add lm interface

deepspeech/models/lm/transformer.py

@@ -23,9 +23,10 @@ import paddle.nn.functional as F
 from deepspeech.modules.mask import subsequent_mask
 from deepspeech.modules.encoder import TransformerEncoder
 from deepspeech.decoders.scorers.scorer_interface import BatchScorerInterface
+from deepspeech.models.lm_interface import 
 #LMInterface
 
-class TransformerLM(nn.Layer, BatchScorerInterface):
+class TransformerLM(nn.Layer, LMInterface, BatchScorerInterface):
     def __init__(
             self,
             n_vocab: int,
@@ -90,7 +91,7 @@ class TransformerLM(nn.Layer, BatchScorerInterface):
         return ys_mask.unsqueeze(-2) & m
 
     def forward(
-        self, x: paddle.Tensor, xlens, t: paddle.Tensor
+        self, x: paddle.Tensor, t: paddle.Tensor
     ) -> Tuple[paddle.Tensor, paddle.Tensor, paddle.Tensor]:
         """Compute LM loss value from buffer sequences.
 
@@ -110,11 +111,11 @@ class TransformerLM(nn.Layer, BatchScorerInterface):
 
         """
         xm = x != 0
+        xlen = xm.sum(axis=1)
         if self.embed_drop is not None:
             emb = self.embed_drop(self.embed(x))
         else:
             emb = self.embed(x)
-        xlen = xm.sum(axis=1)
         h, _ = self.encoder(emb, xlen)
         y = self.decoder(h)
         loss = F.cross_entropy(y.view(-1, y.shape[-1]), t.view(-1), reduction="none")

deepspeech/models/lm_interface.py

@@ -0,0 +1,69 @@
+"""Language model interface."""
+
+import argparse
+
+from deepspeech.decoders.scorers.scorer_interface import ScorerInterface
+from deepspeech.utils.dynamic_import import dynamic_import
+
+class LMInterface(ScorerInterface):
+    """LM Interface for ESPnet model implementation."""
+
+    @staticmethod
+    def add_arguments(parser):
+        """Add arguments to command line argument parser."""
+        return parser
+
+    @classmethod
+    def build(cls, n_vocab: int, **kwargs):
+        """Initialize this class with python-level args.
+
+        Args:
+            idim (int): The number of vocabulary.
+
+        Returns:
+            LMinterface: A new instance of LMInterface.
+
+        """
+        args = argparse.Namespace(**kwargs)
+        return cls(n_vocab, args)
+
+    def forward(self, x, t):
+        """Compute LM loss value from buffer sequences.
+
+        Args:
+            x (torch.Tensor): Input ids. (batch, len)
+            t (torch.Tensor): Target ids. (batch, len)
+
+        Returns:
+            tuple[torch.Tensor, torch.Tensor, torch.Tensor]: Tuple of
+                loss to backward (scalar),
+                negative log-likelihood of t: -log p(t) (scalar) and
+                the number of elements in x (scalar)
+
+        Notes:
+            The last two return values are used
+            in perplexity: p(t)^{-n} = exp(-log p(t) / n)
+
+        """
+        raise NotImplementedError("forward method is not implemented")
+
+
+predefined_lms = {
+    "transformer": "deepspeech.models.lm.transformer:TransformerLM",
+}
+
+def dynamic_import_lm(module):
+    """Import LM class dynamically.
+
+    Args:
+        module (str): module_name:class_name or alias in `predefined_lms`
+
+    Returns:
+        type: LM class
+
+    """
+    model_class = dynamic_import(module, predefined_lms)
+    assert issubclass(
+        model_class, LMInterface
+    ), f"{module} does not implement LMInterface"
+    return model_class