diff --git a/kraken/lib/vgsl.py b/kraken/lib/vgsl.py
index 9a4f0759b..745dda816 100644
--- a/kraken/lib/vgsl.py
+++ b/kraken/lib/vgsl.py
@@ -15,7 +15,7 @@
 from torch import nn
 
 from kraken.lib import layers
-from kraken.lib.codec import PytorchCodec
+from kraken.lib.codec import PytorchCodec, SentencePieceCodec
 from kraken.lib.exceptions import KrakenInvalidModelException
 
 # all tensors are ordered NCHW, the "feature" dimension is C, so the output of
@@ -305,7 +305,9 @@ def _deserialize_layers(name, layer):
             nn.aux_layers = {k: cls(v).nn.get_submodule(k) for k, v in json.loads(mlmodel.user_defined_metadata['aux_layers']).items()}
 
         if 'codec' in mlmodel.user_defined_metadata:
-            nn.add_codec(PytorchCodec(json.loads(mlmodel.user_defined_metadata['codec'])))
+            codec = json.loads(mlmodel.user_defined_metadata['codec'])
+            if codec['type'] == 'SentencePiece':
+                nn.add_codec(SentencePieceCodec(codec['spp']))
 
         nn.user_metadata: Dict[str, Any] = {'accuracy': [],
                                             'metrics': [],
@@ -409,7 +411,7 @@ def _serialize_layer(net, input, net_builder):
             mlmodel.short_description = 'kraken model'
             mlmodel.user_defined_metadata['vgsl'] = '[' + ' '.join(self.named_spec) + ']'
             if self.codec:
-                mlmodel.user_defined_metadata['codec'] = json.dumps(self.codec.c2l)
+                mlmodel.user_defined_metadata['codec'] = json.dumps({'type': 'SentencePiece', 'spp': self.spp.serialized_model_proto()})
             if self.user_metadata:
                 mlmodel.user_defined_metadata['kraken_meta'] = json.dumps(self.user_metadata)
             if self.aux_layers: