Persian-G2P-Tools-Benchmark/benchmarking-scripts/Benchmark_dmort27_epitran.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "VtxEYym69RUH",
        "XjAPkfq7SF87"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WEY5MiKLzurH"
      },
      "source": [
        "# Setup Environment"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install epitran==1.26.0"
      ],
      "metadata": {
        "id": "jviCS0zCmtJc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8d100ba-e606-4956-ee15-81ccc6557ba6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting epitran==1.26.0\n",
            "  Downloading epitran-1.26.0-py2.py3-none-any.whl.metadata (34 kB)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.11/dist-packages (from epitran==1.26.0) (75.2.0)\n",
            "Requirement already satisfied: regex in /usr/local/lib/python3.11/dist-packages (from epitran==1.26.0) (2024.11.6)\n",
            "Collecting panphon>=0.20 (from epitran==1.26.0)\n",
            "  Downloading panphon-0.21.2-py2.py3-none-any.whl.metadata (15 kB)\n",
            "Requirement already satisfied: marisa-trie in /usr/local/lib/python3.11/dist-packages (from epitran==1.26.0) (1.2.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.11/dist-packages (from epitran==1.26.0) (2.32.3)\n",
            "Collecting jamo (from epitran==1.26.0)\n",
            "  Downloading jamo-0.4.1-py3-none-any.whl.metadata (2.3 kB)\n",
            "Collecting unicodecsv (from panphon>=0.20->epitran==1.26.0)\n",
            "  Downloading unicodecsv-0.14.1.tar.gz (10 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.11/dist-packages (from panphon>=0.20->epitran==1.26.0) (6.0.2)\n",
            "Requirement already satisfied: numpy>=1.20.2 in /usr/local/lib/python3.11/dist-packages (from panphon>=0.20->epitran==1.26.0) (2.0.2)\n",
            "Requirement already satisfied: editdistance in /usr/local/lib/python3.11/dist-packages (from panphon>=0.20->epitran==1.26.0) (0.8.1)\n",
            "Collecting munkres (from panphon>=0.20->epitran==1.26.0)\n",
            "  Downloading munkres-1.1.4-py2.py3-none-any.whl.metadata (980 bytes)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests->epitran==1.26.0) (3.4.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests->epitran==1.26.0) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests->epitran==1.26.0) (2.4.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.11/dist-packages (from requests->epitran==1.26.0) (2025.4.26)\n",
            "Downloading epitran-1.26.0-py2.py3-none-any.whl (188 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m188.5/188.5 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading panphon-0.21.2-py2.py3-none-any.whl (75 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.4/75.4 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading jamo-0.4.1-py3-none-any.whl (9.5 kB)\n",
            "Downloading munkres-1.1.4-py2.py3-none-any.whl (7.0 kB)\n",
            "Building wheels for collected packages: unicodecsv\n",
            "  Building wheel for unicodecsv (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for unicodecsv: filename=unicodecsv-0.14.1-py3-none-any.whl size=10744 sha256=9d5442e17e65cdf34cadb6d4681337702fde69e9bea33a290ccb2bc88151e8b5\n",
            "  Stored in directory: /root/.cache/pip/wheels/ec/03/6f/d2e0162d94c0d451556fa43dd4d5531457245c34a36b41ef4a\n",
            "Successfully built unicodecsv\n",
            "Installing collected packages: unicodecsv, munkres, jamo, panphon, epitran\n",
            "Successfully installed epitran-1.26.0 jamo-0.4.1 munkres-1.1.4 panphon-0.21.2 unicodecsv-0.14.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install g2pk==0.9.4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxh7pA-mwSDV",
        "outputId": "f03e0881-3acb-4ab1-fbbb-016a0c4069f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting g2pk==0.9.4\n",
