1. fix typos;

2. add tensorboardX into install requirements.

parakeet/models/waveflow.py

@@ -24,7 +24,7 @@ def fold(x, n_group):
 
     Returns
     ---------
-    Tensor : [shape=(`*, time_steps // n_group, group)]
+    Tensor : [shape=(\*, time_steps // n_group, group)]
         Folded tensor.
     """
     *spatial_shape, time_steps = x.shape
@@ -230,7 +230,7 @@ class ResidualBlock(nn.Layer):
         res : Tensor [shape=(batch_size, channel, 1, width)]
             A row of the the residual output.
             
-        res : Tensor [shape=(batch_size, channel, 1, width)]
+        skip : Tensor [shape=(batch_size, channel, 1, width)]
             A row of the skip output.
         """
         x_row_in = x_row
@@ -349,7 +349,7 @@ class ResidualNet(nn.LayerList):
         res : Tensor [shape=(batch_size, channel, 1, width)]
             A row of the the residual output.
             
-        res : Tensor [shape=(batch_size, channel, 1, width)]
+        skip : Tensor [shape=(batch_size, channel, 1, width)]
             A row of the skip output.
         """
         skip_connections = []
@@ -364,8 +364,8 @@ class Flow(nn.Layer):
     """A bijection (Reversable layer) that transform a density of latent 
     variables p(Z) into a complex data distribution p(X).
 
-    It's an auto regressive flow. The `forward` method implements the 
-    probability density estimation. The `inverse` method implements the 
+    It's an auto regressive flow. The ``forward`` method implements the 
+    probability density estimation. The ``inverse`` method implements the 
     sampling.
     
     Parameters

parakeet/models/wavenet.py

@@ -350,7 +350,7 @@ class ResidualNet(nn.LayerList):
 
     def start_sequence(self):
         """Prepare the ResidualNet to generate a new sequence. This method 
-        should be called before starting calling `add_input` multiple times.
+        should be called before starting calling ``add_input`` multiple times.
         """
         for block in self:
             block.start_sequence()
@@ -372,7 +372,7 @@ class ResidualNet(nn.LayerList):
         Returns
         ----------            
         Tensor [shape=(B, C)]
-            T he skip connection for a step. This output is accumulated with 
+            The skip connection for a step. This output is accumulated with 
             that of other ResidualBlocks. 
         """
         for i, func in enumerate(self):
@@ -514,7 +514,7 @@ class WaveNet(nn.Layer):
         Returns
         --------
         Tensor: [shape=(B, C_output)]
-            A steo of the parameters of the output distributions.
+            A step of the parameters of the output distributions.
         """
         # Causal Conv
         if self.loss_type == "softmax":
@@ -714,7 +714,7 @@ class WaveNet(nn.Layer):
         Parameters
         ----------
         y : Tensor [shape=(B, T, C_output)]
-            The parameterd of the output distribution.
+            The parameters of the output distribution.
             
         t : Tensor [shape=(B, T)]
             The target audio.

setup.py

@@ -69,6 +69,7 @@ setup_info = dict(
         'g2p_en',
         'g2pM',
         'yacs',
+        'tensorboardX',
     ],
     extras_require={
         'doc': ["sphinx", "sphinx-rtd-theme", "numpydoc"],