{
 "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": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YT7fRnKctggU",
    "outputId": "f355a3e0-20da-489f-bd1f-5e508e792a68"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon Jan 24 17:23:29 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   36C    P0    26W / 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": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Mz4bubhxxsad",
    "outputId": "23398525-cc19-43c2-9fec-497e06214f29"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "01/25/2022 02:48:41 - WARNING - __main__ - Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: True\n",
      "01/25/2022 02:48:41 - 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-basaa/runs/Jan25_02-48-41_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=200.0,\n",
      "optim=OptimizerNames.ADAMW_HF,\n",
      "output_dir=./wav2vec2-large-xls-r-300m-basaa,\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-basaa,\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",
      "01/25/2022 02:48:43 - WARNING - datasets.builder - Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/bas/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n",
      "01/25/2022 02:48:46 - WARNING - datasets.builder - Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/bas/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n",
      "01/25/2022 02:48:46 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/bas/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba/cache-3a6331ffb89c104d.arrow\n",
      "01/25/2022 02:48:46 - WARNING - datasets.arrow_dataset - Loading cached processed dataset at /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/bas/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba/cache-5e7d861af8466195.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, 19.86ba/s]\n",
      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00, 38.36ba/s]\n",
      "Didn't find file ./wav2vec2-large-xls-r-300m-basaa/tokenizer_config.json. We won't load it.\n",
      "Didn't find file ./wav2vec2-large-xls-r-300m-basaa/added_tokens.json. We won't load it.\n",
      "Didn't find file ./wav2vec2-large-xls-r-300m-basaa/special_tokens_map.json. We won't load it.\n",
      "Didn't find file ./wav2vec2-large-xls-r-300m-basaa/tokenizer.json. We won't load it.\n",
      "loading file ./wav2vec2-large-xls-r-300m-basaa/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-basaa/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: ['quantizer.codevectors', 'project_q.bias', 'project_hid.bias', 'quantizer.weight_proj.weight', 'project_q.weight', 'quantizer.weight_proj.bias', 'project_hid.weight']\n",
      "- This IS expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing Wav2Vec2ForCTC from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/wav2vec2-xls-r-300m and are newly initialized: ['lm_head.weight', 'lm_head.bias']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "preprocess datasets: 100%|█████████████████| 1008/1008 [00:07<00:00, 138.56ex/s]\n",
      "preprocess datasets: 100%|███████████████████| 375/375 [00:02<00:00, 153.03ex/s]\n",
      "100%|████████████████████████████████████████████| 2/2 [00:00<00:00, 995.09ba/s]\n",
      "100%|████████████████████████████████████████████| 1/1 [00:00<00:00, 970.45ba/s]\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "tokenizer config file saved in ./wav2vec2-large-xls-r-300m-basaa/tokenizer_config.json\n",
      "Special tokens file saved in ./wav2vec2-large-xls-r-300m-basaa/special_tokens_map.json\n",
      "added tokens file saved in ./wav2vec2-large-xls-r-300m-basaa/added_tokens.json\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/config.json\n",
      "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "loading configuration file ./wav2vec2-large-xls-r-300m-basaa/config.json\n",
      "Model config Wav2Vec2Config {\n",
      "  \"_name_or_path\": \"./wav2vec2-large-xls-r-300m-basaa\",\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\": 30,\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\": 33,\n",
      "  \"xvector_output_dim\": 512\n",
      "}\n",
      "\n",
      "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-basaa/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-basaa/tokenizer.json. We won't load it.\n",
      "loading file ./wav2vec2-large-xls-r-300m-basaa/vocab.json\n",
      "loading file ./wav2vec2-large-xls-r-300m-basaa/tokenizer_config.json\n",
      "loading file ./wav2vec2-large-xls-r-300m-basaa/added_tokens.json\n",
      "loading file ./wav2vec2-large-xls-r-300m-basaa/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-basaa into local empty directory.\n",
      "01/25/2022 02:49:13 - WARNING - huggingface_hub.repository - Cloning https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-basaa into local empty directory.\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 = 1008\n",
