diff --git "a/galician_training_script.ipynb" "b/galician_training_script.ipynb"
new file mode 100644--- /dev/null
+++ "b/galician_training_script.ipynb"
@@ -0,0 +1,2143 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# HuggingFace challenge - Debugger notebook\n",
+    "Run this notebook to verify your libraries versions, check GPU config and run a quick training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "id": "T2utsYSKszvv"
+   },
+   "outputs": [],
+   "source": [
+    "import platform\n",
+    "import multiprocessing\n",
+    "\n",
+    "import torch\n",
+    "import transformers\n",
+    "import datasets\n",
+    "\n",
+    "import soundfile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Print main infos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "5P6I-W9ts-kR",
+    "outputId": "939bd550-1486-46a6-8371-e82ada0f448c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Platform: Linux-5.11.0-37-generic-x86_64-with-glibc2.10\n",
+      "CPU cores: 60\n",
+      "Python version: 3.8.8\n",
+      "PyTorch version: 1.10.1+cu102\n",
+      "GPU is visible: True\n",
+      "Transformers version: 4.16.0.dev0\n",
+      "Datasets version: 1.17.1.dev0\n",
+      "soundfile version: 0.10.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Platform: {platform.platform()}\")\n",
+    "print(f\"CPU cores: {multiprocessing.cpu_count()}\")\n",
+    "\n",
+    "print(f\"Python version: {platform.python_version()}\")\n",
+    "\n",
+    "print(f\"PyTorch version: {torch.__version__}\")\n",
+    "print(f\"GPU is visible: {torch.cuda.is_available()}\")\n",
+    "\n",
+    "print(f\"Transformers version: {transformers.__version__}\")\n",
+    "print(f\"Datasets version: {datasets.__version__}\")\n",
+    "\n",
+    "print(f\"soundfile version: {soundfile.__version__}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Check your GPU informations (if any)\n",
+    "If you launched an AI Training job with GPU resources, they should be listed below (Tesla V100s 32GB).\n",
+    "Driver and CUDA version "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "YT7fRnKctggU",
+    "outputId": "f355a3e0-20da-489f-bd1f-5e508e792a68"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Thu Jan 27 07:23:09 2022       \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| NVIDIA-SMI 470.57.02    Driver Version: 470.57.02    CUDA Version: 11.4     |\n",
+      "|-------------------------------+----------------------+----------------------+\n",
+      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
+      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
+      "|                               |                      |               MIG M. |\n",
+      "|===============================+======================+======================|\n",
+      "|   0  Tesla V100S-PCI...  Off  | 00000000:00:06.0 Off |                    0 |\n",
+      "| N/A   34C    P0    25W / 250W |      4MiB / 32510MiB |      0%      Default |\n",
+      "|                               |                      |                  N/A |\n",
+      "+-------------------------------+----------------------+----------------------+\n",
+      "                                                                               \n",
+      "+-----------------------------------------------------------------------------+\n",
+      "| Processes:                                                                  |\n",
+      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
+      "|        ID   ID                                                   Usage      |\n",
+      "|=============================================================================|\n",
+      "|  No running processes found                                                 |\n",
+      "+-----------------------------------------------------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "!nvidia-smi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fa897b4afc049229144599af9e3f807",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(HTML(value='<center>\\n<img src=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from huggingface_hub import notebook_login\n",
+    "\n",
+    "notebook_login()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TorMtpwPv6RQ"
+   },
+   "source": [
+    "## Quick training run with a dummy model and data\n",
+    "more information on https://github.com/huggingface/transformers/tree/master/examples/pytorch/speech-recognition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "fevoJD15u4Ss",
+    "outputId": "5861d34e-745b-45ee-e780-ed363043e655"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2022-01-22 15:01:09--  https://raw.githubusercontent.com/huggingface/transformers/master/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py\n",
+      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.108.133, 185.199.109.133, ...\n",
+      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 30348 (30K) [text/plain]\n",
+      "Saving to: ‘run_speech_recognition_ctc.py’\n",
+      "\n",
+      "run_speech_recognit 100%[===================>]  29.64K  --.-KB/s    in 0.001s  \n",
+      "\n",
+      "2022-01-22 15:01:09 (20.1 MB/s) - ‘run_speech_recognition_ctc.py’ saved [30348/30348]\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget -O run_speech_recognition_ctc.py https://raw.githubusercontent.com/huggingface/transformers/master/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# \t--learning_rate=\"7.5e-5\" \\\n",
+    "# 84.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Mz4bubhxxsad",
+    "outputId": "23398525-cc19-43c2-9fec-497e06214f29"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "01/27/2022 09:23:57 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True\n",
+      "01/27/2022 09:23:57 - INFO - __main__ - Training/evaluation parameters TrainingArguments(\n",
+      "_n_gpu=1,\n",
+      "adafactor=False,\n",
+      "adam_beta1=0.9,\n",
+      "adam_beta2=0.999,\n",
+      "adam_epsilon=1e-08,\n",
+      "bf16=False,\n",
+      "bf16_full_eval=False,\n",
+      "dataloader_drop_last=False,\n",
+      "dataloader_num_workers=0,\n",
+      "dataloader_pin_memory=True,\n",
+      "ddp_bucket_cap_mb=None,\n",
+      "ddp_find_unused_parameters=None,\n",
+      "debug=[],\n",
+      "deepspeed=None,\n",
+      "disable_tqdm=False,\n",
+      "do_eval=True,\n",
+      "do_predict=False,\n",
+      "do_train=True,\n",
+      "eval_accumulation_steps=None,\n",
+      "eval_steps=500,\n",
+      "evaluation_strategy=IntervalStrategy.STEPS,\n",
+      "fp16=True,\n",
+      "fp16_backend=auto,\n",
+      "fp16_full_eval=False,\n",
+      "fp16_opt_level=O1,\n",
+      "gradient_accumulation_steps=1,\n",
+      "gradient_checkpointing=True,\n",
+      "greater_is_better=None,\n",
+      "group_by_length=True,\n",
+      "half_precision_backend=auto,\n",
+      "hub_model_id=None,\n",
+      "hub_strategy=HubStrategy.EVERY_SAVE,\n",
+      "hub_token=<HUB_TOKEN>,\n",
+      "ignore_data_skip=False,\n",
+      "label_names=None,\n",
+      "label_smoothing_factor=0.0,\n",
+      "learning_rate=7e-05,\n",
+      "length_column_name=input_length,\n",
+      "load_best_model_at_end=False,\n",
+      "local_rank=-1,\n",
+      "log_level=-1,\n",
+      "log_level_replica=-1,\n",
+      "log_on_each_node=True,\n",
+      "logging_dir=./wav2vec2-large-xls-r-300m-galician/runs/Jan27_09-23-57_job-8be8b741-e32e-4579-bbec-1e00d9824b4f,\n",
+      "logging_first_step=False,\n",
+      "logging_nan_inf_filter=True,\n",
+      "logging_steps=100,\n",
