line up conditioning better with the paper

e2_tts_pytorch/e2_tts.py

@@ -439,7 +439,7 @@ def forward(
                 text = list_str_to_tensor(text).to(device)
                 assert text.shape[0] == batch
 
-            x = self.embed_text(x, text)
+            x = self.embed_text(x, x, text)
 
         # handle lengths (duration)
 
@@ -623,7 +623,7 @@ def sample(
                 duration = torch.full((batch,), duration, device = device, dtype = torch.long)
 
         elif exists(self.duration_predictor):
-            duration = self.duration_predictor(cond, lens = lens).long()
+            duration = self.duration_predictor(cond, text = text, lens = lens).long()
 
         duration = torch.maximum(lens + 1, duration) # just add one token so something is generated
         duration = duration.clamp(max = max_duration)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "e2-tts-pytorch"
-version = "0.1.10"
+version = "0.2.0"
 description = "E2-TTS in Pytorch"
 authors = [
     { name = "Phil Wang", email = "[email protected]" }