      "  Num Epochs = 200\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 = 6400\n",
      "{'loss': 22.3442, 'learning_rate': 1.3439999999999998e-05, 'epoch': 3.12}       \n",
      "{'loss': 5.9373, 'learning_rate': 2.7439999999999998e-05, 'epoch': 6.25}        \n",
      "{'loss': 3.7179, 'learning_rate': 4.1439999999999996e-05, 'epoch': 9.38}        \n",
      "{'loss': 3.0789, 'learning_rate': 5.544e-05, 'epoch': 12.5}                     \n",
      "{'loss': 2.9287, 'learning_rate': 6.944e-05, 'epoch': 15.62}                    \n",
      "  8%|██▉                                   | 500/6400 [12:29<2:18:06,  1.40s/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 = 375\n",
      "  Batch size = 32\n",
      "\n",
      "  0%|                                                    | 0/12 [00:00<?, ?it/s]\u001b[A\n",
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      " 92%|███████████████████████████████████████▍   | 11/12 [00:11<00:01,  1.14s/it]\u001b[A\n",
      "                                                                                \u001b[A\n",
      "\u001b[A{'eval_loss': 2.877366304397583, 'eval_wer': 1.0, 'eval_runtime': 13.8367, 'eval_samples_per_second': 27.102, 'eval_steps_per_second': 0.867, 'epoch': 15.62}\n",
      "  8%|██▉                                   | 500/6400 [12:43<2:18:06,  1.40s/it]\n",
      "100%|███████████████████████████████████████████| 12/12 [00:12<00:00,  1.01s/it]\u001b[A\n",
      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-basaa/checkpoint-500\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-500/config.json\n",
      "Model weights saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-500/pytorch_model.bin\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-500/preprocessor_config.json\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "{'loss': 2.8183, 'learning_rate': 6.886101694915253e-05, 'epoch': 18.75}        \n",
      "{'loss': 1.6359, 'learning_rate': 6.648813559322033e-05, 'epoch': 25.0}         \n",
      "{'loss': 1.2603, 'learning_rate': 6.530169491525422e-05, 'epoch': 28.12}        \n",
      " 15%|█████▋                                | 951/6400 [24:25<2:29:19,  1.64s/it]Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "{'loss': 1.0279, 'learning_rate': 6.292881355932203e-05, 'epoch': 34.38}        \n",
      "{'loss': 0.9694, 'learning_rate': 6.174237288135593e-05, 'epoch': 37.5}         \n",
      "{'loss': 0.8993, 'learning_rate': 6.0555932203389824e-05, 'epoch': 40.62}       \n",
      "{'loss': 0.8663, 'learning_rate': 5.936949152542372e-05, 'epoch': 43.75}        \n",
      "{'loss': 0.8329, 'learning_rate': 5.818305084745762e-05, 'epoch': 46.88}        \n",
      " 23%|████████▋                            | 1500/6400 [39:45<1:46:24,  1.30s/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 = 375\n",
      "  Batch size = 32\n",
      "\n",
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      "                                                                                \u001b[A\n",
      "\u001b[A{'eval_loss': 0.5573042631149292, 'eval_wer': 0.5792052259118128, 'eval_runtime': 13.9992, 'eval_samples_per_second': 26.787, 'eval_steps_per_second': 0.857, 'epoch': 46.88}\n",
      " 23%|████████▋                            | 1500/6400 [39:59<1:46:24,  1.30s/it]\n",
      "100%|███████████████████████████████████████████| 12/12 [00:12<00:00,  1.01s/it]\u001b[A\n",
      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-basaa/checkpoint-1500\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-1500/config.json\n",
      "Model weights saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-1500/pytorch_model.bin\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-1500/preprocessor_config.json\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-large-xls-r-300m-basaa/checkpoint-500] due to args.save_total_limit\n",
      "{'loss': 0.8219, 'learning_rate': 5.6996610169491526e-05, 'epoch': 50.0}        \n",
      "{'loss': 0.8078, 'learning_rate': 5.581016949152542e-05, 'epoch': 53.12}        \n",
      "{'loss': 0.7718, 'learning_rate': 5.4623728813559317e-05, 'epoch': 56.25}       \n",
      "{'loss': 0.7365, 'learning_rate': 5.3437288135593215e-05, 'epoch': 59.38}       \n",
      "{'loss': 0.7109, 'learning_rate': 5.2250847457627114e-05, 'epoch': 62.5}        \n",
      " 31%|███████████▌                         | 2000/6400 [53:49<1:56:46,  1.59s/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 = 375\n",
      "  Batch size = 32\n",
      "\n",
      "  0%|                                                    | 0/12 [00:00<?, ?it/s]\u001b[A\n",
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      "                                                                                \u001b[A\n",
      "\u001b[A{'eval_loss': 0.54203200340271, 'eval_wer': 0.568317909635275, 'eval_runtime': 13.6769, 'eval_samples_per_second': 27.419, 'eval_steps_per_second': 0.877, 'epoch': 62.5}\n",