            "  Downloading g2pK-0.9.4-py3-none-any.whl.metadata (7.5 kB)\n",
            "Requirement already satisfied: jamo in /usr/local/lib/python3.11/dist-packages (from g2pk==0.9.4) (0.4.1)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.11/dist-packages (from g2pk==0.9.4) (3.9.1)\n",
            "Collecting konlpy (from g2pk==0.9.4)\n",
            "  Downloading konlpy-0.6.0-py2.py3-none-any.whl.metadata (1.9 kB)\n",
            "Collecting python-mecab-ko (from g2pk==0.9.4)\n",
            "  Downloading python_mecab_ko-1.3.7-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (3.4 kB)\n",
            "Collecting JPype1>=0.7.0 (from konlpy->g2pk==0.9.4)\n",
            "  Downloading jpype1-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.9 kB)\n",
            "Requirement already satisfied: lxml>=4.1.0 in /usr/local/lib/python3.11/dist-packages (from konlpy->g2pk==0.9.4) (5.4.0)\n",
            "Requirement already satisfied: numpy>=1.6 in /usr/local/lib/python3.11/dist-packages (from konlpy->g2pk==0.9.4) (2.0.2)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.11/dist-packages (from nltk->g2pk==0.9.4) (8.1.8)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.11/dist-packages (from nltk->g2pk==0.9.4) (1.4.2)\n",
            "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.11/dist-packages (from nltk->g2pk==0.9.4) (2024.11.6)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.11/dist-packages (from nltk->g2pk==0.9.4) (4.67.1)\n",
            "Collecting python-mecab-ko-dic (from python-mecab-ko->g2pk==0.9.4)\n",
            "  Downloading python_mecab_ko_dic-2.1.1.post2-py3-none-any.whl.metadata (1.4 kB)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.11/dist-packages (from JPype1>=0.7.0->konlpy->g2pk==0.9.4) (24.2)\n",
            "Downloading g2pK-0.9.4-py3-none-any.whl (27 kB)\n",
            "Downloading konlpy-0.6.0-py2.py3-none-any.whl (19.4 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.4/19.4 MB\u001b[0m \u001b[31m61.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading python_mecab_ko-1.3.7-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (580 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m580.9/580.9 kB\u001b[0m \u001b[31m34.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading jpype1-1.5.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (494 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m494.1/494.1 kB\u001b[0m \u001b[31m33.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading python_mecab_ko_dic-2.1.1.post2-py3-none-any.whl (34.5 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m34.5/34.5 MB\u001b[0m \u001b[31m18.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: python-mecab-ko-dic, python-mecab-ko, JPype1, konlpy, g2pk\n",
            "Successfully installed JPype1-1.5.2 g2pk-0.9.4 konlpy-0.6.0 python-mecab-ko-1.3.7 python-mecab-ko-dic-2.1.1.post2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install jiwer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stR7NfnfZqB1",
        "outputId": "c5e09b12-0552-4e2d-fd8f-387c8308d1c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting jiwer\n",
            "  Downloading jiwer-3.1.0-py3-none-any.whl.metadata (2.6 kB)\n",
            "Requirement already satisfied: click>=8.1.8 in /usr/local/lib/python3.11/dist-packages (from jiwer) (8.1.8)\n",
            "Collecting rapidfuzz>=3.9.7 (from jiwer)\n",
            "  Downloading rapidfuzz-3.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (12 kB)\n",
            "Downloading jiwer-3.1.0-py3-none-any.whl (22 kB)\n",
            "Downloading rapidfuzz-3.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.1 MB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.1/3.1 MB\u001b[0m \u001b[31m43.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: rapidfuzz, jiwer\n",
