Merge branch 'main' into fix_hydra_omegaconf

docs/source/asr/asr_language_modeling.rst

@@ -169,7 +169,7 @@ Width of the beam search (`--beam_width`) specifies the number of top candidates
 would search for. Larger beams result in more accurate but slower predictions.
 
 There is also a tutorial to learn more about evaluating the ASR models with N-gram LM here:
-`Offline ASR Inference with Beam Search and External Language Model Rescoring <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0/tutorials/asr/Offline_ASR.ipynb>`_
+`Offline ASR Inference with Beam Search and External Language Model Rescoring <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.2/tutorials/asr/Offline_ASR.ipynb>`_
 
 Hyperparameter Grid Search
 --------------------------

docs/source/asr/configs.rst

@@ -10,7 +10,7 @@ for audio files, parameters for any augmentation being performed, as well as the
 this page cover each of these in more detail.
 
 Example configuration files for all of the NeMo ASR scripts can be found in the
-`config directory of the examples <https://github.com/NVIDIA/NeMo/tree/v1.0.0/examples/asr/conf>`_.
+`config directory of the examples <https://github.com/NVIDIA/NeMo/tree/v1.0.2/examples/asr/conf>`_.
 
 
 Dataset Configuration

docs/source/asr/datasets.rst

@@ -235,7 +235,7 @@ Conversion to Tarred Datasets
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 You can easily convert your existing NeMo-compatible ASR datasets using the
-`conversion script here <https://github.com/NVIDIA/NeMo/blob/v1.0.0/scripts/speech_recognition/convert_to_tarred_audio_dataset.py>`_.
+`conversion script here <https://github.com/NVIDIA/NeMo/blob/v1.0.2/scripts/speech_recognition/convert_to_tarred_audio_dataset.py>`_.
 
 .. code::
 

docs/source/asr/intro.rst

@@ -43,8 +43,8 @@ The full documentation tree is as follows:
 Resources and Documentation
 ---------------------------
 
-Hands-on speech recognition tutorial notebooks can be found under `the ASR tutorials folder <https://github.com/NVIDIA/NeMo/tree/v1.0.0/tutorials/asr/>`_.
-If you are a beginner to NeMo, consider trying out the `ASR with NeMo <https://github.com/NVIDIA/NeMo/tree/v1.0.0/tutorials/asr/01_ASR_with_NeMo.ipynb>`_ tutorial.
+Hands-on speech recognition tutorial notebooks can be found under `the ASR tutorials folder <https://github.com/NVIDIA/NeMo/tree/v1.0.2/tutorials/asr/>`_.
+If you are a beginner to NeMo, consider trying out the `ASR with NeMo <https://github.com/NVIDIA/NeMo/tree/v1.0.2/tutorials/asr/01_ASR_with_NeMo.ipynb>`_ tutorial.
 This and most other tutorials can be run on Google Colab by specifying the link to the notebooks' GitHub pages on Colab.
 
 If you are looking for information about a particular ASR model, or would like to find out more about the model

docs/source/asr/speaker_recognition/intro.rst

@@ -28,7 +28,7 @@ Resource and Documentation Guide
 --------------------------------
 
 Hands-on speaker recognition tutorial notebooks can be found under
-`the speaker recognition tutorials folder <https://github.com/NVIDIA/NeMo/tree/v1.0.0/tutorials/speaker_recognition/>`_. This and most other tutorials can be run on Google Colab by specifying the link to the notebooks' GitHub pages on Colab.
+`the speaker recognition tutorials folder <https://github.com/NVIDIA/NeMo/tree/v1.0.2/tutorials/speaker_recognition/>`_. This and most other tutorials can be run on Google Colab by specifying the link to the notebooks' GitHub pages on Colab.
 
 If you are looking for information about a particular SpeakerNet model, or would like to find out more about the model
 architectures available in the ``nemo_asr`` collection, check out the :doc:`Models <./models>` page.
@@ -44,5 +44,5 @@ Documentation for configuration files specific to the ``nemo_asr`` models can be
 :doc:`Configuration Files <./configs>` page.
 