+      "logging_strategy=IntervalStrategy.STEPS,\n",
+      "lr_scheduler_type=SchedulerType.LINEAR,\n",
+      "max_grad_norm=1.0,\n",
+      "max_steps=-1,\n",
+      "metric_for_best_model=None,\n",
+      "mp_parameters=,\n",
+      "no_cuda=False,\n",
+      "num_train_epochs=20.0,\n",
+      "optim=OptimizerNames.ADAMW_HF,\n",
+      "output_dir=./wav2vec2-large-xls-r-300m-galician,\n",
+      "overwrite_output_dir=True,\n",
+      "past_index=-1,\n",
+      "per_device_eval_batch_size=32,\n",
+      "per_device_train_batch_size=32,\n",
+      "prediction_loss_only=False,\n",
+      "push_to_hub=True,\n",
+      "push_to_hub_model_id=None,\n",
+      "push_to_hub_organization=None,\n",
+      "push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n",
+      "remove_unused_columns=True,\n",
+      "report_to=[],\n",
+      "resume_from_checkpoint=None,\n",
+      "run_name=./wav2vec2-large-xls-r-300m-galician,\n",
+      "save_on_each_node=False,\n",
+      "save_steps=500,\n",
+      "save_strategy=IntervalStrategy.STEPS,\n",
+      "save_total_limit=2,\n",
+      "seed=42,\n",
+      "sharded_ddp=[],\n",
+      "skip_memory_metrics=True,\n",
+      "tf32=None,\n",
+      "tpu_metrics_debug=False,\n",
+      "tpu_num_cores=None,\n",
+      "use_legacy_prediction_loop=False,\n",
+      "warmup_ratio=0.0,\n",
+      "warmup_steps=500,\n",
+      "weight_decay=0.0,\n",
+      "xpu_backend=None,\n",
+      ")\n",
+      "01/27/2022 09:24:00 - WARNING - datasets.builder - Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/gl/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n",
+      "01/27/2022 09:24:02 - WARNING - datasets.builder - Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/gl/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n",
+      "01/27/2022 09:24:02 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/gl/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/cache-0553300b2837812b.arrow\n",
+      "01/27/2022 09:24:02 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/gl/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b/cache-46e9370bf6879933.arrow\n",
+      "loading configuration file https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/config.json from cache at /workspace/.cache/huggingface/transformers/dabc27df63e37bd2a7a221c7774e35f36a280fbdf917cf54cadfc7df8c786f6f.a3e4c3c967d9985881e0ae550a5f6f668f897db5ab2e0802f9b97973b15970e6\n",
+      "Model config Wav2Vec2Config {\n",
+      "  \"_name_or_path\": \"facebook/wav2vec2-xls-r-300m\",\n",
+      "  \"activation_dropout\": 0.0,\n",
+      "  \"adapter_kernel_size\": 3,\n",
+      "  \"adapter_stride\": 2,\n",
+      "  \"add_adapter\": false,\n",
+      "  \"apply_spec_augment\": true,\n",
+      "  \"architectures\": [\n",
+      "    \"Wav2Vec2ForPreTraining\"\n",
+      "  ],\n",
+      "  \"attention_dropout\": 0.1,\n",
+      "  \"bos_token_id\": 1,\n",
+      "  \"classifier_proj_size\": 256,\n",
+      "  \"codevector_dim\": 768,\n",
+      "  \"contrastive_logits_temperature\": 0.1,\n",
+      "  \"conv_bias\": true,\n",
+      "  \"conv_dim\": [\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512\n",
+      "  ],\n",
+      "  \"conv_kernel\": [\n",
+      "    10,\n",
+      "    3,\n",
+      "    3,\n",
+      "    3,\n",
+      "    3,\n",
+      "    2,\n",
+      "    2\n",
+      "  ],\n",
+      "  \"conv_stride\": [\n",
+      "    5,\n",
+      "    2,\n",
+      "    2,\n",
+      "    2,\n",
+      "    2,\n",
+      "    2,\n",
+      "    2\n",
+      "  ],\n",
+      "  \"ctc_loss_reduction\": \"sum\",\n",
+      "  \"ctc_zero_infinity\": false,\n",
+      "  \"diversity_loss_weight\": 0.1,\n",
+      "  \"do_stable_layer_norm\": true,\n",
+      "  \"eos_token_id\": 2,\n",
+      "  \"feat_extract_activation\": \"gelu\",\n",
+      "  \"feat_extract_dropout\": 0.0,\n",
+      "  \"feat_extract_norm\": \"layer\",\n",
+      "  \"feat_proj_dropout\": 0.1,\n",
+      "  \"feat_quantizer_dropout\": 0.0,\n",
+      "  \"final_dropout\": 0.0,\n",
+      "  \"gradient_checkpointing\": false,\n",
+      "  \"hidden_act\": \"gelu\",\n",
+      "  \"hidden_dropout\": 0.1,\n",
+      "  \"hidden_size\": 1024,\n",
+      "  \"initializer_range\": 0.02,\n",
+      "  \"intermediate_size\": 4096,\n",
+      "  \"layer_norm_eps\": 1e-05,\n",
+      "  \"layerdrop\": 0.1,\n",
+      "  \"mask_feature_length\": 10,\n",
+      "  \"mask_feature_min_masks\": 0,\n",
+      "  \"mask_feature_prob\": 0.0,\n",
+      "  \"mask_time_length\": 10,\n",
+      "  \"mask_time_min_masks\": 2,\n",
+      "  \"mask_time_prob\": 0.075,\n",
+      "  \"model_type\": \"wav2vec2\",\n",
+      "  \"num_adapter_layers\": 3,\n",
+      "  \"num_attention_heads\": 16,\n",
+      "  \"num_codevector_groups\": 2,\n",
+      "  \"num_codevectors_per_group\": 320,\n",
+      "  \"num_conv_pos_embedding_groups\": 16,\n",
+      "  \"num_conv_pos_embeddings\": 128,\n",
+      "  \"num_feat_extract_layers\": 7,\n",
+      "  \"num_hidden_layers\": 24,\n",
+      "  \"num_negatives\": 100,\n",
+      "  \"output_hidden_size\": 1024,\n",
+      "  \"pad_token_id\": 0,\n",
+      "  \"proj_codevector_dim\": 768,\n",
+      "  \"tdnn_dilation\": [\n",
+      "    1,\n",
+      "    2,\n",
+      "    3,\n",
+      "    1,\n",
+      "    1\n",
+      "  ],\n",
+      "  \"tdnn_dim\": [\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    1500\n",
+      "  ],\n",
+      "  \"tdnn_kernel\": [\n",
+      "    5,\n",
+      "    3,\n",
+      "    3,\n",
+      "    1,\n",
+      "    1\n",
+      "  ],\n",
+      "  \"torch_dtype\": \"float32\",\n",
+      "  \"transformers_version\": \"4.16.0.dev0\",\n",
+      "  \"use_weighted_layer_sum\": false,\n",
+      "  \"vocab_size\": 32,\n",
+      "  \"xvector_output_dim\": 512\n",
+      "}\n",
+      "\n",
+      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00,  5.38ba/s]\n",
+      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00, 20.52ba/s]\n",
+      "Didn't find file ./wav2vec2-large-xls-r-300m-galician/tokenizer_config.json. We won't load it.\n",
+      "Didn't find file ./wav2vec2-large-xls-r-300m-galician/added_tokens.json. We won't load it.\n",
+      "Didn't find file ./wav2vec2-large-xls-r-300m-galician/special_tokens_map.json. We won't load it.\n",
+      "Didn't find file ./wav2vec2-large-xls-r-300m-galician/tokenizer.json. We won't load it.\n",
+      "loading file ./wav2vec2-large-xls-r-300m-galician/vocab.json\n",
+      "loading file None\n",
+      "loading file None\n",
+      "loading file None\n",
+      "loading file None\n",
+      "file ./wav2vec2-large-xls-r-300m-galician/config.json not found\n",
+      "Adding <s> to the vocabulary\n",
+      "Adding </s> to the vocabulary\n",
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
+      "loading configuration file https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/config.json from cache at /workspace/.cache/huggingface/transformers/dabc27df63e37bd2a7a221c7774e35f36a280fbdf917cf54cadfc7df8c786f6f.a3e4c3c967d9985881e0ae550a5f6f668f897db5ab2e0802f9b97973b15970e6\n",
+      "Model config Wav2Vec2Config {\n",
+      "  \"_name_or_path\": \"facebook/wav2vec2-xls-r-300m\",\n",
+      "  \"activation_dropout\": 0.0,\n",
+      "  \"adapter_kernel_size\": 3,\n",
+      "  \"adapter_stride\": 2,\n",
+      "  \"add_adapter\": false,\n",
+      "  \"apply_spec_augment\": true,\n",
+      "  \"architectures\": [\n",
+      "    \"Wav2Vec2ForPreTraining\"\n",
+      "  ],\n",
