{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZG_P29nKcSeI"
   },
   "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": "YacvHugMc1Ka"
   },
   "outputs": [],
   "source": [
    "# %%capture\n",
    "# !pip install https://github.com/kpu/kenlm/archive/master.zip pyctcdecode\n",
    "# !pip install datasets==1.18.3\n",
    "# !pip install git+https://github.com/huggingface/transformers.git\n",
    "# !pip install huggingface_hub==0.1\n",
    "# !pip install torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n",
    "# !pip install jiwer\n",
    "# !pip install -U git+https://github.com/huggingface/transformers.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vy63SoiZbnB5",
    "outputId": "17391c60-b894-4571-b8a4-d46b18cb42e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting git+https://github.com/huggingface/transformers.git\n",
      "  Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-i45amciw\n",
      "  Running command git clone -q https://github.com/huggingface/transformers.git /tmp/pip-req-build-i45amciw\n",
      "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
      "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
      "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (0.1.0)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (3.4.2)\n",
      "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (4.10.1)\n",
      "Requirement already satisfied: tokenizers!=0.11.3,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (0.11.4)\n",
      "Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (0.0.47)\n",
      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (1.19.5)\n",
      "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (4.62.3)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (2.23.0)\n",
      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (6.0)\n",
      "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (21.3)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==4.17.0.dev0) (2019.12.20)\n",
      "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers==4.17.0.dev0) (3.10.0.2)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers==4.17.0.dev0) (3.0.7)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers==4.17.0.dev0) (3.7.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.17.0.dev0) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.17.0.dev0) (2021.10.8)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.17.0.dev0) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==4.17.0.dev0) (1.24.3)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.17.0.dev0) (1.15.0)\n",
      "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.17.0.dev0) (1.1.0)\n",
      "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==4.17.0.dev0) (7.1.2)\n"
     ]
    }
   ],
   "source": [
    "# !pip install -U git+https://github.com/huggingface/transformers.git"
   ]
  },
  {
   "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": {
    "id": "ejKNEyJEcSeO"
   },
   "source": [
    "## Print main infos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5P6I-W9ts-kR",
    "outputId": "bd0c00d8-91c9-4b1a-8f2c-24182c2b227f"
   },
   "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: 1.18.3\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": {
    "id": "_VUKw21PcSeQ"
   },
   "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": "1fb2c851-11c3-4fcd-ad23-9032f25d7f8d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sat Jan 29 03:27:00 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   35C    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": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241,
     "referenced_widgets": [
      "50a1252082d942b09bfc620a9fa9d1d0",
      "e270b7c82f784ebbbba4b17fb07c310d",
      "32eb83bb6fd34c56bb345368e47e8f6f",
      "34417f648cd54ed5b6d91f53af3e2713",
      "7518572223ac480b89af2ab71f38b2ed",
      "ce8bb7d0fb744e7b9ce2ff35cfdbc679",
      "aa47a09bf444413ba95322d979c1908c",
      "0b83a8775ea1441980d8ba945be752fe",
      "127389ec566e423ab9a8f60a9d61caaa",
      "4e4bc5550505497ba35f6bd7dde2893f",
      "e5124c5171e04625b70795e4b7a18819",
      "e410e7aecf23433f880a0f7169a8ce97",
      "0f6b3cf1d33f46f594934874170bcd83",
