Typo fix (NVIDIA#5288) (NVIDIA#5291)

Signed-off-by: Matvei Novikov <[email protected]> Signed-off-by: Matvei Novikov <[email protected]> Signed-off-by: Matvei Novikov <[email protected]> Co-authored-by: Matvei Novikov <[email protected]> Signed-off-by: Hainan Xu <[email protected]>
hainan-xv · Nov 29, 2022 · bbf1c02 · bbf1c02
1 parent 5d05dd2
commit bbf1c02
Showing 1 changed file with 9 additions and 10 deletions.
diff --git 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",