Upload train_model.ipynb

train_model.ipynb

@@ -0,0 +1,1436 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
+    "from datasets import load_dataset, load_metric, Audio, concatenate_datasets\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Login successful\n",
+      "Your token has been saved to /home/ubuntu/.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"
+     ]
+    }
+   ],
+   "source": [
+    "from huggingface_hub import notebook_login\n",
+    "\n",
+    "notebook_login()\n",
+    "repo_name = \"smangrul/xls-r-300m-mr\"\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Reusing dataset open_slr (/home/ubuntu/.cache/huggingface/datasets/open_slr/SLR64/0.0.0/e0fb9e36094eff565efe812d1aba158f6a46ce834cb9705c91d1e2d6ba78ed31)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset({\n",
+      "    features: ['path', 'audio', 'sentence'],\n",
+      "    num_rows: 1569\n",
+      "})\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Reusing dataset common_voice (/home/ubuntu/.cache/huggingface/datasets/mozilla-foundation___common_voice/mr/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)\n",
+      "Reusing dataset common_voice (/home/ubuntu/.cache/huggingface/datasets/mozilla-foundation___common_voice/mr/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset({\n",
+      "    features: ['path', 'audio', 'sentence'],\n",
+      "    num_rows: 698\n",
+      "})\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "openslr = load_dataset(\"openslr\", \"SLR64\", split=\"train\")\n",
+    "print(openslr)\n",
+    "\n",
+    "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"mr\", split=\"train+validation\", use_auth_token=True)\n",
+    "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_8_0\", \"mr\", split=\"test\", use_auth_token=True)\n",
+    "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\"])\n",
+    "print(common_voice_train)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Dataset({\n",
+       "    features: ['path', 'audio', 'sentence'],\n",
+       "    num_rows: 2267\n",
+       "})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data = concatenate_datasets([common_voice_train, openslr])\n",
+    "train_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "import unicodedata\n",
+    "chars_to_remove_regex = '[,?.!\\-\\;\\:\"“%‘”�—’…–\\।\\!\\\"\\,\\-\\.\\?\\:\\|\\“\\”\\–\\;\\'\\’\\‘\\॔\\u200c\\u200d]'\n",
+    "\n",
+    "def remove_special_characters(batch):\n",
+    "    batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n",
+    "    batch[\"sentence\"] = unicodedata.normalize(\"NFKC\", batch[\"sentence\"])\n",
+    "    return batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/ubuntu/.cache/huggingface/datasets/mozilla-foundation___common_voice/mr/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8/cache-86933e1c6f2c17a9.arrow\n",
+      "Loading cached processed dataset at /home/ubuntu/.cache/huggingface/datasets/mozilla-foundation___common_voice/mr/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8/cache-0b71d94dfe9f8e07.arrow\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_dataset = train_data.map(remove_special_characters)\n",
+    "test_dataset = common_voice_test.map(remove_special_characters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "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": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "54586502931b4e99ab8e4cb90cb9fbc0",
+       "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": "8fd87aff4e5f483daf6c6e5a4a00e37b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vocab_train = train_dataset.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=train_dataset.column_names)\n",
+    "vocab_test = test_dataset.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=train_dataset.column_names)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{' ': 0,\n",
+       " 'ँ': 1,\n",
+       " 'ं': 2,\n",
+       " 'ः': 3,\n",
+       " 'अ': 4,\n",
+       " 'आ': 5,\n",
+       " 'इ': 6,\n",
+       " 'ई': 7,\n",
+       " 'उ': 8,\n",
+       " 'ऊ': 9,\n",
+       " 'ऋ': 10,\n",
+       " 'ए': 11,\n",
+       " 'ऐ': 12,\n",
+       " 'ऑ': 13,\n",
+       " 'ओ': 14,\n",
+       " 'औ': 15,\n",
+       " 'क': 16,\n",
+       " 'ख': 17,\n",
+       " 'ग': 18,\n",
+       " 'घ': 19,\n",
+       " 'च': 20,\n",
+       " 'छ': 21,\n",
+       " 'ज': 22,\n",
+       " 'झ': 23,\n",
+       " 'ञ': 24,\n",
+       " 'ट': 25,\n",
+       " 'ठ': 26,\n",
+       " 'ड': 27,\n",
+       " 'ढ': 28,\n",
+       " 'ण': 29,\n",
+       " 'त': 30,\n",
+       " 'थ': 31,\n",
+       " 'द': 32,\n",
+       " 'ध': 33,\n",
+       " 'न': 34,\n",
+       " 'प': 35,\n",
+       " 'फ': 36,\n",
+       " 'ब': 37,\n",
+       " 'भ': 38,\n",
+       " 'म': 39,\n",
+       " 'य': 40,\n",
+       " 'र': 41,\n",
+       " 'ऱ': 42,\n",
+       " 'ल': 43,\n",
+       " 'ळ': 44,\n",
+       " 'व': 45,\n",
+       " 'श': 46,\n",
+       " 'ष': 47,\n",
+       " 'स': 48,\n",
+       " 'ह': 49,\n",
+       " '़': 50,\n",
+       " 'ा': 51,\n",
+       " 'ि': 52,\n",
+       " 'ी': 53,\n",
+       " 'ु': 54,\n",
+       " 'ू': 55,\n",
+       " 'ृ': 56,\n",
+       " 'ॄ': 57,\n",
+       " 'ॅ': 58,\n",
+       " 'े': 59,\n",
+       " 'ै': 60,\n",
+       " 'ॉ': 61,\n",
+       " 'ॊ': 62,\n",
+       " 'ो': 63,\n",
+       " 'ौ': 64,\n",
+       " '्': 65,\n",
+       " 'ॲ': 66}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))\n",
+    "vocab_dict = {v: k for k, v in enumerate(sorted(vocab_list))}\n",
+    "vocab_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vocab_dict[\"|\"] = vocab_dict[\" \"]\n",
+    "del vocab_dict[\" \"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "69"
