Add comments marking nodes with names/shapes known by native client

training/coqui_stt_training/train.py

@@ -912,17 +912,28 @@ def create_inference_graph(batch_size=1, n_steps=16, tflite=False):
     batch_size = batch_size if batch_size > 0 else None
 
     # Create feature computation graph
+
+    # native_client: this node's name and shape are part of the API boundary
+    #   with the native client, if you change them you should sync changes with
+    #   the C++ code.
     input_samples = tfv1.placeholder(
         tf.float32, [Config.audio_window_samples], "input_samples"
     )
     samples = tf.expand_dims(input_samples, -1)
     mfccs, _ = audio_to_features(samples, Config.audio_sample_rate)
+    # native_client: this node's name and shape are part of the API boundary
+    #   with the native client, if you change them you should sync changes with
+    #   the C++ code.
     mfccs = tf.identity(mfccs, name="mfccs")
 
     # Input tensor will be of shape [batch_size, n_steps, 2*n_context+1, n_input]
     # This shape is read by the native_client in STT_CreateModel to know the
     # value of n_steps, n_context and n_input. Make sure you update the code
     # there if this shape is changed.
+    #
+    # native_client: this node's name and shape are part of the API boundary
+    #   with the native client, if you change them you should sync changes with
+    #   the C++ code.
     input_tensor = tfv1.placeholder(
         tf.float32,
         [
@@ -933,15 +944,24 @@ def create_inference_graph(batch_size=1, n_steps=16, tflite=False):
         ],
         name="input_node",
     )
+    # native_client: this node's name and shape are part of the API boundary
+    #   with the native client, if you change them you should sync changes with
+    #   the C++ code.
     seq_length = tfv1.placeholder(tf.int32, [batch_size], name="input_lengths")
 
     if batch_size <= 0:
         # no state management since n_step is expected to be dynamic too (see below)
         previous_state = None
     else:
+        # native_client: this node's name and shape are part of the API boundary
+        #   with the native client, if you change them you should sync changes with
+        #   the C++ code.
         previous_state_c = tfv1.placeholder(
             tf.float32, [batch_size, Config.n_cell_dim], name="previous_state_c"
         )
+        # native_client: this node's name and shape are part of the API boundary
+        #   with the native client, if you change them you should sync changes with
+        #   the C++ code.
         previous_state_h = tfv1.placeholder(
             tf.float32, [batch_size, Config.n_cell_dim], name="previous_state_h"
         )
@@ -971,6 +991,10 @@ def create_inference_graph(batch_size=1, n_steps=16, tflite=False):
     # TF Lite runtime will check that input dimensions are 1, 2 or 4
     # by default we get 3, the middle one being batch_size which is forced to
     # one on inference graph, so remove that dimension
+    #
+    # native_client: this node's name and shape are part of the API boundary
+    #   with the native client, if you change them you should sync changes with
+    #   the C++ code.
     if tflite:
         logits = tf.squeeze(logits, [1])
 
@@ -1045,6 +1069,9 @@ def export():
     graph_version = int(file_relative_read("GRAPH_VERSION").strip())
     assert graph_version > 0
 
+    # native_client: these nodes's names and shapes are part of the API boundary
+    #   with the native client, if you change them you should sync changes with
+    #   the C++ code.
     outputs["metadata_version"] = tf.constant([graph_version], name="metadata_version")
     outputs["metadata_sample_rate"] = tf.constant(
         [Config.audio_sample_rate], name="metadata_sample_rate"