diff --git a/dataspeech/gpu_enrichments/repunct_spacy.py b/dataspeech/gpu_enrichments/repunct_spacy.py
new file mode 100644
index 00000000..66a23b27
--- /dev/null
+++ b/dataspeech/gpu_enrichments/repunct_spacy.py
@@ -0,0 +1,83 @@
+import re
+
+from datasets import load_dataset
+from deepmultilingualpunctuation import PunctuationModel
+from multiprocess import set_start_method
+
+from nltk.tokenize import word_tokenize, sent_tokenize
+from nltk.tag import pos_tag
+
+import nltk
+import spacy
+
+model = PunctuationModel()
+
+dataset_name = "ylacombe/mls-eng-tags"
+output_dataset = "reach-vb/mls-eng-tags-spacy-v2"
+process_split = "train"
+proc = 16
+device_id = 2
+
+ds = load_dataset(dataset_name, split = process_split,  num_proc=proc)
+
+spacy.require_gpu(gpu_id=device_id)
+
+# Load the spaCy model
+nlp = spacy.load('en_core_web_trf')
+
+from spacy.util import compile_infix_regex
+
+def custom_tokenizer(nlp):
+    infixes = nlp.Defaults.infixes + ['\w+(?:-\w+)+']
+    infix_regex = compile_infix_regex(infixes)
+    return spacy.tokenizer.Tokenizer(nlp.vocab, infix_finditer=infix_regex.finditer)
+
+# Use the custom tokenizer
+nlp.tokenizer = custom_tokenizer(nlp)
+
+def true_case_spacy(text):
+    # Process the text with the spaCy model
+    doc = nlp(text)
+    
+    # Initialize an empty list to hold the processed sentences
+    true_cased_sentences = []
+    
+    # Iterate through the sentences in the Doc object
+    for sent in doc.sents:
+        # Initialize an empty list to hold the processed tokens of the current sentence
+        processed_tokens = []
+        
+        # Iterate through the tokens in the current sentence
+        for i, token in enumerate(sent):
+            # Capitalize the first word of the sentence and proper nouns
+            if i == 0 or token.pos_ == 'PROPN':
+                processed_tokens.append(token.text.capitalize())
+            else:
+                processed_tokens.append(token.text)
+        
+        # Join the processed tokens back into a string
+        processed_sentence = ' '.join(processed_tokens)
+        
+        # Remove spaces between punctuations and the preceding word
+        processed_sentence = re.sub(r'(\w) (\W)', r'\1\2', processed_sentence)
+        
+        # Add the processed sentence to the list of processed sentences
+        true_cased_sentences.append(processed_sentence)
+    
+    # Join the processed sentences back into a single string
+    true_cased_text = ' '.join(true_cased_sentences)
+    
+    return true_cased_text
+
+
+def repunctuation_apply_simple(batch):
+    
+    repunct_sample = model.restore_punctuation(batch["text"])
+    batch["repunct_text"] = true_case_spacy(repunct_sample)
+
+    return batch
+
+if __name__ == "__main__":
+    set_start_method("spawn")
+    repunct_ds = ds.map(repunctuation_apply_simple, batch_size=1, num_proc=proc)
+    repunct_ds.push_to_hub(output_dataset, split = process_split)
\ No newline at end of file