+      ]
+     },
+     "execution_count": 11,
+     "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": 12,
+   "metadata": {},
+   "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": 3,
+   "metadata": {},
+   "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",
+      "To https://huggingface.co/smangrul/xls-r-300m-mr\n",
+      "   41422b3..c87c689  main -> main\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'https://huggingface.co/smangrul/xls-r-300m-mr/commit/c87c689895462fd42a184ae74fffebe69a4078e8'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from transformers import Wav2Vec2CTCTokenizer\n",
+    "\n",
+    "tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(\"./\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")\n",
+    "tokenizer.push_to_hub(repo_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "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": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import Wav2Vec2Processor\n",
+    "\n",
+    "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_dataset = train_dataset.cast_column(\"audio\", Audio(sampling_rate=16_000))\n",
+    "test_dataset = test_dataset.cast_column(\"audio\", Audio(sampling_rate=16_000))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "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": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a096ebabad914b1f964e3a88f7763913",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2267 [00:00<?, ?ex/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a35a844a29f748cb9dc8c96c9576cfd6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/306 [00:00<?, ?ex/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "train_dataset = train_dataset.map(prepare_dataset, remove_columns=train_dataset.column_names)\n",
+    "test_dataset = test_dataset.map(prepare_dataset, remove_columns=test_dataset.column_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_from_disk\n",
+    "train_dataset = load_from_disk(\"./Data/train_dataset\")\n",
+    "test_dataset = load_from_disk(\"./Data/test_dataset\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Dataset({\n",
+       "    features: ['input_values', 'input_length', 'labels'],\n",
+       "    num_rows: 2267\n",
+       "})"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Dataset({\n",
+       "    features: ['input_values', 'input_length', 'labels'],\n",
+       "    num_rows: 306\n",
+       "})"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wer_metric = load_metric(\"wer\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "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": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['project_q.bias', 'project_q.weight', 'project_hid.weight', 'quantizer.weight_proj.bias', 'quantizer.codevectors', 'project_hid.bias', 'quantizer.weight_proj.weight']\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"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import Wav2Vec2ForCTC\n",
+    "\n",
+    "model = Wav2Vec2ForCTC.from_pretrained(\n",
+    "    \"facebook/wav2vec2-xls-r-300m\", \n",
+    "    attention_dropout=0.1,\n",
+    "    layerdrop=0.0,\n",
+    "    feat_proj_dropout=0.0,\n",
+    "    mask_time_prob=0.75,\n",
+    "    mask_time_length=10,\n",
+    "    mask_feature_prob=0.25,\n",
+    "    mask_feature_length=64,\n",
+    "    ctc_loss_reduction=\"mean\", \n",
+    "    pad_token_id=processor.tokenizer.pad_token_id,\n",
+    "    vocab_size=len(processor.tokenizer),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/transformers/src/transformers/models/wav2vec2/modeling_wav2vec2.py:1717: 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",
+      "  FutureWarning,\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.freeze_feature_extractor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "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=16,\n",
+    "  gradient_accumulation_steps=2,\n",
+    "  evaluation_strategy=\"steps\",\n",
+    "  num_train_epochs=200,\n",
+    "  gradient_checkpointing=True,\n",
+    "  fp16=True,\n",
+    "  save_steps=400,\n",
+    "  eval_steps=400,\n",
+    "  logging_steps=100,\n",
+    "  learning_rate=1e-4,\n",
+    "  warmup_steps=1000,\n",
+    "  save_total_limit=1,\n",
+    "  push_to_hub=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/ebs/learn/ASR/smangrul/xls-r-300m-mr is already a clone of https://huggingface.co/smangrul/xls-r-300m-mr. Make sure you pull the latest changes with `repo.git_pull()`.\n",
+      "Using amp half precision backend\n"
+     ]
+    }
+   ],
+   "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=train_dataset,\n",
+    "    eval_dataset=test_dataset,\n",
+    "    tokenizer=processor.feature_extractor,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "/home/ubuntu/transformers/src/transformers/optimization.py:309: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use thePyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+      "  FutureWarning,\n",
+      "***** Running training *****\n",
+      "  Num examples = 2267\n",
+      "  Num Epochs = 200\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 = 14200\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='14200' max='14200' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [14200/14200 10:40:12, Epoch 200/200]\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>3.794000</td>\n",
+       "      <td>3.532227</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>800</td>\n",
+       "      <td>3.362400</td>\n",
+       "      <td>3.359044</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1200</td>\n",
+       "      <td>2.293900</td>\n",