            "Successfully installed jiwer-3.1.0 rapidfuzz-3.13.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PfthI4eOqBri"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import re\n",
        "import csv\n",
        "import pandas as pd\n",
        "import json\n",
        "import itertools\n",
        "from tqdm import tqdm\n",
        "from jiwer import cer"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# mapping"
      ],
      "metadata": {
        "id": "VtxEYym69RUH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "output_to_phonetics_map = {\n",
        "    'м': 'm',\n",
        "    'ʷ':' v',\n",
        "    'w': 'v',\n",
        "    'c': 'k',\n",
        "    'ĉ': 'C',\n",
        "    'č': 'C',\n",
        "    '̕': \"?\",\n",
        "    \"'\": '?',\n",
        "    'ʔ': \"?\",\n",
        "    'ꞌ': \"?\",\n",
        "    '̛':  \"?\",\n",
        "    '’': \"?\",\n",
        "    'ʼ': \"?\",\n",
        "    \"'\": '?',\n",
        "    'â': 'A',\n",
        "    'â': 'A',\n",
        "    'ȃ': 'A',\n",
        "    'ž': 'Z',\n",
        "    'š': 'S',\n",
        "    'W': 'v',\n",
        "    'β': 'f',\n",
        "    'е': 'e',\n",
        "    '`': \"?\",\n",
        "    'ɑ': 'A',\n",
        "    'ɑ': 'A',\n",
        "    'ʃ': 'S',\n",
        "    'ð': 'z',\n",
        "    'ɾ': 'r',\n",
        "    'æ': 'a',\n",
        "    'ɪ': 'e',\n",
        "    'χ': 'x',\n",
        "    'ɣ': 'q',\n",
        "    'ʒ': 'Z',\n",
        "    ':': '',\n",
        "    'ː': '',\n",
        "    'ā': 'A',\n",
        "    'ː': '',\n",
        "    'ä': 'A',\n",
        "    'á': 'A',\n",
        "    'š': 'S',\n",
        "    'ū': 'u',\n",
        "    'û': 'u',\n",
        "    'ś': 's',\n",
        "    'ī': 'i',\n",
        "    'í': 'i',\n",
        "    'î': 'i',\n",
        "    'é': 'e',\n",
        "    'ḥ': 'h',\n",
        "    'ɒ': 'A',\n",
        "    'ʰ': '',\n",
        "    'ə': 'e',\n",
        "    'R': 'r',\n",
        "    'W': 'v',\n",
        "    'Q': 'q',\n",
        "    'T': 't',\n",
        "    'Y': 'y',\n",
        "    'P': 'p',\n",
        "    'D': 'd',\n",
        "    'F': 'f',\n",
        "    'H': 'h',\n",
        "    'J': 'j',\n",
        "    'L': 'l',\n",
        "    'X': 'x',\n",
        "    'V': 'v',\n",
        "    'B': 'b',\n",
        "    'N': 'n',\n",
        "    'M': 'm',\n",
        "    'K': 'k',\n",
        "    'G': 'g',\n",
        "    'U': 'u',\n",
        "    'O': 'o',\n",
        "    'I': 'i',\n",
        "    'E': 'e',\n",
        "    'ŋ': 'ng',\n",
        "    '.': '',\n",
        "    'ɛ': 'e',\n",
        "    'ʊ': 'u',\n",
        "    \"ˈ\": '?',\n",
        "    'ù': 'u',\n",
        "    'θ': 's',\n",
        "    '̪': '',\n",
        "    'ũ': 'u',\n",
        "    '_': '',\n",
        "    'ç': 'C',\n",
        "    'ĝ': 'q',\n",
        "    'ɢ': 'q',\n",
        "    'ː': '',\n",
        "    'í': 'i',\n",
        "    'ŝ': 'S',\n",
        "    '!': '',\n",
        "    'ǧ': 'q',\n",
        "    'ʻ': '?',\n",
        "    'è': 'e',\n",
        "    '�': '',\n",
        "    'ú': 'u',\n",
        "    'ô': 'o',\n",
        "    'ē': 'e',\n",
        "    'à': 'A',\n",
        "    'ă': 'A',\n",
        "    'ǐ': 'i',\n",
        "    'ü': 'u',\n",
        "    '\\u200e': '',\n",
        "    'ğ': 'q',\n",
        "    'ṣ': 'S',\n",
        "    'â': 'A',\n",
        "    'â': 'A',\n",
        "    'ȃ': 'A',\n",
        "    'ž': 'Z',\n",
        "    'š': 'S',\n",
        "    'ā': 'A',\n",
        "    'ː': '',\n",
        "    'ä': 'A',\n",
        "    'á': 'A',\n",
        "    'š': 'S',\n",
        "    'ū': 'u',\n",
        "    'û': 'u',\n",
        "    'ś': 'S',\n",
        "    'ī': 'i',\n",
        "    'í': 'i',\n",
        "    'î': 'i',\n",
        "    'é': 'e',\n",
        "}\n",
        "\n",
