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This commit is contained in:
Anjok07
2022-06-13 02:07:19 -05:00
committed by GitHub
parent 8ef8183411
commit b6d6d72bcc
5 changed files with 2518 additions and 711 deletions
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414
inference_MDX.py
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@@ -1,7 +1,4 @@
import os
from pickle import STOP
from tracemalloc import stop
from turtle import update
import subprocess
from unittest import skip
from pathlib import Path
@@ -11,14 +8,18 @@ import pydub
import shutil
import hashlib
import gc
#MDX-Net
#----------------------------------------
import soundfile as sf
import torch
import numpy as np
from demucs.model import Demucs
from demucs.utils import apply_model
from demucs.pretrained import get_model as _gm
from demucs.hdemucs import HDemucs
from demucs.apply import BagOfModels, apply_model
from demucs.audio import AudioFile
import pathlib
from models import get_models, spec_effects
import onnxruntime as ort
import time
@@ -37,38 +38,43 @@ import torch
import tkinter as tk
import traceback # Error Message Recent Calls
import time # Timer
from typing import Literal
class Predictor():
def __init__(self):
pass
def prediction_setup(self, demucs_name,
channels=64):
def prediction_setup(self):
global device
print('Print the gpu setting: ', data['gpu'])
if data['gpu'] >= 0:
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
if data['gpu'] == -1:
device = torch.device('cpu')
if data['demucsmodel']:
self.demucs = Demucs(sources=["drums", "bass", "other", "vocals"], channels=channels)
widget_text.write(base_text + 'Loading Demucs model... ')
if 'UVR' in demucs_model_set:
self.demucs = HDemucs(sources=["other", "vocals"])
else:
self.demucs = HDemucs(sources=["drums", "bass", "other", "vocals"])
widget_text.write(base_text + 'Loading Demucs model...')
update_progress(**progress_kwargs,
step=0.05)
path_d = Path('models/Demucs_Models')
self.demucs = _gm(name=demucs_model_set, repo=path_d)
self.demucs.to(device)
self.demucs.load_state_dict(torch.load(demucs_name))
widget_text.write('Done!\n')
self.demucs.eval()
widget_text.write('Done!\n')
if isinstance(self.demucs, BagOfModels):
widget_text.write(base_text + f"Selected Demucs model is a bag of {len(self.demucs.models)} model(s).\n")
self.onnx_models = {}
c = 0
print('stemtype: ', modeltype)
self.models = get_models('tdf_extra', load=False, device=cpu, stems=modeltype, n_fft_scale=n_fft_scale_set, dim_f=dim_f_set)
if not data['demucs_only']:
widget_text.write(base_text + 'Loading ONNX model... ')
@@ -87,19 +93,17 @@ class Predictor():
elif data['gpu'] == -1:
run_type = ['CPUExecutionProvider']
print(run_type)
print(str(device))
print('Selected Model: ', model_set)
self.onnx_models[c] = ort.InferenceSession(os.path.join('models/MDX_Net_Models', str(model_set) + '.onnx'), providers=run_type)
if not data['demucs_only']:
widget_text.write('Done!\n')
def prediction(self, m):
#mix, rate = sf.read(m)
mix, rate = librosa.load(m, mono=False, sr=44100)
mix, samplerate = librosa.load(m, mono=False, sr=44100)
if mix.ndim == 1:
mix = np.asfortranarray([mix,mix])
mix = np.asfortranarray([mix,mix])
samplerate = samplerate
mix = mix.T
sources = self.demix(mix.T)
widget_text.write(base_text + 'Inferences complete!\n')
@@ -226,13 +230,12 @@ class Predictor():
c += 1
if not data['demucsmodel']:
if data['inst_only']:
widget_text.write(base_text + 'Preparing to save Instrumental...')
