{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vK6B3ubS21Ky",
    "outputId": "45514cb3-fbc5-4156-b58a-8fd89900fbd5"
   },
   "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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      "Collecting xxhash\n",
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      "Collecting aiosignal>=1.1.2\n",
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      "\u001b[?25hCollecting asynctest==0.13.0\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",
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      "\u001b[?25hCollecting sacremoses\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",
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      "\u001b[?25hRequirement already satisfied: sortedcontainers<3.0.0,>=2.1.0 in /usr/local/lib/python3.7/dist-packages (from hypothesis<7,>=6.14->pyctcdecode) (2.4.0)\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=3e1760cae3e3e4b932ad5b40f9521fc521cfb4264fc18c6dad6eb32cef06dd6a\n",
      "  Stored in directory: /root/.cache/pip/wheels/d3/f8/ba/1d828b1603ea422686eb694253a43cb3a5901ea4696c1e0603\n",
      "Successfully built pygtrie\n",
      "Installing collected packages: pygtrie, hypothesis, pyctcdecode\n",
      "Successfully installed hypothesis-6.36.1 pyctcdecode-0.3.0 pygtrie-2.4.2\n",
      "Collecting jiwer\n",
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      "  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=149867 sha256=d73a420a77fb8a29d4d2d2a66007f7304e6a3220c58a8cd107b90776e066ca95\n",
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      "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\n",
      "\u001b[K     / 541 kB 2.6 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=2336650 sha256=f922b3c4b1f55c42d03c7f220392afc2f3083bfaf5c7efddae493f021199a0a9\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-xu_qizqg/wheels/3d/aa/02/7b4a2eab5d7a2a9391bd9680dbad6270808a147bc3b7047e4e\n",
      "Successfully built kenlm\n",
      "Installing collected packages: kenlm\n",
      "Successfully installed kenlm-0.0.0\n",
      "\n",
      "        _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
      "        _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
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      "        _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\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": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
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    },
    "id": "wUoMGUOH24Sv",
    "outputId": "bba7b310-5ed2-4936-fee2-57a495ac3aaa"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97811a06efab4df29d3ef838767a6da9",
       "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": "c0cfd62da1fe4902b3ae977edbbfabd1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "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"
   ]
  },
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      "Downloading and preparing dataset common_voice/mn to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/mn/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/mn/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: 0.313919\n",
      "CER: 0.102565\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=\"mn\"\n",
    "model_id = 'ayameRushia/wav2vec2-large-xls-r-300m-mn'\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",
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    "id": "YSml3Ykr_xeJ"
   },
   "source": [
    "Delete previous model and clear the cuda cache"
   ]
  },
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   "execution_count": 4,
   "metadata": {
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    "outputId": "1f24a4c9-6e9d-4c48-e769-b128d57733fc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mon Feb  7 01:24:30 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 P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   44C    P0    40W / 250W |   2483MiB / 16280MiB |      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"
   ]
  },
  {
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     "text": [
      "Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/mn/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)\n",
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/mn/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8/cache-93c5dc7ad9262723.arrow\n"
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       "  0%|          | 0/471 [00:00<?, ?ba/s]"
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     "text": [
      "WER: 0.582171\n",
      "CER: 0.160670\n"
     ]
    }
   ],
   "source": [
    "dataset_id = \"mozilla-foundation/common_voice_8_0\"\n",
    "language=\"mn\"\n",
    "split=\"test\"\n",
    "model_id = 'ayameRushia/wav2vec2-large-xls-r-300m-mn'\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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