{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Install Requirements and Depedencies, Also login to huggingface through command line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vK6B3ubS21Ky",
    "outputId": "1fc66279-80cf-4dac-95b8-8791cdbc00d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting datasets==1.18.3\n",
      "  Downloading datasets-1.18.3-py3-none-any.whl (311 kB)\n",
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      "\u001b[?25hCollecting aiohttp\n",
      "  Downloading aiohttp-3.8.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.1 MB)\n",
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      "  Downloading asynctest-0.13.0-py3-none-any.whl (26 kB)\n",
      "Collecting frozenlist>=1.1.1\n",
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      "\u001b[?25hCollecting yarl<2.0,>=1.0\n",
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      "Installing collected packages: multidict, frozenlist, yarl, asynctest, async-timeout, aiosignal, fsspec, aiohttp, xxhash, huggingface-hub, datasets\n",
      "Successfully installed aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 datasets-1.18.3 frozenlist-1.3.0 fsspec-2022.1.0 huggingface-hub-0.4.0 multidict-6.0.2 xxhash-2.0.2 yarl-1.7.2\n",
      "Collecting transformers\n",
      "  Downloading transformers-4.16.2-py3-none-any.whl (3.5 MB)\n",
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      "Collecting sacremoses\n",
      "  Downloading sacremoses-0.0.47-py2.py3-none-any.whl (895 kB)\n",
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      "Collecting pyyaml>=5.1\n",
      "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
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      "Installing collected packages: pyyaml, tokenizers, sacremoses, transformers\n",
      "  Attempting uninstall: pyyaml\n",
      "    Found existing installation: PyYAML 3.13\n",
      "    Uninstalling PyYAML-3.13:\n",
      "      Successfully uninstalled PyYAML-3.13\n",
      "Successfully installed pyyaml-6.0 sacremoses-0.0.47 tokenizers-0.11.4 transformers-4.16.2\n",
      "Collecting pyctcdecode\n",
      "  Downloading pyctcdecode-0.3.0-py2.py3-none-any.whl (43 kB)\n",
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      "\u001b[?25hCollecting pygtrie<3.0,>=2.1\n",
      "  Downloading pygtrie-2.4.2.tar.gz (35 kB)\n",
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      "Building wheels for collected packages: pygtrie\n",
      "  Building wheel for pygtrie (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for pygtrie: filename=pygtrie-2.4.2-py3-none-any.whl size=19063 sha256=8df2d93eca581ee990684593e9aea4702e3fc1600c3b12431977e7f7f2a72d2c\n",
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      "Collecting jiwer\n",
      "  Downloading jiwer-2.3.0-py3-none-any.whl (15 kB)\n",
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      "Building wheels for collected packages: python-Levenshtein\n",
      "  Building wheel for python-Levenshtein (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for python-Levenshtein: filename=python_Levenshtein-0.12.2-cp37-cp37m-linux_x86_64.whl size=149862 sha256=9f2040039739ea899365d0e09ec9144de3fa92b4964a8c65af9b5f775f2379a5\n",
      "  Stored in directory: /root/.cache/pip/wheels/05/5f/ca/7c4367734892581bb5ff896f15027a932c551080b2abd3e00d\n",
      "Successfully built python-Levenshtein\n",
      "Installing collected packages: python-Levenshtein, jiwer\n",
      "Successfully installed jiwer-2.3.0 python-Levenshtein-0.12.2\n",
      "Collecting https://github.com/kpu/kenlm/archive/master.zip\n",
      "  Downloading https://github.com/kpu/kenlm/archive/master.zip (541 kB)\n",
      "\u001b[K     |████████████████████████████████| 541 kB 3.0 MB/s \n",
      "\u001b[?25hBuilding wheels for collected packages: kenlm\n",
      "  Building wheel for kenlm (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
      "  Created wheel for kenlm: filename=kenlm-0.0.0-cp37-cp37m-linux_x86_64.whl size=2336711 sha256=8be5703f0c372f4d8b2a3d9bdb5e5c1511bd7167b8ae6c34ec4eccb3eba52d00\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-b2yxhs9a/wheels/3d/aa/02/7b4a2eab5d7a2a9391bd9680dbad6270808a147bc3b7047e4e\n",
      "Successfully built kenlm\n",
      "Installing collected packages: kenlm\n",
      "Successfully installed kenlm-0.0.0\n",
      "\n",
      "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
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      "\n",
      "        To login, `huggingface_hub` now requires a token generated from https://huggingface.co/settings/token.\n",
