Fix inference on cpu device (babysor#241)

synthesizer/inference.py

@@ -62,7 +62,7 @@ def load(self):
                                stop_threshold=hparams.tts_stop_threshold,
                                speaker_embedding_size=hparams.speaker_embedding_size).to(self.device)
 
-        self._model.load(self.model_fpath)
+        self._model.load(self.model_fpath, self.device)
         self._model.eval()
 
         if self.verbose:

synthesizer/models/tacotron.py

@@ -470,7 +470,9 @@ def generate(self, x, speaker_embedding=None, steps=2000, style_idx=0, min_stop_
         # put after encoder 
         if hparams.use_gst and self.gst is not None:
             if style_idx >= 0 and style_idx < 10:
-                query = torch.zeros(1, 1, self.gst.stl.attention.num_units).cuda()
+                query = torch.zeros(1, 1, self.gst.stl.attention.num_units)
+                if device.type == 'cuda':
+                    query = query.cuda()
                 gst_embed = torch.tanh(self.gst.stl.embed)
                 key = gst_embed[style_idx].unsqueeze(0).expand(1, -1, -1)
                 style_embed = self.gst.stl.attention(query, key)
@@ -539,9 +541,9 @@ def log(self, path, msg):
         with open(path, "a") as f:
             print(msg, file=f)
 
-    def load(self, path, optimizer=None):
+    def load(self, path, device, optimizer=None):
         # Use device of model params as location for loaded state
-        checkpoint = torch.load(str(path))
+        checkpoint = torch.load(str(path), map_location=device)
         self.load_state_dict(checkpoint["model_state"], strict=False)
 
         if "optimizer_state" in checkpoint and optimizer is not None:

synthesizer/synthesize.py

@@ -45,7 +45,7 @@ def run_synthesis(in_dir, out_dir, model_dir, hparams):
     model_dir = Path(model_dir)
     model_fpath = model_dir.joinpath(model_dir.stem).with_suffix(".pt")
     print("\nLoading weights at %s" % model_fpath)
-    model.load(model_fpath)
+    model.load(model_fpath, device)
     print("Tacotron weights loaded from step %d" % model.step)
 
     # Synthesize using same reduction factor as the model is currently trained

synthesizer/train.py

@@ -111,7 +111,7 @@ def train(run_id: str, syn_dir: str, models_dir: str, save_every: int,
 
     else:
         print("\nLoading weights at %s" % weights_fpath)
-        model.load(weights_fpath, optimizer)
+        model.load(weights_fpath, device, optimizer)
         print("Tacotron weights loaded from step %d" % model.step)
 
     # Initialize the dataset