+      "  \"attention_dropout\": 0.1,\n",
+      "  \"bos_token_id\": 1,\n",
+      "  \"classifier_proj_size\": 256,\n",
+      "  \"codevector_dim\": 768,\n",
+      "  \"contrastive_logits_temperature\": 0.1,\n",
+      "  \"conv_bias\": true,\n",
+      "  \"conv_dim\": [\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512\n",
+      "  ],\n",
+      "  \"conv_kernel\": [\n",
+      "    10,\n",
+      "    3,\n",
+      "    3,\n",
+      "    3,\n",
+      "    3,\n",
+      "    2,\n",
+      "    2\n",
+      "  ],\n",
+      "  \"conv_stride\": [\n",
+      "    5,\n",
+      "    2,\n",
+      "    2,\n",
+      "    2,\n",
+      "    2,\n",
+      "    2,\n",
+      "    2\n",
+      "  ],\n",
+      "  \"ctc_loss_reduction\": \"sum\",\n",
+      "  \"ctc_zero_infinity\": false,\n",
+      "  \"diversity_loss_weight\": 0.1,\n",
+      "  \"do_stable_layer_norm\": true,\n",
+      "  \"eos_token_id\": 2,\n",
+      "  \"feat_extract_activation\": \"gelu\",\n",
+      "  \"feat_extract_dropout\": 0.0,\n",
+      "  \"feat_extract_norm\": \"layer\",\n",
+      "  \"feat_proj_dropout\": 0.1,\n",
+      "  \"feat_quantizer_dropout\": 0.0,\n",
+      "  \"final_dropout\": 0.0,\n",
+      "  \"gradient_checkpointing\": false,\n",
+      "  \"hidden_act\": \"gelu\",\n",
+      "  \"hidden_dropout\": 0.1,\n",
+      "  \"hidden_size\": 1024,\n",
+      "  \"initializer_range\": 0.02,\n",
+      "  \"intermediate_size\": 4096,\n",
+      "  \"layer_norm_eps\": 1e-05,\n",
+      "  \"layerdrop\": 0.1,\n",
+      "  \"mask_feature_length\": 10,\n",
+      "  \"mask_feature_min_masks\": 0,\n",
+      "  \"mask_feature_prob\": 0.0,\n",
+      "  \"mask_time_length\": 10,\n",
+      "  \"mask_time_min_masks\": 2,\n",
+      "  \"mask_time_prob\": 0.075,\n",
+      "  \"model_type\": \"wav2vec2\",\n",
+      "  \"num_adapter_layers\": 3,\n",
+      "  \"num_attention_heads\": 16,\n",
+      "  \"num_codevector_groups\": 2,\n",
+      "  \"num_codevectors_per_group\": 320,\n",
+      "  \"num_conv_pos_embedding_groups\": 16,\n",
+      "  \"num_conv_pos_embeddings\": 128,\n",
+      "  \"num_feat_extract_layers\": 7,\n",
+      "  \"num_hidden_layers\": 24,\n",
+      "  \"num_negatives\": 100,\n",
+      "  \"output_hidden_size\": 1024,\n",
+      "  \"pad_token_id\": 0,\n",
+      "  \"proj_codevector_dim\": 768,\n",
+      "  \"tdnn_dilation\": [\n",
+      "    1,\n",
+      "    2,\n",
+      "    3,\n",
+      "    1,\n",
+      "    1\n",
+      "  ],\n",
+      "  \"tdnn_dim\": [\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    1500\n",
+      "  ],\n",
+      "  \"tdnn_kernel\": [\n",
+      "    5,\n",
+      "    3,\n",
+      "    3,\n",
+      "    1,\n",
+      "    1\n",
+      "  ],\n",
+      "  \"torch_dtype\": \"float32\",\n",
+      "  \"transformers_version\": \"4.16.0.dev0\",\n",
+      "  \"use_weighted_layer_sum\": false,\n",
+      "  \"vocab_size\": 32,\n",
+      "  \"xvector_output_dim\": 512\n",
+      "}\n",
+      "\n",
+      "loading feature extractor configuration file https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/preprocessor_config.json from cache at /workspace/.cache/huggingface/transformers/6fb028b95b394059e7d3b367bbca2382b576c66aebe896f04d2cd34e1b575f5b.d4484dc1c81456a2461485e7168b04347a7b9a4e3b1ef3aba723323b33e12326\n",
+      "Feature extractor Wav2Vec2FeatureExtractor {\n",
+      "  \"do_normalize\": true,\n",
+      "  \"feature_extractor_type\": \"Wav2Vec2FeatureExtractor\",\n",
+      "  \"feature_size\": 1,\n",
+      "  \"padding_side\": \"right\",\n",
+      "  \"padding_value\": 0,\n",
+      "  \"return_attention_mask\": true,\n",
+      "  \"sampling_rate\": 16000\n",
+      "}\n",
+      "\n",
+      "loading weights file https://huggingface.co/facebook/wav2vec2-xls-r-300m/resolve/main/pytorch_model.bin from cache at /workspace/.cache/huggingface/transformers/1e6a6507f3b689035cd4b247e2a37c154e27f39143f31357a49b4e38baeccc36.1edb32803799e27ed554eb7dd935f6745b1a0b17b0ea256442fe24db6eb546cd\n",
+      "Some weights of the model checkpoint at facebook/wav2vec2-xls-r-300m were not used when initializing Wav2Vec2ForCTC: ['project_q.bias', 'project_hid.weight', 'quantizer.codevectors', 'project_q.weight', 'project_hid.bias', 'quantizer.weight_proj.weight', 'quantizer.weight_proj.bias']\n",
+      "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-300m and are newly initialized: ['lm_head.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",
+      "preprocess datasets: 100%|█████████████████| 3670/3670 [00:27<00:00, 133.09ex/s]\n",
+      "preprocess datasets: 100%|█████████████████| 1716/1716 [00:13<00:00, 124.03ex/s]\n",
+      "100%|████████████████████████████████████████████| 4/4 [00:00<00:00, 797.24ba/s]\n",
+      "100%|████████████████████████████████████████████| 2/2 [00:00<00:00, 765.80ba/s]\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/preprocessor_config.json\n",
+      "tokenizer config file saved in ./wav2vec2-large-xls-r-300m-galician/tokenizer_config.json\n",
+      "Special tokens file saved in ./wav2vec2-large-xls-r-300m-galician/special_tokens_map.json\n",
+      "added tokens file saved in ./wav2vec2-large-xls-r-300m-galician/added_tokens.json\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/config.json\n",
+      "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-galician/preprocessor_config.json\n",
+      "loading configuration file ./wav2vec2-large-xls-r-300m-galician/config.json\n",
+      "Model config Wav2Vec2Config {\n",
+      "  \"_name_or_path\": \"./wav2vec2-large-xls-r-300m-galician\",\n",
+      "  \"activation_dropout\": 0.1,\n",
+      "  \"adapter_kernel_size\": 3,\n",
+      "  \"adapter_stride\": 2,\n",
+      "  \"add_adapter\": false,\n",
+      "  \"apply_spec_augment\": true,\n",
+      "  \"architectures\": [\n",
+      "    \"Wav2Vec2ForPreTraining\"\n",
+      "  ],\n",
+      "  \"attention_dropout\": 0.0,\n",
+      "  \"bos_token_id\": 1,\n",
+      "  \"classifier_proj_size\": 256,\n",
+      "  \"codevector_dim\": 768,\n",
+      "  \"contrastive_logits_temperature\": 0.1,\n",
+      "  \"conv_bias\": true,\n",
+      "  \"conv_dim\": [\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512\n",
+      "  ],\n",
+      "  \"conv_kernel\": [\n",
+      "    10,\n",
+      "    3,\n",
+      "    3,\n",
+      "    3,\n",
+      "    3,\n",
+      "    2,\n",
+      "    2\n",
+      "  ],\n",
+      "  \"conv_stride\": [\n",
+      "    5,\n",
+      "    2,\n",
+      "    2,\n",
+      "    2,\n",
+      "    2,\n",
+      "    2,\n",
+      "    2\n",
+      "  ],\n",
+      "  \"ctc_loss_reduction\": \"mean\",\n",
+      "  \"ctc_zero_infinity\": false,\n",
+      "  \"diversity_loss_weight\": 0.1,\n",
+      "  \"do_stable_layer_norm\": true,\n",
+      "  \"eos_token_id\": 2,\n",
+      "  \"feat_extract_activation\": \"gelu\",\n",
+      "  \"feat_extract_dropout\": 0.0,\n",
+      "  \"feat_extract_norm\": \"layer\",\n",
+      "  \"feat_proj_dropout\": 0.0,\n",
+      "  \"feat_quantizer_dropout\": 0.0,\n",
+      "  \"final_dropout\": 0.0,\n",
+      "  \"hidden_act\": \"gelu\",\n",
+      "  \"hidden_dropout\": 0.0,\n",
+      "  \"hidden_size\": 1024,\n",
+      "  \"initializer_range\": 0.02,\n",
+      "  \"intermediate_size\": 4096,\n",
+      "  \"layer_norm_eps\": 1e-05,\n",
+      "  \"layerdrop\": 0.0,\n",
+      "  \"mask_feature_length\": 64,\n",
+      "  \"mask_feature_min_masks\": 0,\n",
+      "  \"mask_feature_prob\": 0.25,\n",
+      "  \"mask_time_length\": 10,\n",
