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docs/source/asr/speech_classification/configs.rst

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+NeMo Speech Classification Configuration Files
+================================================
+
+This page covers NeMo configuration file setup that is specific to models in the Speech Classification collection.
+For general information about how to set up and run experiments that is common to all NeMo models (e.g.
+experiment manager and PyTorch Lightning trainer parameters), see the :doc:`../../core/core`  page.
+
+The model section of NeMo Speech Classification configuration files will generally require information about the dataset(s) being
+used, the preprocessor for audio files, parameters for any augmentation being performed, as well as the
+model architecture specification.
+The sections on this page cover each of these in more detail.
+
+Example configuration files for all of the NeMo ASR scripts can be found in the
+``<NeMo_git_root>/examples/asr/conf>``.
+
+
+Dataset Configuration
+---------------------
+
+Training, validation, and test parameters are specified using the ``train_ds``, ``validation_ds``, and
+``test_ds`` sections of your configuration file, respectively.
+Depending on the task, you may have arguments specifying the sample rate of your audio files, labels, whether or not to shuffle the dataset, and so on.
+You may also decide to leave fields such as the ``manifest_filepath`` blank, to be specified via the command line
+at runtime.
+
+Any initialization parameters that are accepted for the Dataset class used in your experiment
+can be set in the config file.
+See the :ref:`Datasets <asr-api-datasets>` section of the API for a list of Datasets and their respective parameters.
+
+An example Speech Classification train and validation configuration could look like: 
+
+.. code-block:: yaml
+
+  model:
+    sample_rate: 16000
+    repeat: 2 # number of convolutional sub-blocks within a block, R in <MODEL>_[BxRxC]
+    dropout: 0.0
+    kernel_size_factor: 1.0
+    labels: ['bed', 'bird', 'cat', 'dog', 'down', 'eight', 'five', 'four', 'go', 'happy', 'house', 'left', 'marvin',
+    'nine', 'no', 'off', 'on', 'one', 'right', 'seven', 'sheila', 'six', 'stop', 'three', 'tree', 'two', 'up',
+    'wow', 'yes', 'zero']
+
+    train_ds:
+      manifest_filepath: ???
+      sample_rate: ${model.sample_rate}
+      labels: ${model.labels} # Uses the labels above
+      batch_size: 128
+      shuffle: True
+
+    validation_ds:
+      manifest_filepath: ???
+      sample_rate: ${model.sample_rate}
+      labels: ${model.labels} # Uses the labels above
+      batch_size: 128
+      shuffle: False # No need to shuffle the validation data
+
+If you would like to use tarred dataset, have a look at :ref:`Datasets Configuration <asr-configs-dataset-configuration>`.
+
+
+Preprocessor Configuration
+--------------------------
+Preprocessor helps to compute MFCC or mel spectrogram features that are given as inputs to model. 
+For details on how to write this section, refer to :ref:`Preprocessor Configuration <asr-configs-preprocessor-configuration>`
+
+Check config yaml files in ``<NeMo_git_root>/examples/asr/conf`` to find the processors been used by speech classification models.
+
+
+Augmentation Configurations
+---------------------------
+
+There are a few on-the-fly spectrogram augmentation options for NeMo ASR, which can be specified by the
+configuration file using the ``augmentor`` and ``spec_augment`` section.
+For details on how to write this section, refer to the ASR :ref:`Augmentation Configuration <asr-configs-augmentation-configurations>` section.
+
+Check config yaml files in ``<NeMo_git_root>/tutorials/asr/conf`` to find the processors been used by speech classification models. 
+
+
+Model Architecture Configurations
+---------------------------------
+
+Each configuration file should describe the model architecture being used for the experiment.
+Models in the NeMo ASR collection need a ``encoder`` section and a ``decoder`` section, with the ``_target_`` field
+specifying the module to use for each.
+
+The following sections go into more detail about the specific configurations of each model architecture.
+
+The :ref:`MatchboxNet <MarbleNet_model>` and :ref:`MarbleNet <MarbleNet_model>` models are very similar, and as 
+such the components in their configs are very similar as well.
+
+Decoder Configurations
+------------------------
+
+After features have been computed from ConvASREncoder, we pass the features to decoder to compute embeddings and then to compute log_probs 
+for training models.
+
+.. code-block:: yaml
+
+  model:
+    ...
+    decoder:
+      _target_: nemo.collections.asr.modules.ConvASRDecoderClassification
+      feat_in: *enc_final_filters
+      return_logits: true # return logits if true, else return softmax output
+      pooling_type: 'avg' # AdaptiveAvgPool1d 'avg' or AdaptiveMaxPool1d 'max'
+
+
+Fine-tuning Execution Flow Diagram
+----------------------------------
+
+When preparing your own training or fine-tuning scripts, please follow the execution flow diagram order for correct inference.
+
+Depending on the type of model, there may be extra steps that must be performed -
+
+* Speech Classification models - `Examples directory for Classification Models <https://github.com/NVIDIA/NeMo/blob/stable/examples/asr/speech_classification/README.md>`_
+