{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "vK6B3ubS21Ky",
    "outputId": "7bf79e00-aba4-4702-a2ab-6c6d137af4e3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
      "Requirement already satisfied: pyctcdecode in /usr/local/lib/python3.7/dist-packages (0.3.0)\n",
      "Requirement already satisfied: numpy<2.0.0,>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from pyctcdecode) (1.19.5)\n",
      "Requirement already satisfied: pygtrie<3.0,>=2.1 in /usr/local/lib/python3.7/dist-packages (from pyctcdecode) (2.4.2)\n",
      "Requirement already satisfied: hypothesis<7,>=6.14 in /usr/local/lib/python3.7/dist-packages (from pyctcdecode) (6.36.1)\n",
      "Requirement already satisfied: attrs>=19.2.0 in /usr/local/lib/python3.7/dist-packages (from hypothesis<7,>=6.14->pyctcdecode) (21.4.0)\n",
      "Requirement 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",
      "Requirement already satisfied: jiwer in /usr/local/lib/python3.7/dist-packages (2.3.0)\n",
      "Requirement already satisfied: python-Levenshtein==0.12.2 in /usr/local/lib/python3.7/dist-packages (from jiwer) (0.12.2)\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from python-Levenshtein==0.12.2->jiwer) (57.4.0)\n",
      "Collecting https://github.com/kpu/kenlm/archive/master.zip\n",
      "  Using cached https://github.com/kpu/kenlm/archive/master.zip\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": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 48,
     "referenced_widgets": [
      "1671593a74f54e6aaa2f63ae16347b28",
      "634693e852884de2980acbc9d73fe995",
      "8671b1ab18af4388853dfba14ed18758",
      "28d015fe0f5a481f995374f38ecac3d1",
      "1d1dc74f886a489585fec0372b122214",
      "586e81066c5d47f2959a63cd89e24f5e",
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      "81587ba20e8a47a38a83ae442acfc65e",
      "a8a4239d08564b19872f14440800b910",
      "a7d6c85610594e7ab6c121342c42e189"
     ]
    },
    "id": "wUoMGUOH24Sv",
    "outputId": "af99b7cc-4dda-4054-87d9-d8a8c26c6e75"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1671593a74f54e6aaa2f63ae16347b28",
       "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": "code",
   "execution_count": 6,
   "metadata": {
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     "text": [
      "Downloading and preparing dataset common_voice/el to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/el/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8...\n"
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       "0 examples [00:00, ? examples/s]"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset common_voice downloaded and prepared to /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/el/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8. Subsequent calls will reuse this data.\n"
     ]
    },
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       "0ex [00:00, ?ex/s]"
      ]
     },
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    {
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     },
     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WER: 0.207340\n",
      "CER: 0.060466\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=\"el\"\n",
    "model_id = 'ayameRushia/wav2vec2-large-xls-r-300m-el'\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"
   },
   "source": [
    "Delete previous model and clear the cuda cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "1DzK6MbO_c4r",
    "outputId": "b51116a5-e440-430c-de24-bb5f23bf4db0"
   },
   "outputs": [
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      "Mon Feb  7 01:05:58 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   61C    P0    47W / 250W |   2435MiB / 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"
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    "del model\n",
    "torch.cuda.empty_cache()\n",
    "!nvidia-smi"
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      "Reusing dataset common_voice (/root/.cache/huggingface/datasets/mozilla-foundation___common_voice/el/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8)\n",
      "Loading cached processed dataset at /root/.cache/huggingface/datasets/mozilla-foundation___common_voice/el/8.0.0/b8bc4d453193c06a43269b46cd87f075c70f152ac963b7f28f7a2760c45ec3e8/cache-641b47a82b6cfb24.arrow\n"
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       "  0%|          | 0/421 [00:00<?, ?ba/s]"
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      "WER: 0.311294\n",
      "CER: 0.079509\n"
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    "dataset_id = \"mozilla-foundation/common_voice_8_0\"\n",
    "language=\"el\"\n",
    "split=\"test\"\n",
    "model_id = 'ayameRushia/wav2vec2-large-xls-r-300m-el'\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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