this seems preferable

vall_e/models/ar_nar.py

@@ -173,7 +173,7 @@ class AR_NAR(Base):
 							resps_list[i][t, l] = clamp(token + offset, 1, 1022) # +- 1
 
 			# only apply stop token for RVQ level 0
-			if quant_level <= 0 and timesteps[i] is None:
+			if quant_level <= 0 and timesteps[i] is None and not self.parallel_decoding:
 				# append stop tokens for AR
 				if task not in text_task:
 					resps_list[i] = torch.cat([ resps, audio_stop_sequence ])

vall_e/models/base.py

@@ -1273,20 +1273,21 @@ class Base(nn.Module):
 					input if quant_level == 0 else input[:, :quant_level]
 				)
 
-			if self.version < 7 or not self.parallel_decoding:
+			if self.version < 7: # or not self.parallel_decoding:
 				return self.proms_emb(
 					input if input.dim() == 1 else input[:, : 1 if quant_level == 0 else quant_level],
 					quant_level = 0 if quant_level == 0 else quant_level - 1, # input is one below the target quant level
 					offset = 0,
 				)
-			"""
+			
 			if not self.parallel_decoding:
 				return self.proms_emb(
-					input if input.dim() == 1 else input[:, :quant_level+1],
-					quant_level = quant_level,
+					input,
+					quant_level = 0 if input.dim() == 1 else input.shape[-1],
 					offset = 0,
 				)
 			"""
+			"""
 
 			return self.proms_emb( input )