Update app.py

app.py

@@ -9,26 +9,26 @@ from melgan.model.generator import Generator
 from melgan.utils.hparams import load_hparam
 
 torch.manual_seed(1234)
-MAX_WAV_VALUE = 32768.0
-
-# load trained tacotron2 + GST model:
-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.eval()
-
-# load pre trained MelGAN model for mel2audio:
-vocoder_checkpoint_path = "trained_models/nvidia_tacotron2_LJ11_epoch6400.pt"
-checkpoint = torch.load(vocoder_checkpoint_path)
-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.eval(inference=False)
-
-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()
+
+def init_models(hparams):
+    # load trained tacotron2 + GST model:
+    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.eval()
+    
+    # load pre trained MelGAN model for mel2audio:
+    vocoder_checkpoint_path = "trained_models/nvidia_tacotron2_LJ11_epoch6400.pt"
+    checkpoint = torch.load(vocoder_checkpoint_path)
+    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.eval(inference=False)
+    
+    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()
 
 def synthesize(text):
     sequence = np.array(text_to_sequence(text, ['english_cleaners']))[None, :]
@@ -40,8 +40,13 @@ def synthesize(text):
     with torch.no_grad():
       audio = vocoder_model.inference(mel_outputs_postnet)
     
-    audio_numpy = audio.data.cpu().detach().numpy()    
-    write(save_path, 22050, audio_numpy)
+    audio_numpy = audio.data.cpu().detach().numpy()
 
-iface = gr.Interface(fn=synthesize, inputs="text", outputs="audio")
-iface.launch()
+    return [22050, audio_numpy]
+
+if __name__ == "__main__":
+    
+    init_models(hparams)
+    iface = gr.Interface(fn=synthesize, inputs="text", outputs="audio")
+    iface.launch()
+