Update app.py

app.py

@@ -5,6 +5,7 @@ import gradio as gr
 import matplotlib.pyplot as plt
 import numpy as np
 from scipy.io.wavfile import write
+from text import symbols, text_to_sequence
 import wave
 
 device="cpu"
@@ -61,7 +62,7 @@ def inference(text):
         with torch.no_grad():
             sequence = np.array(text_to_sequence(i, ['transliteration_cleaners']))[None, :]
             sequence = torch.autograd.Variable(torch.from_numpy(sequence)).to(device).long()
-            mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)
+            mel_outputs, mel_outputs_postnet, _, alignments = tacotron2.inference(sequence)
             # plot_data((mel_outputs_postnet.float().data.cpu().numpy()[0], alignments.float().data.cpu().numpy()[0].T))
             audio = waveglow.infer(mel_outputs_postnet, sigma=0.8)
             
@@ -79,7 +80,7 @@ def inference(text):
             
             # sequence = np.array(text_to_sequence(i, ['transliteration_cleaners']))[None, :]
             # sequence = torch.autograd.Variable(torch.from_numpy(sequence)).to(device).long()
-            # mel_outputs, mel_outputs_postnet, _, alignments = model.inference(sequence)
+            # mel_outputs, mel_outputs_postnet, _, alignments = tacotron2.inference(sequence)
             # audio = hifigan(mel_outputs_postnet.float()).to("cpu")
             # audio = audio * MAX_WAV_VALUE
             # data = audio.squeeze().detach().cpu().numpy()