account for variable sequence lengths when generating alignment mask …

…from durations

naturalspeech2_pytorch/naturalspeech2_pytorch.py

@@ -75,21 +75,23 @@ def prob_mask_like(shape, prob, device):
     else:
         return torch.zeros(shape, device = device).float().uniform_(0, 1) < prob
 
-def generate_mask_from_lengths(lengths):
-    src = lengths.int()
-    device = src.device
-    tgt_length = src.sum(dim = -1).amax().item()
+def generate_mask_from_repeats(repeats):
+    repeats = repeats.int()
+    device = repeats.device
 
-    cumsum = src.cumsum(dim = -1)
+    lengths = repeats.sum(dim = -1)
+    max_length = lengths.amax().item()
+    cumsum = repeats.cumsum(dim = -1)
     cumsum_exclusive = F.pad(cumsum, (1, -1), value = 0.)
 
-    tgt_arange = torch.arange(tgt_length, device = device)
-    tgt_arange = repeat(tgt_arange, '... j -> ... i j', i = src.shape[-1])
+    seq = torch.arange(max_length, device = device)
+    seq = repeat(seq, '... j -> ... i j', i = repeats.shape[-1])
 
     cumsum = rearrange(cumsum, '... i -> ... i 1')
     cumsum_exclusive = rearrange(cumsum_exclusive, '... i -> ... i 1')
 
-    mask = (tgt_arange < cumsum) & (tgt_arange >= cumsum_exclusive)
+    lengths = rearrange(lengths, 'b -> b 1 1')
+    mask = (seq < cumsum) & (seq >= cumsum_exclusive) & (seq < lengths)
     return mask
 
 # sinusoidal positional embeds
@@ -1424,7 +1426,7 @@ def sample(
             duration, pitch = self.duration_pitch(phoneme_enc, prompt_enc)
             pitch = rearrange(pitch, 'b n -> b 1 n')
 
-            aln_mask = generate_mask_from_lengths(duration).float()
+            aln_mask = generate_mask_from_repeats(duration).float()
 
             cond = self.expand_encodings(rearrange(phoneme_enc, 'b n d -> b d n'), rearrange(aln_mask, 'b n c -> b 1 n c'), pitch)
 

naturalspeech2_pytorch/version.py

@@ -1 +1 @@
-__version__ = '0.1.2'
+__version__ = '0.1.4'