Embed flag definitions

doc/Flags.rst

@@ -0,0 +1,16 @@
+.. _training-flags:
+
+Command-line flags for the training scripts
+===========================================
+
+Below you can find the definition of all command-line flags supported by the training scripts. This includes ``DeepSpeech.py``, ``evaluate.py``, ``evaluate_tflite.py``, ``transcribe.py`` and ``lm_optimizer.py``.
+
+Flags
+-----
+
+.. literalinclude:: ../training/deepspeech_training/util/flags.py
+   :language: python
+   :linenos:
+   :lineno-match:
+   :start-after: sphinx-doc: training_ref_flags_start
+   :end-before: sphinx-doc: training_ref_flags_end

doc/TRAINING.rst

@@ -123,7 +123,7 @@ The central (Python) script is ``DeepSpeech.py`` in the project's root directory
 
    ./DeepSpeech.py --helpfull
 
-To get the output of this in a slightly better-formatted way, you can also look up the option definitions in :github:`util/flags.py <util/flags.py>`.
+To get the output of this in a slightly better-formatted way, you can also look at the flag definitions in :ref:`training-flags`.
 
 For executing pre-configured training scenarios, there is a collection of convenience scripts in the ``bin`` folder. Most of them are named after the corpora they are configured for. Keep in mind that most speech corpora are *very large*, on the order of tens of gigabytes, and some aren't free. Downloading and preprocessing them can take a very long time, and training on them without a fast GPU (GTX 10 series or newer recommended) takes even longer.
 

training/deepspeech_training/util/flags.py

@@ -5,6 +5,7 @@ import absl.flags
 
 FLAGS = absl.flags.FLAGS
 
+# sphinx-doc: training_ref_flags_start
 def create_flags():
     # Importer
     # ========
@@ -198,3 +199,5 @@ def create_flags():
     f.register_validator('one_shot_infer',
                          lambda value: not value or os.path.isfile(value),
                          message='The file pointed to by --one_shot_infer must exist and be readable.')
+
+# sphinx-doc: training_ref_flags_end