debugged

app.py

@@ -13,7 +13,7 @@ vocoder = HiFiGANXvectorLightningModule.load_from_checkpoint("vocoder_finetuned.
 xvector_model = hydra.utils.instantiate(vocoder.cfg.data.xvector.model)
 xvector_model = xvector_model.to('cpu')
 preprocessor = PreprocessForInfer(miipher.cfg)
-
+preprocessor.cfg.preprocess.text2phone_model.is_cuda=False
 @torch.inference_mode()
 def main(wav_path,transcript,lang_code):
     wav,sr =torchaudio.load(wav_path)
@@ -44,6 +44,7 @@ description = """
 This repository provices pretrained weights and demo of Miipher implementation by [Wataru-Nakata](https://github.com/Wataru-Nakata/miipher)
 Miipher was originally proposed by Koizumi et. al. [arxiv](https://arxiv.org/abs/2303.01664)
 Please note that the model differs in many ways from the paper.
+
 **Non commercial use only** as the weights are provided in CC-BY-NC 2.0.
 """
 inputs = [gr.Audio(label="noisy audio",type='filepath'),gr.Textbox(label="Transcript", value="Your transcript here", max_lines=1), 
@@ -52,4 +53,4 @@ outputs = gr.Audio(label="Output")
 
 demo = gr.Interface(fn=main, inputs=inputs, outputs=outputs,description=description)
 
-demo.launch()
+demo.launch(share=True)