typos and branch name update to r2.0.0rc1

tutorials/asr/Transducers_with_HF_Datasets.ipynb

@@ -31,7 +31,7 @@
     "!pip install matplotlib>=3.3.2\n",
     "\n",
     "## Install NeMo\n",
-    "BRANCH = 'r1.23.0'\n",
+    "BRANCH = 'main'\n",
     "!python -m pip install git+https://github.com/NVIDIA/NeMo.git@$BRANCH#egg=nemo_toolkit[all]\n"
    ]
   },

tutorials/asr/asr_adapters/Multi_Task_Adapters.ipynb

@@ -46,7 +46,7 @@
         "# Multi Task Adaptation with Adapters\n",
         "\n",
         "\n",
-        "In earliier tutorials, we utilized a specific model for one task - for example, an ASR model (CTC, RNN-T etc) for the singular task of Speech Recognition. This is very useful if we want to specialize one task per model, but it can be expensive to deploy a fleet of models for each task, and learn routers to pass user tasks to correct models.\n",
+        "In earlier tutorials, we utilized a specific model for one task - for example, an ASR model (CTC, RNN-T etc) for the singular task of Speech Recognition. This is very useful if we want to specialize one task per model, but it can be expensive to deploy a fleet of models for each task, and learn routers to pass user tasks to correct models.\n",
         "\n",
         "We now support Multi Task models in NeMo, such that a single model can perform multiple tasks such as speech recognition, speech translation, voice activity detection, and more in the future. With one model supporting multiple tasks, we can simplify the task of deploying models and also hope to leverage individual tasks to improve each other (for example: you do need strong speech recognition first before you start doing translation).\n",
         "\n",
@@ -105,7 +105,7 @@
         "id": "6c0c87c9-5290-4634-9338-818f181c936a"
       },
       "source": [
-        "# Enable Adapter Suppport in Model\n",
+        "# Enable Adapter Support in Model\n",
         "\n",
         "New in NeMo 2.0, we now have a simple utility function to convert the model into one that supports adapters, called `replace_adapter_compatible_modules()`.\n",
         "\n",
@@ -837,7 +837,7 @@
         "\n",
         "Data Modules are one way of organizing datasets in PyTorch Lightning. It provides a unified place where data loading and processing can be potentially handled.\n",
         "\n",
-        "**Note**: This isnt strictly necessary - you can achieve the same using just Pytorch dataloaders directly and passing it to Trainer.fit() but we showcase a data module codebase that can be extended by the user."
+        "**Note**: This isn't strictly necessary - you can achieve the same using just Pytorch dataloaders directly and passing it to Trainer.fit() but we showcase a data module codebase that can be extended by the user."
       ]
     },
     {
@@ -1493,10 +1493,10 @@
         "id": "2525bec5-c42b-48c1-b03c-e8126c346238"
       },
       "source": [
-        "# Evaluate after Adaptatation\n",
+        "# Evaluate after Adaptation\n",
         "\n",
-        "Now that the model is done training, lets evalaute its scores on the test set again.\n",
-        "We should see a markedly higher translastion BLEU and lower WER from above."
+        "Now that the model is done training, lets evaluate its scores on the test set again.\n",
+        "We should see a markedly higher translation BLEU and lower WER from above."
       ]
     },
     {
@@ -1657,4 +1657,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 5
-}
+}