diff --git a/app.py b/app.py
index ee6449c..97a7564 100644
--- a/app.py
+++ b/app.py
@@ -1,13 +1,13 @@
 import os
 import sys
+
 import gradio as gr
 
-from tabs.welcome import welcome_tab
 from tabs.conversion.conversion import conversion_tab
 from tabs.conversion.edge_tts import edge_tts_tab
+from tabs.install.install_models import files_upload, url_download, zip_upload
 from tabs.processing.processing import processing_tab
-from tabs.install.install_models import url_download, zip_upload, files_upload
-
+from tabs.welcome import welcome_tab
 
 DEFAULT_PORT = 4000
 MAX_PORT_ATTEMPTS = 10
diff --git a/download_models.py b/download_models.py
index a22e072..a8850d5 100644
--- a/download_models.py
+++ b/download_models.py
@@ -1,4 +1,5 @@
 import os
+
 import requests
 
 PREDICTORS = "https://huggingface.co/Politrees/RVC_resources/resolve/main/predictors/"
diff --git a/rvc/infer/infer.py b/rvc/infer/infer.py
index 39241e9..ab9fb6a 100644
--- a/rvc/infer/infer.py
+++ b/rvc/infer/infer.py
@@ -1,10 +1,12 @@
-import torch
 from multiprocessing import cpu_count
+
+import torch
 from fairseq import checkpoint_utils
 from scipy.io import wavfile
 
 from rvc.lib.algorithm.synthesizers import Synthesizer
 from rvc.lib.my_utils import load_audio
+
 from .pipeline import VC
 
 
diff --git a/rvc/infer/pipeline.py b/rvc/infer/pipeline.py
index e7aa657..1a74bb8 100644
--- a/rvc/infer/pipeline.py
+++ b/rvc/infer/pipeline.py
@@ -1,11 +1,12 @@
-import os
 import gc
-import torch
-import torch.nn.functional as F
-import torchcrepe
+import os
+
 import faiss
 import librosa
 import numpy as np
+import torch
+import torch.nn.functional as F
+import torchcrepe
 from scipy import signal
 
 from rvc.lib.predictors.FCPE import FCPEF0Predictor
@@ -19,7 +20,9 @@
 FILTER_ORDER = 5  # Порядок фильтра
 CUTOFF_FREQUENCY = 48  # Частота среза (в Гц)
 SAMPLE_RATE = 16000  # Частота дискретизации (в Гц)
-bh, ah = signal.butter(N=FILTER_ORDER, Wn=CUTOFF_FREQUENCY, btype="high", fs=SAMPLE_RATE)
+bh, ah = signal.butter(
+    N=FILTER_ORDER, Wn=CUTOFF_FREQUENCY, btype="high", fs=SAMPLE_RATE
+)
 
 
 input_audio_path2wav = {}
@@ -56,7 +59,8 @@ def change_rms(source_audio, source_rate, target_audio, target_rate, rate):
         rms2 = torch.maximum(rms2, torch.zeros_like(rms2) + 1e-6)
 
         adjusted_audio = (
-            target_audio * (torch.pow(rms1, 1 - rate) * torch.pow(rms2, rate - 1)).numpy()
+            target_audio
+            * (torch.pow(rms1, 1 - rate) * torch.pow(rms2, rate - 1)).numpy()
         )
         return adjusted_audio
 
@@ -186,9 +190,13 @@ def get_f0(
             delta_t = np.round(
                 (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1
             ).astype("int16")
-            replace_f0 = np.interp(list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1])
+            replace_f0 = np.interp(
+                list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1]
+            )
             shape = f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)].shape[0]
-            f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[:shape]
+            f0[self.x_pad * tf0 : self.x_pad * tf0 + len(replace_f0)] = replace_f0[
+                :shape
+            ]
 