else:
widget_text.write(base_text + 'Saving vocals... ')
sf.write(non_reduced_vocal_path, sources[c].T, rate)
sf.write(non_reduced_vocal_path, sources[c].T, samplerate)
update_progress(**progress_kwargs,
step=(0.9))
widget_text.write('Done!\n')
@@ -240,7 +243,7 @@ class Predictor():
reduction_sen = float(int(data['noisereduc_s'])/10)
subprocess.call("lib_v5\\sox\\sox.exe" + ' "' +
f"{str(non_reduced_vocal_path)}" + '" "' + f"{str(vocal_path)}" + '" ' +
"noisered lib_v5\\sox\\mdxnetnoisereduc.prof " + f"{reduction_sen}",
"noisered lib_v5\\sox\\" + noise_pro_set + ".prof " + f"{reduction_sen}",
shell=True, stdout=subprocess.PIPE,
stdin=subprocess.PIPE, stderr=subprocess.PIPE)
widget_text.write('Done!\n')
@@ -252,7 +255,11 @@ class Predictor():
else:
widget_text.write(base_text + 'Saving Vocals... ')
sf.write(non_reduced_vocal_path, sources[3].T, rate)
if data['demucs_only']:
if 'UVR' in demucs_model_set:
sf.write(non_reduced_vocal_path, sources[1].T, samplerate)
else:
sf.write(non_reduced_vocal_path, sources[source_val].T, samplerate)
update_progress(**progress_kwargs,
step=(0.9))
widget_text.write('Done!\n')
@@ -275,7 +282,7 @@ class Predictor():
widget_text.write(base_text + 'Preparing Instrumental...')
else:
widget_text.write(base_text + 'Saving Vocals... ')
sf.write(vocal_path, sources[c].T, rate)
sf.write(vocal_path, sources[c].T, samplerate)
update_progress(**progress_kwargs,
step=(0.9))
widget_text.write('Done!\n')
@@ -284,7 +291,15 @@ class Predictor():
widget_text.write(base_text + 'Preparing Instrumental...')
else:
widget_text.write(base_text + 'Saving Vocals... ')
sf.write(vocal_path, sources[3].T, rate)
if data['demucs_only']:
if 'UVR' in demucs_model_set:
sf.write(vocal_path, sources[1].T, samplerate)
else:
sf.write(vocal_path, sources[source_val].T, samplerate)
else:
sf.write(vocal_path, sources[source_val].T, samplerate)
update_progress(**progress_kwargs,
step=(0.9))
widget_text.write('Done!\n')
@@ -470,13 +485,6 @@ class Predictor():
errmessage + f'\nError Time Stamp: [{datetime.now().strftime("%Y-%m-%d %H:%M:%S")}]\n')
except:
pass
try:
print('Is there already a voc file there? ', file_exists_v)
print('Is there already a non_voc file there? ', file_exists_n)
except:
pass
if data['noisereduc_s'] == 'None':
pass
@@ -567,23 +575,37 @@ class Predictor():
segmented_mix[skip] = mix[:,start:end].copy()
if end == samples:
break
if not data['demucsmodel']:
sources = self.demix_base(segmented_mix, margin_size=margin)
elif data['demucs_only']:
sources = self.demix_demucs(segmented_mix, margin_size=margin)
if split_mode == True:
sources = self.demix_demucs_split(mix)
if split_mode == False:
sources = self.demix_demucs(segmented_mix, margin_size=margin)
else: # both, apply spec effects
base_out = self.demix_base(segmented_mix, margin_size=margin)
demucs_out = self.demix_demucs(segmented_mix, margin_size=margin)
print(split_mode)
if split_mode == True:
demucs_out = self.demix_demucs_split(mix)
if split_mode == False:
demucs_out = self.demix_demucs(segmented_mix, margin_size=margin)
nan_count = np.count_nonzero(np.isnan(demucs_out)) + np.count_nonzero(np.isnan(base_out))
if nan_count > 0:
print('Warning: there are {} nan values in the array(s).'.format(nan_count))
demucs_out, base_out = np.nan_to_num(demucs_out), np.nan_to_num(base_out)
sources = {}
print(data['mixing'])
sources[3] = (spec_effects(wave=[demucs_out[source_val],base_out[0]],
algorithm=data['mixing'],
value=b[3])*float(data['compensate'])) # compensation
if 'UVR' in demucs_model_set:
sources[source_val] = (spec_effects(wave=[demucs_out[1],base_out[0]],
algorithm=data['mixing'],
value=b[source_val])*float(data['compensate'])) # compensation
else:
sources[source_val] = (spec_effects(wave=[demucs_out[source_val],base_out[0]],
algorithm=data['mixing'],
value=b[source_val])*float(data['compensate'])) # compensation
return sources
def demix_base(self, mixes, margin_size):
@@ -594,6 +616,7 @@ class Predictor():
widget_text.write(base_text + "Running ONNX Inference...\n")
widget_text.write(base_text + "Processing "f"{onnxitera} slices... ")
print(' Running ONNX Inference...')
