diff --git a/TTS/tts/models/forward_tts.py b/TTS/tts/models/forward_tts.py
index 9e1b1c4097..b6e9ac8a14 100644
--- a/TTS/tts/models/forward_tts.py
+++ b/TTS/tts/models/forward_tts.py
@@ -241,7 +241,7 @@ def __init__(
         )
 
         self.duration_predictor = DurationPredictor(
-            self.args.hidden_channels + self.embedded_speaker_dim,
+            self.args.hidden_channels,
             self.args.duration_predictor_hidden_channels,
             self.args.duration_predictor_kernel_size,
             self.args.duration_predictor_dropout_p,
@@ -249,7 +249,7 @@ def __init__(
 
         if self.args.use_pitch:
             self.pitch_predictor = DurationPredictor(
-                self.args.hidden_channels + self.embedded_speaker_dim,
+                self.args.hidden_channels,
                 self.args.pitch_predictor_hidden_channels,
                 self.args.pitch_predictor_kernel_size,
                 self.args.pitch_predictor_dropout_p,
@@ -263,7 +263,7 @@ def __init__(
 
         if self.args.use_energy:
             self.energy_predictor = DurationPredictor(
-                self.args.hidden_channels + self.embedded_speaker_dim,
+                self.args.hidden_channels,
                 self.args.energy_predictor_hidden_channels,
                 self.args.energy_predictor_kernel_size,
                 self.args.energy_predictor_dropout_p,
@@ -299,7 +299,8 @@ def init_multispeaker(self, config: Coqpit):
         if config.use_d_vector_file:
             self.embedded_speaker_dim = config.d_vector_dim
             if self.args.d_vector_dim != self.args.hidden_channels:
-                self.proj_g = nn.Conv1d(self.args.d_vector_dim, self.args.hidden_channels, 1)
+                #self.proj_g = nn.Conv1d(self.args.d_vector_dim, self.args.hidden_channels, 1)
+                self.proj_g = nn.Linear(in_features=self.args.d_vector_dim, out_features=self.args.hidden_channels)
         # init speaker embedding layer
         if config.use_speaker_embedding and not config.use_d_vector_file:
             print(" > Init speaker_embedding layer.")
@@ -403,10 +404,13 @@ def _forward_encoder(
         # [B, T, C]
         x_emb = self.emb(x)
         # encoder pass
-        o_en = self.encoder(torch.transpose(x_emb, 1, -1), x_mask)
+	#o_en = self.encoder(torch.transpose(x_emb, 1, -1), x_mask)
+        o_en = self.encoder(torch.transpose(x_emb, 1, -1), x_mask, g)
         # speaker conditioning
         # TODO: try different ways of conditioning
-        if g is not None:
+        if g is not None: 
+            if hasattr(self, "proj_g"):
+                g = self.proj_g(g.view(g.shape[0], -1)).unsqueeze(-1)            
             o_en = o_en + g
         return o_en, x_mask, g, x_emb