Merge pull request #930 from Jackwaterveg/join_ctc

Join ctc

deepspeech/decoders/recog.py

@@ -24,6 +24,7 @@ from .utils import add_results_to_json
 from deepspeech.exps import dynamic_import_tester
 from deepspeech.io.reader import LoadInputsAndTargets
 from deepspeech.models.asr_interface import ASRInterface
+from deepspeech.models.lm.transformer import TransformerLM
 from deepspeech.utils.log import Log
 # from espnet.asr.asr_utils import get_model_conf
 # from espnet.asr.asr_utils import torch_load
@@ -78,12 +79,18 @@ def recog_v2(args):
         preprocess_args={"train": False}, )
 
     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

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import logging
 from typing import Any
 from typing import List
 from typing import Tuple
@@ -150,7 +151,7 @@ class TransformerLM(nn.Layer, LMInterface, BatchScorerInterface):
         h, _, cache = self.encoder.forward_one_step(
             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)
@@ -193,7 +194,7 @@ class TransformerLM(nn.Layer, LMInterface, BatchScorerInterface):
         h, _, states = self.encoder.forward_one_step(
             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)]
@@ -219,7 +220,7 @@ 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 +232,14 @@ 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)

deepspeech/modules/encoder.py

@@ -399,7 +399,8 @@ class TransformerEncoder(BaseEncoder):
             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)