+       "      <td>1.011279</td>\n",
+       "      <td>0.829924</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1600</td>\n",
+       "      <td>1.233000</td>\n",
+       "      <td>0.502743</td>\n",
+       "      <td>0.593662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2000</td>\n",
+       "      <td>0.962600</td>\n",
+       "      <td>0.412519</td>\n",
+       "      <td>0.496992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2400</td>\n",
+       "      <td>0.831800</td>\n",
+       "      <td>0.402903</td>\n",
+       "      <td>0.493783</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2800</td>\n",
+       "      <td>0.737000</td>\n",
+       "      <td>0.389773</td>\n",
+       "      <td>0.469314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3200</td>\n",
+       "      <td>0.677100</td>\n",
+       "      <td>0.373987</td>\n",
+       "      <td>0.436021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>3600</td>\n",
+       "      <td>0.634400</td>\n",
+       "      <td>0.383823</td>\n",
+       "      <td>0.432010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4000</td>\n",
+       "      <td>0.586000</td>\n",
+       "      <td>0.375610</td>\n",
+       "      <td>0.419575</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4400</td>\n",
+       "      <td>0.561000</td>\n",
+       "      <td>0.387891</td>\n",
+       "      <td>0.418371</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>4800</td>\n",
+       "      <td>0.518500</td>\n",
+       "      <td>0.386357</td>\n",
+       "      <td>0.417569</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5200</td>\n",
+       "      <td>0.515300</td>\n",
+       "      <td>0.415069</td>\n",
+       "      <td>0.430004</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>5600</td>\n",
+       "      <td>0.478100</td>\n",
+       "      <td>0.399211</td>\n",
+       "      <td>0.408744</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6000</td>\n",
+       "      <td>0.468100</td>\n",
+       "      <td>0.424542</td>\n",
+       "      <td>0.402327</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6400</td>\n",
+       "      <td>0.439400</td>\n",
+       "      <td>0.430979</td>\n",
+       "      <td>0.410750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>6800</td>\n",
+       "      <td>0.429600</td>\n",
+       "      <td>0.427700</td>\n",
+       "      <td>0.409146</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7200</td>\n",
+       "      <td>0.400300</td>\n",
+       "      <td>0.451111</td>\n",
+       "      <td>0.419976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>7600</td>\n",
+       "      <td>0.395100</td>\n",
+       "      <td>0.463446</td>\n",
+       "      <td>0.405134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8000</td>\n",
+       "      <td>0.381800</td>\n",
+       "      <td>0.454752</td>\n",
+       "      <td>0.407942</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8400</td>\n",
+       "      <td>0.371500</td>\n",
+       "      <td>0.461547</td>\n",
+       "      <td>0.404733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>8800</td>\n",
+       "      <td>0.362500</td>\n",
+       "      <td>0.461543</td>\n",
+       "      <td>0.411151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9200</td>\n",
+       "      <td>0.338200</td>\n",
+       "      <td>0.468299</td>\n",
+       "      <td>0.417168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>9600</td>\n",
+       "      <td>0.338800</td>\n",
+       "      <td>0.480989</td>\n",
+       "      <td>0.412355</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10000</td>\n",
+       "      <td>0.317600</td>\n",
+       "      <td>0.475700</td>\n",
+       "      <td>0.410750</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10400</td>\n",
+       "      <td>0.315100</td>\n",
+       "      <td>0.478920</td>\n",
+       "      <td>0.403530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>10800</td>\n",
+       "      <td>0.296200</td>\n",
+       "      <td>0.480600</td>\n",
+       "      <td>0.398315</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11200</td>\n",
+       "      <td>0.299000</td>\n",
+       "      <td>0.477083</td>\n",
+       "      <td>0.393502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>11600</td>\n",
+       "      <td>0.290000</td>\n",
+       "      <td>0.465646</td>\n",
+       "      <td>0.393903</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12000</td>\n",
+       "      <td>0.290900</td>\n",
+       "      <td>0.490041</td>\n",
+       "      <td>0.405937</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12400</td>\n",
+       "      <td>0.275600</td>\n",
+       "      <td>0.489354</td>\n",
+       "      <td>0.399519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>12800</td>\n",
+       "      <td>0.272600</td>\n",
+       "      <td>0.494580</td>\n",
+       "      <td>0.395909</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13200</td>\n",
+       "      <td>0.265900</td>\n",
+       "      <td>0.497918</td>\n",
+       "      <td>0.397112</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>13600</td>\n",
+       "      <td>0.266300</td>\n",
+       "      <td>0.498627</td>\n",
+       "      <td>0.397513</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>14000</td>\n",
+       "      <td>0.259600</td>\n",
+       "      <td>0.504610</td>\n",