        "consonants_regex = '(?=' + '|'.join(['q', 'r', 't', 'y', 'p', 's', 'd', 'f', 'g', 'h', 'j', 'k', 'l', 'z', 'x', 'c', 'v', 'b', 'n', 'm', 'Q', 'R', 'T', 'Y', 'P', 'S', 'D', 'F', 'G', 'H', 'J', 'K', 'L', 'Z', 'X', 'C', 'V', 'B', 'N', 'M' ]) + ')'\n",
        "vowels_regex = '(?=' + '|'.join(['a', 'A', 'e', 'i', 'u', 'o']) + ')'\n",
        "\n",
        "\n",
        "def replace_phonetic_characters(input_string, char_map=output_to_phonetics_map, from_phonetics=False):\n",
        "    substituted = re.sub(r'tʃʰ', 'C', input_string)\n",
        "    substituted = re.sub(r't͡ʃ', 'C', input_string)\n",
        "    substituted = re.sub(r'tʃ', 'C', substituted)\n",
        "    substituted = re.sub(r't͡S', 'C', substituted)\n",
        "    substituted = re.sub(r'ow', 'o', substituted)\n",
        "    substituted = re.sub('d͡ʒ', 'j', substituted)\n",
        "    substituted = re.sub('dʒ', 'j', substituted)\n",
        "\n",
        "    # Create a translation table using str.maketrans\n",
        "    translation_table = str.maketrans(char_map)\n",
        "\n",
        "    # Use str.translate to replace characters based on the translation table\n",
        "    translated = substituted.translate(translation_table)\n",
        "\n",
        "    return translated"
      ],
      "metadata": {
        "id": "TKx8oA1n7rKh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XjAPkfq7SF87"
      },
      "source": [
        "# Get Evaluation Data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://huggingface.co/datasets/MahtaFetrat/SentenceBench/raw/main/SentenceBench.csv"
      ],
      "metadata": {
        "id": "qwCG0jX-88nQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bda9ccb4-f4d8-432b-f460-bfcbea7e462b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2025-05-10 11:19:00--  https://huggingface.co/datasets/MahtaFetrat/SentenceBench/raw/main/SentenceBench.csv\n",
            "Resolving huggingface.co (huggingface.co)... 18.164.174.17, 18.164.174.55, 18.164.174.118, ...\n",
            "Connecting to huggingface.co (huggingface.co)|18.164.174.17|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 56026 (55K) [text/plain]\n",
            "Saving to: ‘SentenceBench.csv’\n",
            "\n",
            "\rSentenceBench.csv     0%[                    ]       0  --.-KB/s               \rSentenceBench.csv   100%[===================>]  54.71K  --.-KB/s    in 0.008s  \n",
            "\n",
            "2025-05-10 11:19:00 (6.90 MB/s) - ‘SentenceBench.csv’ saved [56026/56026]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sentence_bench = pd.read_csv('SentenceBench.csv')"
      ],
      "metadata": {
        "id": "hJO-UAPDQvcb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentence_bench.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "qlYbrnUa9LAN",
        "outputId": "2fa1904b-72eb-4df9-9d92-f3918ce8ccf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     dataset                              grapheme  \\\n",
              "0  homograph                  من قدر تو را می‌دانم   \n",
              "1  homograph  از قضای الهی به قدر الهی پناه می‌برم   \n",
              "2  homograph                به دست و صورتم کرم زدم   \n",
              "\n",
              "                                             phoneme homograph word  \\\n",
              "0                          man qadr-e to rA mi-dAnam            قدر   \n",
              "1  ?az qazAy ?elAhi be qadar-e ?elAhi panAh mi-baram            قدر   \n",
              "2                      be dast-o suratam kerem zadam            کرم   \n",
              "\n",
              "  pronunciation  \n",
              "0          qadr  \n",
              "1         qadar  \n",
              "2         kerem  "
            ],
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              "      <th></th>\n",
              "      <th>dataset</th>\n",
              "      <th>grapheme</th>\n",