         f0bak = f0.copy()
         f0_mel = 1127 * np.log(1 + f0 / 700)
@@ -452,7 +460,9 @@ def pipeline(
                 audio, self.sample_rate, audio_opt, tgt_sr, volume_envelope
             )
         if resample_sr >= self.sample_rate and tgt_sr != resample_sr:
-            audio_opt = librosa.resample(audio_opt, orig_sr=tgt_sr, target_sr=resample_sr)
+            audio_opt = librosa.resample(
+                audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
+            )
 
         audio_max = np.abs(audio_opt).max() / 0.99
         max_int16 = 32768
diff --git a/rvc/lib/algorithm/attentions.py b/rvc/lib/algorithm/attentions.py
index 63258a5..aa6970c 100644
--- a/rvc/lib/algorithm/attentions.py
+++ b/rvc/lib/algorithm/attentions.py
@@ -1,4 +1,5 @@
 import math
+
 import torch
 from torch import nn
 from torch.nn import functional as F
@@ -78,7 +79,9 @@ def attention(self, query, key, value, mask=None):
 
         scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
         if self.window_size is not None:
-            assert t_s == t_t, "Relative attention is only available for self-attention."
+            assert (
+                t_s == t_t
+            ), "Relative attention is only available for self-attention."
             key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
             rel_logits = self._matmul_with_relative_keys(
                 query / math.sqrt(self.k_channels), key_relative_embeddings
@@ -93,7 +96,9 @@ def attention(self, query, key, value, mask=None):
         if mask is not None:
             scores = scores.masked_fill(mask == 0, -1e4)
             if self.block_length is not None:
-                assert t_s == t_t, "Local attention is only available for self-attention."
+                assert (
+                    t_s == t_t
+                ), "Local attention is only available for self-attention."
                 block_mask = (
                     torch.ones_like(scores)
                     .triu(-self.block_length)
@@ -105,7 +110,9 @@ def attention(self, query, key, value, mask=None):
         output = torch.matmul(p_attn, value)
         if self.window_size is not None:
             relative_weights = self._absolute_position_to_relative_position(p_attn)
-            value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
+            value_relative_embeddings = self._get_relative_embeddings(
+                self.emb_rel_v, t_s
+            )
             output = output + self._matmul_with_relative_values(
                 relative_weights, value_relative_embeddings
             )
diff --git a/rvc/lib/algorithm/commons.py b/rvc/lib/algorithm/commons.py
index 6137a0f..3645fb8 100644
--- a/rvc/lib/algorithm/commons.py
+++ b/rvc/lib/algorithm/commons.py
@@ -1,7 +1,8 @@
 import math
+from typing import List, Optional
+
 import torch
 from torch.nn import functional as F
-from typing import List, Optional
 
 
 def init_weights(m, mean=0.0, std=0.01):
@@ -22,7 +23,9 @@ def convert_pad_shape(pad_shape):
 
 def kl_divergence(m_p, logs_p, m_q, logs_q):
     kl = (logs_q - logs_p) - 0.5
-    kl += 0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
+    kl += (
+        0.5 * (torch.exp(2.0 * logs_p) + ((m_p - m_q) ** 2)) * torch.exp(-2.0 * logs_q)
+    )
     return kl
 
 
diff --git a/rvc/lib/algorithm/discriminators.py b/rvc/lib/algorithm/discriminators.py
index a143547..947f1dd 100644
--- a/rvc/lib/algorithm/discriminators.py
+++ b/rvc/lib/algorithm/discriminators.py
@@ -6,7 +6,6 @@
 from .commons import get_padding
 from .residuals import LRELU_SLOPE
 
-
 PERIODS_V1 = [2, 3, 5, 7, 11, 17]
 PERIODS_V2 = [2, 3, 5, 7, 11, 17, 23, 37]
 IN_CHANNELS = [1, 32, 128, 512, 1024]
@@ -18,7 +17,10 @@ def __init__(self, use_spectral_norm=False):
         super(MultiPeriodDiscriminator, self).__init__()
         self.discriminators = nn.ModuleList(
             [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-            + [DiscriminatorP(p, use_spectral_norm=use_spectral_norm) for p in PERIODS_V1]
+            + [
+                DiscriminatorP(p, use_spectral_norm=use_spectral_norm)
+                for p in PERIODS_V1
+            ]
         )
 
