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simul_subs.py
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#!/usr/bin/env python
# coding: utf-8
import ass
import codecs
import sys
import os
import pandas as pd
import datetime
import argparse
from tqdm import tqdm
from nltk.tokenize import word_tokenize
from googletrans import Translator
from langdetect import detect
translator = Translator(service_urls=['translate.google.co.in'])
import re
def clean_text(string):
string = string.replace("）",")").replace("（","(")
string = re.sub(r"\{[^{}]*}","",string)
string = re.sub(r"\([^)]*\)","",string).replace("\\N", " ")
string = string.strip()
if len(string) and string[0] == ")":
string = string[1:].strip()
return string
def jaccard(a, b):
a = set(a)
b = set(b)
c = a.intersection(b)
return float(len(c)) / (len(a) + len(b) - len(c))
parser = argparse.ArgumentParser("Subtitles Mapping")
parser.add_argument("--dir", type=str, help="The common width and height for all images")
parser.add_argument("--anime_name", type=str, help="The name of the anime for naming the output files")
parser.add_argument("--threshold_score", type=float, default=0.1, help="The common width and height for all images")
parser.add_argument("--start", type=int, default=0, help="The episode number where the program ended on the previous run")
args = parser.parse_args()
anime_name = args.anime_name
start = args.start
en_path = args.dir
en_subs_list = os.listdir(en_path)
print(f"Found {len(en_subs_list)} files")
for idx in range(start,len(en_subs_list)):
d1 = dict()
d1['start'] = []
d1['end'] = []
d1['text'] = []
d2 = dict()
d2['start'] = []
d2['end'] = []
d2['text'] = []
extension = en_subs_list[idx].split(".")[-1]
if extension == 'ass':
try:
with open(en_path+"\\"+en_subs_list[idx], encoding='utf_8_sig') as f:
doc1 = ass.parse(f)
except UnicodeError as e:
with open(en_path+"\\"+en_subs_list[idx], encoding='utf16') as f:
doc1 = ass.parse(f)
except Exception as e:
continue
for i in doc1.events:
try:
if detect(i.text) == "en":
d1['start'].append(i.start.total_seconds())
d1['end'].append(i.end.total_seconds())
d1['text'].append(i.text)
elif detect(i.text) == "ja":
d2['start'].append(i.start.total_seconds())
d2['end'].append(i.end.total_seconds())
d2['text'].append(i.text)
except:
continue
elif extension == 'srt':
try:
with open(en_path+"\\"+en_subs_list[idx], encoding='utf_8_sig') as f:
doc2 = srt.parse(f.read())
except UnicodeError as e:
with open(en_path+"\\"+en_subs_list[idx], encoding='utf16') as f:
doc2 = srt.parse(f.read())
except Exception as e:
continue
for i in doc1:
try:
if detect(i.text) == "en":
d1['start'].append(i.start.total_seconds())
d1['end'].append(i.end.total_seconds())
d1['text'].append(i.text)
elif detect(i.text) == "ja":
d2['start'].append(i.start.total_seconds())
d2['end'].append(i.end.total_seconds())
d2['text'].append(i.text)
except:
continue
df1 = pd.DataFrame(d1).sort_values("start")
df1['text'] = df1['text'].apply(clean_text)
df1 = df1[df1['text'] != ""]
df1 = df1.drop_duplicates(subset=['text'])
df2 = pd.DataFrame(d2).sort_values("start")
df2['text'] = df2['text'].apply(clean_text)
df2 = df2[df2['text'] != ""]
df2 = df2.drop_duplicates(subset=['text'])
translated_text = []
for i, row in tqdm(df2.iterrows()):
while True:
if row['text'].strip() == '':
translated_text.append("")
break
try:
translated = translator.translate(row['text'], dest = "en", src="ja")
translated_text.append(translated.text)
except Exception as e:
continue
break
df2['translation'] = translated_text
df_list = []
for i, row in df1.iterrows():
time_diff = abs(df2['start'] - row['start'])
time_diff = time_diff.sort_values()
en_tokenized = word_tokenize(row['text'].lower())
try:
nearest_idxs = time_diff[time_diff < 2].index
if len(nearest_idxs) > 1:
scores = []
for n_idx in nearest_idxs:
translated_text = df2.loc[n_idx].translated
translated_tokenized = word_tokenize(translated_text.lower())
score = jaccard(en_tokenized, translated_tokenized)
scores.append(score)
max_idx = scores.index(max(score))
nearest_idx = nearest_idxs[max_idx]
else:
nearest_idx = nearest_idxs[0]
jp_text = df2.loc[nearest_idx].text
en_text = row['text']
translated_text = df2.loc[nearest_idx].translation
translated_tokenized = word_tokenize(translated_text.lower())
score = jaccard(en_tokenized, translated_tokenized)
df_list.append([en_text, jp_text, translated_text, score, nearest_idx])
except Exception as e:
continue
df = pd.DataFrame(df_list)
df.columns = ["en_text", "jp_text", "translated_text", "score", "index"]
# Merging sentences with same index
merged_df_list = []
dup_indices = []
for i, row in df.iterrows():
if i in dup_indices:
continue
index = row['index']
dup_idx = df.index[df['index'] == index]
if len(dup_idx) == 2:
en_text = df.loc[dup_idx[0]].en_text + " " + df.loc[dup_idx[1]].en_text.lower()
jp_text = df.loc[dup_idx[0]].jp_text
translated_text = df.loc[dup_idx[0]].translated_text
dup_indices.append(dup_idx[1])
en_tokenized = word_tokenize(en_text.lower())
translated_tokenized = word_tokenize(translated_text.lower())
score = jaccard(en_tokenized, translated_tokenized)
merged_df_list.append([en_text, jp_text, translated_text, score])
else:
merged_df_list.append([row[0], row[1], row[2], row[3]])
merged_df = pd.DataFrame(merged_df_list)
merged_df.columns = ["en_text", "jp_text", "google_translated", "score"]
merged_df.to_csv("subs/"+anime_name+"-"+str(idx+1)+".csv", index = False)