diff --git a/tutorials/asr/Offline_ASR_with_VAD_for_CTC_models.ipynb b/tutorials/asr/Offline_ASR_with_VAD_for_CTC_models.ipynb
index af6c8ffc477e..61675ec37e87 100644
--- a/tutorials/asr/Offline_ASR_with_VAD_for_CTC_models.ipynb
+++ b/tutorials/asr/Offline_ASR_with_VAD_for_CTC_models.ipynb
@@ -43,7 +43,7 @@
                 "import torch\n",
                 "import os\n",
                 "from nemo.collections.asr.metrics.wer import word_error_rate\n",
-                "from nemo.collections.asr.parts.utils.vad_utils import stitch_segmented_asr_output, contruct_manfiest_eval"
+                "from nemo.collections.asr.parts.utils.vad_utils import stitch_segmented_asr_output, construct_manfiest_eval"
             ]
         },
         {
@@ -320,7 +320,7 @@
             "cell_type": "markdown",
             "metadata": {},
             "source": [
-                "If we have ground-truth <code>'text'</code> in input_manifest, we can evaluate our performance of stitched output. Let's align the <code>'text'</code> in input manifest and <code>'pred_text'</code> in stitched segmented asr output first, since some samples from input_manfiest might be pure noise and have been removed in VAD output and excluded for ASR inference. "
+                "If we have ground-truth <code>'text'</code> in input_manifest, we can evaluate our performance of stitched output. Let's align the <code>'text'</code> in input manifest and <code>'pred_text'</code> in stitched segmented asr output first, since some samples from input_manifest might be pure noise and have been removed in VAD output and excluded for ASR inference. "
             ]
         },
         {
@@ -329,7 +329,7 @@
             "metadata": {},
             "outputs": [],
             "source": [
-                "aligned_vad_asr_output_manifest = contruct_manfiest_eval(input_manifest, stitched_output_manifest)"
+                "aligned_vad_asr_output_manifest = construct_manifest_eval(input_manifest, stitched_output_manifest)"
             ]
         },
         {
@@ -386,4 +386,4 @@
     },
     "nbformat": 4,
     "nbformat_minor": 4
-}
\ No newline at end of file
+}
diff --git a/tutorials/asr/Speech_Commands.ipynb b/tutorials/asr/Speech_Commands.ipynb
index fc40552aca1c..13c37c33455a 100644
--- a/tutorials/asr/Speech_Commands.ipynb
+++ b/tutorials/asr/Speech_Commands.ipynb
@@ -643,17 +643,13 @@
                 "\n",
                 "We can dramatically improve the time taken to train this model by using Multi GPU training along with Mixed Precision.\n",
                 "\n",
-                "For multi-GPU training, take a look at [the PyTorch Lightning Multi-GPU training section](https://pytorch-lightning.readthedocs.io/en/latest/advanced/multi_gpu.html)\n",
-                "\n",
-                "For mixed-precision training, take a look at [the PyTorch Lightning Mixed-Precision training section](https://pytorch-lightning.readthedocs.io/en/latest/guides/speed.html#mixed-precision-16-bit-training)\n",
-                "\n",
                 "```python\n",
-                "# Mixed precision:\n",
-                "trainer = Trainer(amp_level='O1', precision=16)\n",
-                "\n",
                 "# Trainer with a distributed backend:\n",
                 "trainer = Trainer(devices=2, num_nodes=2, accelerator='gpu', strategy='dp')\n",
                 "\n",
+                "# Mixed precision:\n",
+                "trainer = Trainer(amp_level='O1', precision=16)\n",
+                "\n",
                 "# Of course, you can combine these flags as well.\n",
                 "```"
             ]
diff --git a/tutorials/asr/Voice_Activity_Detection.ipynb b/tutorials/asr/Voice_Activity_Detection.ipynb
index 19a687e0b217..3c7b848c6d5e 100644
--- a/tutorials/asr/Voice_Activity_Detection.ipynb
+++ b/tutorials/asr/Voice_Activity_Detection.ipynb
@@ -657,12 +657,12 @@
                 "We can dramatically improve the time taken to train this model by using Multi GPU training along with Mixed Precision.\n",
                 "\n",
                 "```python\n",
-                "# Mixed precision:\n",
-                "trainer = Trainer(amp_level='O1', precision=16)\n",
-                "\n",
                 "# Trainer with a distributed backend:\n",
                 "trainer = Trainer(devices=2, num_nodes=2, accelerator='gpu', strategy='dp')\n",
                 "\n",
+                "# Mixed precision:\n",
+                "trainer = Trainer(amp_level='O1', precision=16)\n",
+                "\n",
                 "# Of course, you can combine these flags as well.\n",
                 "```"
             ]
diff --git a/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb b/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb
index 5e5b5c9fd4ba..f2d0a45327a2 100644
--- a/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb
+++ b/tutorials/speaker_tasks/Speaker_Identification_Verification.ipynb
@@ -628,18 +628,14 @@
                 "## For Faster Training\n",
                 "We can dramatically improve the time taken to train this model by using Multi GPU training along with Mixed Precision.\n",
                 "\n",
-                "For multi-GPU training, take a look at the [PyTorch Lightning Multi-GPU training section](https://pytorch-lightning.readthedocs.io/en/stable/advanced/multi_gpu.html)\n",
-                "\n",
-                "For mixed-precision training, take a look at the [PyTorch Lightning Mixed-Precision training section](https://pytorch-lightning.readthedocs.io/en/latest/guides/speed.html#mixed-precision-16-bit-training)\n",
+                "### Trainer with a distributed backend:\n",
+                "<pre><code>trainer = Trainer(devices=2, num_nodes=2, accelerator='gpu', strategy='dp')\n",
+                "</code></pre>\n",
                 "\n",
                 "### Mixed precision:\n",
                 "<pre><code>trainer = Trainer(amp_level='O1', precision=16)\n",
                 "</code></pre>\n",
                 "\n",
-                "### Trainer with a distributed backend:\n",
-                "<pre><code>trainer = Trainer(devices=2, num_nodes=2, accelerator='gpu', strategy='dp')\n",
-                "</code></pre>\n",
-                "\n",
                 "Of course, you can combine these flags as well."
             ]
         },