+       "      <td>0.401524</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-400\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-400/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-400/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-400/preprocessor_config.json\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-800\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-800/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-800/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-800/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-400] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-1200\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-1200/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-1200/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-1200/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-800] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-1600\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-1600/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-1600/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-1600/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-1200] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-2000\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-2000/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-2000/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-2000/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-1600] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-2400\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-2400/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-2400/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-2400/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-2800\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-2800/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-2800/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-2800/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-2400] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-3200\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-3200/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-3200/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-3200/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-2800] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-3600\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-3600/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-3600/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-3600/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-3200] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-4000\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-4000/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-4000/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-4000/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-3600] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-4400\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-4400/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-4400/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-4400/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-4800\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-4800/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-4800/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-4800/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-4400] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-5200\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-5200/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-5200/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-5200/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-4800] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-5600\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-5600/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-5600/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-5600/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-5200] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-6000\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-6000/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-6000/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-6000/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-5600] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-6400\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-6400/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-6400/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-6400/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-6800\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-6800/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-6800/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-6800/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-6400] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-7200\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-7200/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-7200/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-7200/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-6800] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-7600\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-7600/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-7600/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-7600/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-7200] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-8000\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-8000/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-8000/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-8000/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-7600] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-8400\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-8400/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-8400/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-8400/