+      "  \"mask_time_min_masks\": 2,\n",
+      "  \"mask_time_prob\": 0.75,\n",
+      "  \"model_type\": \"wav2vec2\",\n",
+      "  \"num_adapter_layers\": 3,\n",
+      "  \"num_attention_heads\": 16,\n",
+      "  \"num_codevector_groups\": 2,\n",
+      "  \"num_codevectors_per_group\": 320,\n",
+      "  \"num_conv_pos_embedding_groups\": 16,\n",
+      "  \"num_conv_pos_embeddings\": 128,\n",
+      "  \"num_feat_extract_layers\": 7,\n",
+      "  \"num_hidden_layers\": 24,\n",
+      "  \"num_negatives\": 100,\n",
+      "  \"output_hidden_size\": 1024,\n",
+      "  \"pad_token_id\": 41,\n",
+      "  \"proj_codevector_dim\": 768,\n",
+      "  \"tdnn_dilation\": [\n",
+      "    1,\n",
+      "    2,\n",
+      "    3,\n",
+      "    1,\n",
+      "    1\n",
+      "  ],\n",
+      "  \"tdnn_dim\": [\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    512,\n",
+      "    1500\n",
+      "  ],\n",
+      "  \"tdnn_kernel\": [\n",
+      "    5,\n",
+      "    3,\n",
+      "    3,\n",
+      "    1,\n",
+      "    1\n",
+      "  ],\n",
+      "  \"torch_dtype\": \"float32\",\n",
+      "  \"transformers_version\": \"4.16.0.dev0\",\n",
+      "  \"use_weighted_layer_sum\": false,\n",
+      "  \"vocab_size\": 44,\n",
+      "  \"xvector_output_dim\": 512\n",
+      "}\n",
+      "\n",
+      "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-galician/preprocessor_config.json\n",
+      "Feature extractor Wav2Vec2FeatureExtractor {\n",
+      "  \"do_normalize\": true,\n",
+      "  \"feature_extractor_type\": \"Wav2Vec2FeatureExtractor\",\n",
+      "  \"feature_size\": 1,\n",
+      "  \"padding_side\": \"right\",\n",
+      "  \"padding_value\": 0,\n",
+      "  \"return_attention_mask\": true,\n",
+      "  \"sampling_rate\": 16000\n",
+      "}\n",
+      "\n",
+      "Didn't find file ./wav2vec2-large-xls-r-300m-galician/tokenizer.json. We won't load it.\n",
+      "loading file ./wav2vec2-large-xls-r-300m-galician/vocab.json\n",
+      "loading file ./wav2vec2-large-xls-r-300m-galician/tokenizer_config.json\n",
+      "loading file ./wav2vec2-large-xls-r-300m-galician/added_tokens.json\n",
+      "loading file ./wav2vec2-large-xls-r-300m-galician/special_tokens_map.json\n",
+      "loading file None\n",
+      "Adding <s> to the vocabulary\n",
+      "Adding </s> to the vocabulary\n",
+      "Cloning https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-galician into local empty directory.\n",
+      "01/27/2022 09:25:02 - WARNING - huggingface_hub.repository - Cloning https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-galician into local empty directory.\n",
+      "Download file pytorch_model.bin:   0%|             | 3.47k/1.18G [00:00<?, ?B/s]\n",
+      "Download file training_args.bin: 100%|█████████████| 2.98k/2.98k [00:00<?, ?B/s]\u001b[A\n",
+      "\n",
+      "Download file pytorch_model.bin:  97%|████▊| 1.14G/1.18G [00:33<00:01, 30.7MB/s]\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:   0%|                | 1.00k/1.18G [00:00<?, ?B/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:   1%|        | 17.1M/1.18G [00:01<01:10, 17.7MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:   5%|▍       | 60.1M/1.18G [00:02<00:35, 33.6MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:   9%|▊        | 107M/1.18G [00:03<00:28, 40.7MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  13%|█▏       | 154M/1.18G [00:04<00:25, 43.6MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  17%|█▍       | 200M/1.18G [00:05<00:23, 45.2MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  20%|█▊       | 245M/1.18G [00:06<00:21, 45.7MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  24%|██▏      | 290M/1.18G [00:07<00:20, 45.9MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  28%|██▍      | 333M/1.18G [00:08<00:20, 45.5MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  31%|██▊      | 379M/1.18G [00:09<00:18, 46.1MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  35%|███▏     | 423M/1.18G [00:10<00:17, 46.2MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  39%|███▌     | 469M/1.18G [00:11<00:16, 46.5MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Download file pytorch_model.bin: 100%|█████| 1.18G/1.18G [00:47<00:00, 30.7MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  46%|████▏    | 559M/1.18G [00:13<00:14, 46.6MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  50%|████▌    | 605M/1.18G [00:14<00:13, 46.8MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  54%|████▊    | 649M/1.18G [00:15<00:12, 46.5MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  58%|█████▏   | 695M/1.18G [00:16<00:11, 47.0MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  62%|█████▌   | 741M/1.18G [00:17<00:10, 47.0MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  65%|█████▉   | 786M/1.18G [00:18<00:09, 47.1MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  69%|██████▏  | 831M/1.18G [00:19<00:08, 46.7MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  73%|██████▌  | 876M/1.18G [00:20<00:07, 46.9MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  77%|██████▉  | 921M/1.18G [00:21<00:06, 46.8MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  80%|███████▏ | 966M/1.18G [00:22<00:05, 46.8MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  84%|██████▋ | 0.99G/1.18G [00:23<00:04, 46.8MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  88%|███████ | 1.03G/1.18G [00:24<00:03, 47.0MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin:  92%|███████▎| 1.08G/1.18G [00:25<00:02, 47.2MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Download file pytorch_model.bin: 100%|█████| 1.18G/1.18G [01:02<00:00, 20.1MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "Download file training_args.bin: 100%|█████████████| 2.98k/2.98k [01:02<?, ?B/s]\u001b[A\n",
+      "Download file training_args.bin: 100%|█████████████| 2.98k/2.98k [01:02<?, ?B/s]\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file training_args.bin: 100%|█████████| 2.98k/2.98k [01:02<00:00, 32.3B/s]\u001b[A\u001b[A\n",
+      "\n",
+      "Clean file training_args.bin: 100%|█████████| 2.98k/2.98k [01:02<00:00, 32.3B/s]\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin: 100%|████████| 1.18G/1.18G [00:27<00:00, 50.1MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "\n",
+      "\n",
+      "Clean file pytorch_model.bin: 100%|████████| 1.18G/1.18G [00:27<00:00, 46.2MB/s]\u001b[A\u001b[A\u001b[A\n",
+      "Using amp half precision backend\n",
+      "The following columns in the training set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "/opt/conda/lib/python3.8/site-packages/transformers/optimization.py:306: 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",
+      "  warnings.warn(\n",
+      "***** Running training *****\n",
+      "  Num examples = 3670\n",
+      "  Num Epochs = 20\n",
+      "  Instantaneous batch size per device = 32\n",
+      "  Total train batch size (w. parallel, distributed & accumulation) = 32\n",
+      "  Gradient Accumulation steps = 1\n",
+      "  Total optimization steps = 2300\n",
+      "{'loss': 12.0308, 'learning_rate': 1.3439999999999998e-05, 'epoch': 0.87}       \n",
+      "{'loss': 4.6075, 'learning_rate': 2.7439999999999998e-05, 'epoch': 1.74}        \n",
+      "{'loss': 3.4071, 'learning_rate': 4.1439999999999996e-05, 'epoch': 2.61}        \n",
+      "{'loss': 3.0602, 'learning_rate': 5.544e-05, 'epoch': 3.48}                     \n",
+      "{'loss': 3.0067, 'learning_rate': 6.944e-05, 'epoch': 4.35}                     \n",
