fix title underline and code-block spacing (#8758)

Signed-off-by: Elena Rastorgueva <[email protected]>

docs/source/asr/asr_language_modeling_and_customization.rst

@@ -1,6 +1,6 @@
-#####################
+#######################################
 ASR Language Modeling and Customization
-#####################
+#######################################
 
 Language models have shown to help the accuracy of ASR models. NeMo supports the following two approaches to incorporate language models into the ASR models:
 
@@ -548,9 +548,9 @@ The following is the list of the arguments for the opengrm script:
 +----------------------+--------+------------------+-----------------------------------------------------------------------------------------------------------------+
 
 
-******************
+***************************************************
 Context-biasing (word boosting) without external LM
-******************
+***************************************************
 
 NeMo toolkit supports a fast context-biasing method for CTC and Transducer (RNN-T) ASR models with CTC-based Word Spotter.
 The method involves decoding CTC log probabilities with a context graph built for words and phrases from the context-biasing list.

docs/source/asr/results.rst

@@ -138,11 +138,12 @@ For more information, see `nemo.collections.asr.modules <./api.html#modules>`__.
 -----
 
 Inference with Multi-task Models
-^^^^^^^^^^^^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Multi-task models that use structured prompts require additionl task tokens as input, in which case it is recommended to use manifest as input. Below is an example of using the `nvidia/canary-1b` model:
 
 .. code-block:: python
+
     from nemo.collections.asr.models import EncDecMultiTaskModel
    
     # load model
@@ -162,6 +163,7 @@ Multi-task models that use structured prompts require additionl task tokens as i
 Here the manifest file should be a json file where each line has the following format:
 
 .. code-block:: bash
+
     {
        "audio_filepath": "/path/to/audio.wav",  # path to the audio file
        "duration": None,  # duration of the audio in seconds, set to `None` to use full audio
@@ -175,6 +177,7 @@ Here the manifest file should be a json file where each line has the following f
 Note that using manifest allows to specify the task configuration for each audio individually. If we want to use the same task configuration for all the audio files, it can be specified in `transcribe` method directly. 
 
 .. code-block:: python
+
     canary_model.transcribe(
             audio=[list of audio files],
             batch_size=4,  # batch size to run the inference with