      " 31%|███████████▌                         | 2000/6400 [54:02<1:56:46,  1.59s/it]\n",
      "100%|███████████████████████████████████████████| 12/12 [00:12<00:00,  1.02it/s]\u001b[A\n",
      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-basaa/checkpoint-2000\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-2000/config.json\n",
      "Model weights saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-2000/pytorch_model.bin\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-2000/preprocessor_config.json\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-large-xls-r-300m-basaa/checkpoint-1000] due to args.save_total_limit\n",
      "{'loss': 0.691, 'learning_rate': 5.106440677966102e-05, 'epoch': 65.62}         \n",
      "{'loss': 0.69, 'learning_rate': 4.987796610169491e-05, 'epoch': 68.75}          \n",
      "{'loss': 0.6662, 'learning_rate': 4.870338983050847e-05, 'epoch': 71.88}        \n",
      "{'loss': 0.6387, 'learning_rate': 4.7528813559322025e-05, 'epoch': 75.0}        \n",
      "{'loss': 0.6295, 'learning_rate': 4.634237288135593e-05, 'epoch': 78.12}        \n",
      " 39%|█████████████▋                     | 2500/6400 [1:07:56<1:40:44,  1.55s/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 = 375\n",
      "  Batch size = 32\n",
      "\n",
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      "                                                                                \u001b[A\n",
      "\u001b[A{'eval_loss': 0.5166199803352356, 'eval_wer': 0.5394665215024497, 'eval_runtime': 13.9794, 'eval_samples_per_second': 26.825, 'eval_steps_per_second': 0.858, 'epoch': 78.12}\n",
      " 39%|█████████████▋                     | 2500/6400 [1:08:10<1:40:44,  1.55s/it]\n",
      "100%|███████████████████████████████████████████| 12/12 [00:12<00:00,  1.00s/it]\u001b[A\n",
      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-basaa/checkpoint-2500\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-2500/config.json\n",
      "Model weights saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-2500/pytorch_model.bin\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-2500/preprocessor_config.json\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-large-xls-r-300m-basaa/checkpoint-1500] due to args.save_total_limit\n",
      "{'loss': 0.6157, 'learning_rate': 4.515593220338983e-05, 'epoch': 81.25}        \n",
      "{'loss': 0.596, 'learning_rate': 4.396949152542372e-05, 'epoch': 84.38}         \n",
      "{'loss': 0.5883, 'learning_rate': 4.2783050847457626e-05, 'epoch': 87.5}        \n",
      "{'loss': 0.5843, 'learning_rate': 4.159661016949152e-05, 'epoch': 90.62}        \n",
      "{'loss': 0.5715, 'learning_rate': 4.041016949152542e-05, 'epoch': 93.75}        \n",
      " 47%|████████████████▍                  | 3000/6400 [1:22:00<1:29:17,  1.58s/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 = 375\n",
      "  Batch size = 32\n",
      "\n",
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      "                                                                                \u001b[A\n",
      "\u001b[A{'eval_loss': 0.5486962795257568, 'eval_wer': 0.562874251497006, 'eval_runtime': 13.6771, 'eval_samples_per_second': 27.418, 'eval_steps_per_second': 0.877, 'epoch': 93.75}\n",
      " 47%|████████████████▍                  | 3000/6400 [1:22:14<1:29:17,  1.58s/it]\n",
      "100%|███████████████████████████████████████████| 12/12 [00:12<00:00,  1.02it/s]\u001b[A\n",
      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-basaa/checkpoint-3000\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-3000/config.json\n",
      "Model weights saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-3000/pytorch_model.bin\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-3000/preprocessor_config.json\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-large-xls-r-300m-basaa/checkpoint-2000] due to args.save_total_limit\n",
      "{'loss': 0.5581, 'learning_rate': 3.922372881355932e-05, 'epoch': 96.88}        \n",
      "{'loss': 0.5262, 'learning_rate': 3.8037288135593213e-05, 'epoch': 100.0}       \n",
      "{'loss': 0.5337, 'learning_rate': 3.685084745762712e-05, 'epoch': 103.12}       \n",
      "{'loss': 0.5229, 'learning_rate': 3.566440677966101e-05, 'epoch': 106.25}       \n",
      " 54%|██████████████████▉                | 3457/6400 [1:34:54<1:17:11,  1.57s/it]Deleting older checkpoint [wav2vec2-large-xls-r-300m-basaa/checkpoint-2500] due to args.save_total_limit\n",
      "{'loss': 0.4817, 'learning_rate': 3.329152542372881e-05, 'epoch': 112.5}        \n",
      "{'loss': 0.4959, 'learning_rate': 3.2105084745762706e-05, 'epoch': 115.62}      \n",
      "{'loss': 0.4786, 'learning_rate': 3.0918644067796605e-05, 'epoch': 118.75}      \n",
      "{'loss': 0.4671, 'learning_rate': 2.9732203389830507e-05, 'epoch': 121.88}      \n",