+      " 22%|████████▋                               | 500/2300 [14:00<55:34,  1.85s/it]The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 1716\n",
+      "  Batch size = 32\n",
+      "\n",
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+      " 11%|████▉                                       | 6/54 [00:07<01:04,  1.35s/it]\u001b[A\n",
+      " 13%|█████▋                                      | 7/54 [00:08<01:05,  1.40s/it]\u001b[A\n",
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+      " 22%|█████████▌                                 | 12/54 [00:15<01:01,  1.47s/it]\u001b[A\n",
+      " 24%|██████████▎                                | 13/54 [00:17<00:59,  1.46s/it]\u001b[A\n",
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+      "{'eval_loss': 2.9632411003112793, 'eval_wer': 1.0, 'eval_runtime': 75.847, 'eval_samples_per_second': 22.624, 'eval_steps_per_second': 0.712, 'epoch': 4.35}\n",
+      "\n",
+      " 22%|████████▋                               | 500/2300 [15:15<55:34,  1.85s/it]\u001b[A\n",
+      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-galician/checkpoint-500\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/checkpoint-500/config.json\n",
+      "Model weights saved in ./wav2vec2-large-xls-r-300m-galician/checkpoint-500/pytorch_model.bin\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/checkpoint-500/preprocessor_config.json\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/preprocessor_config.json\n",
+      "{'loss': 2.9665, 'learning_rate': 6.626666666666666e-05, 'epoch': 5.22}         \n",
+      "{'loss': 2.8916, 'learning_rate': 6.237777777777777e-05, 'epoch': 6.09}         \n",
+      "{'loss': 2.7907, 'learning_rate': 5.8488888888888886e-05, 'epoch': 6.96}        \n",
+      "{'loss': 2.0776, 'learning_rate': 5.46e-05, 'epoch': 7.83}                      \n",
+      "{'loss': 1.4939, 'learning_rate': 5.0711111111111105e-05, 'epoch': 8.7}         \n",
+      " 43%|████████████████▉                      | 1000/2300 [30:25<27:05,  1.25s/it]The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 1716\n",
+      "  Batch size = 32\n",
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+      "{'eval_loss': 0.5004750490188599, 'eval_wer': 0.41569049578927375, 'eval_runtime': 75.9315, 'eval_samples_per_second': 22.599, 'eval_steps_per_second': 0.711, 'epoch': 8.7}\n",
+      "\n",
+      " 43%|████████████████▉                      | 1000/2300 [31:41<27:05,  1.25s/it]\u001b[A\n",
+      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-galician/checkpoint-1000\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/checkpoint-1000/config.json\n",
+      "Model weights saved in ./wav2vec2-large-xls-r-300m-galician/checkpoint-1000/pytorch_model.bin\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/checkpoint-1000/preprocessor_config.json\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/preprocessor_config.json\n",
+      "{'loss': 1.2841, 'learning_rate': 4.682222222222222e-05, 'epoch': 9.57}         \n",
+      "{'loss': 1.1469, 'learning_rate': 4.2933333333333324e-05, 'epoch': 10.43}       \n",
+      "{'loss': 1.0776, 'learning_rate': 3.9044444444444444e-05, 'epoch': 11.3}        \n",
+      "{'loss': 1.0147, 'learning_rate': 3.515555555555555e-05, 'epoch': 12.17}        \n",
+      "{'loss': 0.9982, 'learning_rate': 3.126666666666666e-05, 'epoch': 13.04}        \n",
+      " 65%|█████████████████████████▍             | 1500/2300 [47:00<29:32,  2.22s/it]The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 1716\n",
+      "  Batch size = 32\n",
+      "\n",
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+      "100%|███████████████████████████████████████████| 54/54 [01:12<00:00,  1.20s/it]\u001b[A\n",
+      "{'eval_loss': 0.19668780267238617, 'eval_wer': 0.185651871082125, 'eval_runtime': 75.4326, 'eval_samples_per_second': 22.749, 'eval_steps_per_second': 0.716, 'epoch': 13.04}\n",
+      "\n",
+      " 65%|█████████████████████████▍             | 1500/2300 [48:16<29:32,  2.22s/it]\u001b[A\n",
+      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-galician/checkpoint-1500\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/checkpoint-1500/config.json\n",
+      "Model weights saved in ./wav2vec2-large-xls-r-300m-galician/checkpoint-1500/pytorch_model.bin\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/checkpoint-1500/preprocessor_config.json\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/preprocessor_config.json\n",
+      "Deleting older checkpoint [wav2vec2-large-xls-r-300m-galician/checkpoint-500] due to args.save_total_limit\n",
+      "{'loss': 0.9517, 'learning_rate': 2.7377777777777776e-05, 'epoch': 13.91}       \n",
+      "{'loss': 0.9291, 'learning_rate': 2.3488888888888886e-05, 'epoch': 14.78}       \n",
+      "{'loss': 0.8937, 'learning_rate': 1.96e-05, 'epoch': 15.65}                     \n",
+      "{'loss': 0.8918, 'learning_rate': 1.5711111111111108e-05, 'epoch': 16.52}       \n",
+      "{'loss': 0.8726, 'learning_rate': 1.1822222222222221e-05, 'epoch': 17.39}       \n",
+      " 87%|████████████████████████████████▏    | 2000/2300 [1:03:45<07:42,  1.54s/it]The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 1716\n",
+      "  Batch size = 32\n",
+      "\n",
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+      "100%|███████████████████████████████████████████| 54/54 [01:13<00:00,  1.20s/it]\u001b[A\n",
+      "{'eval_loss': 0.1586812287569046, 'eval_wer': 0.1563984043563604, 'eval_runtime': 75.8623, 'eval_samples_per_second': 22.62, 'eval_steps_per_second': 0.712, 'epoch': 17.39}\n",
+      "\n",
+      " 87%|████████████████████████████████▏    | 2000/2300 [1:05:01<07:42,  1.54s/it]\u001b[A\n",
+      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-galician/checkpoint-2000\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/checkpoint-2000/config.json\n",
+      "Model weights saved in ./wav2vec2-large-xls-r-300m-galician/checkpoint-2000/pytorch_model.bin\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/checkpoint-2000/preprocessor_config.json\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/preprocessor_config.json\n",
+      "Deleting older checkpoint [wav2vec2-large-xls-r-300m-galician/checkpoint-1000] due to args.save_total_limit\n",
+      "{'loss': 0.8785, 'learning_rate': 7.933333333333332e-06, 'epoch': 18.26}        \n",
+      "{'loss': 0.8559, 'learning_rate': 4.044444444444444e-06, 'epoch': 19.13}        \n",
+      "{'loss': 0.8653, 'learning_rate': 1.5555555555555554e-07, 'epoch': 20.0}        \n",
+      "100%|█████████████████████████████████████| 2300/2300 [1:14:46<00:00,  1.34s/it]\n",
+      "\n",
+      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
+      "\n",
+      "\n",
+      "{'train_runtime': 4486.5584, 'train_samples_per_second': 16.36, 'train_steps_per_second': 0.513, 'train_loss': 2.2170696955141813, 'epoch': 20.0}\n",
+      "100%|█████████████████████████████████████| 2300/2300 [1:14:46<00:00,  1.95s/it]\n",
+      "Saving model checkpoint to ./wav2vec2-large-xls-r-300m-galician\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/config.json\n",
+      "Model weights saved in ./wav2vec2-large-xls-r-300m-galician/pytorch_model.bin\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/preprocessor_config.json\n",
+      "***** train metrics *****\n",
+      "  epoch                    =       20.0\n",
+      "  train_loss               =     2.2171\n",
+      "  train_runtime            = 1:14:46.55\n",
