fix some bug and complete the recog.py

deepspeech/decoders/recog.py

@@ -29,6 +29,7 @@ from deepspeech.exps import dynamic_import_tester
 from deepspeech.io.reader import LoadInputsAndTargets
 from deepspeech.models.asr_interface import ASRInterface
 from deepspeech.utils.log import Log
+from deepspeech.models.lm.transformer import TransformerLM
 # from espnet.asr.asr_utils import get_model_conf
 # from espnet.asr.asr_utils import torch_load
 # from espnet.nets.lm_interface import dynamic_import_lm
@@ -83,12 +84,18 @@ def recog_v2(args):
     )
 
     if args.rnnlm:
-        lm_args = get_model_conf(args.rnnlm, args.rnnlm_conf)
-        # NOTE: for a compatibility with less than 0.5.0 version models
-        lm_model_module = getattr(lm_args, "model_module", "default")
-        lm_class = dynamic_import_lm(lm_model_module, lm_args.backend)
-        lm = lm_class(len(char_list), lm_args)
-        torch_load(args.rnnlm, lm)
+        lm_path = args.rnnlm
+        lm = TransformerLM(
+            n_vocab=5002,
+            pos_enc=None,
+            embed_unit=128,
+            att_unit=512,
+            head=8,
+            unit=2048,
+            layer=16,
+            dropout_rate=0.5, )
+        model_dict = paddle.load(lm_path)
+        lm.set_state_dict(model_dict)
         lm.eval()
     else:
         lm = None

deepspeech/models/lm/transformer.py

@@ -23,9 +23,9 @@ 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
+from deepspeech.models.lm_interface import LMInterface
 
+import logging
 class TransformerLM(nn.Layer, LMInterface, BatchScorerInterface):
     def __init__(
             self,
@@ -36,7 +36,7 @@ class TransformerLM(nn.Layer, LMInterface, BatchScorerInterface):
             head: int=2,
             unit: int=1024,
             layer: int=4,
-            dropout_rate: float=0.5, 
+            dropout_rate: float=0.5,
             emb_dropout_rate: float = 0.0,
             att_dropout_rate: float = 0.0,
             tie_weights: bool = False,):
@@ -84,6 +84,8 @@ class TransformerLM(nn.Layer, LMInterface, BatchScorerInterface):
             ), "Tie Weights: True need embedding and final dimensions to match"
             self.decoder.weight = self.embed.weight
 
+
+
     def _target_mask(self, ys_in_pad):
         ys_mask = ys_in_pad != 0
         m = subsequent_mask(ys_mask.size(-1)).unsqueeze(0)
@@ -151,7 +153,7 @@ class TransformerLM(nn.Layer, LMInterface, BatchScorerInterface):
             emb, self._target_mask(y), cache=state
         )
         h = self.decoder(h[:, -1])
-        logp = h.log_softmax(axis=-1).squeeze(0)
+        logp = F.log_softmax(h).squeeze(0)
         return logp, cache
 
     # batch beam search API (see BatchScorerInterface)
@@ -194,7 +196,7 @@ class TransformerLM(nn.Layer, LMInterface, BatchScorerInterface):
             emb, self._target_mask(ys), cache=batch_state
         )
         h = self.decoder(h[:, -1])
-        logp = h.log_softmax(axi=-1)
+        logp = F.log_softmax(h)
 
         # transpose state of [layer, batch] into [batch, layer]
         state_list = [[states[i][b] for i in range(n_layers)] for b in range(n_batch)]
@@ -219,7 +221,7 @@ if __name__ == "__main__":
             # head: int=2,
             # unit: int=1024,
             # layer: int=4,
-            # dropout_rate: float=0.5, 
+            # dropout_rate: float=0.5,
             # emb_dropout_rate: float = 0.0,
             # att_dropout_rate: float = 0.0,
             # tie_weights: bool = False,):
@@ -231,14 +233,14 @@ if __name__ == "__main__":
     #Test the score
     input2 = np.array([5])
     input2 = paddle.to_tensor(input2)
-    state = (None, None, 0)
+    state = None
     output, state = tlm.score(input2, state, None)
 
-    input3 = np.array([10])
+    input3 = np.array([5,10])
     input3 = paddle.to_tensor(input3)
     output, state = tlm.score(input3, state, None)
 
-    input4 = np.array([0])
+    input4 = np.array([5,10,0])
     input4 = paddle.to_tensor(input4)
     output, state = tlm.score(input4, state, None)
     print("output", output)
@@ -256,4 +258,4 @@ if __name__ == "__main__":
     print("output", output)
     #print("cache", cache)
     #np.save("output_pd.npy", output)
-    """
\ No newline at end of file
+    """

deepspeech/modules/encoder.py

@@ -399,7 +399,7 @@ class TransformerEncoder(BaseEncoder):
             #TODO(Hui Zhang): self.embed(xs, masks, offset=0), stride_slice not support bool tensor
             xs, pos_emb, masks = self.embed(xs, masks.astype(xs.dtype), offset=0)
         else:
-            xs = self.embed(xs)
+            xs , pos_emb, masks= self.embed(xs, masks.astype(xs.dtype), offset=0)
         #TODO(Hui Zhang): remove mask.astype, stride_slice not support bool tensor
         masks = masks.astype(paddle.bool)