      "        (Deprecated, will be removed in v0.3.0) To login with username and password instead, interrupt with Ctrl+C.\n",
      "        \n",
      "Token: \n",
      "Login successful\n",
      "Your token has been saved to /root/.huggingface/token\n",
      "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n",
      "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n",
      "\n",
      "git config --global credential.helper store\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets==1.18.3\n",
    "!pip install transformers\n",
    "!pip install pyctcdecode\n",
    "!pip install jiwer\n",
    "!pip install https://github.com/kpu/kenlm/archive/master.zip\n",
    "!huggingface-cli login"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocessing Data, initialize loss function and evaluation function that edited from based on [This Github Repo](https://github.com/harveenchadha/bol/blob/main/demos/robus/evaluate_model_hf_with_lm.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "cd9b21068c814aed8fa9c1653f077ce6",
      "3a0363dd03f34d1383d3583c6c8db403",
      "24a8bce353cb40028d2ce15f26cb5d4a",
      "2a79c7470a854128bcb075508dec31cc",
      "32bb1efb868846ab823f8c97979fc403",
      "0d99cd9e92d642cbba517e3e42b08a34",
      "d322bd1f9b544a72a816a54da9f86fb6",
      "b8124a9bb56b41819242a091136059a8",
      "1eed3e578be7465884d36ebfa649a2e9",
      "d411ccf809aa4510b7467fd688ab5d81",
      "eae4a425b4bc4564ac4ecf7c15421d77",
      "2e9a8d5a111645ae94fdeddb7dac1697",
      "ffb6ee6c418741de85927bb630b02484",
      "cc4af9bf4e7b4d16a554a6b56ab4e18a",
      "def774a3f0d64d45bf9dc275b932bf1c",
      "a12aed326d6e43a790c3bcaa0a0da700",
      "be1fef3458704c0388111b2c0d7a6e9f",
      "838163d1875c46b48659b029a9d1e742",
      "ca6facdc672e419ba3e74b5a5d706733",
      "9a6c4a585d2243c594fe51f80a3e4152",
      "f0dba9aed06149399de6e00f291b361a",
      "8dd56196f17f442a9527dea9788757f3"
     ]
    },
    "id": "wUoMGUOH24Sv",
    "outputId": "e6c82489-de68-4f31-dd90-dd1a6742eade"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd9b21068c814aed8fa9c1653f077ce6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/1.90k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2e9a8d5a111645ae94fdeddb7dac1697",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/2.16k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torchaudio\n",
    "from datasets import load_dataset, load_metric,  Audio\n",
    "from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM\n",
    "import re\n",
    "wer = load_metric(\"wer\")\n",
    "cer = load_metric(\"cer\")\n",
    "\n",
    "\n",
    "chars_to_ignore_regex = '[\\é\\！\\，\\,\\?\\.\\!\\-\\;\\:\\\"\\“\\%\\‘\\”\\�\\'\\’\\—\\–\\·]'\n",
    "\n",
    "def load_data(dataset_id, language, split='test'):\n",
    "    test_dataset = load_dataset(dataset_id, language, split=split, use_auth_token=True)\n",
    "    test_dataset = test_dataset.cast_column(\"audio\", Audio(sampling_rate=16_000))\n",
    "    return test_dataset\n",
    "\n",
    "def speech_file_to_array_fn(batch):\n",
    "    batch[\"sentence\"] = re.sub(chars_to_ignore_regex, \"\", batch[\"sentence\"]).lower() + \" \"\n",
    "    batch[\"sentence\"] = re.sub('!', '', batch[\"sentence\"]).lower() + \" \"\n",
    "    batch[\"sentence\"] = batch[\"sentence\"].replace('\\\"',\"\").replace(\"&\",\"\").replace(\"'\",\"\").replace(\"(\",\"\").lower() + \" \"\n",
    "    batch[\"sentence\"] = batch[\"sentence\"].replace('[',\"\").replace(\"]\",\"\").replace(\"\\\\\",\"\").replace(\"«\",\"\").replace(\"»\",\"\").replace(\")\",\"\").lower() + \" \"\n",
    "    batch[\"sentence\"] = batch[\"sentence\"].replace(\"  \",\" \").replace(\"  \",\" \").replace(\"  \",\" \").lower() + \" \"\n",
    "    \n",
    "    batch[\"speech\"] = batch[\"audio\"][\"array\"]\n",
    "    return batch\n",
    "\n",
    "\n",
    "def evaluate_with_lm(batch):\n",
    "    inputs = processor(batch[\"speech\"], sampling_rate=16_000, return_tensors=\"pt\", padding=True)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        logits = model(**inputs.to('cuda')).logits\n",
    "    int_result = processor.batch_decode(logits.cpu().numpy())\n",
    "\n",
    "    batch[\"pred_strings\"] =  int_result.text\n",
    "\n",
    "    del int_result\n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "    return batch\n",
    "\n",
    "def evaluate(batch):\n",
    "\n",
    "    inputs = processor(batch[\"speech\"], sampling_rate=16_000, return_tensors=\"pt\", padding=True)\n",
    "    with torch.no_grad():\n",
    "        logits = model(inputs.input_values.to('cuda')).logits\n",
    "\n",