     def forward(self, y, y_hat):
@@ -39,7 +41,10 @@ def __init__(self, use_spectral_norm=False):
         super(MultiPeriodDiscriminatorV2, self).__init__()
         self.discriminators = nn.ModuleList(
             [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
-            + [DiscriminatorP(p, use_spectral_norm=use_spectral_norm) for p in PERIODS_V2]
+            + [
+                DiscriminatorP(p, use_spectral_norm=use_spectral_norm)
+                for p in PERIODS_V2
+            ]
         )
 
     def forward(self, y, y_hat):
diff --git a/rvc/lib/algorithm/encoders.py b/rvc/lib/algorithm/encoders.py
index c301dd5..a095019 100644
--- a/rvc/lib/algorithm/encoders.py
+++ b/rvc/lib/algorithm/encoders.py
@@ -1,8 +1,9 @@
 import math
+from typing import Optional
+
 import torch
 from torch import nn
 from torch.nn.utils.weight_norm import remove_weight_norm
-from typing import Optional
 
 from .attentions import FFN, MultiHeadAttention
 from .commons import sequence_mask
diff --git a/rvc/lib/algorithm/generators.py b/rvc/lib/algorithm/generators.py
index 3c7395e..99942e8 100644
--- a/rvc/lib/algorithm/generators.py
+++ b/rvc/lib/algorithm/generators.py
@@ -1,9 +1,10 @@
+from typing import Optional
+
 import torch
 from torch import nn
 from torch.nn import functional as F
-from torch.nn.utils.weight_norm import remove_weight_norm
 from torch.nn.utils.parametrizations import weight_norm
-from typing import Optional
+from torch.nn.utils.weight_norm import remove_weight_norm
 
 from .commons import init_weights
 from .residuals import LRELU_SLOPE, ResBlock1, ResBlock2
@@ -121,10 +122,14 @@ def forward(self, f0: torch.Tensor, upp: int):
             f0_buf[:, :, 0] = f0[:, :, 0]
             f0_buf[:, :, 1:] = (
                 f0_buf[:, :, 0:1]
-                * torch.arange(2, self.harmonic_num + 2, device=f0.device)[None, None, :]
+                * torch.arange(2, self.harmonic_num + 2, device=f0.device)[
+                    None, None, :
+                ]
             )
             rad_values = (f0_buf / float(self.sample_rate)) % 1
-            rand_ini = torch.rand(f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device)
+            rand_ini = torch.rand(
+                f0_buf.shape[0], f0_buf.shape[2], device=f0_buf.device
+            )
             rand_ini[:, 0] = 0
             rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
             tmp_over_one = torch.cumsum(rad_values, 1)
diff --git a/rvc/lib/algorithm/modules.py b/rvc/lib/algorithm/modules.py
index dd3ff31..ed4937d 100644
--- a/rvc/lib/algorithm/modules.py
+++ b/rvc/lib/algorithm/modules.py
@@ -1,7 +1,7 @@
 import torch
 from torch import nn
-from torch.nn.utils.weight_norm import remove_weight_norm
 from torch.nn.utils.parametrizations import weight_norm
+from torch.nn.utils.weight_norm import remove_weight_norm
 
 from .commons import fused_add_tanh_sigmoid_multiply
 
diff --git a/rvc/lib/algorithm/nsf.py b/rvc/lib/algorithm/nsf.py
index 2f19f0f..709843c 100644
--- a/rvc/lib/algorithm/nsf.py
+++ b/rvc/lib/algorithm/nsf.py
@@ -1,10 +1,11 @@
 import math
+from typing import Optional
+
 import torch
 from torch import nn
 from torch.nn import functional as F
-from torch.nn.utils.weight_norm import remove_weight_norm
 from torch.nn.utils.parametrizations import weight_norm
-from typing import Optional
+from torch.nn.utils.weight_norm import remove_weight_norm
 
