mirror of
https://github.com/LibreTranslate/LibreTranslate.git
synced 2024-11-22 07:51:00 +00:00
120 lines
3.5 KiB
Python
120 lines
3.5 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):
|
|
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]
|
|
|
|
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
|
|
|