chore(format): run black on master

app.py

@@ -3,7 +3,9 @@
 from tabs.full_inference import full_inference_tab
 from tabs.download_model import download_model_tab
 from tabs.settings import theme_tab, lang_tab, restart_tab
-from programs.applio_code.rvc.lib.tools.prerequisites_download import prequisites_download_pipeline
+from programs.applio_code.rvc.lib.tools.prerequisites_download import (
+    prequisites_download_pipeline,
+)
 from tabs.presence import load_config_presence, presence_tab
 
 now_dir = os.getcwd()
@@ -15,7 +17,7 @@
 prequisites_download_pipeline(
     False,
     False,
-    True, 
+    True,
     False,
 )
 
@@ -32,15 +34,14 @@
     RPCManager.start_presence()
 
 
-
 rvc_theme = loadThemes.load_theme() or "NoCrypt/miku"
 
-with gr.Blocks(
-    theme=rvc_theme, title="Advanced RVC Inference"
-) as rvc:
+with gr.Blocks(theme=rvc_theme, title="Advanced RVC Inference") as rvc:
     gr.Markdown('<div align="center"><h1>Advanced RVC Inference</h1></a></div>')
-    gr.Markdown('<div align="center">this project Maintained by <a href="https://discord.com/1314204512814235689">NeoDev</a></div>')
-
+    gr.Markdown(
+        '<div align="center">this project Maintained by <a href="https://discord.com/1314204512814235689">NeoDev</a></div>'
+    )
+
     with gr.Tab(i18n("Full Inference")):
         full_inference_tab()
     with gr.Tab(i18n("Download Model")):

assets/discord_presence.py

@@ -30,8 +30,14 @@ def update_presence(self):
                 state="Advanced-RVC",
                 details="Advaced voice cloning with UVR5 feature",
                 buttons=[
-                    {"label": "Home", "url": "https://github.com/ArkanDash/Advanced-RVC-Inference"},
-                    {"label": "Download", "url": "https://github.com/ArkanDash/Advanced-RVC-Inference/archive/refs/heads/master.zip"},
+                    {
+                        "label": "Home",
+                        "url": "https://github.com/ArkanDash/Advanced-RVC-Inference",
+                    },
+                    {
+                        "label": "Download",
+                        "url": "https://github.com/ArkanDash/Advanced-RVC-Inference/archive/refs/heads/master.zip",
+                    },
                 ],
                 large_image="logo",
                 large_text="Experimenting with Advanced-RVC",

assets/i18n/languages/id_ID.py

@@ -85,5 +85,5 @@
     "Export Audio": "Ekspor Audio",
     "Music URL": "URL Musik",
     "Download": "Unduh",
-    "Model URL": "URL Model"
+    "Model URL": "URL Model",
 }

assets/themes/Grheme.py

@@ -1,5 +1,4 @@
 from __future__ import annotations
-import time
 
 from typing import Iterable
 import gradio as gr

core.py

@@ -15,8 +15,7 @@
 from programs.music_separation_code.inference import proc_file
 
 logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(levelname)s - %(message)s"
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
 )
 now_dir = os.getcwd()
 sys.path.append(now_dir)
@@ -1015,7 +1014,6 @@ def download_model(link):
     return "Model downloaded with success"
 
 
-
 def download_music(link):
     if not link or not isinstance(link, str):
         logging.error("Invalid link provided.")
@@ -1035,9 +1033,11 @@ def download_music(link):
     command = [
         "yt-dlp",
         "-x",
-        "--audio-format", "wav",
-        "--output", output_template,
-        link
+        "--audio-format",
+        "wav",
+        "--output",
+        output_template,
+        link,
     ]
 
     try:

programs/music_separation_code/ensemble.py

@@ -1,5 +1,5 @@
 # coding: utf-8
-__author__ = 'Roman Solovyev (ZFTurbo): https://github.com/ZFTurbo/'
+__author__ = "Roman Solovyev (ZFTurbo): https://github.com/ZFTurbo/"
 
 import os
 import librosa
@@ -81,42 +81,42 @@ def average_waveforms(pred_track, weights, algorithm):
 
     mod_track = []
     for i in range(pred_track.shape[0]):
-        if algorithm == 'avg_wave':
+        if algorithm == "avg_wave":
             mod_track.append(pred_track[i] * weights[i])
-        elif algorithm in ['median_wave', 'min_wave', 'max_wave']:
+        elif algorithm in ["median_wave", "min_wave", "max_wave"]:
             mod_track.append(pred_track[i])
-        elif algorithm in ['avg_fft', 'min_fft', 'max_fft', 'median_fft']:
+        elif algorithm in ["avg_fft", "min_fft", "max_fft", "median_fft"]:
             spec = stft(pred_track[i], nfft=2048, hl=1024)
-            if algorithm in ['avg_fft']:
+            if algorithm in ["avg_fft"]:
                 mod_track.append(spec * weights[i])
             else:
                 mod_track.append(spec)
     pred_track = np.array(mod_track)
 
