Update app.py

app.py

@@ -11,32 +11,63 @@ from infer import *
 import logging
 from infer_tools.infer_tool import *
 import io
+import tempfile
+##Render function
 def render_audio(audio_file):
     print(audio_file)
+    print(ckpt)
+    print(yaml)
     ############
     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'
+    model_path = ckpt
+    config_path= yaml
     hubert_gpu=True
     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 = 0 # 音高调整，支持正负（半音）
+    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)
-                                
+############################################
+#Transform ckpt binary into .ckpt
+def transform_binary(ckpt_file):
+    # Create a temporary file and write the binary contents to it
+    temp_file = tempfile.NamedTemporaryFile(suffix='.ckpt', delete=False)
+    temp_file.write(ckpt_file)
+    print("CKPT Path is:", temp_file.name)
+    global ckpt
+    ckpt = temp_file.name
+    
+    print(ckpt)
+    print(ckpt)
+    print(ckpt)
+    return temp_file.name
 
+#Transform yaml binary into .yaml
+def transform_binary2(yaml_file):
+    # Create a temporary file and write the binary contents to it
+    temp_file = tempfile.NamedTemporaryFile(suffix='.yaml', delete=False)
+    temp_file.write(yaml_file)
+    print("YAML Path is:", temp_file.name)
+    global yaml
+    yaml = temp_file.name
+    print(yaml)
+    print(yaml)
+    return temp_file.name
 
-def segment(audio):
-    pass  # Implement your image segmentation model here...
+#Play audio
+def play(audio_file):
+    print(audio_file)
+upload_input = gr.inputs.File()
+output_label = gr.outputs.Label()
 
 demo = gr.Blocks()
 with demo:
@@ -49,12 +80,22 @@ with demo:
         </p>
         """
     )
-    ckpt_file = gr.File(label= 'Load your CKPT', type="file")
-    config_file = gr.File(label= 'Load your Config File', type="file")
+    ckpt_file = gr.File(label= 'Load your CKPT', type="binary")
+    yaml_file = gr.File(label= 'Load your YAML', type="binary")
     audio_file = gr.Audio(label = 'Load your WAV', type="filepath")
-    gr.Slider(2, 20, value=4)
-    b1 = gr.Button("Render")
-    b1.click(fn=render_audio, inputs=audio_file)
-
+    #Button 1
+    b1 = gr.Button("Decompile CKPT")
+    b1.click(transform_binary, inputs=ckpt_file)
+    #Button 2
+    b2 = gr.Button("Decompile YAML")
+    b2.click(transform_binary2, inputs=yaml_file)
+    #Button 4
+    b4 = gr.Button("Render")
+    b4.click(fn=render_audio, inputs=[audio_file])
+    def spam():
+        print(yaml)
+        print(ckpt)
+    #b5 = gr.Button("SPAM ME")
+    #b5.click(fn=spam)
 
-demo.launch()
+demo.launch()