      "{'loss': 0.4661, 'learning_rate': 2.8545762711864405e-05, 'epoch': 125.0}       \n",
      " 62%|███████████████████████▏             | 4000/6400 [1:49:56<46:49,  1.17s/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 = 375\n",
      "  Batch size = 32\n",
      "\n",
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      "                                                                                \u001b[A\n",
      "\u001b[A{'eval_loss': 0.5621253252029419, 'eval_wer': 0.5394665215024497, 'eval_runtime': 13.7208, 'eval_samples_per_second': 27.331, 'eval_steps_per_second': 0.875, 'epoch': 125.0}\n",
      " 62%|███████████████████████▏             | 4000/6400 [1:50:09<46:49,  1.17s/it]\n",
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      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-basaa/checkpoint-4000\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-4000/config.json\n",
      "Model weights saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-4000/pytorch_model.bin\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-4000/preprocessor_config.json\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-large-xls-r-300m-basaa/checkpoint-3000] due to args.save_total_limit\n",
      "{'loss': 0.4538, 'learning_rate': 2.7359322033898304e-05, 'epoch': 128.12}      \n",
      "{'loss': 0.4482, 'learning_rate': 2.61728813559322e-05, 'epoch': 131.25}        \n",
      "{'loss': 0.4371, 'learning_rate': 2.38e-05, 'epoch': 137.5}                     \n",
      "{'loss': 0.423, 'learning_rate': 2.2613559322033898e-05, 'epoch': 140.62}       \n",
      " 70%|██████████████████████████           | 4500/6400 [2:03:53<44:15,  1.40s/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 = 375\n",
      "  Batch size = 32\n",
      "\n",
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      "                                                                                \u001b[A\n",
      "\u001b[A{'eval_loss': 0.5657923221588135, 'eval_wer': 0.5247686445291235, 'eval_runtime': 13.7521, 'eval_samples_per_second': 27.269, 'eval_steps_per_second': 0.873, 'epoch': 140.62}\n",
      " 70%|██████████████████████████           | 4500/6400 [2:04:06<44:15,  1.40s/it]\n",
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      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-basaa/checkpoint-4500\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-4500/config.json\n",
      "Model weights saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-4500/pytorch_model.bin\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-4500/preprocessor_config.json\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-large-xls-r-300m-basaa/checkpoint-3500] due to args.save_total_limit\n",
      "{'loss': 0.4134, 'learning_rate': 2.1427118644067796e-05, 'epoch': 143.75}      \n",
      "{'loss': 0.4014, 'learning_rate': 2.024067796610169e-05, 'epoch': 146.88}       \n",
      "{'loss': 0.4001, 'learning_rate': 1.905423728813559e-05, 'epoch': 150.0}        \n",
      "{'loss': 0.3777, 'learning_rate': 1.786779661016949e-05, 'epoch': 153.12}       \n",
      "{'loss': 0.3793, 'learning_rate': 1.6681355932203387e-05, 'epoch': 156.25}      \n",
      " 78%|████████████████████████████▉        | 5000/6400 [2:17:49<38:02,  1.63s/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 = 375\n",
      "  Batch size = 32\n",
      "\n",
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      "                                                                                \u001b[A\n",
      "\u001b[A{'eval_loss': 0.5920763611793518, 'eval_wer': 0.4980947196516059, 'eval_runtime': 13.612, 'eval_samples_per_second': 27.549, 'eval_steps_per_second': 0.882, 'epoch': 156.25}\n",
      " 78%|████████████████████████████▉        | 5000/6400 [2:18:03<38:02,  1.63s/it]\n",
      "100%|███████████████████████████████████████████| 12/12 [00:12<00:00,  1.02it/s]\u001b[A\n",
      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-basaa/checkpoint-5000\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-5000/config.json\n",
      "Model weights saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-5000/pytorch_model.bin\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-5000/preprocessor_config.json\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-large-xls-r-300m-basaa/checkpoint-4000] due to args.save_total_limit\n",
      "{'loss': 0.3843, 'learning_rate': 1.549491525423729e-05, 'epoch': 159.38}       \n",
      "{'loss': 0.3918, 'learning_rate': 1.4308474576271186e-05, 'epoch': 162.5}       \n",
      "{'loss': 0.3593, 'learning_rate': 1.3122033898305082e-05, 'epoch': 165.62}      \n",
      "{'loss': 0.3674, 'learning_rate': 1.1935593220338983e-05, 'epoch': 168.75}      \n",
      "{'loss': 0.3651, 'learning_rate': 1.074915254237288e-05, 'epoch': 171.88}       \n",