              "      <th>phoneme</th>\n",
              "      <th>homograph word</th>\n",
              "      <th>pronunciation</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>homograph</td>\n",
              "      <td>من قدر تو را می‌دانم</td>\n",
              "      <td>man qadr-e to rA mi-dAnam</td>\n",
              "      <td>قدر</td>\n",
              "      <td>qadr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>homograph</td>\n",
              "      <td>از قضای الهی به قدر الهی پناه می‌برم</td>\n",
              "      <td>?az qazAy ?elAhi be qadar-e ?elAhi panAh mi-baram</td>\n",
              "      <td>قدر</td>\n",
              "      <td>qadar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>homograph</td>\n",
              "      <td>به دست و صورتم کرم زدم</td>\n",
              "      <td>be dast-o suratam kerem zadam</td>\n",
              "      <td>کرم</td>\n",
              "      <td>kerem</td>\n",
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              "\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "\n",
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              "        async function quickchart(key) {\n",
              "          const quickchartButtonEl =\n",
              "            document.querySelector('#' + key + ' button');\n",
              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "          try {\n",
              "            const charts = await google.colab.kernel.invokeFunction(\n",
              "                'suggestCharts', [key], {});\n",
              "          } catch (error) {\n",
              "            console.error('Error during call to suggestCharts:', error);\n",
              "          }\n",
              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sentence_bench",
              "summary": "{\n  \"name\": \"sentence_bench\",\n  \"rows\": 400,\n  \"fields\": [\n    {\n      \"column\": \"dataset\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"homograph\",\n          \"mana-tts\",\n          \"commonvoice\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"grapheme\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 400,\n        \"samples\": [\n          \"\\u0622\\u06cc\\u0627 \\u0628\\u0627\\u06cc\\u062f \\u062d\\u0642\\u06cc\\u0642\\u062a \\u0631\\u0627 \\u0628\\u0647 \\u0622\\u0646\\u200c\\u0647\\u0627 \\u0628\\u06af\\u0648\\u06cc\\u06cc\\u0645\\u061f\",\n          \"\\u06a9\\u0647 \\u067e\\u06cc\\u0634 \\u0627\\u0632 \\u0627\\u0646\\u0642\\u0644\\u0627\\u0628 \\u0628\\u0647 \\u062e\\u0648\\u0627\\u0628\\u06af\\u0627\\u0647 \\u062f\\u062e\\u062a\\u0631\\u0627\\u0646 \\u0648 \\u0632\\u0646\\u0627\\u0646 \\u0646\\u0627\\u0628\\u06cc\\u0646\\u0627 \\u0627\\u062e\\u062a\\u0635\\u0627\\u0635\\u200c\\u06cc\\u0627\\u0641\\u062a\\u0647 \\u0628\\u0648\\u062f. \\u0627\\u063a\\u0644\\u0628 \\u0632\\u0646\\u0627\\u0646\\u06cc \\u06a9\\u0647 \\u062f\\u0631 \\u0627\\u06cc\\u0646 \\u062e\\u0648\\u0627\\u0628\\u06af\\u0627\\u0647 \\u0632\\u0646\\u062f\\u06af\\u06cc \\u0645\\u06cc\\u200c\\u06a9\\u0631\\u062f\\u0646\\u062f\\u060c \",\n          \"\\u062f\\u0648\\u062f \\u0648 \\u0645\\u0647 \\u063a\\u0644\\u06cc\\u0638\\u06cc \\u062f\\u0631 \\u0645\\u062d\\u06cc\\u0637 \\u067e\\u06cc\\u0686\\u06cc\\u062f\\u0647 \\u0628\\u0648\\u062f\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"phoneme\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 400,\n        \"samples\": [\n          \"?AyA bAyad haqiqat rA be ?AnhA beguyim\\u061f\",\n          \"ke piS ?az ?enqelAb be xAbgAh-e doxtarAn va zanAn-e nAbinA ?extesAsyAfte bud ?aqlab-e zanAni ke dar ?in xAbgAh zendegi mikardand\",\n          \"dud-o meh-e qalizi dar mohit piCide bud\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"homograph word\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 101,\n        \"samples\": [\n          \"\\u06af\\u0631\\u06cc\\u0645\",\n          \"\\u0633\\u0628\\u06a9\\u06cc\",\n          \"\\u06a9\\u0645\\u06cc\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pronunciation\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 210,\n        \"samples\": [\n          \"darham\",\n          \"Sum\",\n          \"moSk\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wDV7ysXf2b_H"