    "    pred_ids = torch.argmax(logits, dim=-1)\n",
    "    batch[\"pred_strings\"] = processor.batch_decode(pred_ids, skip_special_tokens=True)\n",
    "    return batch\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate using language model\n",
    "Results: <br>\n",
    "WER: 0.086074 (8.6074) <br>\n",
    "CER: 0.024147 (2.4147)"
   ]
  },
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      "Downloading and preparing dataset common_voice/ia to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/ia/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8...\n"
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     "text": [
      "Dataset common_voice downloaded and prepared to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/ia/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8. Subsequent calls will reuse this data.\n"
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       "0ex [00:00, ?ex/s]"
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     "text": [
      "WER: 0.086074\n",
      "CER: 0.024147\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torchaudio\n",
    "from datasets import load_dataset, load_metric,  Audio\n",
    "from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, Wav2Vec2ProcessorWithLM\n",
    "import re\n",
    "\n",
    "language=\"ia\"\n",
    "model_id = 'ayameRushia/wav2vec2-large-xls-r-300m-ia'\n",
    "dataset_id = \"mozilla-foundation/common_voice_8_0\"\n",
    "split=\"test\"\n",
    "\n",
    "processor = Wav2Vec2ProcessorWithLM.from_pretrained(model_id,use_auth_token=True)\n",
    "model = Wav2Vec2ForCTC.from_pretrained(model_id,use_auth_token=True)\n",
    "model.to('cuda')\n",
    "\n",
    "test_dataset = load_data(dataset_id, language, split)\n",
    "test_dataset = test_dataset.map(speech_file_to_array_fn)\n",
    "\n",
    "result = test_dataset.map(evaluate_with_lm, batched=True, batch_size=4)\n",
    "\n",
    "print(\"WER: {:2f}\".format(wer.compute(predictions=result[\"pred_strings\"], references=result[\"sentence\"])))\n",
    "print(\"CER: {:2f}\".format(cer.compute(predictions=result[\"pred_strings\"], references=result[\"sentence\"])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YSml3Ykr_xeJ"
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   "source": [
    "Delete previous model and clear the cuda cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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    "outputId": "2a5ebca6-caf3-4dd4-ac82-cb379cdc0cce"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Feb  6 06:38:45 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\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 T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   77C    P0    38W /  70W |   2810MiB / 15109MiB |      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",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "del model\n",
    "torch.cuda.empty_cache()\n",
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate without using language model\n",
    "Results: <br>\n",
    "WER: 0.201776 (20.1776) <br>\n",
    "CER: 0.047205 (4.7205) <br>"
   ]
  },
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      "Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/ia/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)\n",
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/ia/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8/cache-f4febe81b8fd7934.arrow\n"
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       "  0%|          | 0/427 [00:00<?, ?ba/s]"
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      "WER: 0.201776\n",
      "CER: 0.047205\n"
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   ],
   "source": [
    "dataset_id = \"mozilla-foundation/common_voice_8_0\"\n",
    "language=\"ia\"\n",
    "split=\"test\"\n",
    "model_id = 'ayameRushia/wav2vec2-large-xls-r-300m-ia'\n",
    "\n",
    "processor = Wav2Vec2Processor.from_pretrained(model_id,use_auth_token=True)\n",
    "model = Wav2Vec2ForCTC.from_pretrained(model_id,use_auth_token=True)\n",
    "model.to(\"cuda\")\n",
    "\n",
    "test_dataset = load_data(dataset_id, language, split)\n",
    "test_dataset = test_dataset.map(speech_file_to_array_fn)\n",
    "\n",
    "result = test_dataset.map(evaluate, batched=True, batch_size=4)\n",
    "\n",
    "print(\"WER: {:2f}\".format(wer.compute(predictions=result[\"pred_strings\"], references=result[\"sentence\"])))\n",
    "print(\"CER: {:2f}\".format(cer.compute(predictions=result[\"pred_strings\"], references=result[\"sentence\"])))"
   ]
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