Introduce code from Rust monorepo. This is a mess

tts/NeMo/docs/source/asr/results.rst

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+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.
+
+Refer to 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, use the Experiment Manager to do so by setting the ``resume_if_exists`` flag to ``True``.
+
+Loading Local Checkpoints
+-------------------------
+
+NeMo automatically saves checkpoints of a model that is trained in a ``.nemo`` format. Alternatively, to manually save the model at any 
+point, issue :code:`model.save_to(<checkpoint_path>.nemo)`.
+
+If there is a local ``.nemo`` checkpoint that you'd like to load, 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.
+
+NGC Pretrained Checkpoints
+--------------------------
+
+The ASR 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 ASR models available from NGC. 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.ASRModel.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 base English QuartzNet model for speech recognition, run:
+
+.. code-block:: python
+
+  model = nemo_asr.models.ASRModel.from_pretrained(model_name="QuartzNet15x5Base-En")
+
+You can also call :code:`from_pretrained()` from the specific model class (such as :code:`EncDecCTCModel`
+for QuartzNet) if you need to access a 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()
+
+Transcribing/Inference
+^^^^^^^^^^^^^^^^^^^^^^
+
+To perform inference and transcribe a sample of speech after loading the model, use the ``transcribe()`` method:
+
+.. code-block:: python
+
+    model.transcribe(paths2audio_files=[list of audio files], batch_size=BATCH_SIZE, logprobs=False)
+
+Setting the argument ``logprobs`` to ``True`` returns the log probabilities instead of transcriptions. For more information, see :doc:`./api.html#modules`.
+The audio files should be 16KHz monochannel wav files.
+
+Fine-tuning on Different Datasets
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+There are multiple ASR tutorials provided in the :ref:`Tutorials <tutorials>` section. Most of these tutorials explain how to instantiate a pre-trained model, prepare the model for fine-tuning on some dataset (in the same language) as a demonstration.
+
+
+Automatic Speech Recognition Models
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+   :file: data/asr_results.csv
+   :align: left
+   :widths: 30, 30, 40
+   :header-rows: 1
+
+Language Models for ASR
+^^^^^^^^^^^^^^^^^^^^^^^
+
+.. csv-table::
+   :file: data/asrlm_results.csv
+   :align: left
+   :widths: 30, 30, 40
+   :header-rows: 1
+
+   
+Speech Recognition (Languages)
+------------------------------
+
+English
+^^^^^^^
+.. csv-table::
+   :file: data/benchmark_en.csv
+   :align: left
+   :widths: 40, 10, 50
+   :header-rows: 1
+
+-----------------------------
+
+Mandarin
+^^^^^^^^
+.. csv-table::
+   :file: data/benchmark_zh.csv
+   :align: left
+   :widths: 40, 10, 50
+   :header-rows: 1
+
+-----------------------------
+
+German
+^^^^^^
+.. csv-table::
+   :file: data/benchmark_de.csv
+   :align: left
+   :widths: 40, 10, 50
+   :header-rows: 1
+
+-----------------------------
+
+Polish
+^^^^^^
+.. csv-table::
+   :file: data/benchmark_pl.csv
+   :align: left
+   :widths: 40, 10, 50
+   :header-rows: 1
+
+-----------------------------
+
+Italian
+^^^^^^^
+.. csv-table::
+   :file: data/benchmark_it.csv
+   :align: left
+   :widths: 40, 10, 50
+   :header-rows: 1
+
+-----------------------------
+
+Russian
+^^^^^^^
+.. csv-table::
+   :file: data/benchmark_ru.csv
+   :align: left
+   :widths: 40, 10, 50
+   :header-rows: 1
+
+-----------------------------
+
+Spanish
+^^^^^^^
+.. csv-table::
+   :file: data/benchmark_es.csv
+   :align: left
+   :widths: 40, 10, 50
+   :header-rows: 1
+
+
+-----------------------------
+
+Catalan
+^^^^^^^
+.. csv-table::
+   :file: data/benchmark_ca.csv
+   :align: left
+   :widths: 40, 10, 50
+   :header-rows: 1
+
+