 from .commons import init_weights
 from .generators import SineGen
@@ -59,7 +60,9 @@ def __init__(
         self.num_kernels = len(resblock_kernel_sizes)
         self.num_upsamples = len(upsample_rates)
         self.f0_upsamp = nn.Upsample(scale_factor=math.prod(upsample_rates))
-        self.m_source = SourceModuleHnNSF(sample_rate=sr, harmonic_num=0, is_half=is_half)
+        self.m_source = SourceModuleHnNSF(
+            sample_rate=sr, harmonic_num=0, is_half=is_half
+        )
 
         self.conv_pre = nn.Conv1d(
             initial_channel, upsample_initial_channel, 7, 1, padding=3
@@ -70,7 +73,8 @@ def __init__(
         self.noise_convs = nn.ModuleList()
 
         channels = [
-            upsample_initial_channel // (2 ** (i + 1)) for i in range(len(upsample_rates))
+            upsample_initial_channel // (2 ** (i + 1))
+            for i in range(len(upsample_rates))
         ]
         stride_f0s = [
             math.prod(upsample_rates[i + 1 :]) if i + 1 < len(upsample_rates) else 1
diff --git a/rvc/lib/algorithm/residuals.py b/rvc/lib/algorithm/residuals.py
index 45d851b..236565f 100644
--- a/rvc/lib/algorithm/residuals.py
+++ b/rvc/lib/algorithm/residuals.py
@@ -1,14 +1,14 @@
+from typing import Optional
+
 import torch
 from torch import nn
 from torch.nn import functional as F
-from torch.nn.utils.weight_norm import remove_weight_norm
 from torch.nn.utils.parametrizations import weight_norm
-from typing import Optional
+from torch.nn.utils.weight_norm import remove_weight_norm
 
 from .commons import get_padding, init_weights
 from .modules import WaveNet
 
-
 LRELU_SLOPE = 0.1
 
 
diff --git a/rvc/lib/algorithm/synthesizers.py b/rvc/lib/algorithm/synthesizers.py
index 35aae21..83877bb 100644
--- a/rvc/lib/algorithm/synthesizers.py
+++ b/rvc/lib/algorithm/synthesizers.py
@@ -1,10 +1,11 @@
+from typing import Optional
+
 import torch
 from torch import nn
 from torch.nn.utils.weight_norm import remove_weight_norm
-from typing import Optional
 
-from .commons import slice_segments, rand_slice_segments
-from .encoders import TextEncoder, PosteriorEncoder
+from .commons import rand_slice_segments, slice_segments
+from .encoders import PosteriorEncoder, TextEncoder
 from .generators import Generator
 from .nsf import GeneratorNSF
 from .residuals import ResidualCouplingBlock
diff --git a/rvc/lib/predictors/FCPE.py b/rvc/lib/predictors/FCPE.py
index ca49736..bf26fe5 100644
--- a/rvc/lib/predictors/FCPE.py
+++ b/rvc/lib/predictors/FCPE.py
@@ -1,21 +1,20 @@
+import math
+import os
+from functools import partial
 from typing import Union
 
-import torch.nn.functional as F
+import librosa
 import numpy as np
+import soundfile as sf
 import torch
 import torch.nn as nn
-from torch.nn.utils.parametrizations import weight_norm
-from torchaudio.transforms import Resample
-import os
-import librosa
-import soundfile as sf
+import torch.nn.functional as F
 import torch.utils.data
-from librosa.filters import mel as librosa_mel_fn
-import math
-from functools import partial
-
 from einops import rearrange, repeat
+from librosa.filters import mel as librosa_mel_fn
 from local_attention import LocalAttention
+from torch.nn.utils.parametrizations import weight_norm
+from torchaudio.transforms import Resample
 
 os.environ["LRU_CACHE_CAPACITY"] = "3"
 
@@ -47,7 +46,9 @@ def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False
         return [], sample_rate or target_sr or 48000
     if target_sr is not None and sample_rate != target_sr:
         data = torch.from_numpy(
-            librosa.core.resample(data.numpy(), orig_sr=sample_rate, target_sr=target_sr)
+            librosa.core.resample(
+                data.numpy(), orig_sr=sample_rate, target_sr=target_sr
+            )
         )
         sample_rate = target_sr
 