      },
      "source": [
        "### Get ManaTTS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TcL5ZLvSSnVB",
        "outputId": "59e9cd68-4665-4b68-bc35-9d80d2cc03d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('در این نوشته بنا داریم با یک ابزار ساده و مکانیکی افزایش بینایی برای افراد کم\\u200cبینا ',\n",
              "  'dar ?in neveSte banA dArim bA yek ?abzAr-e sAde va mekAniki-ye ?afzAyeS-e binAyi barAye ?afrAd-e kam\\u200cbinA '),\n",
              " ('به نام بی\\u200cوپتیک یا عدسی دورنما آشنا شویم. ',\n",
              "  'be nAm-e biyoptik yA ?adasi-ye durnamA ?ASnA Savim'),\n",
              " ('دراین\\u200cصورت، انجام خودارزیابی و ارائه بازخورد بر عهده خودتان است. ',\n",
              "  'dar ?in surat ?anjAm-e xod?arzyAbi va ?erA?e-ye bAzxord bar ?ohde-ye xodetAn ?ast ')]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "filtered_rows = sentence_bench[sentence_bench['dataset'] == 'mana-tts'][['grapheme', 'phoneme']]\n",
        "\n",
        "# Convert to a list of tuples\n",
        "mana_evaluation_data = list(filtered_rows.itertuples(index=False, name=None))\n",
        "\n",
        "mana_evaluation_data[:3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jjacw9Mp2eoX"
      },
      "source": [
        "### Get CommonVoice"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-yQnqCGw26sk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "253e406c-5fb7-4b8f-fc2e-25a289e5bb0d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('در اکثر شهرها، مرکزی برای خرید دوچرخه وجود دارد.',\n",
              "  'dar ?aksar-e Sahr-hA, markazi barAye xarid-e  doCarxe vojud dArad.'),\n",
              " ('پس از مدرسه کودکان به سوی خانه جست و خیز کردند.',\n",
              "  'pas ?az madrese kudakAn be suye xAne jast-o-xiz kardand.'),\n",
              " ('شما نگران زن و بچه این نباش.', 'SomA negarAn-e zan-o-baCCe-ye ?in nabAS.')]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "filtered_rows = sentence_bench[sentence_bench['dataset'] == 'commonvoice'][['grapheme', 'phoneme']]\n",
        "\n",
        "# Convert to a list of tuples\n",
        "commonvoice_evaluation_data = list(filtered_rows.itertuples(index=False, name=None))\n",
        "\n",
        "commonvoice_evaluation_data[:3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ciSPyhRc3Rvo"
      },
      "source": [
        "### Get Homograph"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XlFc5JbN3Rvz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d6b2c71-afe5-4e1b-dc9d-16c0581e3222"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('من قدر تو را می\\u200cدانم', 'man qadr-e to rA mi-dAnam', 'قدر', 'qadr'),\n",
              " ('از قضای الهی به قدر الهی پناه می\\u200cبرم',\n",
              "  '?az qazAy ?elAhi be qadar-e ?elAhi panAh mi-baram',\n",
              "  'قدر',\n",
              "  'qadar'),\n",
              " ('به دست و صورتم کرم زدم', 'be dast-o suratam kerem zadam', 'کرم', 'kerem')]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "filtered_rows = sentence_bench[sentence_bench['dataset'] == 'homograph'][['grapheme', 'phoneme', 'homograph word',\t'pronunciation']]\n",
        "\n",
        "# Convert to a list of tuples\n",
        "homograph_evaluation_data = list(filtered_rows.itertuples(index=False, name=None))\n",
        "\n",
        "homograph_evaluation_data[:3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R6PE5ds45TPr"
      },
      "source": [
        "# Evaluate Method Outputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLKaERek4u_D"
