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127 lines
4.1 KiB
Python
127 lines
4.1 KiB
Python
import string
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from argostranslate import translate
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from polyglot.detect.base import Detector, UnknownLanguage
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from polyglot.transliteration.base import Transliterator
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__languages = None
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def load_languages():
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global __languages
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if __languages is None or len(__languages) == 0:
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__languages = translate.get_installed_languages()
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return __languages
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def detect_languages(text):
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# detect batch processing
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if isinstance(text, list):
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is_batch = True
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else:
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is_batch = False
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text = [text]
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# get the candidates
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candidates = []
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for t in text:
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try:
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d = Detector(t).languages
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for i in range(len(d)):
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d[i].text_length = len(t)
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candidates.extend(d)
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except UnknownLanguage:
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pass
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# total read bytes of the provided text
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text_length_total = sum(c.text_length for c in candidates)
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# Load language codes
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languages = load_languages()
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lang_codes = [l.code for l in languages]
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# only use candidates that are supported by argostranslate
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candidate_langs = list(
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filter(lambda l: l.text_length != 0 and l.code in lang_codes, candidates)
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)
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# this happens if no language could be detected
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if not candidate_langs:
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# use language "en" by default but with zero confidence
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return [{"confidence": 0.0, "language": "en"}]
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# for multiple occurrences of the same language (can happen on batch detection)
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# calculate the average confidence for each language
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if is_batch:
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temp_average_list = []
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for lang_code in lang_codes:
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# get all candidates for a specific language
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lc = list(filter(lambda l: l.code == lang_code, candidate_langs))
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if len(lc) > 1:
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# if more than one is present, calculate the average confidence
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lang = lc[0]
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lang.confidence = sum(l.confidence for l in lc) / len(lc)
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lang.text_length = sum(l.text_length for l in lc)
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temp_average_list.append(lang)
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elif lc:
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# otherwise just add it to the temporary list
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temp_average_list.append(lc[0])
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if temp_average_list:
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# replace the list
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candidate_langs = temp_average_list
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# sort the candidates descending based on the detected confidence
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candidate_langs.sort(
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key=lambda l: (l.confidence * l.text_length) / text_length_total, reverse=True
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)
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return [{"confidence": l.confidence, "language": l.code} for l in candidate_langs]
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def __transliterate_line(transliterator, line_text):
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new_text = []
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# transliteration is done word by word
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for orig_word in line_text.split(" "):
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# remove any punctuation on the right side
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r_word = orig_word.rstrip(string.punctuation)
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r_diff = set(char for char in orig_word) - set(char for char in r_word)
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# and on the left side
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l_word = orig_word.lstrip(string.punctuation)
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l_diff = set(char for char in orig_word) - set(char for char in l_word)
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# the actual transliteration of the word
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t_word = transliterator.transliterate(orig_word.strip(string.punctuation))
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# if transliteration fails, default back to the original word
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if not t_word:
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t_word = orig_word
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else:
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# add back any stripped punctuation
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if r_diff:
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t_word = t_word + "".join(r_diff)
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if l_diff:
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t_word = "".join(l_diff) + t_word
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new_text.append(t_word)
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# rebuild the text
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return " ".join(new_text)
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def transliterate(text, target_lang="en"):
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# initialize the transliterator from polyglot
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transliterator = Transliterator(target_lang=target_lang)
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# check for multiline string
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if "\n" in text:
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lines = []
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# process each line separate
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for line in text.split("\n"):
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lines.append(__transliterate_line(transliterator, line))
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# rejoin multiline string
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return "\n".join(lines)
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else:
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return __transliterate_line(transliterator, text)
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