      " 86%|███████████████████████████████▊     | 5500/6400 [2:31:45<19:02,  1.27s/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 = 375\n",
      "  Batch size = 32\n",
      "\n",
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      "                                                                                \u001b[A\n",
      "\u001b[A{'eval_loss': 0.5986579060554504, 'eval_wer': 0.48884050081654873, 'eval_runtime': 13.8361, 'eval_samples_per_second': 27.103, 'eval_steps_per_second': 0.867, 'epoch': 171.88}\n",
      " 86%|███████████████████████████████▊     | 5500/6400 [2:31:59<19:02,  1.27s/it]\n",
      "100%|███████████████████████████████████████████| 12/12 [00:12<00:00,  1.01it/s]\u001b[A\n",
      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-basaa/checkpoint-5500\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-5500/config.json\n",
      "Model weights saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-5500/pytorch_model.bin\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-5500/preprocessor_config.json\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-large-xls-r-300m-basaa/checkpoint-4500] due to args.save_total_limit\n",
      "{'loss': 0.345, 'learning_rate': 9.56271186440678e-06, 'epoch': 175.0}          \n",
      "{'loss': 0.3527, 'learning_rate': 8.376271186440677e-06, 'epoch': 178.12}       \n",
      "{'loss': 0.3446, 'learning_rate': 7.189830508474576e-06, 'epoch': 181.25}       \n",
      "{'loss': 0.3461, 'learning_rate': 6.003389830508474e-06, 'epoch': 184.38}       \n",
      "{'loss': 0.3351, 'learning_rate': 4.816949152542372e-06, 'epoch': 187.5}        \n",
      " 94%|██████████████████████████████████▋  | 6000/6400 [2:45:42<10:44,  1.61s/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 = 375\n",
      "  Batch size = 32\n",
      "\n",
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      " 33%|██████████████▋                             | 4/12 [00:02<00:06,  1.25it/s]\u001b[A\n",
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      " 58%|█████████████████████████▋                  | 7/12 [00:06<00:05,  1.10s/it]\u001b[A\n",
      " 67%|█████████████████████████████▎              | 8/12 [00:07<00:04,  1.17s/it]\u001b[A\n",
      " 75%|█████████████████████████████████           | 9/12 [00:08<00:03,  1.11s/it]\u001b[A\n",
      " 83%|███████████████████████████████████▊       | 10/12 [00:10<00:02,  1.12s/it]\u001b[A\n",
      " 92%|███████████████████████████████████████▍   | 11/12 [00:11<00:01,  1.12s/it]\u001b[A\n",
      "                                                                                \u001b[A\n",
      "\u001b[A{'eval_loss': 0.6017099022865295, 'eval_wer': 0.49482852476864453, 'eval_runtime': 13.6253, 'eval_samples_per_second': 27.522, 'eval_steps_per_second': 0.881, 'epoch': 187.5}\n",
      " 94%|██████████████████████████████████▋  | 6000/6400 [2:45:56<10:44,  1.61s/it]\n",
      "100%|███████████████████████████████████████████| 12/12 [00:12<00:00,  1.02it/s]\u001b[A\n",
      "                                                                                \u001b[ASaving model checkpoint to ./wav2vec2-large-xls-r-300m-basaa/checkpoint-6000\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-6000/config.json\n",
      "Model weights saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-6000/pytorch_model.bin\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/checkpoint-6000/preprocessor_config.json\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "Deleting older checkpoint [wav2vec2-large-xls-r-300m-basaa/checkpoint-5000] due to args.save_total_limit\n",
      "{'loss': 0.3368, 'learning_rate': 3.630508474576271e-06, 'epoch': 190.62}       \n",
      "{'loss': 0.3405, 'learning_rate': 2.444067796610169e-06, 'epoch': 193.75}       \n",
      "{'loss': 0.3271, 'learning_rate': 1.2576271186440677e-06, 'epoch': 196.88}      \n",
      "{'loss': 0.334, 'learning_rate': 7.118644067796609e-08, 'epoch': 200.0}         \n",
      "100%|█████████████████████████████████████| 6400/6400 [2:57:08<00:00,  1.22s/it]\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n",
      "{'train_runtime': 10628.5861, 'train_samples_per_second': 18.968, 'train_steps_per_second': 0.602, 'train_loss': 1.1891312944889068, 'epoch': 200.0}\n",
      "100%|█████████████████████████████████████| 6400/6400 [2:57:08<00:00,  1.66s/it]\n",
      "Saving model checkpoint to ./wav2vec2-large-xls-r-300m-basaa\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/config.json\n",
      "Model weights saved in ./wav2vec2-large-xls-r-300m-basaa/pytorch_model.bin\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "***** train metrics *****\n",
      "  epoch                    =      200.0\n",
      "  train_loss               =     1.1891\n",
      "  train_runtime            = 2:57:08.58\n",
      "  train_samples            =       1008\n",
      "  train_samples_per_second =     18.968\n",