 
-For a clear step-by-step tutorial we advice you to refer tutorials found in `folder <https://github.com/NVIDIA/NeMo/tree/v1.0.0/tutorials/speaker_recognition/>`_.
+For a clear step-by-step tutorial we advice you to refer tutorials found in `folder <https://github.com/NVIDIA/NeMo/tree/v1.0.2/tutorials/speaker_recognition/>`_.
 

docs/source/core/core.rst

@@ -16,7 +16,7 @@ NeMo models contain everything needed to train and reproduce Conversational AI m
 
 NeMo uses `Hydra <https://hydra.cc/>`_ for configuring both NeMo models and the PyTorch Lightning Trainer.
 
-.. note:: Every NeMo model has an example configuration file and training script that can be found `here <https://github.com/NVIDIA/NeMo/tree/v1.0.0/examples>`_.
+.. note:: Every NeMo model has an example configuration file and training script that can be found `here <https://github.com/NVIDIA/NeMo/tree/v1.0.2/examples>`_.
 
 The end result of using NeMo, `Pytorch Lightning <https://github.com/PyTorchLightning/pytorch-lightning>`_, and Hydra is that NeMo models all have the same look and feel and are also fully compatible with the PyTorch ecosystem. 
 
@@ -182,7 +182,7 @@ PyTorch Lightning Trainer
 ~~~~~~~~~~~~~~~~~~~~~~~~~
 
 Since every NeMo model is a ``LightningModule``, we can automatically take advantage of the PyTorch Lightning ``Trainer``. Every NeMo 
-`example <https://github.com/NVIDIA/NeMo/tree/v1.0.0/examples>`_ training script uses the ``Trainer`` object to fit the model.
+`example <https://github.com/NVIDIA/NeMo/tree/v1.0.2/examples>`_ training script uses the ``Trainer`` object to fit the model.
 
 First, instantiate the model and trainer, then call ``.fit``:
 
@@ -230,7 +230,7 @@ With Hydra, we can configure everything needed for NeMo with three interfaces:
 YAML
 ~~~~
 
-NeMo provides YAML configuration files for all of our `example <https://github.com/NVIDIA/NeMo/tree/v1.0.0/examples>`_ training scripts.
+NeMo provides YAML configuration files for all of our `example <https://github.com/NVIDIA/NeMo/tree/v1.0.2/examples>`_ training scripts.
 YAML files make it easy to experiment with different model and training configurations.
 
 Every NeMo example YAML has the same underlying configuration structure:
@@ -297,7 +297,7 @@ CLI
 With NeMo and Hydra, every aspect of model training can be modified from the command-line. This is extremely helpful for running lots 
 of experiments on compute clusters or for quickly testing parameters while developing.
 
-All NeMo `examples <https://github.com/NVIDIA/NeMo/tree/v1.0.0/examples>`_ come with instructions on how to
+All NeMo `examples <https://github.com/NVIDIA/NeMo/tree/v1.0.2/examples>`_ come with instructions on how to
 run the training/inference script from the command-line (see `here <https://github.com/NVIDIA/NeMo/blob/4e9da75f021fe23c9f49404cd2e7da4597cb5879/examples/asr/speech_to_text.py#L24>`_
 for an example).
 
@@ -401,7 +401,7 @@ configuration for a Novograd optimizer with Cosine Annealing learning rate sched
         warmup_ratio: null
         min_lr: 1e-9:
 
-.. note:: `NeMo Examples <https://github.com/NVIDIA/NeMo/tree/v1.0.0/examples>`_ has optimizer and scheduler configurations for
+.. note:: `NeMo Examples <https://github.com/NVIDIA/NeMo/tree/v1.0.2/examples>`_ has optimizer and scheduler configurations for
 every NeMo model. 
 