@@ -187,7 +188,9 @@ def softmax_kernel(
     if is_query:
         data_dash = ratio * (
             torch.exp(
-                data_dash - diag_data - torch.max(data_dash, dim=-1, keepdim=True).values
+                data_dash
+                - diag_data
+                - torch.max(data_dash, dim=-1, keepdim=True).values
             )
             + eps
         )
@@ -355,12 +358,16 @@ def gaussian_orthogonal_random_matrix(
     block_list = []
 
     for _ in range(nb_full_blocks):
-        q = orthogonal_matrix_chunk(nb_columns, qr_uniform_q=qr_uniform_q, device=device)
+        q = orthogonal_matrix_chunk(
+            nb_columns, qr_uniform_q=qr_uniform_q, device=device
+        )
         block_list.append(q)
 
     remaining_rows = nb_rows - nb_full_blocks * nb_columns
     if remaining_rows > 0:
-        q = orthogonal_matrix_chunk(nb_columns, qr_uniform_q=qr_uniform_q, device=device)
+        q = orthogonal_matrix_chunk(
+            nb_columns, qr_uniform_q=qr_uniform_q, device=device
+        )
         block_list.append(q[:remaining_rows])
 
     final_matrix = torch.cat(block_list)
@@ -661,7 +668,9 @@ def forward(
     def cents_decoder(self, y, mask=True):
         B, N, _ = y.size()
         ci = self.cent_table[None, None, :].expand(B, N, -1)
-        rtn = torch.sum(ci * y, dim=-1, keepdim=True) / torch.sum(y, dim=-1, keepdim=True)
+        rtn = torch.sum(ci * y, dim=-1, keepdim=True) / torch.sum(
+            y, dim=-1, keepdim=True
+        )
         if mask:
             confident = torch.max(y, dim=-1, keepdim=True)[0]
             confident_mask = torch.ones_like(confident)
@@ -778,7 +787,9 @@ def extract_mel(self, audio, sample_rate, keyshift=0, train=False):
 
         mel = self.extract_nvstft(audio_res, keyshift=keyshift, train=train)
         n_frames = int(audio.shape[1] // self.hop_size) + 1
-        mel = torch.cat((mel, mel[:, -1:, :]), 1) if n_frames > int(mel.shape[1]) else mel
+        mel = (
+            torch.cat((mel, mel[:, -1:, :]), 1) if n_frames > int(mel.shape[1]) else mel
+        )
         mel = mel[:, :n_frames, :] if n_frames < int(mel.shape[1]) else mel
         return mel
 
@@ -833,7 +844,9 @@ def repeat_expand(
     ):
         ndim = content.ndim
         content = (
-            content[None, None] if ndim == 1 else content[None] if ndim == 2 else content
+            content[None, None]
+            if ndim == 1
+            else content[None] if ndim == 2 else content
         )
         assert content.ndim == 3
         is_np = isinstance(content, np.ndarray)
diff --git a/rvc/lib/predictors/RMVPE.py b/rvc/lib/predictors/RMVPE.py
index 929f399..32f2c75 100644
--- a/rvc/lib/predictors/RMVPE.py
+++ b/rvc/lib/predictors/RMVPE.py
@@ -1,10 +1,10 @@
-import torch
 import numpy as np
+import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from librosa.filters import mel
+from librosa.util import normalize, pad_center, tiny
 from scipy.signal import get_window
-from librosa.util import pad_center, tiny, normalize
 
 
 def window_sumsquare(
@@ -176,7 +176,9 @@ def forward(self, x):
 
