python code is beter for integrity ;)

[gyroing]

import hazm
import typing
import sys

normalizer = hazm.Normalizer()
sent_tokenizer = hazm.SentenceTokenizer()
word_tokenizer = hazm.WordTokenizer()

tagger = hazm.POSTagger(
model=str("pos_tagger.model")
)

def preprocess_text(text: str) -> typing.List[typing.List[str]]:

    text = normalizer.normalize(text)
    processed_sentences = []

    for sentence in sent_tokenizer.tokenize(text):
        words = word_tokenizer.tokenize(sentence)
        processed_words = fix_words(words)
        processed_sentences.append(" ".join(processed_words))

    return  " ".join(processed_sentences)

def fix_words(words: typing.List[str]) -> typing.List[str]:
fixed_words = []

    for word, pos in tagger.tag(words):
        if pos[-1] == "Z":
            if word[-1] != "ِ":
                if (word[-1] == "ه") and (word[-2] != "ا"):
                    word += "‌ی"
            word += "ِ"
                

        fixed_words.append(word)

    return fixed_words

text = preprocess_text(sys.argv[1])
sys.stdout.write(text)

[gyroing]

import hazm import typing import sys

normalizer = hazm.Normalizer() sent_tokenizer = hazm.SentenceTokenizer() word_tokenizer = hazm.WordTokenizer()

tagger = hazm.POSTagger( model=str("pos_tagger.model") )

def preprocess_text(text: str) -> typing.List[typing.List[str]]:

    text = normalizer.normalize(text)
    processed_sentences = []

    for sentence in sent_tokenizer.tokenize(text):
        words = word_tokenizer.tokenize(sentence)
        processed_words = fix_words(words)
        processed_sentences.append(" ".join(processed_words))

    return  " ".join(processed_sentences)

def fix_words(words: typing.List[str]) -> typing.List[str]: fixed_words = []

    for word, pos in tagger.tag(words):
        if pos[-1] == "Z":
            if word[-1] != "ِ":
                if (word[-1] == "ه") and (word[-2] != "ا"):
                    word += "‌ی"
            word += "ِ"
                

        fixed_words.append(word)

    return fixed_words

text = preprocess_text(sys.argv[1]) sys.stdout.write(text)

originally from mimic3 tts but it have been changed to works new Hazm posttagger

HuggingFace Kasreh Ezafeh Detection with Hazm Demo

[Soebb]

import hazm import typing import sys

normalizer = hazm.Normalizer() sent_tokenizer = hazm.SentenceTokenizer() word_tokenizer = hazm.WordTokenizer()

tagger = hazm.POSTagger( model=str("pos_tagger.model") )

def preprocess_text(text: str) -> typing.List[typing.List[str]]:

    text = normalizer.normalize(text)
    processed_sentences = []

    for sentence in sent_tokenizer.tokenize(text):
        words = word_tokenizer.tokenize(sentence)
        processed_words = fix_words(words)
        processed_sentences.append(" ".join(processed_words))

    return  " ".join(processed_sentences)

def fix_words(words: typing.List[str]) -> typing.List[str]: fixed_words = []

    for word, pos in tagger.tag(words):
        if pos[-1] == "Z":
            if word[-1] != "ِ":
                if (word[-1] == "ه") and (word[-2] != "ا"):
                    word += "‌ی"
            word += "ِ"
                

        fixed_words.append(word)

    return fixed_words

text = preprocess_text(sys.argv[1]) sys.stdout.write(text)

Wow, thanks for explaining clearly.
I will try surely.

[Soebb]

Done.