Delete initial.py

initial.py

@@ -1,101 +0,0 @@
-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
-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 = 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 = -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
-
-#Play audio
-def play(audio_file):
-    print(audio_file)
-upload_input = gr.inputs.File()
-output_label = gr.outputs.Label()
-
-demo = gr.Blocks()
-with demo:
-    gr.Markdown("# **<p align='center'>DIFF-SVC Inference</p>**")
-
-    gr.Markdown(
-        """
-        <p style='text-align: center'>
-        Render whatever model you want with this space!
-        </p>
-        """
-    )
-    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")
-    #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()