nasty bandaid because some of my DAC dataset only has 8 RVQ levels instead of the full 9

vall_e/models/ar_nar.py

@@ -134,6 +134,8 @@ class AR_NAR(Base):
 		tone_list: list[Tensor] | None = None,
 		len_list: list[Tensor] | None = None,
 
+		training: bool | None = None,
+
 		max_steps: int = 1000,
 		max_levels: int = 0,
 		max_resp_context: int = -1,
@@ -157,8 +159,11 @@ class AR_NAR(Base):
 			n_levels_set = {r.shape[-1] for r in resps_list}
 			n_levels = next(iter(n_levels_set))
 
+			if training is None:
+				training = n_levels == self.n_resp_levels
+
 			# is training
-			if n_levels == self.n_resp_levels:
+			if training:
 				# to-do: make this YAML configurable
 				def sample_task():
 					return "tts"

vall_e/train.py

@@ -64,6 +64,8 @@ def train_feeder(engine, batch):
 				proms_list=batch["proms"],
 				resps_list=batch["resps"],
 				lang_list=batch["lang"],
+
+				training=True,
 			)
 
 		losses = engine.gather_attribute("loss")