Add train notebook

wav2vec2/wav2vec_train.ipynb

@@ -0,0 +1,1665 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 829,
+     "status": "ok",
+     "timestamp": 1641588786523,
+     "user": {
+      "displayName": "Yurii Paniv",
+      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
+      "userId": "13095662915325887123"
+     },
+     "user_tz": -120
+    },
+    "id": "YELVqGxMxnbG",
+    "outputId": "876761c1-2e03-411b-e61b-07ac4ad61377"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Wed Dec 28 21:13:08 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 515.86.01    Driver Version: 515.86.01    CUDA Version: 11.7     |\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  NVIDIA GeForce ...  Off  | 00000000:0A:00.0  On |                  N/A |\n",
+      "|  0%   41C    P8    24W / 390W |   1364MiB / 24576MiB |      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",
+      "|    0   N/A  N/A      1345      G   /usr/lib/xorg/Xorg                528MiB |\n",
+      "|    0   N/A  N/A      2100      G   /usr/bin/kwalletd5                  4MiB |\n",
+      "|    0   N/A  N/A      2266      G   ...ec/xdg-desktop-portal-kde        4MiB |\n",
+      "|    0   N/A  N/A      2303      G   /usr/bin/ksmserver                  4MiB |\n",
+      "|    0   N/A  N/A      2305      G   /usr/bin/kded5                      4MiB |\n",
+      "|    0   N/A  N/A      2306      G   /usr/bin/kwin_x11                 102MiB |\n",
+      "|    0   N/A  N/A      2367      G   /usr/bin/plasmashell              133MiB |\n",
+      "|    0   N/A  N/A      2396      G   ...de-authentication-agent-1        4MiB |\n",
+      "|    0   N/A  N/A      2443      G   ...x-gnu/libexec/kdeconnectd        4MiB |\n",
+      "|    0   N/A  N/A      2445      G   .../usr/bin/telegram-desktop        7MiB |\n",
+      "|    0   N/A  N/A      2459      G   /usr/bin/kaccess                    4MiB |\n",
+      "|    0   N/A  N/A      2484      G   ...1/usr/lib/firefox/firefox      214MiB |\n",
+      "|    0   N/A  N/A      2499      G   .../libexec/DiscoverNotifier        4MiB |\n",
+      "|    0   N/A  N/A      2784      G   /usr/bin/dolphin                    4MiB |\n",
+      "|    0   N/A  N/A      2917      G   /usr/bin/dolphin                    4MiB |\n",
+      "|    0   N/A  N/A      2997      G   /usr/bin/dolphin                    4MiB |\n",
+      "|    0   N/A  N/A      3138      G   ...gnu/libexec/kf5/kioslave5        4MiB |\n",
+      "|    0   N/A  N/A      3158      G   ...gnu/libexec/kf5/kioslave5        4MiB |\n",
+      "|    0   N/A  N/A      3663      G   /usr/bin/dolphin                    4MiB |\n",
+      "|    0   N/A  N/A      3768      G   /usr/bin/dolphin                    4MiB |\n",
+      "|    0   N/A  N/A      3908      G   ...gnu/libexec/kf5/kioslave5        4MiB |\n",
+      "|    0   N/A  N/A      3964      G   ...gnu/libexec/kf5/kioslave5        4MiB |\n",
+      "|    0   N/A  N/A      4610      G   ...RendererForSitePerProcess      293MiB |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "gpu_info = !nvidia-smi\n",
+    "gpu_info = '\\n'.join(gpu_info)\n",
+    "if gpu_info.find('failed') >= 0:\n",
+    "  print('Not connected to a GPU')\n",
+    "else:\n",
+    "  print(gpu_info)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 5334,
+     "status": "ok",
+     "timestamp": 1641588811766,
+     "user": {
+      "displayName": "Yurii Paniv",
+      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
+      "userId": "13095662915325887123"
+     },
+     "user_tz": -120
+    },
+    "id": "2MMXcWFFgCXU",
+    "outputId": "be9fd72e-4395-4cd0-ff87-631dad046e71"
+   },
+   "outputs": [],
+   "source": [
+    "from datasets import load_from_disk, load_metric, Audio\n",
+    "\n",
+    "common_voice_train = load_from_disk(\"cached_dataset/cv_train\")\n",
+    "common_voice_test = load_from_disk(\"cached_dataset/cv_test\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "kAR0-2KLkopp"
+   },
+   "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": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from transformers import Wav2Vec2CTCTokenizer\n",
+    "\n",
+    "tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(\"./\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "KYZtoW-tlZgl"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import Wav2Vec2Processor\n",
+    "\n",
+    "processor = Wav2Vec2Processor(feature_extractor=feature_extractor, tokenizer=tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "tborvC9hx88e"
+   },
+   "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": 7,
+   "metadata": {
+    "id": "lbQf5GuZyQ4_"