-    if algorithm in ['avg_wave']:
+    if algorithm in ["avg_wave"]:
         pred_track = pred_track.sum(axis=0)
         pred_track /= np.array(weights).sum().T
-    elif algorithm in ['median_wave']:
+    elif algorithm in ["median_wave"]:
         pred_track = np.median(pred_track, axis=0)
-    elif algorithm in ['min_wave']:
+    elif algorithm in ["min_wave"]:
         pred_track = np.array(pred_track)
         pred_track = lambda_min(pred_track, axis=0, key=np.abs)
-    elif algorithm in ['max_wave']:
+    elif algorithm in ["max_wave"]:
         pred_track = np.array(pred_track)
         pred_track = lambda_max(pred_track, axis=0, key=np.abs)
-    elif algorithm in ['avg_fft']:
+    elif algorithm in ["avg_fft"]:
         pred_track = pred_track.sum(axis=0)
         pred_track /= np.array(weights).sum()
         pred_track = istft(pred_track, 1024, final_length)
-    elif algorithm in ['min_fft']:
+    elif algorithm in ["min_fft"]:
         pred_track = np.array(pred_track)
         pred_track = lambda_min(pred_track, axis=0, key=np.abs)
         pred_track = istft(pred_track, 1024, final_length)
-    elif algorithm in ['max_fft']:
+    elif algorithm in ["max_fft"]:
         pred_track = np.array(pred_track)
         pred_track = absmax(pred_track, axis=0)
         pred_track = istft(pred_track, 1024, final_length)
-    elif algorithm in ['median_fft']:
+    elif algorithm in ["median_fft"]:
         pred_track = np.array(pred_track)
         pred_track = np.median(pred_track, axis=0)
         pred_track = istft(pred_track, 1024, final_length)
@@ -125,37 +125,58 @@ def average_waveforms(pred_track, weights, algorithm):
 
 def ensemble_files(args):
     parser = argparse.ArgumentParser()
-    parser.add_argument("--files", type=str, required=True, nargs='+', help="Path to all audio-files to ensemble")
-    parser.add_argument("--type", type=str, default='avg_wave', help="One of avg_wave, median_wave, min_wave, max_wave, avg_fft, median_fft, min_fft, max_fft")
-    parser.add_argument("--weights", type=float, nargs='+', help="Weights to create ensemble. Number of weights must be equal to number of files")
-    parser.add_argument("--output", default="res.wav", type=str, help="Path to wav file where ensemble result will be stored")
+    parser.add_argument(
+        "--files",
+        type=str,
+        required=True,
+        nargs="+",
+        help="Path to all audio-files to ensemble",
+    )
+    parser.add_argument(
+        "--type",
+        type=str,
+        default="avg_wave",
+        help="One of avg_wave, median_wave, min_wave, max_wave, avg_fft, median_fft, min_fft, max_fft",
+    )
+    parser.add_argument(
+        "--weights",
+        type=float,
+        nargs="+",
+        help="Weights to create ensemble. Number of weights must be equal to number of files",
+    )
+    parser.add_argument(
+        "--output",
+        default="res.wav",
+        type=str,
+        help="Path to wav file where ensemble result will be stored",
+    )
     if args is None:
         args = parser.parse_args()
     else:
         args = parser.parse_args(args)
 
-    print('Ensemble type: {}'.format(args.type))
-    print('Number of input files: {}'.format(len(args.files)))
+    print("Ensemble type: {}".format(args.type))
+    print("Number of input files: {}".format(len(args.files)))
     if args.weights is not None:
         weights = args.weights
     else:
         weights = np.ones(len(args.files))
-    print('Weights: {}'.format(weights))
-    print('Output file: {}'.format(args.output))
+    print("Weights: {}".format(weights))
+    print("Output file: {}".format(args.output))
     data = []
     for f in args.files:
         if not os.path.isfile(f):
-            print('Error. Can\'t find file: {}. Check paths.'.format(f))
+            print("Error. Can't find file: {}. Check paths.".format(f))
             exit()
-        print('Reading file: {}'.format(f))
+        print("Reading file: {}".format(f))
         wav, sr = librosa.load(f, sr=None, mono=False)
         # wav, sr = sf.read(f)
         print("Waveform shape: {} sample rate: {}".format(wav.shape, sr))
         data.append(wav)
     data = np.array(data)
     res = average_waveforms(data, weights, args.type)
-    print('Result shape: {}'.format(res.shape))
-    sf.write(args.output, res.T, sr, 'FLOAT')
+    print("Result shape: {}".format(res.shape))
+    sf.write(args.output, res.T, sr, "FLOAT")
 
 
 if __name__ == "__main__":

programs/music_separation_code/inference.py

@@ -7,7 +7,6 @@
 from tqdm import tqdm
 import sys
 import os
-import glob
 import torch
 import numpy as np
 import soundfile as sf