[cm] Adding categorical params to non realtime wrapper

examples/example_clipper_gen.py

@@ -85,7 +85,7 @@ def get_native_buffer_sizes(self) -> List[int]:
     def is_one_shot_model(self) -> bool:
         return False
 
-    def aggregate_cont_params(self, cont_params: Tensor) -> Tensor:
+    def aggregate_continuous_params(self, cont_params: Tensor) -> Tensor:
         return cont_params  # We want sample-level control, so no aggregation
 
     def do_forward_pass(self,

examples/music_gen.py

@@ -5,7 +5,8 @@
 import torch as tr
 from torch import Tensor
 
-from neutone_sdk import NeutoneParameter, KnobNeutoneParameter, TextNeutoneParameter
+from neutone_sdk import NeutoneParameter, TextNeutoneParameter, \
+    ContinuousNeutoneParameter
 from neutone_sdk.non_realtime_wrapper import NonRealtimeBase
 
 logging.basicConfig()
@@ -48,7 +49,7 @@ def get_neutone_parameters(self) -> List[NeutoneParameter]:
         return [
             TextNeutoneParameter("prompt", "testing"),
             # TODO(cm): convert to categorical
-            KnobNeutoneParameter("duration", "how many seconds to generate", default_value=0.0),
+            ContinuousNeutoneParameter("duration", "how many seconds to generate", default_value=0.0),
         ]
 
     @tr.jit.export
@@ -120,15 +121,15 @@ def do_forward_pass(self,
     wrapper = MusicGenModelWrapper(model)
     audio_out = wrapper.forward(curr_block_idx=0,
                                 audio_in=[],
-                                cont_params=tr.tensor([0.0, 1.0]).unsqueeze(1),
+                                knob_params=tr.tensor([0.0, 1.0]).unsqueeze(1),
                                 text_params=["testing"])
     log.info(audio_out[0].shape)
 
     # wrapper.prepare_for_inference()
     ts = tr.jit.script(wrapper)
     audio_out = ts.forward(curr_block_idx=0,
                            audio_in=[],
-                           cont_params=tr.tensor([0.0]).unsqueeze(1),
+                           knob_params=tr.tensor([0.0]).unsqueeze(1),
                            text_params=["testing"])
     log.info(audio_out[0].shape)