      "  train_steps_per_second   =      0.602\n",
      "01/25/2022 05:46:29 - 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 = 375\n",
      "  Batch size = 32\n",
      "100%|███████████████████████████████████████████| 12/12 [00:11<00:00,  1.01it/s]\n",
      "***** eval metrics *****\n",
      "  epoch                   =      200.0\n",
      "  eval_loss               =     0.5975\n",
      "  eval_runtime            = 0:00:13.50\n",
      "  eval_samples            =        375\n",
      "  eval_samples_per_second =     27.761\n",
      "  eval_steps_per_second   =      0.888\n",
      "  eval_wer                =     0.4981\n",
      "Saving model checkpoint to ./wav2vec2-large-xls-r-300m-basaa\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/config.json\n",
      "Model weights saved in ./wav2vec2-large-xls-r-300m-basaa/pytorch_model.bin\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-basaa/preprocessor_config.json\n",
      "Upload file pytorch_model.bin: 100%|██████▉| 1.18G/1.18G [01:02<00:00, 12.8MB/s]To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-basaa\n",
      "   4004a5d..cba4cc3  main -> main\n",
      "\n",
      "01/25/2022 05:49:03 - WARNING - huggingface_hub.repository - To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-basaa\n",
      "   4004a5d..cba4cc3  main -> main\n",
      "\n",
      "Upload file pytorch_model.bin: 100%|███████| 1.18G/1.18G [01:03<00:00, 19.8MB/s]\n",
      "Dropping the following result as it does not have all the necessary fields:\n",
      "{'dataset': {'name': 'MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BAS', 'type': 'common_voice', 'args': 'Config: bas, Training split: train+validation, Eval split: test'}}\n",
      "To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-basaa\n",
      "   cba4cc3..5a62cc8  main -> main\n",
      "\n",
      "01/25/2022 05:49:12 - WARNING - huggingface_hub.repository - To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-basaa\n",
      "   cba4cc3..5a62cc8  main -> main\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=\"bas\" \\\n",
    "\t--output_dir=\"./wav2vec2-large-xls-r-300m-basaa\" \\\n",
    "\t--overwrite_output_dir \\\n",
    "\t--num_train_epochs=\"200\" \\\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-basaa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!ls -ltr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filesystem      Size  Used Avail Use% Mounted on\n",
      "overlay         3.5T  963G  2.4T  29% /\n",
      "tmpfs            64M     0   64M   0% /dev\n",
      "tmpfs            87G     0   87G   0% /sys/fs/cgroup\n",
      "tmpfs            87G  8.0K   87G   1% /dev/shm\n",
      "/dev/md0        3.5T  963G  2.4T  29% /etc/group\n",
      "tmpfs            87G   12K   87G   1% /proc/driver/nvidia\n",
      "/dev/vda1        49G  6.4G   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": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading and preparing dataset common_voice/bas to /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/bas/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65173ad791c049b582dc4533c3555696",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/52.1M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset common_voice downloaded and prepared to /workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/bas/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba. Subsequent calls will reuse this data.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/bas/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1008\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset, load_metric, Audio\n",
    "\n",
    "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"bas\", use_auth_token=True, split=\"train+validation\")\n",
    "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"bas\", use_auth_token=True, split=\"test\")\n",
    "\n",
    "print(len(common_voice_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6300.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(common_voice_train) * 200 / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Column name accent not in the dataset. Current columns in the dataset: ['path', 'audio', 