diff --git a/utils/normalize.py b/utils/normalize.py
index bc15dbd..37b2b9d 100644
--- a/utils/normalize.py
+++ b/utils/normalize.py
@@ -1,48 +1,80 @@
+import logging
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# Initialize the T5 model and tokenizer
+tokenizer = T5Tokenizer.from_pretrained("google/byt5-small")
+model = T5ForConditionalGeneration.from_pretrained("google/byt5-small")
+
 def is_russian_wording(text):
     """
     Check if the text contains any Russian characters by checking 
     each character against the Unicode range for Cyrillic.
     """
-    # Check if any character in the text is a Cyrillic character
     for char in text:
         if '\u0400' <= char <= '\u04FF':  # Unicode range for Cyrillic characters
             return True
     return False
 
+def segment_text(text):
+    """
+    Use a neural network model to segment text into words.
+    """
+    # Encode the input text for the model
+    inputs = tokenizer.encode("segment: " + text, return_tensors="pt")
+    
+    # Generate predictions
+    with torch.no_grad():
+        outputs = model.generate(inputs)
+    
+    # Decode the generated tokens back to text
+    segmented_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return segmented_text
+
 def normalize(text):
     """
     Normalize English text to resemble Russian characters.
     """
-    text = text.lower()
-    if is_russian_wording(text):
-        # Normalize the text by replacing characters
-        text = (text
-                    .replace('  ', ' ')
-                    .replace('  ', ' ')
-                    .replace('  ', ' ')
-                    .replace('e', 'е')
-                    .replace('o', 'о')
-                    .replace('x', 'х')
-                    .replace('a', 'а')
-                    .replace('r', 'г')
-                    .replace('m', 'м')
-                    .replace('u', 'и')
-                    .replace('n', 'п')
-                    .replace('p', 'р')
-                    .replace('t', 'т')
-                    .replace('y', 'у')
-                    .replace('h', 'н')
-                    .replace('p', 'р')
-                    .replace('i', 'й')
-                    .replace('c', 'с')
-                    .replace('k', 'к')
-                    .replace('b', 'в')
-                    .replace('3', 'з')
-                    .replace('4', 'ч')
-                    .replace('0', 'о')
-                    .replace('e', 'е')
-                    .replace('d', 'д')
-                    .replace('z', 'з')
-                    )
+    # Segment the text first
+    segmented_text = segment_text(text.replace('  ', ' ').replace('  ', ' ').replace('  ', ' '))
     
-    return text
+    # Normalize after segmentation
+    segmented_text = segmented_text.lower()
+    
+    if is_russian_wording(segmented_text):
+        # Normalize the text by replacing characters
+        normalized_text = (segmented_text
+                           .replace('e', 'е')
+                           .replace('o', 'о')
+                           .replace('x', 'х')
+                           .replace('a', 'а')
+                           .replace('r', 'г')
+                           .replace('m', 'м')
+                           .replace('u', 'и')
+                           .replace('n', 'п')
+                           .replace('p', 'р')
+                           .replace('t', 'т')
+                           .replace('y', 'у')
+                           .replace('h', 'н')
+                           .replace('i', 'й')
+                           .replace('c', 'с')
+                           .replace('k', 'к')
+                           .replace('b', 'в')
+                           .replace('3', 'з')
+                           .replace('4', 'ч')
+                           .replace('0', 'о')
+                           .replace('d', 'д')
+                           .replace('z', 'з'))
+        
+        return normalized_text
+    
+    return segmented_text
+
+# Example usage
+if __name__ == "__main__":
+    input_text = "Hello, this is a test input."
+    
+    normalized_output = normalize(input_text)
+    print(normalized_output)
+