heimoshuiyu/ultimatevocalremovergui
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This commit is contained in:
Anjok07
2022-06-03 04:08:37 -05:00
committed by GitHub
parent 3f62d7e877
commit aa2dd10834
5 changed files with 845 additions and 361 deletions
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195
inference_MDX.py
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@@ -62,11 +62,16 @@ class Predictor():
self.demucs.load_state_dict(torch.load(demucs_name))
widget_text.write('Done!\n')
self.demucs.eval()
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)
widget_text.write(base_text + 'Loading ONNX model... ')
if not data['demucs_only']:
widget_text.write(base_text + 'Loading ONNX model... ')
update_progress(**progress_kwargs,
step=0.1)
c+=1
@@ -85,7 +90,9 @@ class Predictor():
print(str(device))
self.onnx_models[c] = ort.InferenceSession(os.path.join('models/MDX_Net_Models', str(model_set) + '.onnx'), providers=run_type)
widget_text.write('Done!\n')
if not data['demucs_only']:
widget_text.write('Done!\n')
def prediction(self, m):
#mix, rate = sf.read(m)
@@ -100,8 +107,18 @@ class Predictor():
#Main Save Path
save_path = os.path.dirname(_basename)
print('stemset_n: ', stemset_n)
#Vocal Path
vocal_name = '(Vocals)'
if stemset_n == '(Vocals)':
vocal_name = '(Vocals)'
elif stemset_n == '(Other)':
vocal_name = '(Other)'
elif stemset_n == '(Drums)':
vocal_name = '(Drums)'
elif stemset_n == '(Bass)':
vocal_name = '(Bass)'
if data['modelFolder']:
vocal_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
@@ -124,7 +141,16 @@ class Predictor():
file_name = f'{os.path.basename(_basename)}_{vocal_name}',)
#Instrumental Path
Instrumental_name = '(Instrumental)'
if stemset_n == '(Vocals)':
Instrumental_name = '(Instrumental)'
elif stemset_n == '(Other)':
Instrumental_name = '(No_Other)'
elif stemset_n == '(Drums)':
Instrumental_name = '(No_Drums)'
elif stemset_n == '(Bass)':
Instrumental_name = '(No_Bass)'
if data['modelFolder']:
Instrumental_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
@@ -147,7 +173,15 @@ class Predictor():
file_name = f'{os.path.basename(_basename)}_{Instrumental_name}',)
#Non-Reduced Vocal Path
vocal_name = '(Vocals)'
if stemset_n == '(Vocals)':
vocal_name = '(Vocals)'
elif stemset_n == '(Other)':
vocal_name = '(Other)'
elif stemset_n == '(Drums)':
vocal_name = '(Drums)'
elif stemset_n == '(Bass)':
vocal_name = '(Bass)'
if data['modelFolder']:
non_reduced_vocal_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
@@ -222,7 +256,7 @@ class Predictor():
step=(0.9))
widget_text.write('Done!\n')
widget_text.write(base_text + 'Performing Noise Reduction... ')
reduction_sen = float(int(data['noisereduc_s'])/10)
reduction_sen = float(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}",
@@ -442,8 +476,6 @@ class Predictor():
except:
pass
if data['noisereduc_s'] == 'None':
pass
elif data['non_red'] == True:
@@ -536,7 +568,8 @@ class Predictor():
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)
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)
@@ -545,10 +578,10 @@ class Predictor():
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 = {}
sources[3] = (spec_effects(wave=[demucs_out[3],base_out[0]],
algorithm='default',
value=b[3])*1.03597672895) # compensation
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
return sources
def demix_base(self, mixes, margin_size):
@@ -607,6 +640,7 @@ class Predictor():
return _sources
def demix_demucs(self, mix, margin_size):
print('shift_set ', shift_set)
processed = {}
demucsitera = len(mix)
demucsitera_calc = demucsitera * 2
@@ -622,7 +656,6 @@ class Predictor():
cmix = torch.tensor(cmix, dtype=torch.float32)
ref = cmix.mean(0)
cmix = (cmix - ref.mean()) / ref.std()
shift_set = 0
with torch.no_grad():
sources = apply_model(self.demucs, cmix.to(device), split=True, overlap=overlap_set, shifts=shift_set)
sources = (sources * ref.std() + ref.mean()).cpu().numpy()
@@ -650,16 +683,22 @@ data = {
'chunks': 10,
'non_red': False,
'noisereduc_s': 3,
'mixing': 'default',
'modelFolder': False,
'voc_only': False,
'inst_only': False,
'break': False,
'n_fft_scale': 6144,
'dim_f': 2048,
'overlap': 0.5,
'shifts': 0,
'margin': 44100,
'channel': 64,
'compensate': 1.03597672895,
'demucs_only': False,
'mixing': 'Default',
'DemucsModel': 'demucs_extra-3646af93_org.th',
# Choose Model
'mdxnetModel': 'UVR-MDX-NET 1',
