fix: attempt automatic embedding table update for previous models

fs2/model.py

@@ -15,6 +15,7 @@
 from everyvoice.text.features import N_PHONOLOGICAL_FEATURES
 from everyvoice.text.lookups import LookupTable
 from everyvoice.text.text_processor import TextProcessor
+from everyvoice.text.utils import get_symbols_from_symbol_dict, symbol_sorter
 from everyvoice.utils import pydantic_validation_error_shortener
 from everyvoice.utils.heavy import expand
 from loguru import logger
@@ -35,7 +36,7 @@
 
 
 class FastSpeech2(pl.LightningModule):
-    _VERSION: str = "1.1"
+    _VERSION: str = "1.2"
 
     def __init__(
         self,
@@ -296,6 +297,57 @@ def check_and_upgrade_checkpoint(self, checkpoint):
             # Upgrading from 0.0 to 1.0 requires no changes; future versions might require changes
             checkpoint["model_info"]["version"] = "1.0"
 
+        # We changed the handling of phonological features in everyvoice==0.3.0
+        if (
+            ckpt_version < Version("1.2")
+            and checkpoint["hyper_parameters"]["config"]["model"][
+                "target_text_representation_level"
+            ]
+            == TargetTrainingTextRepresentationLevel.phonological_features.value
+        ):
+            raise ValueError(
+                f"""There were breaking changes to the handling of phonological features in version 1.2, introduced in version 0.3.0 of EveryVoice.
+                               Your model is version {ckpt_version} and your model may not work as a result. Please downgrade to everyvoice 0.2.0."""
+            )
+
+        elif ckpt_version < Version("1.2"):
+            old_hardcoded_symbols = [
+                "\x80",
+                " ",
+                "<EXCL>",
+                "<QINT>",
+                "<QUOTE>",
+                "<BB>",
+                "<SB>",
+                "<EPS>",
+            ]
+            checkpoint_symbols = symbol_sorter(
+                get_symbols_from_symbol_dict(
+                    checkpoint["hyper_parameters"]["config"]["text"]["symbols"]
+                ),
+                hardcoded_initial_symbols=old_hardcoded_symbols,
+            )
+            model_symbols = self.text_processor.symbols
+            assert (
+                checkpoint_symbols <= model_symbols
+            ), "Unfortunately we are unable to automatically update your embedding table. Please re-train your model or downgrade to everyvoice 0.2.0"
+            checkpoint_symbol_indices = torch.Tensor(
+                [
+                    model_symbols.index(c) if c in model_symbols else 0
+                    for c in checkpoint_symbols
+                ]
+            ).int()
+            new_weights = torch.zeros(self.text_input_layer.weight.size())
+            # Copy data into the correct positions
+            new_weights[checkpoint_symbol_indices, :] = checkpoint["state_dict"][
+                "text_input_layer.weight"
+            ]
+            # Update the checkpoint's state_dict with the new weights
+            checkpoint["state_dict"]["text_input_layer.weight"] = new_weights
+            logger.warning(
+                f"Your checkpoint was trained using version {ckpt_version} but your code is currently running {self._VERSION}. We have attempted to update your checkpoint automatically, but if you encounter issues, please re-train your model or downgrade to everyvoice 0.2.0"
+            )
+
         return checkpoint
 
     def on_load_checkpoint(self, checkpoint):