bug fix

app.py

@@ -19,16 +19,30 @@ os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
 os.environ["TEMP"] = tmp
 warnings.filterwarnings("ignore")
 torch.manual_seed(114514)
+import soundfile as sf
+from fairseq import checkpoint_utils
+import gradio as gr
+import logging
+from vc_infer_pipeline import VC
+from config import Config
 from i18n import I18nAuto
-
 import signal
-
 import math
-
 from utils import load_audio, CSVutil
 
 global DoFormant, Quefrency, Timbre
 
+
+i18n = I18nAuto()
+config = Config()
+weight_root = "weights"
+index_root = "logs"
+names = []
+index_paths = ["./logs/joel/added_IVF479_Flat_nprobe_1.index","./logs/jenny/added_IVF533_Flat_nprobe_1.index"]
+file_index=None
+
+hubert_model = None
+
 if not os.path.isdir('csvdb/'):
     os.makedirs('csvdb')
     frmnt, stp = open("csvdb/formanting.csv", 'w'), open("csvdb/stop.csv", 'w')
@@ -158,7 +172,7 @@ def update_fshift_presets(preset, qfrency, tmbre):
         {"value": tmbre, "__type__": "update"},
     )
 
-i18n = I18nAuto()
+
 #i18n.print()
 # Determine if there are N cards that can be used to train and accelerate inference
 ngpu = torch.cuda.device_count()
@@ -213,20 +227,10 @@ from lib.infer_pack.models import (
     SynthesizerTrnMs768NSFsid,
     SynthesizerTrnMs768NSFsid_nono,
 )
-import soundfile as sf
-from fairseq import checkpoint_utils
-import gradio as gr
-import logging
-from vc_infer_pipeline import VC
-from config import Config
 
-config = Config()
-cpt=None
 # from trainset_preprocess_pipeline import PreProcess
 logging.getLogger("numba").setLevel(logging.WARNING)
 
-hubert_model = None
-
 def load_hubert():
     global hubert_model
     models, _, _ = checkpoint_utils.load_model_ensemble_and_task(
@@ -241,14 +245,6 @@ def load_hubert():
         hubert_model = hubert_model.float()
     hubert_model.eval()
 
-
-weight_root = "weights"
-index_root = "logs"
-names = []
-index_paths = ["./logs/joel/added_IVF479_Flat_nprobe_1.index","./logs/jenny/added_IVF533_Flat_nprobe_1.index"]
-file_index=None
-
-
 # Define a function to calculate a "similarity score" to identify potential copyright infringement
 def calculate_similarity_score(
     audio0,
@@ -1557,7 +1553,7 @@ def zip_downloader(model):
     else:
         return f'./weights/{model}.pth', "Could not find Index file."
 
-with gr.Blocks(theme=gr.themes.Base(), title='RVC RULE1 v1') as app:
+with gr.Blocks(title='RVC RULE1 v1', theme='step-3-profit/Midnight-Deep@=0.0.2') as app:
     global person
     with gr.Tabs():
         with gr.TabItem("Inference"):