 
 class ResEncoderBlock(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01):
+    def __init__(
+        self, in_channels, out_channels, kernel_size, n_blocks=1, momentum=0.01
+    ):
         super(ResEncoderBlock, self).__init__()
         self.conv = nn.ModuleList(
             [
diff --git a/rvc/modules/download_source.py b/rvc/modules/download_source.py
index a1e108f..c786437 100644
--- a/rvc/modules/download_source.py
+++ b/rvc/modules/download_source.py
@@ -1,7 +1,8 @@
 import urllib.request
+
 import gdown
-import requests
 import gradio as gr
+import requests
 from mega import Mega
 
 
diff --git a/rvc/modules/model_manager.py b/rvc/modules/model_manager.py
index 3e1792e..a66dd21 100644
--- a/rvc/modules/model_manager.py
+++ b/rvc/modules/model_manager.py
@@ -1,7 +1,8 @@
 import os
-import sys
 import shutil
+import sys
 import zipfile
+
 import gradio as gr
 
 from rvc.modules.download_source import download_file
@@ -151,7 +152,9 @@ def upload_separate_files(pth_file, index_file, dir_name, progress=gr.Progress()
 # Основная функция для вызова из командной строки
 def main():
     if len(sys.argv) != 3:
-        print('\nИспользование:\npython3 -m rvc.modules.model_manager "url" "dir_name"\n')
+        print(
+            '\nИспользование:\npython3 -m rvc.modules.model_manager "url" "dir_name"\n'
+        )
         sys.exit(1)
 
     url = sys.argv[1]
diff --git a/rvc/scripts/audio_processing.py b/rvc/scripts/audio_processing.py
index 5198b3b..1fa27ed 100644
--- a/rvc/scripts/audio_processing.py
+++ b/rvc/scripts/audio_processing.py
@@ -1,22 +1,22 @@
 import os
+
+import gradio as gr
 import librosa
 import numpy as np
-import gradio as gr
 import soundfile as sf
 from pedalboard import (
-    Pedalboard,
-    Reverb,
+    Chorus,
     Compressor,
     HighpassFilter,
-    LowShelfFilter,
     HighShelfFilter,
+    LowShelfFilter,
     NoiseGate,
-    Chorus,
+    Pedalboard,
+    Reverb,
 )
 from pedalboard.io import AudioFile
 from pydub import AudioSegment
 
-
 OUTPUT_DIR = os.path.join(os.getcwd(), "output")
 
 
diff --git a/rvc/scripts/edge_tts_conversion.py b/rvc/scripts/edge_tts_conversion.py
index e480251..e423de1 100644
--- a/rvc/scripts/edge_tts_conversion.py
+++ b/rvc/scripts/edge_tts_conversion.py
@@ -1,15 +1,15 @@
+import asyncio
 import gc
 import os
-import torch
+
+import edge_tts
+import gradio as gr
 import librosa
 import numpy as np
-import gradio as gr
 import soundfile as sf
-import edge_tts
-import asyncio
-
-from rvc.infer.infer import Config, load_hubert, get_vc, rvc_infer
+import torch
 
+from rvc.infer.infer import Config, get_vc, load_hubert, rvc_infer
 
 RVC_MODELS_DIR = os.path.join(os.getcwd(), "models")
 HUBERT_MODEL_PATH = os.path.join(
diff --git a/rvc/scripts/voice_conversion.py b/rvc/scripts/voice_conversion.py
index 5e5eba3..c9df367 100644
--- a/rvc/scripts/voice_conversion.py
+++ b/rvc/scripts/voice_conversion.py
@@ -1,13 +1,13 @@
 import gc
 import os
-import torch
+
+import gradio as gr
 import librosa
 import numpy as np
-import gradio as gr
 import soundfile as sf
+import torch
 
-from rvc.infer.infer import Config, load_hubert, get_vc, rvc_infer
-
+from rvc.infer.infer import Config, get_vc, load_hubert, rvc_infer
 
 RVC_MODELS_DIR = os.path.join(os.getcwd(), "models")
 HUBERT_MODEL_PATH = os.path.join(
diff --git a/tabs/conversion/conversion.py b/tabs/conversion/conversion.py
index a1b59cf..5020e2b 100644
--- a/tabs/conversion/conversion.py
+++ b/tabs/conversion/conversion.py
@@ -1,16 +1,16 @@
 import os
+
 import gradio as gr
 
