Init

utils/preprocessing.py

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+import re
+import math
+
+
+# List of words which might lead to inaccurate translation
+DICTIONARY = {
+    'violated': 'raped',
+    'semen':    'cum'
+}
+
+def replace_english_words(text: str) -> str:
+    return ' '.join(DICTIONARY.get(word, word) for word in text.split())
+
+def remove_ass_statements(text: str) -> str:
+    # Regular expression to match everything between curly braces (non-greedy)
+    return re.sub(r'\{.*?\}', '', text)
+
+def remove_new_line_statements(text: str) -> str:
+    return text.replace("\\N", " ")
+
+def split_sentence(sentence, n, translated_sentence):
+    original_commas = sentence.count(',')
+    translated_commas = translated_sentence.count(',')
+
+    # Split by commas if commas align
+    if original_commas != 0 and original_commas == translated_commas:
+        return [chunk.strip() for chunk in translated_sentence.split(',')]
+    
+    # Otherwise, split into n roughly equal chunks
+    words = translated_sentence.split()
+    chunk_size = math.ceil(len(words) / n)
+    chunks = [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
+    return chunks

utils/translator.py

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+from transformers import AutoProcessor, SeamlessM4Tv2Model
+
+processor = AutoProcessor.from_pretrained("facebook/seamless-m4t-v2-large")
+model = SeamlessM4Tv2Model.from_pretrained("facebook/seamless-m4t-v2-large")
+
+def translator(subtitle_line: str, target_language: str) -> str:
+    # Ensure the model and processor are loaded to the GPU
+    model.to('cuda')
+    # Move input tensors to GPU
+    text_inputs = processor(text=subtitle_line, src_lang="eng", return_tensors="pt")
+    text_inputs = {key: value.to('cuda') for key, value in text_inputs.items()}
+    # Generate output tokens on GPU
+    output_tokens = model.generate(**text_inputs, tgt_lang=target_language, num_beams=5, generate_speech=False)
+    # Decode the result
+    return processor.decode(output_tokens[0].tolist()[0], skip_special_tokens=True)