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-8800\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-8800/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-8800/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-8800/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-8400] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-9200\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-9200/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-9200/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-9200/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-8800] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-9600\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-9600/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-9600/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-9600/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-9200] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-10000\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-10000/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-10000/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-10000/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-9600] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-10400\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-10400/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-10400/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-10400/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-10800\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-10800/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-10800/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-10800/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-10400] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-11200\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-11200/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-11200/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-11200/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-10800] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-11600\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-11600/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-11600/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-11600/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-11200] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-12000\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-12000/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-12000/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-12000/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-11600] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-12400\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-12400/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-12400/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-12400/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-12800\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-12800/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-12800/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-12800/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-12400] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-13200\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-13200/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-13200/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-13200/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-12800] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-13600\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-13600/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-13600/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-13600/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-13200] 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. If input_length are not expected by `Wav2Vec2ForCTC.forward`,  you can safely ignore this message.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 306\n",
+      "  Batch size = 8\n",
+      "Saving model checkpoint to smangrul/xls-r-300m-mr/checkpoint-14000\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-14000/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/checkpoint-14000/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/checkpoint-14000/preprocessor_config.json\n",
+      "Deleting older checkpoint [smangrul/xls-r-300m-mr/checkpoint-13600] 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"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrainOutput(global_step=14200, training_loss=0.8374653981437146, metrics={'train_runtime': 38417.9883, 'train_samples_per_second': 11.802, 'train_steps_per_second': 0.37, 'total_flos': 9.128944889276437e+19, 'train_loss': 0.8374653981437146, 'epoch': 200.0})"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Saving model checkpoint to smangrul/xls-r-300m-mr\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/config.json\n",
+      "Model weights saved in smangrul/xls-r-300m-mr/pytorch_model.bin\n",
+      "Configuration saved in smangrul/xls-r-300m-mr/preprocessor_config.json\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d6ee5a61abd46f4a91acb7e34864e06",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Upload file pytorch_model.bin:   0%|          | 3.39k/1.18G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "remote: -------------------------------------------------------------------------\u001b[31m        \n",
+      "remote: Your push was rejected because it contains files larger than 10M.        \n",
+      "remote: Please use https://git-lfs.github.com/ to store larger files.\u001b(B\u001b[m        \n",
+      "remote: -------------------------------------------------------------------------        \n",