for mix in mixes:
gui_progress_bar_onnx += 1
if data['demucsmodel']:
@@ -602,6 +625,7 @@ class Predictor():
else:
update_progress(**progress_kwargs,
step=(0.1 + (0.9/onnxitera * gui_progress_bar_onnx)))
cmix = mixes[mix]
sources = []
n_sample = cmix.shape[1]
@@ -634,7 +658,6 @@ class Predictor():
end = None
sources.append(tar_signal[:,start:end])
chunked_sources.append(sources)
_sources = np.concatenate(chunked_sources, axis=-1)
del self.onnx_models
@@ -647,6 +670,7 @@ class Predictor():
demucsitera = len(mix)
demucsitera_calc = demucsitera * 2
gui_progress_bar_demucs = 0
widget_text.write(base_text + "Split Mode is off. (Chunks enabled for Demucs Model)\n")
widget_text.write(base_text + "Running Demucs Inference...\n")
widget_text.write(base_text + "Processing "f"{len(mix)} slices... ")
print(' Running Demucs Inference...')
@@ -659,7 +683,8 @@ class Predictor():
ref = cmix.mean(0)
cmix = (cmix - ref.mean()) / ref.std()
with torch.no_grad():
sources = apply_model(self.demucs, cmix.to(device), split=True, overlap=overlap_set, shifts=shift_set)
print(split_mode)
sources = apply_model(self.demucs, cmix[None], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0]
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
sources[[0,1]] = sources[[1,0]]
@@ -673,6 +698,27 @@ class Predictor():
sources = np.concatenate(sources, axis=-1)
widget_text.write('Done!\n')
return sources
def demix_demucs_split(self, mix):
print('shift_set ', shift_set)
widget_text.write(base_text + "Split Mode is on. (Chunks disabled for Demucs Model)\n")
widget_text.write(base_text + "Running Demucs Inference...\n")
widget_text.write(base_text + "Processing "f"{len(mix)} slices... ")
print(' Running Demucs Inference...')
mix = torch.tensor(mix, dtype=torch.float32)
ref = mix.mean(0)
mix = (mix - ref.mean()) / ref.std()
with torch.no_grad():
sources = apply_model(self.demucs, mix[None], split=split_mode, device=device, overlap=overlap_set, shifts=shift_set, progress=False)[0]
widget_text.write('Done!\n')
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
sources[[0,1]] = sources[[1,0]]
return sources
data = {
# Paths
@@ -694,11 +740,11 @@ data = {
'overlap': 0.5,
'shifts': 0,
'margin': 44100,
'channel': 64,
'split_mode': False,
'compensate': 1.03597672895,
'demucs_only': False,
'mixing': 'Default',
'DemucsModel': 'demucs_extra-3646af93_org.th',
'DemucsModel_MDX': 'UVR_Demucs_Model_1',
# Choose Model
'mdxnetModel': 'UVR-MDX-NET 1',
'mdxnetModeltype': 'Vocals (Custom)',
@@ -751,6 +797,7 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
global model_set_name
global stemset_n
global noise_pro_set
global demucs_model_set
global mdx_model_hash
@@ -759,6 +806,9 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
global overlap_set
global shift_set
global source_val
global split_mode
global demucs_switch
# Update default settings
default_chunks = data['chunks']
@@ -823,161 +873,90 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
source_val_set = 0
stem_name = '(Bass)'
try:
if data['mdxnetModel'] == 'UVR-MDX-NET 1':
if data['mdxnetModel'] == 'UVR-MDX-NET 1':
if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_1_9703.onnx'):
model_set = 'UVR_MDXNET_1_9703'
model_set_name = 'UVR_MDXNET_1_9703'
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
elif data['mdxnetModel'] == 'UVR-MDX-NET 2':
model_set = 'UVR_MDXNET_2_9682'
model_set_name = 'UVR_MDXNET_2_9682'
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
elif data['mdxnetModel'] == 'UVR-MDX-NET 3':
model_set = 'UVR_MDXNET_3_9662'
model_set_name = 'UVR_MDXNET_3_9662'
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