'sentence']",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-31dcc77bf46f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcommon_voice_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcommon_voice_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove_columns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"accent\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"age\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"client_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"down_votes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"gender\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"locale\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"segment\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"up_votes\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mcommon_voice_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcommon_voice_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove_columns\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"accent\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"age\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"client_id\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"down_votes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"gender\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"locale\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"segment\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"up_votes\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    520\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"Dataset\"\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"self\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    521\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 522\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    523\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/datasets/fingerprint.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0;31m# Call actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m             \u001b[0;31m# Update fingerprint of in-place transforms + update in-place history of transforms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mremove_columns\u001b[0;34m(self, column_names, new_fingerprint)\u001b[0m\n\u001b[1;32m   1498\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcolumn_name\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcolumn_names\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1499\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcolumn_name\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumn_names\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1500\u001b[0;31m                 raise ValueError(\n\u001b[0m\u001b[1;32m   1501\u001b[0m                     \u001b[0;34mf\"Column name {column_name} not in the dataset. \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1502\u001b[0m                     \u001b[0;34mf\"Current columns in the dataset: {dataset._data.column_names}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Column name accent not in the dataset. Current columns in the dataset: ['path', 'audio', 'sentence']"
     ]
    }
   ],
   "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": 12,
   "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": 13,
   "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>Hiônde hi mbémba.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Me mbagbana le kiñe a tagbe.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Péé i sômi, i mbem kôsi.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yokel yaga nyen u bike i mok.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Me mbada kômôl i wom.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Hibee hi mbudla.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mudaa nunu a nyi téñ añgis yéé!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Me ñkon i jôl.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A nsugul bijek.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Di bak i ntoa pôôga i kôdôk.</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": 14,
   "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": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ef8fa5c4a82429da47176ab713a7b66",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1008 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88de76e4a37d4e79849a1dbe49b80455",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/375 [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": 16,
   "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": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ed486613cdb34d73975c1410a1b757fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1008 [00:00<?, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "72a48662a2ef4ad9acf0e085c1049072",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/375 [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": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c613e2c2c3445a1a2b29223e22006c2",
       "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": "8c3bfa22a48e47378d752091079a42a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vocab_train = common_voice_train.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_train.column_names)\n",
    "vocab_test = common_voice_test.map(extract_all_chars, batched=True, batch_size=-1, keep_in_memory=True, remove_columns=common_voice_test.column_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' ': 0,\n",
       " 'a': 1,\n",
       " 'b': 2,\n",
       " 'd': 3,\n",
       " 'e': 4,\n",
       " 'f': 5,\n",
       " 'g': 6,\n",
       " 'h': 7,\n",
       " 'i': 8,\n",
