Update app.py

app.py

@@ -19,7 +19,7 @@ def init_models(hparams):
     model = load_model(hparams)
     checkpoint_path = "trained_models/checkpoint_78000.model"
     model.load_state_dict(torch.load(checkpoint_path)['state_dict'])
-    model.to('cuda')
+    # model.to('cuda')
     _ = model.eval()
     
     # load pre trained MelGAN model for mel2audio:
@@ -28,15 +28,15 @@ def init_models(hparams):
     hp_melgan = load_hparam("melgan/config/default.yaml")
     vocoder_model = Generator(80)
     vocoder_model.load_state_dict(checkpoint['model_g'])
-    vocoder_model = vocoder_model.to('cuda')
+    # vocoder_model = vocoder_model.to('cuda')
     vocoder_model.eval(inference=False)
 
 def synthesize(text):
     sequence = np.array(text_to_sequence(text, ['english_cleaners']))[None, :]
-    sequence = torch.from_numpy(sequence).to(device='cuda', dtype=torch.int64)
+    sequence = torch.from_numpy(sequence).to(device='cpu', dtype=torch.int64)
 
     gst_head_scores = np.array([0.5, 0.15, 0.35])  # originally ([0.5, 0.15, 0.35])
-    gst_scores = torch.from_numpy(gst_head_scores).cuda().float()
+    gst_scores = torch.from_numpy(gst_head_scores).float()
 
     mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence, gst_scores)