      "e549178ba75f4939aba6ae1cf743722a",
      "9c28978adf974326a21259ae56f47fe9",
      "7d3231a0b7794b11af662170b352d9e0"
     ]
    },
    "id": "3Wj2W4tWcSeR",
    "outputId": "ad4eb63f-d643-45bd-b8d7-6adfefd9f773"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Login successful\n",
      "Your token has been saved to /root/.huggingface/token\n",
      "\u001b[1m\u001b[31mAuthenticated through git-crendential store but this isn't the helper defined on your machine.\n",
      "You will have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal to set it as the default\n",
      "\n",
      "git config --global credential.helper store\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "wHpUxFQPeWE2"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "!apt install git-lfs"
   ]
  },
  {
   "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": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "fevoJD15u4Ss",
    "outputId": "64745ecf-65b0-494d-a88d-52826eaae0f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-01-28 09:12:30--  https://raw.githubusercontent.com/huggingface/transformers/master/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.109.133, ...\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: 31209 (30K) [text/plain]\n",
      "Saving to: ‘run_speech_recognition_ctc.py’\n",
      "\n",
      "run_speech_recognit 100%[===================>]  30.48K  --.-KB/s    in 0.001s  \n",
      "\n",
      "2022-01-28 09:12:30 (21.4 MB/s) - ‘run_speech_recognition_ctc.py’ saved [31209/31209]\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\n",
    "# !wget -O run_speech_recognition_ctc.py https://raw.githubusercontent.com/huggingface/transformers/master/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "XJRA51HjcSeT"
   },
   "outputs": [],
   "source": [
    "# \t--learning_rate=\"7.5e-5\" \\\n",
    "# 84.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hZOB6ZAnsvDX",
    "outputId": "7b6a85b5-950c-46a1-c005-b885f8a9bd17"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nvcc: NVIDIA (R) Cuda compiler driver\n",
      "Copyright (c) 2005-2020 NVIDIA Corporation\n",
      "Built on Mon_Oct_12_20:09:46_PDT_2020\n",
      "Cuda compilation tools, release 11.1, V11.1.105\n",
      "Build cuda_11.1.TC455_06.29190527_0\n"
     ]
    }
   ],
   "source": [
    "!nvcc --version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NKlgW0E-sldT",
    "outputId": "b925521a-29d2-4787-dd5b-6520dda688e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting bitsandbytes-cuda111\n",
      "  Downloading bitsandbytes_cuda111-0.26.0-py3-none-any.whl (4.0 MB)\n",
      "\u001b[K     |████████████████████████████████| 4.0 MB 4.3 MB/s \n",
      "\u001b[?25hInstalling collected packages: bitsandbytes-cuda111\n",
      "Successfully installed bitsandbytes-cuda111-0.26.0\n"
     ]
    }
   ],
   "source": [
    "!pip install bitsandbytes-cuda111"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "remove special characters from datasets: 2558ex [00:00, 4238.92ex/s]\n",
      "remove special characters from datasets: 1184ex [00:00, 5236.89ex/s]\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, 10.20ba/s]\n",
      "100%|█████████████████████████████████████████████| 1/1 [00:00<00:00, 31.05ba/s]\n",
      "Didn't find file ./wav2vec2-large-xls-r-300m-slovenian/tokenizer.json. We won't load it.\n",
      "loading file ./wav2vec2-large-xls-r-300m-slovenian/vocab.json\n",
      "loading file ./wav2vec2-large-xls-r-300m-slovenian/tokenizer_config.json\n",
      "loading file ./wav2vec2-large-xls-r-300m-slovenian/added_tokens.json\n",
      "loading file ./wav2vec2-large-xls-r-300m-slovenian/special_tokens_map.json\n",
      "loading file None\n",
      "Adding <s> to the vocabulary\n",
      "Adding </s> to the vocabulary\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.weight', 'quantizer.codevectors', 'quantizer.weight_proj.weight', 'project_hid.bias', 'project_q.bias', 'project_hid.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: 2558ex [00:18, 135.52ex/s]\n",
      "preprocess datasets: 1184ex [00:09, 123.58ex/s]\n",
      "100%|████████████████████████████████████████████| 3/3 [00:00<00:00, 604.02ba/s]\n",
