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LibreTranslate/app/language.py

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import string
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from argostranslate import translate
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from polyglot.detect.base import Detector, UnknownLanguage
from polyglot.transliteration.base import Transliterator
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languages = translate.load_installed_languages()
__lang_codes = [l.code for l in languages]
def detect_languages(text):
# detect batch processing
if isinstance(text, list):
is_batch = True
else:
is_batch = False
text = [text]
# get the candidates
candidates = []
for t in text:
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try:
candidates.extend(Detector(t).languages)
except UnknownLanguage as e:
pass
# total read bytes of the provided text
read_bytes_total = sum(c.read_bytes for c in candidates)
# only use candidates that are supported by argostranslate
candidate_langs = list(filter(lambda l: l.read_bytes != 0 and l.code in __lang_codes, candidates))
# this happens if no language could be detected
if not candidate_langs:
# use language "en" by default but with zero confidence
return [
{
'confidence': 0.0,
'language': "en"
}
]
# for multiple occurrences of the same language (can happen on batch detection)
# calculate the average confidence for each language
if is_batch:
temp_average_list = []
for lang_code in __lang_codes:
# get all candidates for a specific language
lc = list(filter(lambda l: l.code == lang_code, candidate_langs))
if len(lc) > 1:
# if more than one is present, calculate the average confidence
lang = lc[0]
lang.confidence = sum(l.confidence for l in lc) / len(lc)
lang.read_bytes = sum(l.read_bytes for l in lc)
temp_average_list.append(lang)
elif lc:
# otherwise just add it to the temporary list
temp_average_list.append(lc[0])
if temp_average_list:
# replace the list
candidate_langs = temp_average_list
# sort the candidates descending based on the detected confidence
candidate_langs.sort(key=lambda l: (l.confidence * l.read_bytes) / read_bytes_total, reverse=True)
return [
{
'confidence': l.confidence,
'language': l.code
}
for l in candidate_langs
]
def __transliterate_line(transliterator, line_text):
new_text = []
# transliteration is done word by word
for orig_word in line_text.split(" "):
# remove any punctuation on the right side
r_word = orig_word.rstrip(string.punctuation)
r_diff = set(char for char in orig_word) - set(char for char in r_word)
# and on the left side
l_word = orig_word.lstrip(string.punctuation)
l_diff = set(char for char in orig_word) - set(char for char in l_word)
# the actual transliteration of the word
t_word = transliterator.transliterate(orig_word.strip(string.punctuation))
# if transliteration fails, default back to the original word
if not t_word:
t_word = orig_word
else:
# add back any stripped punctuation
if r_diff:
t_word = t_word + ''.join(r_diff)
if l_diff:
t_word = ''.join(l_diff) + t_word
new_text.append(t_word)
# rebuild the text
return " ".join(new_text)
def transliterate(text, target_lang="en"):
# initialize the transliterator from polyglot
transliterator = Transliterator(target_lang=target_lang)
# check for multiline string
if "\n" in text:
lines = []
# process each line separate
for line in text.split("\n"):
lines.append(__transliterate_line(transliterator, line))
# rejoin multiline string
return "\n".join(lines)
else:
return __transliterate_line(transliterator, text)