forked from mirrors/LibreTranslate
118 lines
3.8 KiB
Python
118 lines
3.8 KiB
Python
import string
|
|
|
|
from argostranslate import translate
|
|
from polyglot.detect.base import Detector, UnknownLanguage
|
|
from polyglot.transliteration.base import Transliterator
|
|
|
|
|
|
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:
|
|
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)
|