app.py

import os
import shutil
import zipfile
import gradio as gr


os.system('gdown https://drive.google.com/uc?id=1Flw6Z0K2QdRrTn5F-gVt6HdR9TRPiaKy')
with zipfile.ZipFile('VQMIVC-pretrained models.zip', 'r') as zip_ref:
    zip_ref.extractall('.')
    
shutil.move('VQMIVC-pretrained models/checkpoints/', '.')
shutil.move('VQMIVC-pretrained models/vocoder/', '.')



def inference(audio1, audio2):
  os.system("python convert_example.py -s "+ audio1.name+" -r "+ audio2.name+ " -c converted -m 'checkpoints/useCSMITrue_useCPMITrue_usePSMITrue_useAmpTrue/VQMIVC-model.ckpt-500.pt'")
  out = os.path.basename(str(audio1)).split(".")[0] + "_converted_gen.wav"
  return out

inputs = [gr.inputs.Audio(label="Source Audio", type=file),gr.inputs.Audio(label="Reference Audio", type=file)]
outputs =  gr.outputs.Audio(label="Output Audio", type=file)


title = "VITS"
description = "demo for VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2106.06103'>Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech</a> | <a href='https://github.com/jaywalnut310/vits'>Github Repo</a></p>"

examples = [
 ["We propose VITS, Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech."],
 ["Our method adopts variational inference augmented with normalizing flows and an adversarial training process, which improves the expressive power of generative modeling."]   
]

gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()