ugh

vall_e/models/base.py

@@ -1004,7 +1004,9 @@ class Base(nn.Module):
 
 					# store dropout mask
 					if "len" in self.capabilities and quant_level == 0:
-						dropout_mask = _dropout_mask( resps_list[i], p=0.8 )
+						t = random.random()
+						p = math.cos(t * math.pi * 0.5)
+						dropout_mask = _dropout_mask( resps_list[i], p=p )
 						inputs[i].append( ("dropout_mask", dropout_mask ) )
 		
 			# Audio length prediction task

vall_e/samplers.py

@@ -527,7 +527,7 @@ def add_gumbel_noise(t, temperature, device):
 	return (t + torch.Tensor(temperature * np.random.gumbel(size=t.shape)).to(device))
 
 # derived from https://github.com/LeapLabTHU/ImprovedNAT/blob/main/libs/nat_misc.py#L39
-# this 
+# this provides mostly poor output, but it might just be a matter of how I'm naively training the model for """diffusion"""
 class SampleScheduler:
 	def __init__(
 		self,