fix get_texts() to work with recent changes in k2.

snowfall/common.py

@@ -12,6 +12,7 @@
 from typing import Any, Dict, Iterable, List, Optional, TextIO, Tuple, Union
 
 import k2
+import k2.ragged as k2r
 import kaldialign
 import torch
 import torch.distributed as dist
@@ -314,18 +315,28 @@ def get_texts(best_paths: k2.Fsa, indices: Optional[torch.Tensor] = None) -> Lis
           decoded.
     '''
     # remove any 0's or -1's (there should be no 0's left but may be -1's.)
-    aux_labels = k2.ragged.remove_values_leq(best_paths.aux_labels, 0)
-    aux_shape = k2.ragged.compose_ragged_shapes(best_paths.arcs.shape(),
-                                                aux_labels.shape())
-    # remove the states and arcs axes.
-    aux_shape = k2.ragged.remove_axis(aux_shape, 1)
-    aux_shape = k2.ragged.remove_axis(aux_shape, 1)
-    aux_labels = k2.RaggedInt(aux_shape, aux_labels.values())
+
+    if isinstance(best_paths.aux_labels, k2.RaggedInt):
+        aux_labels = k2r.remove_values_leq(best_paths.aux_labels, 0)
+        aux_shape = k2r.compose_ragged_shapes(best_paths.arcs.shape(),
+                                                    aux_labels.shape())
+
+        # remove the states and arcs axes.
+        aux_shape = k2r.remove_axis(aux_shape, 1)
+        aux_shape = k2r.remove_axis(aux_shape, 1)
+        aux_labels = k2.RaggedInt(aux_shape, aux_labels.values())
+    else:
+        # remove axis corresponding to states.
+        aux_shape = k2r.remove_axis(best_paths.arcs.shape(), 1)
+        aux_labels = k2.RaggedInt(aux_shape, best_paths.aux_labels)
+        # remove 0's and -1's.
+        aux_labels = k2r.remove_values_leq(aux_labels, 0)
+
     assert (aux_labels.num_axes() == 2)
-    aux_labels, _ = k2.ragged.index(aux_labels,
+    aux_labels, _ = k2r.index(aux_labels,
                                     invert_permutation(indices).to(dtype=torch.int32,
                                                                    device=best_paths.device))
-    return k2.ragged.to_list(aux_labels)
+    return k2r.to_list(aux_labels)
 
 
 def invert_permutation(indices: torch.Tensor) -> torch.Tensor: