mirror of
https://github.com/LibreTranslate/LibreTranslate.git
synced 2025-11-04 03:27:14 -05:00
136 lines
4.1 KiB
Python
136 lines
4.1 KiB
Python
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from functools import lru_cache
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from argostranslate import translate
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from libretranslate.detect import Detector
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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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@lru_cache(maxsize=None)
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def load_lang_codes():
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languages = load_languages()
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return tuple(l.code for l in 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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lang_codes = load_lang_codes()
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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(lang_codes).detect(t)
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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 Exception as e:
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print(str(e))
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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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# this happens if no language could be detected
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if not candidates:
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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, candidates))
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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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candidates = temp_average_list
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# sort the candidates descending based on the detected confidence
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candidates.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 candidates]
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def improve_translation_formatting(source, translation, improve_punctuation=True, remove_single_word_duplicates=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 not len(translation):
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return source
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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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# A workaround for certain language models that output
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# the single word repeated ad-infinitum (the "salad" bug)
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# https://github.com/LibreTranslate/LibreTranslate/issues/46
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if remove_single_word_duplicates:
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if len(source) < 20 and source.count(" ") == 0 and translation.count(" ") > 0:
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bow = translation.split()
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count = {}
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for word in bow:
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count[word] = count.get(word, 0) + 1
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for word in count:
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if count[word] / len(count) >= 2:
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translation = bow[0]
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break
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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 len(translation) == 0:
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return source
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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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