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app.py

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+import os
+import gradio as gr
+path = "Kororinpa/Amadeus_Project"
+os.chdir(path)
+print(os.getcwd())
+
+%matplotlib inline
+import matplotlib.pyplot as plt
+import IPython.display as ipd
+
+import os
+import json
+import math
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+
+import commons
+import utils
+from data_utils import TextAudioLoader, TextAudioCollate, TextAudioSpeakerLoader, TextAudioSpeakerCollate
+from models import SynthesizerTrn
+from text.symbols import symbols
+from text import text_to_sequence
+
+from scipy.io.wavfile import write
+
+
+def get_text(text, hps):
+    text_norm = text_to_sequence(text, hps.data.text_cleaners)
+    if hps.data.add_blank:
+        text_norm = commons.intersperse(text_norm, 0)
+    text_norm = torch.LongTensor(text_norm)
+    return text_norm
+
+hps = utils.get_hparams_from_file("Kororinpa/Amadeus_Project/configs/steins_gate_base.json")
+
+net_g = SynthesizerTrn(
+    len(symbols),
+    hps.data.filter_length // 2 + 1,
+    hps.train.segment_size // hps.data.hop_length,
+    **hps.model).cuda()
+_ = net_g.eval()
+
+_ = utils.load_checkpoint("Kororinpa/Amadeus_Project/G_265000.pth", net_g, None)
+
+def syn(content):
+  stn_tst = get_text(content, hps)
+  with torch.no_grad():
+      x_tst = stn_tst.cuda().unsqueeze(0)
+      x_tst_lengths = torch.LongTensor([stn_tst.size(0)]).cuda()
+      audio = net_g.infer(x_tst, x_tst_lengths, noise_scale=.667, noise_scale_w=0.8, length_scale=1)[0][0,0].data.cpu().float().numpy()
+  return (hps.data.sampling_rate,audio)
+  #ipd.display(ipd.Audio(audio, rate=hps.data.sampling_rate))
+
+demo = gr.Interface(fn=syn,inputs="text",outputs=gr.Audio)
+
+app = gr.Blocks()
+with app:
+    with gr.Tabs():
+        with gr.TabItem("Basic"):
+            input1 = gr.Textbox()
+            submit = gr.Button("Convert", variant="primary")
+            output1 = gr.Audio(label="Output Audio")
+        submit.click(syn,input1,output1)
+        
+app.launch()