      "100%|████████████████████████████████████████████| 2/2 [00:00<00:00, 849.91ba/s]\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-slovenian/preprocessor_config.json\n",
      "tokenizer config file saved in ./wav2vec2-large-xls-r-300m-slovenian/tokenizer_config.json\n",
      "Special tokens file saved in ./wav2vec2-large-xls-r-300m-slovenian/special_tokens_map.json\n",
      "added tokens file saved in ./wav2vec2-large-xls-r-300m-slovenian/added_tokens.json\n",
      "Configuration saved in ./wav2vec2-large-xls-r-300m-slovenian/config.json\n",
      "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-slovenian/preprocessor_config.json\n",
      "loading configuration file ./wav2vec2-large-xls-r-300m-slovenian/config.json\n",
      "Model config Wav2Vec2Config {\n",
      "  \"_name_or_path\": \"./wav2vec2-large-xls-r-300m-slovenian\",\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\": 32,\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\": 35,\n",
      "  \"xvector_output_dim\": 512\n",
      "}\n",
      "\n",
      "loading feature extractor configuration file ./wav2vec2-large-xls-r-300m-slovenian/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-slovenian/tokenizer.json. We won't load it.\n",
      "loading file ./wav2vec2-large-xls-r-300m-slovenian/vocab.json\n",
      "loading file ./wav2vec2-large-xls-r-300m-slovenian/tokenizer_config.json\n",
      "loading file ./wav2vec2-large-xls-r-300m-slovenian/added_tokens.json\n",
      "loading file ./wav2vec2-large-xls-r-300m-slovenian/special_tokens_map.json\n",
      "loading file None\n",
      "Adding <s> to the vocabulary\n",
      "Adding </s> to the vocabulary\n",
      "/workspace/votic_training/./wav2vec2-large-xls-r-300m-slovenian is already a clone of https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-slovenian. Make sure you pull the latest changes with `repo.git_pull()`.\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 = 2558\n",
      "  Num Epochs = 100\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 = 8000\n",
      "  7%|██▋                                   | 577/8000 [13:11<2:08:41,  1.04s/it]"
     ]
    }
   ],
   "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=\"sl\" \\\n",
    "\t--output_dir=\"./wav2vec2-large-xls-r-300m-slovenian\" \\\n",
    "\t--overwrite_output_dir \\\n",
    "\t--num_train_epochs=\"100\" \\\n",
    "\t--per_device_train_batch_size=\"32\" \\\n",
    "\t--per_device_eval_batch_size=\"16\" \\\n",
    "\t--gradient_accumulation_steps=\"1\" \\\n",
    "\t--learning_rate=\"7e-5\" \\\n",
    "\t--warmup_steps=\"1000\" \\\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=\"1000\" \\\n",
    "\t--eval_steps=\"1000\" \\\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 > out.log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "0zBb4QMVcSeV"
   },
   "outputs": [],
   "source": [
    "# !rm -rf wav2vec2-large-xls-r-300m-bashkir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jxvhTTQ2cSeV"
   },
   "outputs": [],
   "source": [
    "!ls -ltr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "okCO9-XTcSeV",
    "outputId": "a47bb25e-904a-4c1e-8871-d996a16b6bcc"
   },
   "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": null,
   "metadata": {
    "id": "axSDvjOMdkxW"
   },
   "outputs": [],
   "source": [
    "# !pip install -U datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238,
     "referenced_widgets": [
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      "2de6d3927c534397ab122a9cf6332a33",
      "f3891dcc62b74ccd8d5a61b0ca761b2a",
      "9958cd546fbe477092527a14bb3bfe21",
      "639f180d5e02425dba7d4c4bca07c59b",
      "4da0d9054bd74fb2a77bb40371c99a7b",
      "3f8a5e226fbf4175b4fa7f39a2a9d290",
      "41515b22976648aabe660b8df3506c4c",
      "b2a72b0caf104aee8dd95bff01cc52a4",
      "6b8769a26838449e9d7d45fc5cc7a6f6",
      "50862512d9c14dbd92f8cc3d795d4cd2",
      "352fc0a527024af8a284c53f4d521fec",
      "67653ac95966464994b1e0a889cfc5d9",
      "778d0a9a7de243eba8dd1c0caf3aa82e",
      "14eb779636914797867b7315f347839d",
      "25a5802292874e49bb42a1489ff54b31",
      "89a05d4149534d78935e169c6623f458",
      "49f46100f43346d2bdb402e2fd1a1951",
      "5e2e7ad6aa8f4f51adf7f6376b84f618",
