Fix glow tts initialization

TTS/tts/models/glow_tts.py

@@ -11,6 +11,7 @@ from TTS.tts.layers.glow_tts.monotonic_align import generate_path, maximum_path
 from TTS.tts.models.base_tts import BaseTTS
 from TTS.tts.utils.data import sequence_mask
 from TTS.tts.utils.measures import alignment_diagonal_score
+from TTS.tts.utils.speakers import get_speaker_manager
 from TTS.tts.utils.visual import plot_alignment, plot_spectrogram
 from TTS.utils.audio import AudioProcessor
 
@@ -50,9 +51,9 @@ class GlowTTS(BaseTTS):
         for key in config:
             setattr(self, key, config[key])
 
-        chars, self.config = self.get_characters(config)
-        self.num_chars = len(chars)
+        chars, self.config, self.num_chars = self.get_characters(config)
         self.decoder_output_dim = config.out_channels
+
         self.init_multispeaker(config)
 
         # if is a multispeaker and c_in_channels is 0, set to 256
@@ -91,9 +92,23 @@ class GlowTTS(BaseTTS):
             c_in_channels=self.c_in_channels,
         )
 
-        if self.num_speakers > 1 and not self.d_vector_dim:
-            # speaker embedding layer
-            self.emb_g = nn.Embedding(self.num_speakers, self.c_in_channels)
+    def init_multispeaker(self, config: "Coqpit", data: list = None) -> None:
+        """Initialize multi-speaker modules of a model. A model can be trained either with a speaker embedding layer
+        or with external `d_vectors` computed from a speaker encoder model.
+
+        If you need a different behaviour, override this function for your model.
+
+        Args:
+            config (Coqpit): Model configuration.
+            data (List, optional): Dataset items to infer number of speakers. Defaults to None.
+        """
+        # init speaker manager
+        self.speaker_manager = get_speaker_manager(config, data=data)
+        self.num_speakers = self.speaker_manager.num_speakers
+        # init speaker embedding layer
+        if config.use_speaker_embedding and not config.use_d_vector_file:
+            self.embedded_speaker_dim = self.c_in_channels
+            self.emb_g = nn.Embedding(self.num_speakers, self.embedded_speaker_dim)
             nn.init.uniform_(self.emb_g.weight, -0.1, 0.1)
 
     @staticmethod
@@ -260,6 +275,7 @@ class GlowTTS(BaseTTS):
     def inference(self, x, aux_input={"x_lengths": None, "d_vectors": None}):  # pylint: disable=dangerous-default-value
         x_lengths = aux_input["x_lengths"]
         g = aux_input["d_vectors"] if aux_input is not None and "d_vectors" in aux_input else None
+
         if g is not None:
             if self.d_vector_dim:
                 g = F.normalize(g).unsqueeze(-1)