Update app.py

function 'synthesize' created to convert text to speech passing through Tacotron2-GST + MelGAN vocoder

app.py

@@ -1,7 +1,47 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+from hyper_parameters import tacotron_params as hparams
+from training import load_model
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+from text import text_to_sequence
+
+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 synthesize(text):
+    sequence = np.array(text_to_sequence(text, ['english_cleaners']))[None, :]
+    sequence = torch.from_numpy(sequence).to(device='cuda', dtype=torch.int64)
+
+    mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence, gst_scores)
+
+    # mel2wav inference:
+    with torch.no_grad():
+      audio = vocoder_model.inference(mel_outputs_postnet)
+    
+    audio_numpy = audio.data.cpu().detach().numpy()    
+    write(save_path, 22050, audio_numpy)
+
+iface = gr.Interface(fn=synthesize, inputs="text", outputs="audio")
 iface.launch()