      "2e918f153be0489dbf0ad64bc45c563c",
      "c319fa946f3e4380864aed6d3fbb77e7"
     ]
    },
    "id": "82uZWUF_cSeW",
    "outputId": "e78215f2-d452-4d92-a94c-0a469f8760d4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/sl/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n",
      "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/sl/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2558\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset, load_metric, Audio\n",
    "\n",
    "common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"sl\", use_auth_token=True, split=\"train+validation\")\n",
    "common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"sl\", use_auth_token=True, split=\"test\")\n",
    "\n",
    "print(len(common_voice_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1Qa9wKa4cSeW",
    "outputId": "da721286-89ac-421c-a269-e779449488c6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['client_id', 'path', 'audio', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent', 'locale', 'segment'],\n",
       "    num_rows: 2558\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_voice_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "H_KRIMbEcSeX",
    "outputId": "90601843-d465-4cd3-dff0-9d2302e02699"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7993.75"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(common_voice_train) * 100 / 32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "ZUc_UAMbcSeX"
   },
   "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": 7,
   "metadata": {
    "id": "OKxWKzjMcSeX"
   },
   "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": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "uR3e--0AcSeY",
    "outputId": "efb84606-2717-4040-ca02-86975a2f4824"
   },
   "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>Njegove oči so večje od njegovega trebuha.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cesar lahko postane vsak berač, če ga le ljudstvo izvoli.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ne, zelene.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tišina je manj škodljiva kot slab odgovor.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vsega je konec.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Imam video posnetek mojega včerajšnjega pevskega nastopa.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dokler sreča pomaga, se da tudi proti toku plavati.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prijatelje hitreje izgubimo, kot dobimo.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Stara mera, stara vera.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Kislina hudo opeče kožo.</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": 9,
   "metadata": {
    "id": "x_zfqqoVcSeY"
   },
   "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": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "d8426e73abce4cbaa58a89aef1fce8b7",
      "ae51183c24fe42809d080fd15c298f92",
      "21c9c4302a76449784f314c15ca59bea",
      "fcc23e29fde64cde92f2ae57d7cabd78",
      "61ac7115c9b24ebb855343cc01b1d3f4",
      "90b3e47068e747c7be958d22fb56fe4f",
      "820d84c1afc7416e9368a246ab8d5ce9",
      "07447e6083b04bfeb04e5a601fe475bd",
      "822d95bb43c44a4394441d92e25120d7",
      "138580d9724141448ff8a5e11ef415ce",
      "1a03059af7bb40da924ecf3e709d7e0d",
      "b9d888877a7e4a24b07f4fb91ceda179",
      "36db5c636fcf46518685b91a168d9c11",
      "4407f3810d5d4820acf8db794ce305e6",
      "72fee1a44b5343a7add71c9649139317",
      "9b22b13729bf4f20b8b96da540cfaa3f",
      "90bde27c6e564ca285a65d6b594d6865",
      "256669df6862481cbd0bbcee229e2efe",
      "07b40214652e48adbae525787288795d",
      "57e054662b5d497b8e1f3d99fb72034f",
      "81a7889575ed4e0293f7ce56032e6edb",
      "00a619827a094be4ae891726e44ddd97"
     ]
    },
    "id": "BQfNU564cSeZ",
    "outputId": "6ca3ff91-8acb-4096-d746-aed7edb4055a"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5003f23353224ac48d49a352dd1aa96a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0ex [00:00, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93e83a03299a4ca5843bcd6d8d282da8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0ex [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": 11,
