin\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-1200/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-large-xls-r-300m-pa-in/checkpoint-800] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  return (input_length - kernel_size) // stride + 1\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 = 343\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-1600\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-1600/config.json\n",
            "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-1600/pytorch_model.bin\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-1600/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-large-xls-r-300m-pa-in/checkpoint-1200] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  return (input_length - kernel_size) // stride + 1\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 = 343\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-2000\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2000/config.json\n",
            "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2000/pytorch_model.bin\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2000/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-large-xls-r-300m-pa-in/checkpoint-1600] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  return (input_length - kernel_size) // stride + 1\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 = 343\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-2400\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2400/config.json\n",
            "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2400/pytorch_model.bin\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2400/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-large-xls-r-300m-pa-in/checkpoint-2000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  return (input_length - kernel_size) // stride + 1\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 = 343\n",
            "  Batch size = 8\n",
            "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in/checkpoint-2800\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2800/config.json\n",
            "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2800/pytorch_model.bin\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/checkpoint-2800/preprocessor_config.json\n",
            "Deleting older checkpoint [wav2vec2-large-xls-r-300m-pa-in/checkpoint-2400] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1093: UserWarning: __floordiv__ is deprecated, and its behavior will change in a future version of pytorch. It currently rounds toward 0 (like the 'trunc' function NOT 'floor'). This results in incorrect rounding for negative values. To keep the current behavior, use torch.div(a, b, rounding_mode='trunc'), or for actual floor division, use torch.div(a, b, rounding_mode='floor').\n",
            "  return (input_length - kernel_size) // stride + 1\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=2880, training_loss=1.0195181330045064, metrics={'train_runtime': 11634.7947, 'train_samples_per_second': 8.055, 'train_steps_per_second': 0.248, 'total_flos': 1.4789924329646787e+19, 'train_loss': 1.0195181330045064, 'epoch': 119.98})"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370,
          "referenced_widgets": [
            "115fbb1c21b145618fc0d391e201f922",
            "d30c33ce5b674dd583cb880197136fad",
            "e0810a85c656407d935c4959c29c52a5",
            "092a6ab3acac4c22ad6dc15777ab031e",
            "14b5af3cbe594ff282ae0415ae8e6d18",
            "a8bd35737fd4472b98494d6b048ae119",
            "997561dd68bb418ea86d6c957f919bfa",
            "e7ec33dd62df4063bb6d0abd3c8c1aa6",
            "d9cc3eaea19644f1baafabb4cf52f8f0",
            "93eb52140f654537be2b64c045a0d65b",
            "710fc56bc545449da082a953769a5a92",
            "81557dc72fbb4a18b2207e61acf83968",
            "fd36cbd237054c569438ecce05d20120",
            "9516bb75b27841629f9bb735a5240c5a",
            "a35b729be41f4b11bb8e4d94541476ba",
            "4112dded89304231b1438af48e7bb9ec",
            "1f5913397eb14f5c9226af6a864df7e5",
            "13ae6e7ece3049ecb241c3cc94fb279c",
            "25efb9576ecf4d3eaab3125b5d1c7c20",
            "7eba46668fad435c8ae8fee539e1f961",
            "aa32eeb3e38a44b09f87125be320f1b3",
            "780bf497ebad453c98d48f8a73c71c4e"
          ]
        },
        "id": "ocBhbxkrGN5A",
        "outputId": "b1ddc7c5-10ad-49ab-dc0b-a49771bd7b8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to wav2vec2-large-xls-r-300m-pa-in\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/config.json\n",
            "Model weights saved in wav2vec2-large-xls-r-300m-pa-in/pytorch_model.bin\n",
            "Configuration saved in wav2vec2-large-xls-r-300m-pa-in/preprocessor_config.json\n",
            "Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "115fbb1c21b145618fc0d391e201f922",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 3.37k/1.18G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "81557dc72fbb4a18b2207e61acf83968",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Upload file runs/Jan22_00-06-54_7fa4f467060a/events.out.tfevents.1642810066.7fa4f467060a.75.0:  41%|####1     …"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-pa-in\n",
            "   50e2ea6..1cdd79d  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': 'pa-IN'}}\n",
            "To https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-pa-in\n",
            "   1cdd79d..8e03b20  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/anuragshas/wav2vec2-large-xls-r-300m-pa-in/commit/1cdd79df0dfa4f2075b214fcad940488dea7edf2'"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "2Lyjc1qQHB37"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}