Update app.py

app.py

@@ -1,6 +1,43 @@
 import gradio as gr
+from utils.hparams import hparams
+from preprocessing.data_gen_utils import get_pitch_parselmouth,get_pitch_crepe
+import numpy as np
+import matplotlib.pyplot as plt
+import IPython.display as ipd
+import utils
+import librosa
+import torchcrepe
+from infer import *
+import logging
+from infer_tools.infer_tool import *
+import io
+def render_audio(audio_file):
+    print(audio_file)
+    ############
+    logging.getLogger('numba').setLevel(logging.WARNING)
+
+    # 工程文件夹名，训练时用的那个
+    project_name = "Unnamed"
+    model_path = f'./checkpoints/Unnamed/model_ckpt_steps_192000.ckpt'
+    config_path=f'./checkpoints/Unnamed/config.yaml'
+    hubert_gpu=False
+    svc_model = Svc(project_name,config_path,hubert_gpu, model_path)
+    print('model loaded')
+    wav_fn = audio_file
+    demoaudio, sr = librosa.load(wav_fn)
+    key = -8 # 音高调整，支持正负（半音）
+    # 加速倍数
+
+    pndm_speedup = 20
+    wav_gen='queeeeee.wav'#直接改后缀可以保存不同格式音频，如flac可无损压缩
+    f0_tst, f0_pred, audio = run_clip(svc_model,file_path=wav_fn, key=key, acc=pndm_speedup, use_crepe=True, use_pe=True, thre=0.05,
+                                        use_gt_mel=False, add_noise_step=500,project_name=project_name,out_path=wav_gen)
+                                
+
+
+def segment(audio):
+    pass  # Implement your image segmentation model here...
 
-model_path = get_model_path()
 demo = gr.Blocks()
 with demo:
     gr.Markdown("# **<p align='center'>DIFF-SVC Inference</p>**")
@@ -12,11 +49,12 @@ with demo:
         </p>
         """
     )
-    gr.File(label= 'Load your CKPT')
-    gr.File(label= 'Load your Config File')
-    
+    ckpt_file = gr.File(label= 'Load your CKPT', type="file")
+    config_file = gr.File(label= 'Load your Config File', type="file")
     audio_file = gr.Audio(label = 'Load your WAV', type="filepath")
     gr.Slider(2, 20, value=4)
     b1 = gr.Button("Render")
-    #b1.click(speech_to_text, inputs=audio_file, outputs=text)
-demo.launch()
+    b1.click(fn=render_audio, inputs=audio_file)
+
+
+demo.launch()