   "metadata": {
    "id": "liHWiYx9cSeZ"
   },
   "outputs": [],
   "source": [
    "# start_with_ar = common_voice_train.filter(lambda example: \"⅛\" in example['sentence'])\n",
    "# start_with_ar[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "gdGzeDEVcSeZ"
   },
   "outputs": [],
   "source": [
    "# start_with_ar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "d9Fe6xH1cSea"
   },
   "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",
    "#     batch[\"sentence\"] = re.sub('&', 'and', batch[\"sentence\"])\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "e106fee906b8408ebba822b4ff70693f",
      "af14186997de449f997936a81d69876b",
      "2d251c97292c4824a6cb218548c17353",
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      "8aaba2878c2c484e8fbf9d81354f924f",
      "95c3ba1f3cef4ca887dd8461db595c0a",
      "7092d73471c44875b496924bc82858df",
      "2d63896d07f64d91abb45c257ace72db",
      "781d2e3803574e2c841acfd1e090b84f",
      "d43443cf2579467a9cc3e9f05c607615",
      "19b70fdd00dc47f8b79f0d3acc68d11a",
      "992b23b615fb4e88a92782c64ad60cc2",
      "46031a26e4ff42819f1169eb07718b46",
      "0c8b1327b27449a49f9b71fdc80d831c",
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      "b17ceb2a58d64747aafd29836e681e02",
      "cb8b0320a4794b278f86c33bbfc9825f",
      "1e7f593023d544e1afe46359567abfca",
      "33fffaf4bc4a405187a2dd4eaa7ffc67",
      "173c6246acdb4a3bbf6dce2e236499a2",
      "478464758f2b455bb8848ef4046ed11d",
      "ce845667dcf54353b02299f85dcda29d"
     ]
    },
    "id": "f8K35VABcSea",
    "outputId": "3c0f55b5-3d44-4058-b362-896ebb58901c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "46212f83adbc482f92eaafed315ba529",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0ex [00:00, ?ex/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70ce1182600543b19d80066a5ec047ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0ex [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": 15,
   "metadata": {
    "id": "DOIBoakvcSea"
   },
   "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": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "43a3718f19944ea7bda27a96e3816a78",
      "2ec6b96f3d8449c08a8b62b85ccb57dc",
      "8137d055bfd14a058a8755d00e5fe260",
      "6fa1c2f06d0c43c38c76a7ace7c178a5",
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      "a33fe3bf150949fd9679ff1fe91d4433",
      "49aa86ddf29944b281b830b99e1ac4fe",
      "efb5e6e4970f43d1b6197e4aaedac0b7",
      "6e1517cd2bed4a35b52de6cdc812b75c",
      "557ddd52295c43a69b6fd2689edb46b4",
      "19630b62bebe4a8f9e775e59ee46fb9c",
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      "c6c51e30cbb1472f9e1a49bc0f5bb2cc",
      "002decc6fb504205ae9f61a4871e1532"
     ]
    },
    "id": "ZvFltuhucSea",
    "outputId": "fb969f7f-7172-4d82-8116-3dab25bd85a2"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b2f6ecf4162d4ccd86dce7c79620814a",
       "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": "a9e5c2b1152742d9af33530bac3506df",
       "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": 17,
   "metadata": {
    "id": "FJD7IXLkcSeb"
   },
   "outputs": [],
   "source": [
    "vocab_list = list(set(vocab_train[\"vocab\"][0]) | set(vocab_test[\"vocab\"][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e50XUYe3cSeb",
    "outputId": "769619ae-7ad5-4504-f454-34a24d642534"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' ': 0,\n",
       " 'a': 1,\n",
       " 'b': 2,\n",
       " 'c': 3,\n",
       " 'd': 4,\n",
       " 'e': 5,\n",
       " 'f': 6,\n",
       " 'g': 7,\n",
       " 'h': 8,\n",
       " 'i': 9,\n",
       " 'j': 10,\n",
       " 'k': 11,\n",
       " 'l': 12,\n",
       " 'm': 13,\n",
       " 'n': 14,\n",
       " 'o': 15,\n",
       " 'p': 16,\n",
       " 'r': 17,\n",
       " 's': 18,\n",
       " 't': 19,\n",
       " 'u': 20,\n",
       " 'v': 21,\n",
       " 'w': 22,\n",
       " 'x': 23,\n",
       " 'y': 24,\n",
       " 'z': 25,\n",
       " '«': 26,\n",
       " '»': 27,\n",
       " 'č': 28,\n",
       " 'š': 29,\n",
       " 'ž': 30}"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 128
    },
    "id": "wWofPlfMcSeb",
