diff --git a/galvasr2/deduplicate.py b/galvasr2/deduplicate.py
new file mode 100644
index 00000000..1c76ab62
--- /dev/null
+++ b/galvasr2/deduplicate.py
@@ -0,0 +1,123 @@
+import logging
+from galvasr2.align.spark.align_lib import load_audio_id_text_id_mapping, load_transcripts
+from datasketch import MinHash, MinHashLSH, MinHashLSHForest
+from nltk import ngrams
+from tqdm import tqdm
+import numpy as np
+import itertools
+
+
+class DataDeduplication:
+
+    def __init__(self, num_rows: int = 1):
+        self.num_rows = num_rows
+
+    def read_transcription_data(
+        self,
+        spark: SparkSession,
+        data_trans_index: str,
+        data_trans: str,
+    ) -> pd.DataFrame:
+        """Read the transcriptions
+        Returns
+        -------
+        transcripts_pdf: dataframe
+            pandas dataframe with the transcriptions
+        """
+        # spark.sparkContext.setLogLevel("INFO") # "ALL" for very verbose logging
+        logging.getLogger("py4j").setLevel(logging.ERROR)
+        catalogue_df = load_audio_id_text_id_mapping(spark, data_trans_index)
+        training_sample_rows = catalogue_df.collect()
+        # Comment this out to load everything. It might takes ~15 minute, in my experience, on an 8 core machine.
+        if self.num_rows > 1:
+            training_sample_rows = training_sample_rows[: self.num_rows]
+        transcripts_df = load_transcripts(
+            spark, data_trans, training_sample_rows)
+        transcripts_pdf = transcripts_df.toPandas()
+        return transcripts_pdf
+
+    def generate_buckets(self, data:list, threshold:float=0.9, n_grams:int=30, num_perm:int=128) -> dict:
+        """Hashing are generated subsequently buckets
+        Returns
+        -------
+        lsh: 
+            Locality-sensitive hashing object
+        minhashes: 
+            Hashing values for each transcription
+        """
+        lsh = MinHashLSH(threshold=threshold, num_perm=num_perm)
+        # Create MinHash objects
+        minhashes = {}
+        error = []
+        for c, i in enumerate(tqdm(data)):
+            try:
+                if c % 5000 == 0:
+                    print(c)
+                minhash = MinHash(num_perm=num_perm)
+                for d in ngrams(i, n_grams):
+                    minhash.update("".join(d).encode('utf-8'))
+                lsh.insert(c, minhash)
+                minhashes[c] = minhash
+            except:
+                error.append(c)
+                pass
+        return lsh, minhashes
+
+    def create_cand_pairs(self, lsh, minhashes):
+        """Compare each element inside a bucket using the Jaccard distance
+        Returns
+        -------
+        big_list: list
+            List with all the possibles duplicates for each transcription
+        """
+        big_list = []
+        for query in minhashes.keys():
+            bucket = lsh.query(minhashes[query])
+            if len(bucket) == 1:
+                big_list.append([bucket[0], "None"])
+            if len(bucket) > 1:
+                first_val = bucket[0]
+                for val in bucket[1:2]:
+                    second_val = val
+                    big_list.append([first_val, second_val])
+        return big_list
+
+    def find_duplicates(self, lsh, minhashes) -> list:
+        """Compare each element inside a bucket using the Jaccard distance and 
+        let only the duplicates elements
+        Returns
+        -------
+        duplicate: list
+            List with only the duplicates transcriptions
+        """
+        duplicate = []
+        for i in range(len(minhashes.keys())):
+            try:
+                result = lsh.query(minhashes[i])
+                if len(result) > 1:
+                    result.sort()
+                    duplicate.append(result)
+                    print((result))
+            except:
+                pass
+        duplicate.sort()
+        duplicate = list(duplicate for duplicate,
+                         _ in itertools.groupby(duplicate))
+        return duplicate
+
+    def data_to_delete(self, lsh, minhashes, transcripts_pdf) -> list:
+        """Only one of the duplicate elements is saved, and the identifier and text_document_id of
+        the elements to be eliminated is returned
+        Returns
+        -------
+        doc_delete: list
+            List with identifier and text_document_id
+        """
+        duplicate = self.find_duplicates(lsh, minhashes)
+        index_delete = []
+        for value in duplicate:
+            index_delete.append(value[1:])
+        index_delete = list(itertools.chain(*index_delete))
+        doc_delete = list(transcripts_pdf[transcripts_pdf.index.isin(
+            index_delete)][['identifier', 'text_document_id']].values)
+        return doc_delete