forked from mirrors/LibreTranslate
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
from argostranslate import translate
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from polyglot.detect.base import Detector
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languages = translate.load_installed_languages()
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__lang_codes = [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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# get the candidates
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candidates = []
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for t in text:
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candidates.extend(Detector(t).languages)
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# total read bytes of the provided text
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read_bytes_total = sum(c.read_bytes for c in candidates)
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# only use candidates that are supported by argostranslate
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candidate_langs = list(filter(lambda l: l.read_bytes != 0 and l.code in __lang_codes, candidates))
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# this happens if no language could be detected
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if not candidate_langs:
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# use language "en" by default but with zero confidence
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return [
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{
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'confidence': 0.0,
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'language': "en"
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}
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]
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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, candidate_langs))
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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.read_bytes = sum(l.read_bytes 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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candidate_langs = temp_average_list
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# sort the candidates descending based on the detected confidence
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candidate_langs.sort(key=lambda l: (l.confidence * l.read_bytes) / read_bytes_total, reverse=True)
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return [
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{
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'confidence': l.confidence,
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'language': l.code
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}
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for l in candidate_langs
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]
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