+   },
+   "outputs": [],
+   "source": [
+    "data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "9Xsux2gmyXso"
+   },
+   "outputs": [],
+   "source": [
+    "wer_metric = load_metric(\"wer\")\n",
+    "cer_metric = load_metric(\"cer\")\n",
+    "metrics = [wer_metric, cer_metric]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "id": "1XZ-kjweyTy_"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\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",
+    "    cer = cer_metric.compute(predictions=pred_str, references=label_str)\n",
+    "\n",
+    "    return {\"wer\": wer, \"cer\": cer}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 9496,
+     "status": "ok",
+     "timestamp": 1641588938616,
+     "user": {
+      "displayName": "Yurii Paniv",
+      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
+      "userId": "13095662915325887123"
+     },
+     "user_tz": -120
+    },
+    "id": "e7cqAWIayn6w",
+    "outputId": "b7b20ce9-e1b2-473f-8032-2a75f98dfa9e"
+   },
+   "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.weight', 'quantizer.weight_proj.weight', 'project_q.bias', 'quantizer.weight_proj.bias', 'project_hid.bias', 'project_hid.weight', 'quantizer.codevectors']\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.bias', 'lm_head.weight']\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=0.3,\n",
+    "    hidden_dropout=0.3,\n",
+    "    feat_proj_dropout=0.3,\n",
+    "    mask_time_prob=0.05,\n",
+    "    layerdrop=0.3,\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": 11,
+   "metadata": {
+    "id": "oGI8zObtZ3V0"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/robinhad/Projects/unchanged/voice-recognition-ua/env/lib/python3.10/site-packages/transformers/models/wav2vec2/modeling_wav2vec2.py:1618: FutureWarning: The method `freeze_feature_extractor` is deprecated and will be removed in Transformers v5.Please use the equivalent `freeze_feature_encoder` method instead.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.freeze_feature_extractor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "KbeKSV7uzGPP"
+   },
+   "outputs": [],
+   "source": [
+    "from transformers import TrainingArguments\n",
+    "\n",
+    "repo_name = \"wav2vec2-xls-r-base-uk\"\n",
+    "\n",
+    "training_args = TrainingArguments(\n",
+    "  output_dir=repo_name,\n",
+    "  group_by_length=True,\n",
+    "  per_device_train_batch_size=24,\n",
+    "  per_device_eval_batch_size=24,  \n",
+    "  gradient_accumulation_steps=6,\n",
+    "  eval_accumulation_steps=6,\n",
+    "  evaluation_strategy=\"epoch\",\n",
+    "  save_strategy=\"epoch\",\n",
+    "  logging_strategy=\"epoch\",\n",
+    "  num_train_epochs=150,\n",
+    "  gradient_checkpointing=True,\n",
+    "  fp16=True,\n",
+    "  #save_steps=1,\n",
+    "  #eval_steps=1,\n",
+    "  #logging_steps=1,\n",
+    "  learning_rate=3e-4,\n",
+    "  warmup_steps=500,\n",
+    "  save_total_limit=2,\n",
+    "  report_to=\"tensorboard\",\n",
+    "  load_best_model_at_end=True,\n",
+    "  metric_for_best_model=\"cer\",\n",
+    "  greater_is_better=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "executionInfo": {
+     "elapsed": 11063,
+     "status": "ok",
+     "timestamp": 1641588949674,
+     "user": {
+      "displayName": "Yurii Paniv",
+      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
+      "userId": "13095662915325887123"
+     },
+     "user_tz": -120
+    },
+    "id": "rY7vBmFCPFgC",
+    "outputId": "2e89d5ea-5b25-44bf-8492-a6220b0b1c38"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using cuda_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=common_voice_train,\n",
+    "    eval_dataset=common_voice_test,\n",
+    "    tokenizer=processor.feature_extractor,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 409
+    },
+    "id": "9fRr9TG5pGBl",
+    "outputId": "c2a7c797-326c-4bd2-b167-9d2f41d77def"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading model from wav2vec2-xls-r-base-uk/checkpoint-7505.\n",
+      "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/robinhad/Projects/unchanged/voice-recognition-ua/env/lib/python3.10/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
+      "  warnings.warn(\n",
+      "***** Running training *****\n",
+      "  Num examples = 11463\n",
+      "  Num Epochs = 150\n",
+      "  Instantaneous batch size per device = 24\n",
+      "  Total train batch size (w. parallel, distributed & accumulation) = 144\n",
+      "  Gradient Accumulation steps = 6\n",
+      "  Total optimization steps = 11850\n",
+      "  Continuing training from checkpoint, will skip to saved global_step\n",
