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INDIC_NLP_LIB_HOME = "indic_nlp_library"
INDIC_NLP_RESOURCES = "indic_nlp_resources"
import sys
sys.path.append(r"{}".format(INDIC_NLP_LIB_HOME))
from indicnlp import common
common.set_resources_path(INDIC_NLP_RESOURCES)
from indicnlp import loader
loader.load()
from sacremoses import MosesPunctNormalizer
from sacremoses import MosesTokenizer
from sacremoses import MosesDetokenizer
from collections import defaultdict
from tqdm import tqdm
from joblib import Parallel, delayed
from indicnlp.tokenize import indic_tokenize
from indicnlp.tokenize import indic_detokenize
from indicnlp.normalize import indic_normalize
from indicnlp.transliterate import unicode_transliterate
en_tok = MosesTokenizer(lang="en")
en_normalizer = MosesPunctNormalizer()
def preprocess_line(line, normalizer, lang, transliterate=False):
if lang == "en":
return " ".join(
en_tok.tokenize(en_normalizer.normalize(line.strip()), escape=False)
)
elif transliterate:
# line = indic_detokenize.trivial_detokenize(line.strip(), lang)
return unicode_transliterate.UnicodeIndicTransliterator.transliterate(
" ".join(
indic_tokenize.trivial_tokenize(
normalizer.normalize(line.strip()), lang
)
),
lang,
"hi",
).replace(" ् ", "्")
else:
# we only need to transliterate for joint training
return " ".join(
indic_tokenize.trivial_tokenize(normalizer.normalize(line.strip()), lang)
)
def preprocess(infname, outfname, lang, transliterate=False):
"""
Normalize, tokenize and script convert(for Indic)
return number of sentences input file
"""
n = 0
num_lines = sum(1 for line in open(infname, "r"))
if lang == "en":
with open(infname, "r", encoding="utf-8") as infile, open(
outfname, "w", encoding="utf-8"
) as outfile:
out_lines = Parallel(n_jobs=-1, backend="multiprocessing")(
delayed(preprocess_line)(line, None, lang)
for line in tqdm(infile, total=num_lines)
)
for line in out_lines:
outfile.write(line + "\n")
n += 1
else:
normfactory = indic_normalize.IndicNormalizerFactory()
normalizer = normfactory.get_normalizer(lang)
# reading
with open(infname, "r", encoding="utf-8") as infile, open(
outfname, "w", encoding="utf-8"
) as outfile:
out_lines = Parallel(n_jobs=-1, backend="multiprocessing")(
delayed(preprocess_line)(line, normalizer, lang, transliterate)
for line in tqdm(infile, total=num_lines)
)
for line in out_lines:
outfile.write(line + "\n")
n += 1
return n
def old_preprocess(infname, outfname, lang):
"""
Preparing each corpus file:
- Normalization
- Tokenization
- Script coversion to Devanagari for Indic scripts
"""
n = 0
num_lines = sum(1 for line in open(infname, "r"))
# reading
with open(infname, "r", encoding="utf-8") as infile, open(
outfname, "w", encoding="utf-8"
) as outfile:
if lang == "en":
en_tok = MosesTokenizer(lang="en")
en_normalizer = MosesPunctNormalizer()
for line in tqdm(infile, total=num_lines):
outline = " ".join(
en_tok.tokenize(en_normalizer.normalize(line.strip()), escape=False)
)
outfile.write(outline + "\n")
n += 1
else:
normfactory = indic_normalize.IndicNormalizerFactory()
normalizer = normfactory.get_normalizer(lang)
for line in tqdm(infile, total=num_lines):
outline = (
unicode_transliterate.UnicodeIndicTransliterator.transliterate(
" ".join(
indic_tokenize.trivial_tokenize(
normalizer.normalize(line.strip()), lang
)
),
lang,
"hi",
).replace(" ् ", "्")
)
outfile.write(outline + "\n")
n += 1
return n
if __name__ == "__main__":
# INDIC_NLP_LIB_HOME = "indic_nlp_library"
# INDIC_NLP_RESOURCES = "indic_nlp_resources"
# sys.path.append(r'{}'.format(INDIC_NLP_LIB_HOME))
# common.set_resources_path(INDIC_NLP_RESOURCES)
# data_dir = '../joint_training/v1'
# new_dir = data_dir + '.norm'
# for path, subdirs, files in os.walk(data_dir):
# for name in files:
# infile = os.path.join(path, name)
# lang = infile.split('.')[-1]
# outfile = os.path.join(path.replace(data_dir, new_dir), name)
# preprocess(infile, outfile, lang)
# loader.load()
infname = sys.argv[1]
outfname = sys.argv[2]
lang = sys.argv[3]
if len(sys.argv) == 4:
transliterate = False
elif len(sys.argv) == 5:
transliterate = sys.argv[4]
if transliterate.lower() == "true":
transliterate = True
else:
transliterate = False
else:
print(f"Invalid arguments: {sys.argv}")
exit()
print(preprocess(infname, outfname, lang, transliterate))
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