diff --git a/i6_models/parts/rasr_fsa.py b/i6_models/parts/rasr_fsa.py
index ececc34c..00d7ad9f 100644
--- a/i6_models/parts/rasr_fsa.py
+++ b/i6_models/parts/rasr_fsa.py
@@ -306,6 +306,43 @@ def build_batched_fsa(self, fsas: Iterable[FsaTuple]) -> WeightedFsa:
         return out_fsa
 
 
+def join_fsas_fbw_v2(fsas: Iterable[FsaTuple]) -> WeightedFsaV2:
+    """
+    Joins a set of FSAs represented as tuples into a single :classref:`WeightedFsaV2` object,
+    for consumption by the FBW V2 op.
+
+    :param fsas: FSAs to be concatenated, represented as tuples with the following fields:
+        * number of states S
+        * number of edges E
+        * integer edge array of shape [E, 3] where each row is an edge
+            consisting of from-state, to-state and the emission idx
+        * float weight array of shape [E,]
+    :return: Single FSA object corresponding to the joined FSAs passed as parameter.
+    """
+    fsas = list(fsas)  # ensure we can iterate multiple times over this iterable
+    num_states = [f[0] for f in fsas]
+    num_edges = [f[1] for f in fsas]
+    start_states = np.cumsum(np.array([0] + num_states, dtype=np.uint32))[:-1]
+    end_states = np.cumsum(num_states) - 1
+    weights = np.concatenate(tuple(f[3] for f in fsas))
+
+    edges = []
+    for idx, f in enumerate(fsas):
+        f_edges = f[2].reshape(3, -1).copy()
+        f_edges[:2, :] += start_states[idx]
+        edges.append(f_edges)
+
+    out_fsa = WeightedFsaV2(
+        torch.IntTensor(num_states).to(torch.uint32),
+        torch.IntTensor(num_edges).to(torch.uint32),
+        torch.IntTensor(np.concatenate(edges, axis=1)).contiguous(),
+        torch.Tensor(weights),
+        torch.IntTensor(np.array([start_states, end_states])),
+    )
+
+    return out_fsa
+
+
 class _RasrFsaBuilderFbw2(_AbstractRasrFsaBuilder):
     """
     Abstract base class for building an FSA.
@@ -334,28 +371,7 @@ def build_batched_fsa(self, fsas: Iterable[FsaTuple]) -> WeightedFsaV2:
         :return: Single FSA object corresponding to the joined FSAs passed as parameter.
         """
 
-        fsas = list(fsas)  # ensure we can iterate multiple times over this iterable
-        num_states = [f[0] for f in fsas]
-        num_edges = [f[1] for f in fsas]
-        start_states = np.cumsum(np.array([0] + num_states, dtype=np.uint32))[:-1]
-        end_states = np.cumsum(num_states) - 1
-        weights = np.concatenate(tuple(f[3] for f in fsas))
-
-        edges = []
-        for idx, f in enumerate(fsas):
-            f_edges = f[2].reshape(3, -1).copy()
-            f_edges[:2, :] += start_states[idx]
-            edges.append(f_edges)
-
-        out_fsa = WeightedFsaV2(
-            torch.IntTensor(num_states).to(torch.uint32),
-            torch.IntTensor(num_edges).to(torch.uint32),
-            torch.IntTensor(np.concatenate(edges, axis=1)).contiguous(),
-            torch.Tensor(weights),
-            torch.IntTensor(np.array([start_states, end_states])),
-        )
-
-        return out_fsa
+        return join_fsas_fbw_v2(fsas)
 
 
 class RasrFsaBuilderV2(_RasrFsaBuilderFbw2):