+      "  train_samples            =       3670\n",
+      "  train_samples_per_second =      16.36\n",
+      "  train_steps_per_second   =      0.513\n",
+      "01/27/2022 10:41:00 - INFO - __main__ - *** Evaluate ***\n",
+      "The following columns in the evaluation set  don't have a corresponding argument in `Wav2Vec2ForCTC.forward` and have been ignored: input_length.\n",
+      "***** Running Evaluation *****\n",
+      "  Num examples = 1716\n",
+      "  Batch size = 32\n",
+      "100%|███████████████████████████████████████████| 54/54 [01:14<00:00,  1.39s/it]\n",
+      "***** eval metrics *****\n",
+      "  epoch                   =       20.0\n",
+      "  eval_loss               =     0.1525\n",
+      "  eval_runtime            = 0:01:16.38\n",
+      "  eval_samples            =       1716\n",
+      "  eval_samples_per_second =     22.464\n",
+      "  eval_steps_per_second   =      0.707\n",
+      "  eval_wer                =     0.1542\n",
+      "Saving model checkpoint to ./wav2vec2-large-xls-r-300m-galician\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/config.json\n",
+      "Model weights saved in ./wav2vec2-large-xls-r-300m-galician/pytorch_model.bin\n",
+      "Configuration saved in ./wav2vec2-large-xls-r-300m-galician/preprocessor_config.json\n",
+      "Upload file pytorch_model.bin: 100%|██████▉| 1.17G/1.18G [00:52<00:00, 29.1MB/s]To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-galician\n",
+      "   1485f51..85ab499  main -> main\n",
+      "\n",
+      "01/27/2022 10:44:30 - WARNING - huggingface_hub.repository - To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-galician\n",
+      "   1485f51..85ab499  main -> main\n",
+      "\n",
+      "Upload file pytorch_model.bin: 100%|███████| 1.18G/1.18G [00:53<00:00, 23.6MB/s]\n",
+      "Dropping the following result as it does not have all the necessary fields:\n",
+      "{'dataset': {'name': 'MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - GL', 'type': 'common_voice', 'args': 'Config: gl, Training split: train+validation, Eval split: test'}}\n",
+      "error: RPC failed; HTTP 504 curl 22 The requested URL returned error: 504 Gateway Time-out\n",
+      "fatal: The remote end hung up unexpectedly\n",
+      "fatal: The remote end hung up unexpectedly\n",
+      "Everything up-to-date\n",
+      "\n",
+      "01/27/2022 10:45:38 - WARNING - huggingface_hub.repository - error: RPC failed; HTTP 504 curl 22 The requested URL returned error: 504 Gateway Time-out\n",
+      "fatal: The remote end hung up unexpectedly\n",
+      "fatal: The remote end hung up unexpectedly\n",
+      "Everything up-to-date\n",
+      "\n",
+      "Error pushing update to the model card. Please read logs and retry.\n",
+      "$error: RPC failed; HTTP 504 curl 22 The requested URL returned error: 504 Gateway Time-out\n",
+      "fatal: The remote end hung up unexpectedly\n",
+      "fatal: The remote end hung up unexpectedly\n",
+      "Everything up-to-date\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python run_speech_recognition_ctc.py \\\n",
+    "\t--dataset_name=\"mozilla-foundation/common_voice_7_0\" \\\n",
+    "\t--model_name_or_path=\"facebook/wav2vec2-xls-r-300m\" \\\n",
+    "\t--dataset_config_name=\"gl\" \\\n",
+    "\t--output_dir=\"./wav2vec2-large-xls-r-300m-galician\" \\\n",
+    "\t--overwrite_output_dir \\\n",
+    "\t--num_train_epochs=\"20\" \\\n",
+    "\t--per_device_train_batch_size=\"32\" \\\n",
+    "\t--per_device_eval_batch_size=\"32\" \\\n",
+    "\t--gradient_accumulation_steps=\"1\" \\\n",
+    "\t--learning_rate=\"7e-5\" \\\n",
+    "\t--warmup_steps=\"500\" \\\n",
+    "\t--length_column_name=\"input_length\" \\\n",
+    "\t--evaluation_strategy=\"steps\" \\\n",
+    "\t--text_column_name=\"sentence\" \\\n",
+    "\t--chars_to_ignore , ? . ! \\- \\; \\: \\\" “ % ‘ ” � — ’ … – \\\n",
+    "\t--save_steps=\"500\" \\\n",
+    "\t--eval_steps=\"500\" \\\n",
+    "\t--logging_steps=\"100\" \\\n",
+    "\t--layerdrop=\"0.0\" \\\n",
+    "\t--activation_dropout=\"0.1\" \\\n",
+    "\t--save_total_limit=\"2\" \\\n",
+    "\t--freeze_feature_encoder \\\n",
+    "\t--feat_proj_dropout=\"0.0\" \\\n",
+    "\t--mask_time_prob=\"0.75\" \\\n",
+    "\t--mask_time_length=\"10\" \\\n",
+    "\t--mask_feature_prob=\"0.25\" \\\n",
+    "\t--mask_feature_length=\"64\" \\\n",
+    "\t--gradient_checkpointing \\\n",
+    "\t--use_auth_token \\\n",
+    "\t--fp16 \\\n",
+    "\t--group_by_length \\\n",
+    "\t--do_train --do_eval \\\n",
+    "    --push_to_hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !rm -rf wav2vec2-large-xls-r-300m-bashkir"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!ls -ltr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Filesystem      Size  Used Avail Use% Mounted on\n",
+      "overlay         3.5T  1.2T  2.2T  34% /\n",
+      "tmpfs            64M     0   64M   0% /dev\n",
+      "tmpfs            87G     0   87G   0% /sys/fs/cgroup\n",
+      "tmpfs            87G     0   87G   0% /dev/shm\n",
+      "/dev/md0        3.5T  1.2T  2.2T  34% /etc/group\n",
+      "tmpfs            87G   12K   87G   1% /proc/driver/nvidia\n",
+      "/dev/vda1        49G  6.5G   42G  14% /usr/bin/nvidia-smi\n",
+      "udev             87G     0   87G   0% /dev/nvidia0\n",
+      "tmpfs            87G     0   87G   0% /proc/acpi\n",
+      "tmpfs            87G     0   87G   0% /proc/scsi\n",
+      "tmpfs            87G     0   87G   0% /sys/firmware\n"
+     ]
+    }
+   ],
+   "source": [
+    "!df -h"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/gl/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n",
+      "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/gl/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3670\n"
+     ]
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset, load_metric, Audio\n",
+    "\n",
+    "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"gl\", use_auth_token=True, split=\"train+validation\")\n",
+    "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"gl\", use_auth_token=True, split=\"test\")\n",
+    "\n",
+    "print(len(common_voice_train))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5734.375"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(common_voice_train) * 50 / 32"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "common_voice_train = common_voice_train.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])\n",
+    "common_voice_test = common_voice_test.remove_columns([\"accent\", \"age\", \"client_id\", \"down_votes\", \"gender\", \"locale\", \"segment\", \"up_votes\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import ClassLabel\n",
+    "import random\n",
+    "import pandas as pd\n",
+    "from IPython.display import display, HTML\n",
+    "\n",
+    "def show_random_elements(dataset, num_examples=10):\n",
+    "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
+    "    picks = []\n",
+    "    for _ in range(num_examples):\n",
+    "        pick = random.randint(0, len(dataset)-1)\n",
+    "        while pick in picks:\n",
+    "            pick = random.randint(0, len(dataset)-1)\n",
+    "        picks.append(pick)\n",
+    "    \n",
+    "    df = pd.DataFrame(dataset[picks])\n",