-from rvc.scripts.voice_conversion import voice_pipeline
 from rvc.modules.model_manager import get_folders, update_models_list
 from rvc.modules.ui_updates import (
     process_file_upload,
     show_hop_slider,
-    update_button_text,
-    swap_visibility,
     swap_buttons,
+    swap_visibility,
+    update_button_text,
 )
-
+from rvc.scripts.voice_conversion import voice_pipeline
 from tabs.install.install_huberts import install_hubert_tab
 
 rvc_models_dir = os.path.join(os.getcwd(), "models")
diff --git a/tabs/conversion/edge_tts.py b/tabs/conversion/edge_tts.py
index d918c54..efc8687 100644
--- a/tabs/conversion/edge_tts.py
+++ b/tabs/conversion/edge_tts.py
@@ -1,10 +1,10 @@
 import os
+
 import gradio as gr
 
-from rvc.scripts.edge_tts_conversion import edge_tts_pipeline
 from rvc.modules.model_manager import get_folders, update_models_list
 from rvc.modules.ui_updates import show_hop_slider
-
+from rvc.scripts.edge_tts_conversion import edge_tts_pipeline
 from tabs.install.install_huberts import install_hubert_tab
 
 rvc_models_dir = os.path.join(os.getcwd(), "models")
diff --git a/tabs/install/install_huberts.py b/tabs/install/install_huberts.py
index 6c24913..a317b6f 100644
--- a/tabs/install/install_huberts.py
+++ b/tabs/install/install_huberts.py
@@ -2,6 +2,7 @@
 import re
 import shutil
 import urllib.request
+
 import gradio as gr
 
 embedders_dir = os.path.join(os.getcwd(), "rvc", "models", "embedders")
diff --git a/tabs/install/install_models.py b/tabs/install/install_models.py
index 9e35757..8a74e9f 100644
--- a/tabs/install/install_models.py
+++ b/tabs/install/install_models.py
@@ -2,8 +2,8 @@
 
 from rvc.modules.model_manager import (
     download_from_url,
-    upload_zip_file,
     upload_separate_files,
+    upload_zip_file,
 )
 
 
@@ -66,9 +66,13 @@ def zip_upload():
                         info="Дайте вашей загружаемой модели уникальное имя, "
                         "отличное от других голосовых моделей.",
                     )
-                    model_upload_button = gr.Button("Загрузить модель", variant="primary")
+                    model_upload_button = gr.Button(
+                        "Загрузить модель", variant="primary"
+                    )
 
-        local_upload_output_message = gr.Text(label="Сообщение вывода", interactive=False)
+        local_upload_output_message = gr.Text(
+            label="Сообщение вывода", interactive=False
+        )
         model_upload_button.click(
             upload_zip_file,
             inputs=[zip_file, local_model_name],
@@ -93,7 +97,9 @@ def files_upload():
                     info="Дайте вашей загружаемой модели уникальное имя, "
                     "отличное от других голосовых моделей.",
                 )
-                separate_upload_button = gr.Button("Загрузить модель", variant="primary")
+                separate_upload_button = gr.Button(
+                    "Загрузить модель", variant="primary"
+                )
 
         separate_upload_output_message = gr.Text(
             label="Сообщение вывода", interactive=False
diff --git a/tabs/processing/processing.py b/tabs/processing/processing.py
index b2e34ef..df5e20f 100644
--- a/tabs/processing/processing.py
+++ b/tabs/processing/processing.py
@@ -1,14 +1,14 @@
 import gradio as gr
 
-from rvc.scripts.audio_processing import process_audio
 from rvc.modules.ui_updates import (
     process_file_upload,
-    update_button_text_voc,
-    update_button_text_inst,
-    swap_visibility,
-    swap_buttons,
     show_effects,
+    swap_buttons,
+    swap_visibility,
+    update_button_text_inst,
+    update_button_text_voc,
 )
+from rvc.scripts.audio_processing import process_audio
 
 
 def processing_tab():