+      "remote: Offending files:        \n",
+      "remote:  - language_model/unigrams.txt (ref: refs/heads/main)        \n",
+      "To https://huggingface.co/smangrul/xls-r-300m-mr\n",
+      " ! [remote rejected] main -> main (pre-receive hook declined)\n",
+      "error: failed to push some refs to 'https://user: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@huggingface.co/smangrul/xls-r-300m-mr'\n",
+      "\n"
+     ]
+    },
+    {
+     "ename": "OSError",
+     "evalue": "remote: -------------------------------------------------------------------------\u001b[31m        \nremote: Your push was rejected because it contains files larger than 10M.        \nremote: Please use https://git-lfs.github.com/ to store larger files.\u001b(B\u001b[m        \nremote: -------------------------------------------------------------------------        \nremote: Offending files:        \nremote:  - language_model/unigrams.txt (ref: refs/heads/main)        \nTo https://huggingface.co/smangrul/xls-r-300m-mr\n ! [remote rejected] main -> main (pre-receive hook declined)\nerror: failed to push some refs to 'https://user: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@huggingface.co/smangrul/xls-r-300m-mr'\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mCalledProcessError\u001b[0m                        Traceback (most recent call last)",
+      "\u001b[0;32m~/hf/lib/python3.7/site-packages/huggingface_hub/repository.py\u001b[0m in \u001b[0;36mgit_push\u001b[0;34m(self, upstream, blocking, auto_lfs_prune)\u001b[0m\n\u001b[1;32m   1018\u001b[0m                         raise subprocess.CalledProcessError(\n\u001b[0;32m-> 1019\u001b[0;31m                             \u001b[0mreturn_code\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1020\u001b[0m                         )\n",
+      "\u001b[0;31mCalledProcessError\u001b[0m: Command '['git', 'push', '--set-upstream', 'origin', 'main']' returned non-zero exit status 1.",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[0;32m/tmp/ipykernel_39173/1405518398.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/transformers/src/transformers/trainer.py\u001b[0m in \u001b[0;36mpush_to_hub\u001b[0;34m(self, commit_message, blocking, **kwargs)\u001b[0m\n\u001b[1;32m   2807\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2808\u001b[0m         git_head_commit_url = self.repo.push_to_hub(\n\u001b[0;32m-> 2809\u001b[0;31m             \u001b[0mcommit_message\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcommit_message\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblocking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblocking\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mauto_lfs_prune\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2810\u001b[0m         )\n\u001b[1;32m   2811\u001b[0m         \u001b[0;31m# push separately the model card to be independant from the rest of the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/hf/lib/python3.7/site-packages/huggingface_hub/repository.py\u001b[0m in \u001b[0;36mpush_to_hub\u001b[0;34m(self, commit_message, blocking, clean_ok, auto_lfs_prune)\u001b[0m\n\u001b[1;32m   1252\u001b[0m             \u001b[0mupstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mf\"origin {self.current_branch}\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1253\u001b[0m             \u001b[0mblocking\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mblocking\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1254\u001b[0;31m             \u001b[0mauto_lfs_prune\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mauto_lfs_prune\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1255\u001b[0m         )\n\u001b[1;32m   1256\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/hf/lib/python3.7/site-packages/huggingface_hub/repository.py\u001b[0m in \u001b[0;36mgit_push\u001b[0;34m(self, upstream, blocking, auto_lfs_prune)\u001b[0m\n\u001b[1;32m   1021\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1022\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0msubprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCalledProcessError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1023\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mEnvironmentError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1024\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1025\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mblocking\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mOSError\u001b[0m: remote: -------------------------------------------------------------------------\u001b[31m        \nremote: Your push was rejected because it contains files larger than 10M.        \nremote: Please use https://git-lfs.github.com/ to store larger files.\u001b(B\u001b[m        \nremote: -------------------------------------------------------------------------        \nremote: Offending files:        \nremote:  - language_model/unigrams.txt (ref: refs/heads/main)        \nTo https://huggingface.co/smangrul/xls-r-300m-mr\n ! [remote rejected] main -> main (pre-receive hook declined)\nerror: failed to push some refs to 'https://user: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@huggingface.co/smangrul/xls-r-300m-mr'\n"
+     ]
+    }
+   ],
+   "source": [
+    "trainer.push_to_hub()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# train_dataset.save_to_disk(\"./Data/train_dataset\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# test_dataset.save_to_disk(\"./Data/test_dataset\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "hf",
+   "language": "python",
+   "name": "hf"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}