+    "    display(HTML(df.to_html()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>sentence</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>A torre do campanario amosa tres corpos.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Viaxou por Inglaterra e Europa.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Foi veciño do Concello de Ordes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Butch é outro personaxe.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Na colmea máis vella había unha alza sen mel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Tiña carácter humorístico.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>No soto están as salas Thorne.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Alfredo Guisado tamén escribiu poesía usando o pseudónimo de Pedro de Meneses.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Tamén aparece nun dos laterais unha fornela coa imaxe do San Miguel.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>A letra provén das coplas típicas.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "show_random_elements(common_voice_train.remove_columns([\"path\", \"audio\"]), num_examples=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "chars_to_remove_regex = '[\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\—\\’\\…\\–]'\n",
+    "\n",
+    "def remove_special_characters(batch):\n",
+    "    batch[\"sentence\"] = re.sub(chars_to_remove_regex, '', batch[\"sentence\"]).lower()\n",
+    "    return batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "02d1d9c1963e44aa9031276fe6495ab9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3670 [00:00<?, ?ex/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "146077caeacd46398c35152f26b0c8ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1716 [00:00<?, ?ex/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "common_voice_train = common_voice_train.map(remove_special_characters)\n",
+    "common_voice_test = common_voice_test.map(remove_special_characters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# start_with_ar = common_voice_train.filter(lambda example: \"−\" in example['sentence'])\n",
+    "# start_with_ar[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# start_with_ar"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def replace_hatted_characters(batch):\n",
+    "#     batch[\"sentence\"] = re.sub('[â]', 'a', batch[\"sentence\"])\n",
+    "#     batch[\"sentence\"] = re.sub('[î]', 'i', batch[\"sentence\"])\n",
+    "#     batch[\"sentence\"] = re.sub('[ô]', 'o', batch[\"sentence\"])\n",
+    "#     batch[\"sentence\"] = re.sub('[û]', 'u', batch[\"sentence\"])\n",
+    "    return batch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "97bd448c04fa4fcebfd77c91614c8004",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3670 [00:00<?, ?ex/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d61ba742cd6c40e2a304266e9220284b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1716 [00:00<?, ?ex/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "common_voice_train = common_voice_train.map(replace_hatted_characters)\n",
+    "common_voice_test = common_voice_test.map(replace_hatted_characters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "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": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5794b701250d4e41b6163d91f9f298bb",
+       "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": "e23e45ea6c0640b59cb5f3adf2db9583",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n",
+    "vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{' ': 0,\n",
+       " \"'\": 1,\n",
+       " '_': 2,\n",
+       " 'a': 3,\n",
+       " 'b': 4,\n",
+       " 'c': 5,\n",
+       " 'd': 6,\n",
+       " 'e': 7,\n",
+       " 'f': 8,\n",
+       " 'g': 9,\n",
+       " 'h': 10,\n",
+       " 'i': 11,\n",
+       " 'j': 12,\n",
+       " 'k': 13,\n",
+       " 'l': 14,\n",
+       " 'm': 15,\n",
+       " 'n': 16,\n",
+       " 'o': 17,\n",
+       " 'p': 18,\n",
+       " 'q': 19,\n",
+       " 'r': 20,\n",
+       " 's': 21,\n",
+       " 't': 22,\n",
+       " 'u': 23,\n",
+       " 'v': 24,\n",
+       " 'w': 25,\n",
+       " 'x': 26,\n",
+       " 'y': 27,\n",
+       " 'z': 28,\n",
+       " '¡': 29,\n",
+       " '«': 30,\n",
+       " '»': 31,\n",
+       " '¿': 32,\n",
+       " 'á': 33,\n",
+       " 'é': 34,\n",
+       " 'í': 35,\n",
+       " 'ñ': 36,\n",
+       " 'ó': 37,\n",
+       " 'ú': 38,\n",
+       " 'ü': 39,\n",
+       " '−': 40}"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vocab_dict = {v: k for k, v in enumerate(sorted(vocab_list))}\n",
+    "vocab_dict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "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"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "43\n"
+     ]
+    }
+   ],
+   "source": [
+    "vocab_dict[\"|\"] = vocab_dict[\" \"]\n",
+    "del vocab_dict[\" \"]\n",
+    "\n",
+    "vocab_dict[\"[UNK]\"] = len(vocab_dict)\n",
+    "vocab_dict[\"[PAD]\"] = len(vocab_dict)\n",
+    "print(len(vocab_dict))\n",
+    "\n",
+    "import json\n",
+    "with open('./vocab.json', 'w') as vocab_file:\n",
+    "    json.dump(vocab_dict, vocab_file)\n",
+    "    \n",
+    "from transformers import Wav2Vec2CTCTokenizer\n",
+    "\n",
+    "tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(\"./\", unk_token=\"[UNK]\", pad_token=\"[PAD]\", word_delimiter_token=\"|\")\n",
+    "\n",
+    "repo_name = \"wav2vec2-large-xls-r-300m-galician\"\n",
+    "\n",
+    "tokenizer.push_to_hub(repo_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--2022-01-27 08:52:26--  https://raw.githubusercontent.com/huggingface/transformers/master/examples/research_projects/robust-speech-event/eval.py\n",
+      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...\n",
+      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
+      "HTTP request sent, awaiting response... 200 OK\n",
+      "Length: 4421 (4.3K) [text/plain]\n",
+      "Saving to: ‘eval.py’\n",
+      "\n",
+      "eval.py             100%[===================>]   4.32K  --.-KB/s    in 0s      \n",
+      "\n",
+      "2022-01-27 08:52:27 (15.4 MB/s) - ‘eval.py’ saved [4421/4421]\n",
+      "\n",
+      "total 1232592\n",
+      "-rw-r--r-- 1 ovh ovh        398 Jan 27 08:09 vocab.json\n",
+      "-rw-r--r-- 1 ovh ovh        260 Jan 27 08:09 tokenizer_config.json\n",
+      "-rw-r--r-- 1 ovh ovh        309 Jan 27 08:09 special_tokens_map.json\n",
+      "-rw-r--r-- 1 ovh ovh         23 Jan 27 08:09 added_tokens.json\n",
+      "drwxr-xr-x 2 ovh ovh       4096 Jan 27 08:22 checkpoint-400\n",
+      "drwxr-xr-x 2 ovh ovh       4096 Jan 27 08:35 checkpoint-800\n",
+      "-rw-r--r-- 1 ovh ovh       2369 Jan 27 08:46 trainer_state.json\n",
+      "-rw-r--r-- 1 ovh ovh        194 Jan 27 08:46 train_results.json\n",
+      "-rw-r--r-- 1 ovh ovh        224 Jan 27 08:47 eval_results.json\n",
+      "-rw-r--r-- 1 ovh ovh        397 Jan 27 08:47 all_results.json\n",
+      "-rw-r--r-- 1 ovh ovh       2033 Jan 27 08:47 config.json\n",
+      "-rw-r--r-- 1 ovh ovh 1262104049 Jan 27 08:47 pytorch_model.bin\n",
+      "-rw-r--r-- 1 ovh ovh       3055 Jan 27 08:47 training_args.bin\n",
+      "-rw-r--r-- 1 ovh ovh        212 Jan 27 08:47 preprocessor_config.json\n",
+      "-rw-r--r-- 1 ovh ovh       1642 Jan 27 08:49 README.md\n",
+      "-rw-r--r-- 1 ovh ovh       4421 Jan 27 08:52 eval.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "!wget -O eval.py https://raw.githubusercontent.com/huggingface/transformers/master/examples/research_projects/robust-speech-event/eval.py\n",
+    "!cp eval.py wav2vec2-large-xls-r-300m-galician\n",
+    "!ls -ltr wav2vec2-large-xls-r-300m-galician"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading: 100%|█████████████████████████| 9.88k/9.88k [00:00<00:00, 4.62MB/s]\n",