 Optimizers can be configured from the CLI as well:

docs/source/nlp/machine_translation.rst

@@ -5,8 +5,8 @@ Machine Translation Models
 Machine translation is the task of translating text from one language to another. For example, from English to Spanish. Models are 
 based on the Transformer sequence-to-sequence architecture :cite:`nlp-machine_translation-vaswani2017attention`.
 
-An example script on how to train the model can be found here: `NeMo/examples/nlp/machine_translation/enc_dec_nmt.py <https://github.com/NVIDIA/NeMo/blob/v1.0.0/examples/nlp/machine_translation/enc_dec_nmt.py>`__.
-The default configuration file for the model can be found at: `NeMo/examples/nlp/machine_translation/conf/aayn_base.yaml <https://github.com/NVIDIA/NeMo/blob/v1.0.0/examples/nlp/machine_translation/conf/aayn_base.yaml>`__.
+An example script on how to train the model can be found here: `NeMo/examples/nlp/machine_translation/enc_dec_nmt.py <https://github.com/NVIDIA/NeMo/blob/v1.0.2/examples/nlp/machine_translation/enc_dec_nmt.py>`__.
+The default configuration file for the model can be found at: `NeMo/examples/nlp/machine_translation/conf/aayn_base.yaml <https://github.com/NVIDIA/NeMo/blob/v1.0.2/examples/nlp/machine_translation/conf/aayn_base.yaml>`__.
 
 Quick Start Guide
 -----------------
@@ -213,7 +213,7 @@ as follows:
 Applying BPE Tokenization, Batching, Bucketing and Padding
 ----------------------------------------------------------
 
-Given BPE tokenizers, and a cleaned parallel corpus, the following steps are applied to create a `TranslationDataset <https://github.com/NVIDIA/NeMo/blob/v1.0.0/nemo/collections/nlp/data/machine_translation/machine_translation_dataset.py#L64>`__ object.
+Given BPE tokenizers, and a cleaned parallel corpus, the following steps are applied to create a `TranslationDataset <https://github.com/NVIDIA/NeMo/blob/v1.0.2/nemo/collections/nlp/data/machine_translation/machine_translation_dataset.py#L64>`__ object.
 
 #. Text to IDs - This performs subword tokenization with the BPE model on an input string and maps it to a sequence of tokens for the 
    source and target text.
@@ -285,7 +285,7 @@ Tarred datasets can be configured as follows:
 
 Tarred datasets can be created in two ways:
 
-#. Using the Hydra config and `training script <https://github.com/NVIDIA/NeMo/blob/v1.0.0/examples/nlp/machine_translation/enc_dec_nmt.py>`__.
+#. Using the Hydra config and `training script <https://github.com/NVIDIA/NeMo/blob/v1.0.2/examples/nlp/machine_translation/enc_dec_nmt.py>`__.
 
    For example:
 

docs/source/nlp/text_classification.rst

@@ -12,7 +12,7 @@ An example script on how to train the model can be found here: `NeMo/examples/nl
 The default configuration file for the model can be found at: `NeMo/examples/nlp/text_classification/conf/text_classification_config.yaml <https://github.com/NVIDIA/NeMo/blob/main/examples/nlp/text_classification/conf/text_classification_config.yaml>`__.
 
 There is also a Jupyter notebook which explains how to work with this model. We recommend you try this model in the Jupyter notebook (can run on `Google's Colab <https://colab.research.google.com/notebooks/intro.ipynb>`_.):
-`NeMo/tutorials/nlp/Text_Classification_Sentiment_Analysis.ipynb <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.0/tutorials/nlp/Text_Classification_Sentiment_Analysis.ipynb>`__.
+`NeMo/tutorials/nlp/Text_Classification_Sentiment_Analysis.ipynb <https://colab.research.google.com/github/NVIDIA/NeMo/blob/v1.0.2/tutorials/nlp/Text_Classification_Sentiment_Analysis.ipynb>`__.
 This tutorial shows an example of how run the Text Classification model on a sentiment analysis task. You may connect to an instance with a GPU (**Runtime** -> **Change runtime type** -> select **GPU** for the hardware accelerator) to run the notebook.
 
 Data Format