'high_end_process': 'mirroring',
'mdxnetModeltype': 'Vocals (Custom)',
}
default_chunks = data['chunks']
default_noisereduc_s = data['noisereduc_s']
@@ -696,9 +735,6 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
global widget_text
global gui_progress_bar
global music_file
global channel_set
global margin_set
global overlap_set
global default_chunks
global default_noisereduc_s
global _basename
@@ -710,14 +746,17 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
global base_text
global model_set
global model_set_name
global stemset_n
global channel_set
global margin_set
global overlap_set
global shift_set
global source_val
# Update default settings
default_chunks = data['chunks']
default_noisereduc_s = data['noisereduc_s']
channel_set = int(64)
margin_set = int(44100)
overlap_set = float(0.5)
widget_text = text_widget
gui_progress_bar = progress_var
@@ -735,7 +774,6 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
ffmp_err = """audioread\__init__.py", line 116, in audio_open"""
sf_write_err = "sf.write"
try:
with open('errorlog.txt', 'w') as f:
f.write(f'No errors to report at this time.' + f'\n\nLast Process Method Used: MDX-Net' +
@@ -745,38 +783,112 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
data.update(kwargs)
if data['mdxnetModeltype'] == 'Vocals (Custom)':
stemset = 'v'
source_val_set = 3
stem_name = '(Vocals)'
if data['mdxnetModeltype'] == 'Other (Custom)':
stemset = 'o'
source_val_set = 2
stem_name = '(Other)'
if data['mdxnetModeltype'] == 'Drums (Custom)':
stemset = 'd'
source_val_set = 1
stem_name = '(Drums)'
if data['mdxnetModeltype'] == 'Bass (Custom)':
stemset = 'b'
source_val_set = 0
stem_name = '(Bass)'
if data['mdxnetModeltype'] == 'Vocals (Default)':
stemset = 'v'
source_val_set = 3
stem_name = '(Vocals)'
if data['mdxnetModeltype'] == 'Other (Default)':
stemset = 'o'
source_val_set = 2
stem_name = '(Other)'
if data['mdxnetModeltype'] == 'Drums (Default)':
stemset = 'd'
source_val_set = 1
stem_name = '(Drums)'
if data['mdxnetModeltype'] == 'Bass (Default)':
stemset = 'b'
source_val_set = 0
stem_name = '(Bass)'
if data['mdxnetModel'] == 'UVR-MDX-NET 1':
model_set = 'UVR_MDXNET_9703'
model_set_name = 'UVR_MDXNET_9703'
modeltype = 'vocals-one'
model_set = 'UVR_MDXNET_1_9703'
model_set_name = 'UVR_MDXNET_1_9703'
modeltype = 'v'
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_9682'
model_set_name = 'UVR_MDXNET_9682'
modeltype = 'vocals-one'
model_set = 'UVR_MDXNET_2_9682'
model_set_name = 'UVR_MDXNET_2_9682'
modeltype = 'v'
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_9662'
model_set_name = 'UVR_MDXNET_9662'
model_set = 'UVR_MDXNET_3_9662'
model_set_name = 'UVR_MDXNET_3_9662'
modeltype = 'v'
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 = 'vocals-one'
modeltype = 'v'
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'
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'
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'
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 = 'vocals-two'
modeltype = stemset
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'])
print(n_fft_scale_set)
print(dim_f_set)
print(data['DemucsModel'])
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)
@@ -799,11 +911,20 @@ def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button, progress
print(model_set)
try:
if float(data['noisereduc_s']) >= 10:
text_widget.write('Error: Noise Reduction only supports values between 0-10.\nPlease set a value between 0-10 (with or without decimals) and try again.')
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
total, used, free = shutil.disk_usage("/")
total_space = int(total/1.074e+9)
used_space = int(used/1.074e+9)
free_space = int(free/1.074e+9)
if int(free/1.074e+9) <= int(2):
text_widget.write('Error: Not enough storage on main drive to continue. Your main drive must have \nat least 3 GB\'s of storage in order for this application function properly. \n\nPlease ensure your main drive has at least 3 GB\'s of storage and try again.\n\n')
@@ -837,12 +958,14 @@ 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/demucs_extra-3646af93_org.th'
demucsmodel = 'models/Demucs_Model/' + str(data['DemucsModel'])
pred = Predictor()
pred.prediction_setup(demucs_name=demucsmodel,
channels=channel_set)
print(demucsmodel)
# split
pred.prediction(
m=music_file,