I read the blogs and a few others and found out how to inspect my current signature and my saved model gave me this, what would I need to change to just pass a read sound file and get the predictions back?
Maybe what I am trying to say is how would I talk to the input and output tensors given this is my default signature?
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['__saved_model_init_op']:
  The given SavedModel SignatureDef contains the following input(s):
  The given SavedModel SignatureDef contains the following output(s):
    outputs['__saved_model_init_op'] tensor_info:
        dtype: DT_INVALID
        shape: unknown_rank
        name: NoOp
  Method name is: 

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['input_1'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 246000)
        name: serving_default_input_1:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['output_1'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 768, 32)
        name: StatefulPartitionedCall:0
  Method name is: tensorflow/serving/predict
WARNING: Logging before flag parsing goes to stderr.
W0810 10:24:07.810713 140706217662336 deprecation.py:506] From /usr/local/lib/python2.7/dist-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling __init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.
Instructions for updating:
If using Keras pass *_constraint arguments to layers.

Defined Functions:
  Function Name: '__call__'
    Option #1
      Callable with:
        Argument #1
          speech: TensorSpec(shape=(None, 246000), dtype=tf.float32, name=u'speech')
        Argument #2
          DType: bool
          Value: False
    Option #2
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 246000), dtype=tf.float32, name=u'input_1')
        Argument #2
          DType: bool
          Value: False
    Option #3
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 246000), dtype=tf.float32, name=u'input_1')
        Argument #2
          DType: bool
          Value: True
    Option #4
      Callable with:
        Argument #1
          speech: TensorSpec(shape=(None, 246000), dtype=tf.float32, name=u'speech')
        Argument #2
          DType: bool
          Value: True

  Function Name: '_default_save_signature'
    Option #1
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 246000), dtype=tf.float32, name=u'input_1')

  Function Name: 'call'
    Option #1
      Callable with:
        Argument #1
          speech: TensorSpec(shape=(None, 246000), dtype=tf.float32, name=u'speech')
        Argument #2
          DType: bool
          Value: True
    Option #2
      Callable with:
        Argument #1
          speech: TensorSpec(shape=(None, 246000), dtype=tf.float32, name=u'speech')
        Argument #2
          DType: bool
          Value: False

  Function Name: 'call_and_return_all_conditional_losses'
    Option #1
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 246000), dtype=tf.float32, name=u'input_1')
        Argument #2
          DType: bool
          Value: False
    Option #2
      Callable with:
        Argument #1
          input_1: TensorSpec(shape=(None, 246000), dtype=tf.float32, name=u'input_1')
        Argument #2
          DType: bool
          Value: True
    Option #3
      Callable with:
        Argument #1
          speech: TensorSpec(shape=(None, 246000), dtype=tf.float32, name=u'speech')
        Argument #2
          DType: bool
          Value: True
    Option #4
      Callable with:
        Argument #1
          speech: TensorSpec(shape=(None, 246000), dtype=tf.float32, name=u'speech')
        Argument #2