+      "Downloading: 100%|█████████████████████████| 2.98k/2.98k [00:00<00:00, 1.68MB/s]\n",
+      "Downloading and preparing dataset common_voice/gl to /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/gl/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b...\n",
+      "Dataset common_voice downloaded and prepared to /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/gl/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b. Subsequent calls will reuse this data.\n",
+      "100%|███████████████████████████████████████| 1716/1716 [15:28<00:00,  1.85ex/s]\n",
+      "WER: 1.0185525232698032\n",
+      "CER: 3.5303200243189132\n",
+      "100%|████████████████████████████████████| 1716/1716 [00:00<00:00, 20749.40ex/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "!cd wav2vec2-large-xls-r-300m-galician; python eval.py \\\n",
+    "    --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config gl --split test --log_outputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "24592b0be30e4eafb1949cf09d1c4fb4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/260 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f9bf2ab0d2fa4d3f9235cc6d1ab772f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/574 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b0791474a34043da8057e06741472ade",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/23.0 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1ccbd582d616458b87c76ac8dc5b6b36",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/309 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# from transformers import AutoModelForCTC, Wav2Vec2Processor\n",
+    "\n",
+    "# model = AutoModelForCTC.from_pretrained(\"infinitejoy/wav2vec2-large-xls-r-300m-bashkir\")\n",
+    "# processor = Wav2Vec2Processor.from_pretrained(\"infinitejoy/wav2vec2-large-xls-r-300m-bashkir\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "013fabff2ea243a0a728a79b8f54ae09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/1.99k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a8d9ca6d024f46f58301bfbcc475e41a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/1.18G [00:00<?, ?B/s]"
+      ]
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+    },
+    {
+     "data": {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "AssertionError",
+     "evalue": "55",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-23-c6863db4730f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogits\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0mlogits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m: 55"
+     ]
+    }
+   ],
+   "source": [
+    "# from transformers import AutoModelForCTC, AutoProcessor\n",
+    "# from datasets import load_dataset\n",
+    "\n",
+    "# model = AutoModelForCTC.from_pretrained(\"infinitejoy/wav2vec2-large-xls-r-300m-bashkir\")\n",
+    "# processor = AutoProcessor.from_pretrained(\"infinitejoy/wav2vec2-large-xls-r-300m-bashkir\")\n",
+    "\n",
+    "# input_values = processor(common_voice_test[0][\"audio\"][\"array\"], return_tensors=\"pt\", sampling_rate=16_000).input_values\n",
+    "# # input_values = input_values.to(\"cuda\")\n",
+    "\n",
+    "# logits = model(input_values).logits\n",
+    "\n",
+    "# assert logits.shape[-1] == 32, logits.shape[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/gl/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15f27ed4e1f547229a8e9c441c01dfc5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/1.18G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c0f5833953e4f2aa060f7146b9a0d91",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10 [00:00<?, ?ex/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['[UNK]_[UNK] [UNK]sh[UNK] c[UNK]d ptd[UNK] [UNK]ld[UNK]á[UNK]r [UNK]rd[UNK]m[UNK]cn[UNK]', '[UNK]h[UNK]ln[UNK]r r[UNK]h[UNK]k_[UNK]m[UNK]cd[UNK]h[UNK]q[UNK]n[UNK]r[UNK] [UNK]ad[UNK]h[UNK]ch[UNK]í_[UNK] cn[UNK] [UNK]_[UNK]q[UNK] _[UNK]m[UNK]s[UNK]d[UNK]r [UNK]ptd[UNK] [UNK]d[UNK]rd[UNK] [UNK]qé[UNK]n[UNK] [UNK]n[UNK] [UNK]kd[UNK]u[UNK]d[UNK] [UNK]o[UNK]_[UNK]q_ n[UNK] [UNK]l_[UNK]q[UNK]', '[UNK]r[UNK]d[UNK] [UNK]u_[UNK]r[UNK] [UNK]on[UNK]k_[UNK]r [UNK]ok[UNK]_[UNK]sd[UNK]qé[UNK]_[UNK]r[UNK] [UNK]q[UNK]d[UNK]y_[UNK]q[UNK] [UNK]m_[UNK] [UNK]bn[UNK]q[UNK]s[UNK]h[UNK]bd[UNK]k_[UNK] [UNK]sn[UNK]l_[UNK]q¿[UNK]r _[UNK]t[UNK]fn [UNK]ud[UNK]m[UNK]c[UNK]h[UNK]s_[UNK] [UNK]cn[UNK]r [UNK]b[UNK]_[UNK]a_[UNK]kh[UNK]í[UNK]n[UNK]r[UNK] [UNK]cd[UNK] [UNK]od[UNK]c[UNK]q_[UNK]', '[UNK]t[UNK]mg_ [UNK]ud[UNK]q[UNK]rhñ[UNK]m [UNK]mn[UNK]q[UNK]l_[UNK]k cn[UNK] [UNK]o[UNK]k_[UNK]md[UNK]s_[UNK]', '[UNK]d[UNK]t[UNK] [UNK]o[UNK]d[UNK]q[UNK]c[UNK]é[UNK]m[UNK] [UNK] [UNK]sn[UNK]c_[UNK] [UNK]¿[UNK] [UNK]e_[UNK]lh[UNK]kh[UNK]_ ptd [UNK]sh[UNK]í[UNK]_[UNK]', '[UNK]n[UNK] ld[UNK]t[UNK] [UNK]b[UNK]_[UNK]l[UNK] [UNK]o[UNK]hk[UNK]kn[UNK]t tmg_[UNK] [UNK]ln[UNK]r[UNK]b[UNK]_[UNK]', '[UNK]ptd[UNK] [UNK]wt[UNK]r[UNK]sh[UNK]y_[UNK]', '[UNK]s[UNK]ñ[UNK]l_[UNK]r[UNK]kd n[UNK]t[UNK]s[UNK]q_[UNK]', '[UNK]o_[UNK]r[UNK]_[UNK] [UNK]o_[UNK]r[UNK]_ [UNK]o_[UNK]q_ [UNK]cd[UNK]m[UNK]s[UNK]qn[UNK] [UNK]ptd [UNK]m[UNK]ñ[UNK]r _[UNK]ém[UNK]c_ dr[UNK]s_[UNK]ln[UNK]r [UNK]b[UNK]d[UNK]_[UNK]m[UNK]cn[UNK]', '[UNK]_[UNK]m[UNK]s[UNK]d[UNK]r dr[UNK]s_[UNK]a[UNK]_[UNK] _[UNK]ké[UNK]']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'E ti de quen ves sendo?'"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from datasets import Audio, Dataset, load_dataset, load_metric\n",
+    "from transformers import AutoFeatureExtractor, pipeline\n",
+    "\n",
+    "dataset = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"gl\", use_auth_token=True, split=\"train+validation\")\n",
+    "\n",
+    "# for testing: only process the first two examples as a test\n",
+    "dataset = dataset.select(range(10))\n",
+    "\n",
+    "repo_name = 'infinitejoy/wav2vec2-large-xls-r-300m-galician'\n",
+    "\n",
+    "# load processor\n",
+    "feature_extractor = AutoFeatureExtractor.from_pretrained(repo_name)\n",
+    "# feature_extractor = processor_with_lm.feature_extractor\n",
+    "sampling_rate = feature_extractor.sampling_rate\n",
+    "\n",
+    "# resample audio\n",
+    "dataset = dataset.cast_column(\"audio\", Audio(sampling_rate=sampling_rate))\n",
+    "\n",
+    "# load eval pipeline\n",
+    "asr = pipeline(\"automatic-speech-recognition\", model=repo_name, feature_extractor=feature_extractor)\n",
+    "\n",
+    "# map function to decode audio\n",
+    "def map_to_pred(batch):\n",
+    "    prediction = asr(\n",
+    "        batch[\"audio\"][\"array\"])\n",
+    "\n",
+    "    batch[\"prediction\"] = prediction[\"text\"]\n",
+    "    batch[\"target\"] = batch[\"sentence\"]\n",
+    "    return batch\n",
+    "\n",
+    "# run inference on all examples\n",
+    "result = dataset.map(map_to_pred, remove_columns=dataset.column_names)\n",
+    "print(result[\"prediction\"])\n",
+    "\n",
+    "result[0]['target']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"qtod'sjn qdllhhm u'h ds\""
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result[2]['prediction'].replace('[UNK]', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Rupeatko remmiin, vai et?'"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result[2]['target'].replace('[UNK]', '')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "authorship_tag": "ABX9TyM3OaMlm9YQtKpl28c8gBBd",
+   "include_colab_link": true,
+   "name": "DebugOVHTransformers.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}