    "outputId": "cf9d5cdb-ffe1-46d5-af5c-f7c34cb4b59d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33\n"
     ]
    },
    {
     "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",
      "/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-slovenian into local empty directory.\n",
      "To https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-slovenian\n",
      "   89f59d6..3368867  main -> main\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'https://huggingface.co/infinitejoy/wav2vec2-large-xls-r-300m-slovenian/commit/3368867f07d5d4180c1224cf0523b6862469c435'"
      ]
     },
     "execution_count": 19,
     "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-slovenian\"\n",
    "\n",
    "# tokenizer.save_pretrained(repo_name)\n",
    "\n",
    "tokenizer.push_to_hub(repo_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1XVJcIykcSeb",
    "outputId": "67c53812-24ce-4dee-bb95-608971d61338"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-01-30 07:10:29--  https://raw.githubusercontent.com/huggingface/transformers/master/examples/research_projects/robust-speech-event/eval.py\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 4738 (4.6K) [text/plain]\n",
      "Saving to: ‘eval.py’\n",
      "\n",
      "eval.py             100%[===================>]   4.63K  --.-KB/s    in 0s      \n",
      "\n",
      "2022-01-30 07:10:29 (16.6 MB/s) - ‘eval.py’ saved [4738/4738]\n",
      "\n",
      "total 1232584\n",
      "-rw-r--r-- 1 ovh ovh        300 Jan 30 02:51 vocab.json\n",
      "-rw-r--r-- 1 ovh ovh        260 Jan 30 02:51 tokenizer_config.json\n",
      "-rw-r--r-- 1 ovh ovh        309 Jan 30 02:51 special_tokens_map.json\n",
      "-rw-r--r-- 1 ovh ovh         23 Jan 30 02:51 added_tokens.json\n",
      "drwxr-xr-x 2 ovh ovh       4096 Jan 30 04:36 checkpoint-500\n",
      "drwxr-xr-x 2 ovh ovh       4096 Jan 30 06:22 checkpoint-1000\n",
      "-rw-r--r-- 1 ovh ovh       2521 Jan 30 07:06 trainer_state.json\n",
      "-rw-r--r-- 1 ovh ovh        197 Jan 30 07:06 train_results.json\n",
      "-rw-r--r-- 1 ovh ovh        224 Jan 30 07:06 eval_results.json\n",
      "-rw-r--r-- 1 ovh ovh       2033 Jan 30 07:06 config.json\n",
      "-rw-r--r-- 1 ovh ovh        398 Jan 30 07:06 all_results.json\n",
      "-rw-r--r-- 1 ovh ovh 1262063089 Jan 30 07:06 pytorch_model.bin\n",
      "-rw-r--r-- 1 ovh ovh        212 Jan 30 07:06 preprocessor_config.json\n",
      "-rw-r--r-- 1 ovh ovh       3055 Jan 30 07:06 training_args.bin\n",
      "-rw-r--r-- 1 ovh ovh       1709 Jan 30 07:08 README.md\n",
      "-rw-r--r-- 1 ovh ovh       4738 Jan 30 07:10 eval.py\n",
      "-rw-r--r-- 1 ovh ovh      30348 Jan 30 07:10 run_speech_recognition_ctc.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-irish\n",
    "!cp run_speech_recognition_ctc.py wav2vec2-large-xls-r-300m-irish\n",
    "!ls -ltr wav2vec2-large-xls-r-300m-irish"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "id": "OLB-MXricSec",
    "outputId": "784016e5-2c0a-4235-b432-96bf126b33ba"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: mkl-service + Intel(R) MKL: MKL_THREADING_LAYER=INTEL is incompatible with libgomp-a34b3233.so.1 library.\n",
      "\tTry to import numpy first or set the threading layer accordingly. Set MKL_SERVICE_FORCE_INTEL to force it.\n"
     ]
    }
   ],
   "source": [
    "!cd wav2vec2-large-xls-r-300m-i;python eval.py \\\n",
    "    --model_id ./ --dataset mozilla-foundation/common_voice_7_0 --config kmr --split test --log_outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "aoMHnv5ocSec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error: mkl-service + Intel(R) MKL: MKL_THREADING_LAYER=INTEL is incompatible with libgomp-a34b3233.so.1 library.\n",
      "\tTry to import numpy first or set the threading layer accordingly. Set MKL_SERVICE_FORCE_INTEL to force it.\n"
     ]
    }
   ],
   "source": [
    "!cd wav2vec2-large-xls-r-300m-irish; python eval.py \\\n",
    "    --model_id ./ --dataset speech-recognition-community-v2/dev_data \\\n",
    "    --config kmr --split validation --chunk_length_s 10 --stride_length_s 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "referenced_widgets": [
      "24592b0be30e4eafb1949cf09d1c4fb4",