       " 'j': 9,\n",
       " 'k': 10,\n",
       " 'l': 11,\n",
       " 'm': 12,\n",
       " 'n': 13,\n",
       " 'o': 14,\n",
       " 'p': 15,\n",
       " 'r': 16,\n",
       " 's': 17,\n",
       " 't': 18,\n",
       " 'u': 19,\n",
       " 'v': 20,\n",
       " 'w': 21,\n",
       " 'y': 22,\n",
       " 'z': 23,\n",
       " 'é': 24,\n",
       " 'ñ': 25,\n",
       " '̃': 26,\n",
       " '̈': 27}"
      ]
     },
     "execution_count": 21,
     "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": 22,
   "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": [
      "30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/huggingface_hub/hf_api.py:1001: FutureWarning: `create_repo` now takes `token` as an optional positional argument. Be sure to adapt your code!\n",
      "  warnings.warn(\n",
      "Cloning https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-basaa into local empty directory.\n",
      "To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-basaa\n",
      "   535f413..1d27e6b  main -> main\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-basaa/commit/1d27e6b41659e85ed1b87d2bb3db58e96472fe1f'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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-basaa\"\n",
    "\n",
    "tokenizer.push_to_hub(repo_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-01-25 05:51:53--  https://raw.githubusercontent.com/huggingface/transformers/master/examples/research_projects/robust-speech-event/eval.py\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.111.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|: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",
      "2022-01-25 05:51:53 (11.6 MB/s) - ‘eval.py’ saved [4421/4421]\n",
      "\n",
      "total 1232556\n",
      "-rw-r--r-- 1 ovh ovh        272 Jan 25 02:49 vocab.json\n",
      "-rw-r--r-- 1 ovh ovh        260 Jan 25 02:49 tokenizer_config.json\n",
      "-rw-r--r-- 1 ovh ovh        309 Jan 25 02:49 special_tokens_map.json\n",
      "-rw-r--r-- 1 ovh ovh         23 Jan 25 02:49 added_tokens.json\n",
      "drwxr-xr-x 2 ovh ovh       4096 Jan 25 05:21 checkpoint-5500\n",
      "drwxr-xr-x 2 ovh ovh       4096 Jan 25 05:35 checkpoint-6000\n",
      "-rw-r--r-- 1 ovh ovh        197 Jan 25 05:46 train_results.json\n",
      "-rw-r--r-- 1 ovh ovh      11278 Jan 25 05:46 trainer_state.json\n",
      "-rw-r--r-- 1 ovh ovh        224 Jan 25 05:46 eval_results.json\n",
      "-rw-r--r-- 1 ovh ovh       2033 Jan 25 05:46 config.json\n",
      "-rw-r--r-- 1 ovh ovh        399 Jan 25 05:46 all_results.json\n",
      "-rw-r--r-- 1 ovh ovh 1262058993 Jan 25 05:46 pytorch_model.bin\n",
      "-rw-r--r-- 1 ovh ovh       3055 Jan 25 05:46 training_args.bin\n",
      "-rw-r--r-- 1 ovh ovh        212 Jan 25 05:46 preprocessor_config.json\n",
      "-rw-r--r-- 1 ovh ovh       2253 Jan 25 05:49 README.md\n",
      "-rw-r--r-- 1 ovh ovh       4421 Jan 25 05:51 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-basaa\n",
    "!ls -ltr wav2vec2-large-xls-r-300m-basaa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/bas/7.0.0/33e08856cfa0d0665e837bcad73ffd920a0bc713ce8c5fffb55dbdf1c084d5ba)\n",
      "100%|█████████████████████████████████████████| 375/375 [03:03<00:00,  2.04ex/s]\n",
      "WER: 1.0408274360370169\n",
      "CER: 2.2848350566223536\n",
      "100%|██████████████████████████████████████| 375/375 [00:00<00:00, 20474.93ex/s]\n"
     ]
    }
   ],
   "source": [
    "!cd wav2vec2-large-xls-r-300m-basaa; python eval.py --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config bas --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": [
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      ]
     },
     "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": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from transformers import AutoModelForCTC, Wav2Vec2Processor\n",
    "\n",
    "model = AutoModelForCTC.from_pretrained(\"infinitejoy/wav2vec2-large-xls-r-300m-basaa\")\n",
    "processor = Wav2Vec2Processor.from_pretrained(\"infinitejoy/wav2vec2-large-xls-r-300m-basaa\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "33",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-5156c922a4eb>\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: 33"
     ]
    }
   ],
   "source": [
    "from transformers import AutoModelForCTC, AutoProcessor\n",
    "from datasets import load_dataset\n",
    "\n",
    "model = AutoModelForCTC.from_pretrained(\"infinitejoy/wav2vec2-large-xls-r-300m-basaa\")\n",
    "processor = AutoProcessor.from_pretrained(\"infinitejoy/wav2vec2-large-xls-r-300m-basaa\")\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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "name": "DebugOVHTransformers.ipynb",
   "provenance": []
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  "kernelspec": {
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