Format Align TTS docstrings

TTS/tts/models/align_tts.py

@@ -73,13 +73,7 @@ class AlignTTS(BaseTTS):
 
     Encoder -> DurationPredictor -> Decoder
 
-    Check ```AlignTTSArgs``` for the class arguments.
+    Check :class:`AlignTTSArgs` for the class arguments.
-
-    Examples:
-        >>> from TTS.tts.configs import AlignTTSConfig
-        >>> config = AlignTTSConfig()
-        >>> config.model_args.num_chars = 50
-        >>> model = AlignTTS(config)
 
     Paper Abstract:
         Targeting at both high efficiency and performance, we propose AlignTTS to predict the
@@ -99,6 +93,11 @@ class AlignTTS(BaseTTS):
         Original model uses Transormers in encoder and decoder layers. However, here you can set the architecture
         differently based on your requirements using ```encoder_type``` and ```decoder_type``` parameters.
 
+    Examples:
+        >>> from TTS.tts.configs import AlignTTSConfig
+        >>> config = AlignTTSConfig()
+        >>> model = AlignTTS(config)
+
     """
 
     # pylint: disable=dangerous-default-value
@@ -113,6 +112,11 @@ class AlignTTS(BaseTTS):
             if isinstance(config.model_args.length_scale, int)
             else config.model_args.length_scale
         )
+
+        if not self.config.model_args.num_chars:
+            chars, self.config, num_chars = self.get_characters(config)
+            self.config.model_args.num_chars = num_chars
+
         self.emb = nn.Embedding(self.config.model_args.num_chars, self.config.model_args.hidden_channels)
 
         self.embedded_speaker_dim = 0
@@ -173,15 +177,15 @@ class AlignTTS(BaseTTS):
         """Generate attention alignment map from durations and
         expand encoder outputs
 
-        Example:
+        Examples::
-            encoder output: [a,b,c,d]
+            - encoder output: [a,b,c,d]
-            durations: [1, 3, 2, 1]
+            - durations: [1, 3, 2, 1]
 
-            expanded: [a, b, b, b, c, c, d]
+            - expanded: [a, b, b, b, c, c, d]
-            attention map: [[0, 0, 0, 0, 0, 0, 1],
+            - attention map: [[0, 0, 0, 0, 0, 0, 1],
-                            [0, 0, 0, 0, 1, 1, 0],
+                             [0, 0, 0, 0, 1, 1, 0],
-                            [0, 1, 1, 1, 0, 0, 0],
+                             [0, 1, 1, 1, 0, 0, 0],
-                            [1, 0, 0, 0, 0, 0, 0]]
+                             [1, 0, 0, 0, 0, 0, 0]]
         """
         attn = self.convert_dr_to_align(dr, x_mask, y_mask)
         o_en_ex = torch.matmul(attn.squeeze(1).transpose(1, 2), en.transpose(1, 2)).transpose(1, 2)
@@ -257,11 +261,11 @@ class AlignTTS(BaseTTS):
     ):  # pylint: disable=unused-argument
         """
         Shapes:
-            x: [B, T_max]
+            - x: :math:`[B, T_max]`
-            x_lengths: [B]
+            - x_lengths: :math:`[B]`
-            y_lengths: [B]
+            - y_lengths: :math:`[B]`
-            dr: [B, T_max]
+            - dr: :math:`[B, T_max]`
-            g: [B, C]
+            - g: :math:`[B, C]`
         """
         y = y.transpose(1, 2)
         g = aux_input["d_vectors"] if "d_vectors" in aux_input else None
@@ -311,9 +315,9 @@ class AlignTTS(BaseTTS):
     def inference(self, x, aux_input={"d_vectors": None}):  # pylint: disable=unused-argument
         """
         Shapes:
-            x: [B, T_max]
+            - x: :math:`[B, T_max]`
-            x_lengths: [B]
+            - x_lengths: :math:`[B]`
-            g: [B, C]
+            - g: :math:`[B, C]`
         """
         g = aux_input["d_vectors"] if "d_vectors" in aux_input else None
         x_lengths = torch.tensor(x.shape[1:2]).to(x.device)