      "f9bf2ab0d2fa4d3f9235cc6d1ab772f1",
      "b0791474a34043da8057e06741472ade",
      "1ccbd582d616458b87c76ac8dc5b6b36"
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    },
    "id": "5vvo9g7HcSec",
    "outputId": "c2cab8b0-2b67-4039-b8ca-9ab13c9629c3"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "24592b0be30e4eafb1949cf09d1c4fb4",
       "version_major": 2,
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      },
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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-bashkir\")\n",
    "# processor = Wav2Vec2Processor.from_pretrained(\"infinitejoy/wav2vec2-large-xls-r-300m-bashkir\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "referenced_widgets": [
      "013fabff2ea243a0a728a79b8f54ae09",
      "a8d9ca6d024f46f58301bfbcc475e41a",
      "b336e2647c05466d87a11dfa326e30d6",
      "8e6962320ad944439261482617be4869",
      "99de2ef750aa49fd986965d66853a5ea",
      "765670f93e5f4c2e849c98d53e616f38",
      "812abafc8f6b49e3a498718d034a379b"
     ]
    },
    "id": "9yVf94nEcSec",
    "outputId": "60fbb3b8-692c-4418-c222-c734e48c4db1"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "013fabff2ea243a0a728a79b8f54ae09",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "a8d9ca6d024f46f58301bfbcc475e41a",
       "version_major": 2,
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      },
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     },
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    },
    {
     "data": {
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    {
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     },
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    {
     "data": {
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     },
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    },
    {
     "data": {
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       "model_id": "765670f93e5f4c2e849c98d53e616f38",
       "version_major": 2,
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      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "812abafc8f6b49e3a498718d034a379b",
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       "version_minor": 0
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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": 1,
   "metadata": {
    "colab": {
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      "42ac3a57a96b4987b0c62aa41aa13702",
      "29dbfe94c1e9436ea3feefd9a7ba5d34",
      "acd0b1dccad24943bf760273d89aced3"
     ]
    },
    "id": "z26_Ce-kcSed",
    "outputId": "fb46fc7b-5450-4d76-e2cb-3b6417133d44"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset common_voice (/workspace/.cache/huggingface/datasets/mozilla-foundation___common_voice/lv/7.0.0/fe20cac47c166e25b1f096ab661832e3da7cf298ed4a91dcaa1343ad972d175b)\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['nebija nekā tīra ko uzvilkt', 'cēlonis tam ne viens vien', 'visi vilki nav pelēki', 'iedzert aukstu alu būtu labi', 'vai mani mati bija glīti', 'lēnām nesasteidz', 'nerunā man rupjības', 'es vairs nevaru būt tavs elks', 'es atradu mūsu zemes gabalu', 'ko tas sīkais sūds ar mani darītu']\n",
      "['Nebija nekā tīra, ko uzvilkt?', 'Cēlonis tam - ne viens vien.', 'Visi vilki nav pelēki.', 'Iedzert aukstu alu būtu labi.', 'Vai mani mati bija glīti?', 'Lēnām, nesasteidz.', 'Nerunā man rupjības.', 'Es vairs nevaru būt tavs elks.', 'Es atradu mūsu zemes gabalu.', 'Ko tas sīkais sūds ar mani darītu?']\n"
     ]
    }
   ],
   "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\", \"lv\", 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-latvian'\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",
    "print(result['target'])"
   ]
  },
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    "id": "W0ajacuBcSed"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"e'ess' qted j'ms' ' ɓ'jhm s' s'm' jtj' jtr'm 'v' ɓ'x'\""
      ]
     },
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     "metadata": {},
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   ],
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   ]
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