      },
      "source": [
        "## PER Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nBee9xG54u_E"
      },
      "outputs": [],
      "source": [
        "def remove_non_word_chars(text):\n",
        "    pattern = r'[^\\w\\s\\?]'\n",
        "    cleaned_text = re.sub(pattern, ' ', text)\n",
        "    return cleaned_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "W8PoNV9V4u_E"
      },
      "outputs": [],
      "source": [
        "def remove_white_spaces(text):\n",
        "    cleaned_text = re.sub(r'\\s+', ' ', text)\n",
        "    return cleaned_text.strip()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YD0cvnn74u_E"
      },
      "outputs": [],
      "source": [
        "def get_word_only_text(text):\n",
        "  word_only_text = remove_non_word_chars(text)\n",
        "  extra_space_removed_text = remove_white_spaces(word_only_text)\n",
        "\n",
        "  return extra_space_removed_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6OQQDual4u_E"
      },
      "outputs": [],
      "source": [
        "def get_texts_cer(reference, model_output):\n",
        "  # Preprocess input texts to only contain word characters\n",
        "  word_only_reference = get_word_only_text(reference)\n",
        "  word_only_output = get_word_only_text(model_output)\n",
        "\n",
        "  # Return +infinity for CER if any of the texts is empty\n",
        "  if not word_only_reference.strip() or not word_only_output.strip():\n",
        "    return float('inf')\n",
        "\n",
        "  return cer(word_only_reference, word_only_output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ncWQnPdW4u_E"
      },
      "outputs": [],
      "source": [
        "def get_avg_cer_of_method(method_outputs, references):\n",
        "  cers = []\n",
        "  for idx, o in enumerate(method_outputs):\n",
        "    cer = get_texts_cer(o.replace('-', ''), references[idx][1].replace('-', ''))\n",
        "    if cer != float('inf'):\n",
        "      cers.append(cer)\n",
        "\n",
        "  return sum(cers) / len(cers)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Homograph Evaluation"
      ],
      "metadata": {
        "id": "oBgNtpFQDwku"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_homograph_performance(outputs, references):\n",
        "  corrects = 0\n",
        "  total = 0\n",
        "\n",
        "  for idx, (g, p, homograph, right) in enumerate(references):\n",
        "    if homograph != '':\n",
        "      total += 1\n",
        "      if right in outputs[idx]:\n",
        "        corrects += 1\n",
        "\n",
        "  return corrects / total"
      ],
      "metadata": {
        "id": "J445ULEvEEDn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Full bench"
      ],
      "metadata": {
        "id": "JGEUIrbi9kNH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "benchmark = []\n",
        "\n",
        "for g, p in mana_evaluation_data:\n",
        "  benchmark.append((g, p, '', ''))\n",
        "\n",
        "for g, p in commonvoice_evaluation_data:\n",
        "  benchmark.append((g, p, '', ''))\n",
        "\n",
        "for g, p, w, r in homograph_evaluation_data:\n",
        "  benchmark.append((g, p, w, r))\n",
        "\n",
        "benchmark = benchmark[:400]"
      ],
      "metadata": {
        "id": "fGzQvL8V9mln"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def print_all_metrics(predictions):\n",
        "  per = get_avg_cer_of_method(predictions, benchmark) * 100\n",
        "  homograph = get_homograph_performance(predictions, benchmark) * 100\n",
        "\n",
        "  print(f\"PER: \\t\\t\\t{per:.4f}\")\n",
        "  print(f\"HOMOGRAPH: \\t\\t{homograph:.4f}\")"
      ],
      "metadata": {
        "id": "DpSqE5oPbmAy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Epitran"
      ],
