Fix typos

tutorials/asr/ASR_Example_CommonVoice_Finetuning.ipynb

@@ -273,7 +273,7 @@
     "```bash\n",
     "TOKENIZER=${YOUR_DATA_ROOT}/esperanto/tokenizers/tokenizer_spe_bpe_v128\n",
     "TRAIN_MANIFEST=${YOUR_DATA_ROOT}/train_tarred_1bk/tarred_audio_manifest.json\n",
-    "TARRED_AUDIO_FILEPATHS=${YOUR_DATA_ROOT}/train_tarred_1bk/audio__OP_0..1023_CL_.tar # \"_OP_0..1023_CL_\" is the range for the banch of files audio_0.tar, audio_1.tar, ..., audio_1023.tar\n",
+    "TARRED_AUDIO_FILEPATHS=${YOUR_DATA_ROOT}/train_tarred_1bk/audio__OP_0..1023_CL_.tar # \"_OP_0..1023_CL_\" is the range for the batch of files audio_0.tar, audio_1.tar, ..., audio_1023.tar\n",
     "DEV_MANIFEST=${YOUR_DATA_ROOT}/validation/validation_mozilla-foundation_common_voice_11_0_manifest.json.clean\n",
     "TEST_MANIFEST=${YOUR_DATA_ROOT}/test/test_mozilla-foundation_common_voice_11_0_manifest.json.clean\n",
     "\n",

tutorials/asr/README.md

@@ -32,7 +32,9 @@ In this repository, you will find several tutorials discussing what is Automatic
 
 12) `Multilang_ASR`: We will learn how to work with existing checkpoints of multilingual ASR models and how to train new ones. It is possible to create a multilingual version of any ASR model that uses tokenizers. This notebook shows how to create a multilingual version of the small monolingual Conformer Transducer model.
 
-13) `ASR_Example_CommonVoice_Finetuning`: Learn how to fine-tune an ASR model using CommonVoice to a new alphabet, Esperanto.
+13) `ASR_Example_CommonVoice_Finetuning`: Learn how to fine-tune an ASR model using CommonVoice to a new alphabet, Esperanto. We walk through the data processing steps of MCV data using HuggingFace Datasets, preparation of the tokenizer, model and then setup fine-tuning.
+
+
 ----------------
 
 # Automatic Speech Recogntion with Adapters

tutorials/cloud/aws/ASR_Finetuning_at_Scale_with_AWS_SageMaker.ipynb

@@ -15,7 +15,7 @@
    "source": [
     "In this tutorial we show how you can train a NeMo ASR Model using [Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html) using the CommonVoice Esperanto dataset (~250hrs), and scale onto multiple GPUs and nodes with near-linear scaling.\n",
     "\n",
-    "AWS SageMaker is useful for practioners/researchers who are familiar with training locally or on a remote instance (via SSH). SageMaker also supports multi-GPU & Multi-node."
+    "AWS SageMaker is useful for practitioners/researchers who are familiar with training locally or on a remote instance (via SSH). SageMaker also supports multi-GPU & Multi-node."
    ]
   },
   {

tutorials/cloud/aws/SageMaker_ASR_Training.ipynb

@@ -15,7 +15,7 @@
    "source": [
     "In this tutorial we show how you can train a NeMo ASR Model using [Amazon SageMaker](https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html). This is meant to be a minimalistic example of how to use SageMaker with NeMo.\n",
     "\n",
-    "AWS SageMaker is useful for practioners/researchers who are familiar with training locally or on a remote instance (via SSH). SageMaker also supports multi-GPU & Multi-node.\n",
+    "AWS SageMaker is useful for practitioners/researchers who are familiar with training locally or on a remote instance (via SSH). SageMaker also supports multi-GPU & Multi-node.\n",
     "\n",
     "Using AWS SageMaker we train a simple Conformer CTC model using the AN4 dataset on a remote instance with a GPU (p3.2xlarge). We use S3 to store the data and our checkpoints/logs.\n",
     "\n",