mirror of
https://github.com/LibreTranslate/LibreTranslate.git
synced 2024-12-18 08:27:03 +02:00
ee638b407d
The model server for the Polyglot transliteration library is currently down and the project looks unmaintained; this currently is breaking LibreTranslate installs. This commit removes Polyglot for transliteration but keeps using Polyglot for language detection. - https://github.com/LibreTranslate/LibreTranslate/issues/344 - https://community.libretranslate.com/t/improving-transliteration-in-libretranslate/400
115 lines
3.5 KiB
Python
115 lines
3.5 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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__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 improve_translation_formatting(source, translation, improve_punctuation=True):
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source = source.strip()
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if not len(source):
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return ""
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if improve_punctuation:
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source_last_char = source[len(source) - 1]
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translation_last_char = translation[len(translation) - 1]
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punctuation_chars = ['!', '?', '.', ',', ';']
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if source_last_char in punctuation_chars:
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if translation_last_char != source_last_char:
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if translation_last_char in punctuation_chars:
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translation = translation[:-1]
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translation += source_last_char
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elif translation_last_char in punctuation_chars:
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translation = translation[:-1]
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if source.islower():
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return translation.lower()
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if source.isupper():
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return translation.upper()
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if source[0].islower():
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return translation[0].lower() + translation[1:]
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if source[0].isupper():
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return translation[0].upper() + translation[1:]
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return translation
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