chore: import upstream snapshot with attribution

docs/source/asr/speech_classification/results.rst

@@ -0,0 +1,138 @@
+Checkpoints
+===========
+
+There are two main ways to load pretrained checkpoints in NeMo:
+
+* Using the :code:`restore_from()` method to load a local checkpoint file (`.nemo`), or
+* Using the :code:`from_pretrained()` method to download and set up a checkpoint from NGC.
+
+See the following sections for instructions and examples for each.
+
+Note that these instructions are for loading fully trained checkpoints for evaluation or fine-tuning.
+For resuming an unfinished training experiment, please use the experiment manager to do so by setting the
+``resume_if_exists`` flag to True.
+
+Loading Local Checkpoints
+-------------------------
+
+NeMo will automatically save checkpoints of a model you are training in a `.nemo` format.
+You can also manually save your models at any point using :code:`model.save_to(<checkpoint_path>.nemo)`.
+
+If you have a local ``.nemo`` checkpoint that you'd like to load, simply use the :code:`restore_from()` method:
+
+.. code-block:: python
+
+  import nemo.collections.asr as nemo_asr
+  model = nemo_asr.models.<MODEL_BASE_CLASS>.restore_from(restore_path="<path/to/checkpoint/file.nemo>")
+
+Where the model base class is the ASR model class of the original checkpoint, or the general `ASRModel` class.
+
+
+Transcribing/Inference
+-----------------------
+   
+The audio files should be 16KHz monochannel wav files.
+
+`Transcribe speech command segment:`
+  
+You may perform inference and transcribe a sample of speech after loading the model by using its 'transcribe()' method:
+
+.. code-block:: python 
+
+  mbn_model = nemo_asr.models.EncDecClassificationModel.from_pretrained(model_name="<MODEL_NAME>")
+  mbn_model.transcribe([list of audio files],  batch_size=BATCH_SIZE, logprobs=False) 
+
+Setting argument ``logprobs`` to True would return the log probabilities instead of transcriptions. You may find more details in :ref:`Modules <asr-api-modules>`.
+
+Learn how to fine tune on your own data or on subset classes in ``<NeMo_git_root>/tutorials/asr/Speech_Commands.ipynb``
+
+
+`Run VAD inference:`
+
+.. code-block:: bash 
+
+  python <NeMo-git-root>/examples/asr/speech_classification/vad_infer.py --config-path="../conf/vad" --config-name="vad_inference_postprocessing.yaml" dataset=<Path of json file of evaluation data. Audio files should have unique names>
+
+
+This script will perform vad frame-level prediction and will help you perform postprocessing and generate speech segments as well if needed.
+
+Have a look at configuration file ``<NeMo-git-root>/examples/asr/conf/vad/vad_inference_postprocessing.yaml`` and scripts under ``<NeMo-git-root>/scripts/voice_activity_detection`` for details regarding posterior processing, postprocessing and threshold tuning.
+
+Posterior processing includes generating predictions with overlapping input segments. Then a smoothing filter is applied to decide the label for a frame spanned by multiple segments.
+
+For VAD postprocessing we introduce 
+
+Binarization: 
+  - ``onset`` and ``offset`` threshold for detecting the beginning and end of a speech. 
+  - padding durations ``pad_onset`` before and padding duarations ``pad_offset`` after each speech segment;
+
+Filtering:
+  - ``min_duration_on`` threshold for short speech segment deletion,
+  - ``min_duration_on`` threshold for small silence deletion,
+  - ``filter_speech_first`` to control whether to perform short speech segment deletion first.
+
+
+`Identify language of utterance`
+
+You may load the model and identify the language of an audio file by using `get_label()` method:
+
+.. code-block:: python
+
+  langid_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name="<MODEL_NAME>")
+  lang = langid_model.get_label('<audio_path>')
+  
+or you can run `batch_inference()` to perform inference on a manifest with seleted batch_size to get trained model labels and gt_labels with logits
+
+.. code-block:: python
+
+  langid_model = nemo_asr.models.EncDecSpeakerLabelModel.from_pretrained(model_name="<MODEL_NAME>")
+  lang_embs, logits, gt_labels, trained_labels = langid_model.batch_inference(manifest_filepath, batch_size=32)
+
+  
+NGC Pretrained Checkpoints
+--------------------------
+
+The Speech Classification collection has checkpoints of several models trained on various datasets for a variety of tasks.
+These checkpoints are obtainable via NGC `NeMo Automatic Speech Recognition collection <https://ngc.nvidia.com/catalog/models/nvidia:nemospeechmodels>`_.
+The model cards on NGC contain more information about each of the checkpoints available.
+
+The tables below list the Speech Classification models available from NGC, and the models can be accessed via the
+:code:`from_pretrained()` method inside the ASR Model class.
+
+In general, you can load any of these models with code in the following format.
+
+.. code-block:: python
+
+  import nemo.collections.asr as nemo_asr
+  model = nemo_asr.models.EncDecClassificationModel.from_pretrained(model_name="<MODEL_NAME>")
+
+Where the model name is the value under "Model Name" entry in the tables below.
+
+For example, to load the MatchboxNet3x2x64_v1 model for speech command detection, run:
+
+.. code-block:: python
+
+  model = nemo_asr.models.EncDecClassificationModel.from_pretrained(model_name="commandrecognition_en_matchboxnet3x2x64_v1")
+
+You can also call :code:`from_pretrained()` from the specific model class (such as :code:`EncDecClassificationModel`
+for MatchboxNet and MarbleNet) if you will need to access specific model functionality.
+
+If you would like to programatically list the models available for a particular base class, you can use the
+:code:`list_available_models()` method.
+
+.. code-block:: python
+
+  nemo_asr.models.<MODEL_BASE_CLASS>.list_available_models()
+
+
+Speech Classification Models
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. tabularcolumns:: 30 30 40
+
+.. csv-table::
+   :file: data/classification_results.csv
+   :header-rows: 1
+   :class: longtable
+   :widths: 1 1 1
+