fix cornercase deepspeech

Signed-off-by: Ngoc Minh Tran <[email protected]>

Author: minhitbk

art/estimators/speech_recognition/pytorch_deep_speech.py

@@ -285,7 +285,7 @@ def predict(
         """
         import torch  # lgtm [py/repeated-import]
 
-        x_ = x.copy()
+        x_ = np.array([x_i for x_i in x] + [np.array([0.1]), np.array([0.1, 0.2])])[:-2]
 
         # Put the model in the eval mode
         self._model.eval()
@@ -370,7 +370,7 @@ def loss_gradient(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
         """
         from warpctc_pytorch import CTCLoss
 
-        x_ = x.copy()
+        x_ = np.array([x_i for x_i in x] + [np.array([0.1]), np.array([0.1, 0.2])])[:-2]
 
         # Put the model in the training mode
         self._model.train()
@@ -432,8 +432,6 @@ def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: in
         """
         import random
 
-        import torch  # lgtm [py/repeated-import]
-
         from warpctc_pytorch import CTCLoss
 
         # Put the model in the training mode
@@ -466,8 +464,10 @@ def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: in
                 )
 
                 # Extract random batch
-                i_batch = x_preprocessed[ind[begin:end]].copy()
-                o_batch = y_preprocessed[ind[begin:end]].copy()
+                i_batch = np.array(
+                    [x_i for x_i in x_preprocessed[ind[begin : end]]] + [np.array([0.1]), np.array([0.1, 0.2])]
+                )[:-2]
+                o_batch = y_preprocessed[ind[begin : end]]
 
                 # Transform data into the model input space
                 inputs, targets, input_rates, target_sizes, batch_idx = self.transform_model_input(