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mirror of https://github.com/LibreTranslate/LibreTranslate.git synced 2024-12-18 08:27:03 +02:00
LibreTranslate/libretranslate/language.py
2023-12-11 17:14:27 -05:00

136 lines
4.1 KiB
Python

from functools import lru_cache
from argostranslate import translate
from libretranslate.detect import Detector
__languages = None
def load_languages():
global __languages
if __languages is None or len(__languages) == 0:
__languages = translate.get_installed_languages()
return __languages
@lru_cache(maxsize=None)
def load_lang_codes():
languages = load_languages()
return tuple(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]
lang_codes = load_lang_codes()
# get the candidates
candidates = []
for t in text:
try:
d = Detector(lang_codes).detect(t)
for i in range(len(d)):
d[i].text_length = len(t)
candidates.extend(d)
except Exception as e:
print(str(e))
# total read bytes of the provided text
text_length_total = sum(c.text_length for c in candidates)
# this happens if no language could be detected
if not candidates:
# 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, candidates))
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.text_length = sum(l.text_length 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
candidates = temp_average_list
# sort the candidates descending based on the detected confidence
candidates.sort(
key=lambda l: (l.confidence * l.text_length) / text_length_total, reverse=True
)
return [{"confidence": l.confidence, "language": l.code} for l in candidates]
def improve_translation_formatting(source, translation, improve_punctuation=True, remove_single_word_duplicates=True):
source = source.strip()
if not len(source):
return ""
if not len(translation):
return source
if improve_punctuation:
source_last_char = source[len(source) - 1]
translation_last_char = translation[len(translation) - 1]
punctuation_chars = ['!', '?', '.', ',', ';', '']
if source_last_char in punctuation_chars:
if translation_last_char != source_last_char:
if translation_last_char in punctuation_chars:
translation = translation[:-1]
translation += source_last_char
elif translation_last_char in punctuation_chars:
translation = translation[:-1]
# A workaround for certain language models that output
# the single word repeated ad-infinitum (the "salad" bug)
# https://github.com/LibreTranslate/LibreTranslate/issues/46
if remove_single_word_duplicates:
if len(source) < 20 and source.count(" ") == 0 and translation.count(" ") > 0:
bow = translation.split()
count = {}
for word in bow:
count[word] = count.get(word, 0) + 1
for word in count:
if count[word] / len(count) >= 2:
translation = bow[0]
break
if source.islower():
return translation.lower()
if source.isupper():
return translation.upper()
if len(translation) == 0:
return source
if source[0].islower():
return translation[0].lower() + translation[1:]
if source[0].isupper():
return translation[0].upper() + translation[1:]
return translation