elif data['mdxnetModel'] == 'UVR-MDX-NET Karaoke':
model_set = 'UVR_MDXNET_KARA'
model_set_name = 'UVR_MDXNET_Karaoke'
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
elif data['mdxnetModel'] == 'other':
model_set = 'other'
model_set_name = 'other'
modeltype = 'o'
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = '(Other)'
source_val = 2
n_fft_scale_set=8192
dim_f_set=2048
elif data['mdxnetModel'] == 'drums':
model_set = 'drums'
model_set_name = 'drums'
modeltype = 'd'
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = '(Drums)'
source_val = 1
n_fft_scale_set=4096
dim_f_set=2048
elif data['mdxnetModel'] == 'bass':
model_set = 'bass'
model_set_name = 'bass'
modeltype = 'b'
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = '(Bass)'
source_val = 0
n_fft_scale_set=16384
dim_f_set=2048
else:
model_set = data['mdxnetModel']
model_set_name = data['mdxnetModel']
modeltype = stemset
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = stem_name
source_val = source_val_set
n_fft_scale_set=int(data['n_fft_scale'])
dim_f_set=int(data['dim_f'])
MDXModelName=('models/MDX_Net_Models/' + model_set + '.onnx')
mdx_model_hash = hashlib.md5(open(MDXModelName, 'rb').read()).hexdigest()
print(mdx_model_hash)
except:
if data['mdxnetModel'] == 'UVR-MDX-NET 1':
model_set = 'UVR_MDXNET_9703'
model_set_name = 'UVR_MDXNET_9703'
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
elif data['mdxnetModel'] == 'UVR-MDX-NET 2':
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
elif data['mdxnetModel'] == 'UVR-MDX-NET 2':
if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_2_9682.onnx'):
model_set = 'UVR_MDXNET_2_9682'
model_set_name = 'UVR_MDXNET_2_9682'
else:
model_set = 'UVR_MDXNET_9682'
model_set_name = 'UVR_MDXNET_9682'
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
elif data['mdxnetModel'] == 'UVR-MDX-NET 3':
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
elif data['mdxnetModel'] == 'UVR-MDX-NET 3':
if os.path.isfile('models/MDX_Net_Models/UVR_MDXNET_3_9662.onnx'):
model_set = 'UVR_MDXNET_3_9662'
model_set_name = 'UVR_MDXNET_3_9662'
else:
model_set = 'UVR_MDXNET_9662'
model_set_name = 'UVR_MDXNET_9662'
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
elif data['mdxnetModel'] == 'UVR-MDX-NET Karaoke':
model_set = 'UVR_MDXNET_KARA'
model_set_name = 'UVR_MDXNET_Karaoke'
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
elif data['mdxnetModel'] == 'other':
model_set = 'other'
model_set_name = 'other'
modeltype = 'o'
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = '(Other)'
source_val = 2
n_fft_scale_set=8192
dim_f_set=2048
elif data['mdxnetModel'] == 'drums':
model_set = 'drums'
model_set_name = 'drums'
modeltype = 'd'
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = '(Drums)'
source_val = 1
n_fft_scale_set=4096
dim_f_set=2048
elif data['mdxnetModel'] == 'bass':
model_set = 'bass'
model_set_name = 'bass'
modeltype = 'b'
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = '(Bass)'
source_val = 0
n_fft_scale_set=16384
dim_f_set=2048
else:
model_set = data['mdxnetModel']
model_set_name = data['mdxnetModel']
modeltype = stemset
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = stem_name
source_val = source_val_set
n_fft_scale_set=int(data['n_fft_scale'])
dim_f_set=int(data['dim_f'])
MDXModelName=('models/MDX_Net_Models/' + model_set_name + '.onnx')
mdx_model_hash = hashlib.md5(open(MDXModelName, 'rb').read()).hexdigest()
print(mdx_model_hash)
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
elif data['mdxnetModel'] == 'UVR-MDX-NET Karaoke':
model_set = 'UVR_MDXNET_KARA'
model_set_name = 'UVR_MDXNET_Karaoke'
modeltype = 'v'
noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
stemset_n = '(Vocals)'
source_val = 3
n_fft_scale_set=6144
dim_f_set=2048
elif 'other' in data['mdxnetModel']:
model_set = 'other'
model_set_name = 'other'
modeltype = 'o'
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = '(Other)'
source_val = 2
n_fft_scale_set=8192
dim_f_set=2048
elif 'drums' in data['mdxnetModel']:
model_set = 'drums'
model_set_name = 'drums'
modeltype = 'd'
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = '(Drums)'
source_val = 1
n_fft_scale_set=4096
dim_f_set=2048
elif 'bass' in data['mdxnetModel']:
model_set = 'bass'
model_set_name = 'bass'
modeltype = 'b'
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = '(Bass)'
source_val = 0
n_fft_scale_set=16384
dim_f_set=2048
else:
model_set = data['mdxnetModel']
model_set_name = data['mdxnetModel']
modeltype = stemset
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
stemset_n = stem_name
source_val = source_val_set
n_fft_scale_set=int(data['n_fft_scale'])
dim_f_set=int(data['dim_f'])
if data['noise_pro_select'] == 'Auto Select':
@@ -988,12 +967,8 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
print(n_fft_scale_set)
print(dim_f_set)
print(data['DemucsModel'])
print(data['DemucsModel_MDX'])
overlap_set = float(data['overlap'])
channel_set = int(data['channel'])
margin_set = int(data['margin'])
shift_set = int(data['shifts'])
stime = time.perf_counter()
progress_var.set(0)
@@ -1002,7 +977,46 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
try: #Load File(s)
for file_num, music_file in tqdm(enumerate(data['input_paths'], start=1)):
overlap_set = float(data['overlap'])
channel_set = int(data['channel'])
margin_set = int(data['margin'])
shift_set = int(data['shifts'])
demucs_model_set = data['DemucsModel_MDX']
split_mode = data['split_mode']
demucs_switch = data['demucsmodel']
if stemset_n == '(Bass)':
if 'UVR' in demucs_model_set:
text_widget.write('The selected Demucs model can only be used with vocal stems.\n')
text_widget.write('Please select a 4 stem Demucs model and try again.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
else:
pass
if stemset_n == '(Drums)':
if 'UVR' in demucs_model_set:
text_widget.write('The selected Demucs model can only be used with vocal stems.\n')
text_widget.write('Please select a 4 stem Demucs model and try again.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
else:
pass
if stemset_n == '(Other)':
if 'UVR' in demucs_model_set:
text_widget.write('The selected Demucs model can only be used with vocal stems.\n')
text_widget.write('Please select a 4 stem Demucs model and try again.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
else:
pass
_mixture = f'{data["input_paths"]}'
_basename = f'{data["export_path"]}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
@@ -1063,11 +1077,10 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
e = os.path.join(data["export_path"])
demucsmodel = 'models/Demucs_Model/' + str(data['DemucsModel'])
demucsmodel = 'models/Demucs_Models/' + str(data['DemucsModel_MDX'])
pred = Predictor()
pred.prediction_setup(demucs_name=demucsmodel,
channels=channel_set)
pred.prediction_setup()
print(demucsmodel)
@@ -1373,7 +1386,10 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
text_widget.write("\n" + f'Please address the error and try again.' + "\n")
text_widget.write(f'If this error persists, please contact the developers with the error details.\n\n')
text_widget.write(f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}')
torch.cuda.empty_cache()
try:
torch.cuda.empty_cache()
except:
pass
button_widget.configure(state=tk.NORMAL) # Enable Button
return