      "metadata": {
        "id": "k6XT11uMBnGp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import epitran\n",
        "epi = epitran.Epitran('fas-Arab')"
      ],
      "metadata": {
        "id": "A53DAk2_Dakd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epi.transliterate(u'دلم میخواست برم ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "L84ue_vWwdZl",
        "outputId": "06e6a744-7fd5-46d2-d0e2-48a2ef9dc133"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'dlm mjxvɒst brm '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "replace_phonetic_characters(epi.transliterate(u'دلم میخواست برم '))"
      ],
      "metadata": {
        "id": "I_1WYcyaZyTR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "927eb9fa-2bd3-44f2-8abd-92d90c9767af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'dlm mjxvAst brm '"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# outputs"
      ],
      "metadata": {
        "id": "NLgJTtoCg4m_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm import tqdm\n",
        "import time\n",
        "\n",
        "outputs = []\n",
        "start_time = time.time()\n",
        "\n",
        "for g, p, _, _ in tqdm(benchmark):\n",
        "    o = epi.transliterate(g)\n",
        "    outputs.append(o)\n",
        "\n",
        "total_time = time.time() - start_time\n",
        "avg_time = total_time / len(benchmark) if len(benchmark) > 0 else 0\n",
        "print(f\"Total: {total_time:.2f}s | Avg: {avg_time:.4f}s/sample\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ECW_8Ja5g7FY",
        "outputId": "2c778f9b-7957-4b6f-9116-08681762d1e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 400/400 [00:00<00:00, 3625.70it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total: 0.12s | Avg: 0.0003s/sample\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mapped_outputs = []\n",
        "for o in outputs:\n",
        "  mapped = replace_phonetic_characters(o)\n",
        "  mapped_outputs.append(mapped)\n",
        "  mapped.replace('j', 'y')"
      ],
      "metadata": {
        "id": "K-catlB6Esuf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print_all_metrics(mapped_outputs)\n",
        "print(f\"TOTAL TIME:\\t\\t{total_time:.4f} (s)\")\n",
        "print(f\"AVG TIME:\\t\\t{avg_time:.4f} (s)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H2taHCPWCnls",
        "outputId": "c3e8950a-898b-45ea-bfb3-0ac9f384c296"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PER: \t\t\t45.1223\n",
            "HOMOGRAPH: \t\t0.0000\n",
            "TOTAL TIME:\t\t0.1184 (s)\n",
            "AVG TIME:\t\t0.0003 (s)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Runs\n",
        "\n",
        "## First:\n",
        "\n",
        "```\n",
        "PER: \t\t\t45.1223\n",
        "HOMOGRAPH: \t\t0.0000\n",
        "TOTAL TIME:\t\t0.1172 (s)\n",
        "AVG TIME:\t\t0.0003 (s)\n",
        "```\n",
        "\n",
        "## Second\n",
        "\n",
        "```\n",
        "PER: \t\t\t45.1223\n",
        "HOMOGRAPH: \t\t0.0000\n",
        "TOTAL TIME:\t\t0.1074 (s)\n",
        "AVG TIME:\t\t0.0003 (s)\n",
        "```\n",
        "\n",
        "## Third\n",
        "\n",
        "```\n",
        "PER: \t\t\t45.1223\n",
        "HOMOGRAPH: \t\t0.0000\n",
        "TOTAL TIME:\t\t0.1296 (s)\n",
        "AVG TIME:\t\t0.0003 (s)\n",
        "```\n",
        "\n",
        "## Fourth\n",
        "\n",
        "```\n",
        "PER: \t\t\t45.1223\n",
        "HOMOGRAPH: \t\t0.0000\n",
        "TOTAL TIME:\t\t0.1085 (s)\n",
        "AVG TIME:\t\t0.0003 (s)\n",
        "```\n",
        "\n",
        "## Fifth\n",
        "\n",
        "```\n",
        "PER: \t\t\t45.1223\n",
        "HOMOGRAPH: \t\t0.0000\n",
        "TOTAL TIME:\t\t0.1184 (s)\n",
        "AVG TIME:\t\t0.0003 (s)\n",
        "```"
      ],
      "metadata": {
        "id": "dq7_g71Wivog"
      }
    }
  ]
}