+      "  Continuing training from epoch 95\n",
+      "  Continuing training from global step 7505\n",
+      "  Will skip the first 95 epochs then the first 0 batches in the first epoch. If this takes a lot of time, you can add the `--ignore_data_skip` flag to your launch command, but you will resume the training on data already seen by your model.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d39c143147e7431a91cf50b54464cbee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0it [00:00, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='7910' max='11850' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [ 7910/11850 45:49 < 7:28:05, 0.15 it/s, Epoch 100.11/150]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Epoch</th>\n",
+       "      <th>Training Loss</th>\n",
+       "      <th>Validation Loss</th>\n",
+       "      <th>Wer</th>\n",
+       "      <th>Cer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>95</td>\n",
+       "      <td>0.271200</td>\n",
+       "      <td>0.596927</td>\n",
+       "      <td>0.519565</td>\n",
+       "      <td>0.128453</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>96</td>\n",
+       "      <td>0.279300</td>\n",
+       "      <td>0.595789</td>\n",
+       "      <td>0.516518</td>\n",
+       "      <td>0.128272</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>97</td>\n",
+       "      <td>0.276800</td>\n",
+       "      <td>0.623400</td>\n",
+       "      <td>0.512582</td>\n",
+       "      <td>0.127275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>98</td>\n",
+       "      <td>0.266000</td>\n",
+       "      <td>0.617245</td>\n",
+       "      <td>0.519181</td>\n",
+       "      <td>0.130092</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>99</td>\n",
+       "      <td>0.281600</td>\n",
+       "      <td>0.606772</td>\n",
+       "      <td>0.512401</td>\n",
+       "      <td>0.128527</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 = 6783\n",
+      "  Batch size = 24\n",
+      "Saving model checkpoint to wav2vec2-xls-r-base-uk/checkpoint-7584\n",
+      "Configuration saved in wav2vec2-xls-r-base-uk/checkpoint-7584/config.json\n",
+      "Model weights saved in wav2vec2-xls-r-base-uk/checkpoint-7584/pytorch_model.bin\n",
+      "Feature extractor saved in wav2vec2-xls-r-base-uk/checkpoint-7584/preprocessor_config.json\n",
+      "Deleting older checkpoint [wav2vec2-xls-r-base-uk/checkpoint-7505] 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 = 6783\n",
+      "  Batch size = 24\n",
+      "Saving model checkpoint to wav2vec2-xls-r-base-uk/checkpoint-7663\n",
+      "Configuration saved in wav2vec2-xls-r-base-uk/checkpoint-7663/config.json\n",
+      "Model weights saved in wav2vec2-xls-r-base-uk/checkpoint-7663/pytorch_model.bin\n",
+      "Feature extractor saved in wav2vec2-xls-r-base-uk/checkpoint-7663/preprocessor_config.json\n",
+      "Deleting older checkpoint [wav2vec2-xls-r-base-uk/checkpoint-7584] 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 = 6783\n",
+      "  Batch size = 24\n",
+      "Saving model checkpoint to wav2vec2-xls-r-base-uk/checkpoint-7742\n",
+      "Configuration saved in wav2vec2-xls-r-base-uk/checkpoint-7742/config.json\n",
+      "Model weights saved in wav2vec2-xls-r-base-uk/checkpoint-7742/pytorch_model.bin\n",
+      "Feature extractor saved in wav2vec2-xls-r-base-uk/checkpoint-7742/preprocessor_config.json\n",
+      "Deleting older checkpoint [wav2vec2-xls-r-base-uk/checkpoint-7663] 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 = 6783\n",
+      "  Batch size = 24\n",
+      "Saving model checkpoint to wav2vec2-xls-r-base-uk/checkpoint-7821\n",
+      "Configuration saved in wav2vec2-xls-r-base-uk/checkpoint-7821/config.json\n",
+      "Model weights saved in wav2vec2-xls-r-base-uk/checkpoint-7821/pytorch_model.bin\n",
+      "Feature extractor saved in wav2vec2-xls-r-base-uk/checkpoint-7821/preprocessor_config.json\n",
+      "Deleting older checkpoint [wav2vec2-xls-r-base-uk/checkpoint-7742] 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 = 6783\n",
+      "  Batch size = 24\n",
+      "Saving model checkpoint to wav2vec2-xls-r-base-uk/checkpoint-7900\n",
+      "Configuration saved in wav2vec2-xls-r-base-uk/checkpoint-7900/config.json\n",
+      "Model weights saved in wav2vec2-xls-r-base-uk/checkpoint-7900/pytorch_model.bin\n",
+      "Feature extractor saved in wav2vec2-xls-r-base-uk/checkpoint-7900/preprocessor_config.json\n",
+      "Deleting older checkpoint [wav2vec2-xls-r-base-uk/checkpoint-7821] due to args.save_total_limit\n"
+     ]
+    }
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