diff --git a/tutorials/nlp/Punctuation_and_Capitalization_Lexical_Audio.ipynb b/tutorials/nlp/Punctuation_and_Capitalization_Lexical_Audio.ipynb
index dc78ddcc7408..fb544c24e0a2 100644
--- a/tutorials/nlp/Punctuation_and_Capitalization_Lexical_Audio.ipynb
+++ b/tutorials/nlp/Punctuation_and_Capitalization_Lexical_Audio.ipynb
@@ -117,7 +117,7 @@
    },
    "source": [
     "## Architecture\n",
-    "Punctuation and capitaalization lexical audio model is based on [Multimodal Semi-supervised Learning Framework for Punctuation Prediction in Conversational Speech](https://arxiv.org/pdf/2008.00702.pdf). Model consists of lexical encoder (BERT-like model), acoustic encoder (i.e. Conformer's audio encoder), fusion of lexical and audio features (attention based fusion) and prediction layers.\n",
+    "Punctuation and capitalization lexical audio model is based on [Multimodal Semi-supervised Learning Framework for Punctuation Prediction in Conversational Speech](https://arxiv.org/pdf/2008.00702.pdf). Model consists of lexical encoder (BERT-like model), acoustic encoder (i.e. Conformer's audio encoder), fusion of lexical and audio features (attention based fusion) and prediction layers.\n",
     "\n",
     "Fusion is needed because encoded text and audio might have different length therefore can't be aligned one-to-one. As model predicts punctuation and capitalization per text token we use cross-attention between encoded lexical and encoded audio input."
    ]
@@ -279,14 +279,7 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "outputs": [],
+   "cell_type": "markdown",
    "source": [
     "## download get_libritts_data.py script to download and preprocess the LibriTTS data\n",
     "os.makedirs(WORK_DIR, exist_ok=True)\n",
@@ -295,7 +288,13 @@
     "    wget.download(f'https://raw.githubusercontent.com/NVIDIA/NeMo/{BRANCH}/examples/nlp/token_classification/data/get_libritts_data.py', WORK_DIR)\n",
     "else:\n",
     "    print ('get_libritts_data.py already exists')"
-   ]
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   }
   },
   {
    "cell_type": "code",