def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button,
progress_var: tk.Variable, **kwargs: dict):
def load_models():
text_widget.write('Loading models...\n') # nopep8 Write Command Text
models = defaultdict(lambda: None)
devices = defaultdict(lambda: None)
# -Instrumental-
if os.path.isfile(data['instrumentalModel']):
device = torch.device('cpu')
model = nets.CascadedASPPNet()
model.load_state_dict(
torch.load(data['instrumentalModel'], map_location=device))
if torch.cuda.is_available() and data['gpu'] >= 0:
device = torch.device('cuda:{}'.format(data['gpu']))
model.to(device)
models['instrumental'] = model
devices['instrumental'] = device
# -Vocal-
elif os.path.isfile(data['vocalModel']):
device = torch.device('cpu')
model = nets.CascadedASPPNet()
model.load_state_dict(
torch.load(data['vocalModel'], map_location=device))
if torch.cuda.is_available() and data['gpu'] >= 0:
device = torch.device('cuda:{}'.format(data['gpu']))
model.to(device)
models['vocal'] = model
devices['vocal'] = device
# -Stack-
if os.path.isfile(data['stackModel']):
device = torch.device('cpu')
model = nets.CascadedASPPNet()
model.load_state_dict(
torch.load(data['stackModel'], map_location=device))
if torch.cuda.is_available() and data['gpu'] >= 0:
device = torch.device('cuda:{}'.format(data['gpu']))
model.to(device)
models['stack'] = model
devices['stack'] = device
text_widget.write('Done!\n')
return models, devices
def load_wave_source():
X, sr = librosa.load(music_file,
data['sr'],
False,
dtype=np.float32,
res_type='kaiser_fast')
return X, sr
def stft_wave_source(X, model, device):
X = spec_utils.calc_spec(X, data['hop_length'])
X, phase = np.abs(X), np.exp(1.j * np.angle(X))
coeff = X.max()
X /= coeff
offset = model.offset
l, r, roi_size = dataset.make_padding(X.shape[2], data['window_size'],
offset)
X_pad = np.pad(X, ((0, 0), (0, 0), (l, r)), mode='constant')
X_roll = np.roll(X_pad, roi_size // 2, axis=2)
model.eval()
with torch.no_grad():
masks = []
masks_roll = []
length = int(np.ceil(X.shape[2] / roi_size))
for i in tqdm(range(length)):
update_progress(**progress_kwargs,
step=0.1 + 0.5 * (i / (length - 1)))
start = i * roi_size
X_window = torch.from_numpy(
np.asarray([
X_pad[:, :, start:start + data['window_size']],
X_roll[:, :, start:start + data['window_size']]
])).to(device)
pred = model.predict(X_window)
pred = pred.detach().cpu().numpy()
masks.append(pred[0])
masks_roll.append(pred[1])
mask = np.concatenate(masks, axis=2)[:, :, :X.shape[2]]
mask_roll = np.concatenate(masks_roll, axis=2)[:, :, :X.shape[2]]
mask = (mask + np.roll(mask_roll, -roi_size // 2, axis=2)) / 2
if data['postprocess']:
vocal = X * (1 - mask) * coeff
mask = spec_utils.mask_uninformative(mask, vocal)
inst = X * mask * coeff
vocal = X * (1 - mask) * coeff
return inst, vocal, phase, mask
def invert_instrum_vocal(inst, vocal, phase):
wav_instrument = spec_utils.spec_to_wav(inst, phase,
data['hop_length']) # nopep8
wav_vocals = spec_utils.spec_to_wav(vocal, phase,
data['hop_length']) # nopep8
return wav_instrument, wav_vocals
def save_files(wav_instrument, wav_vocals):
"""Save output music files"""
vocal_name = None
instrumental_name = None
save_path = os.path.dirname(base_name)
# Get the Suffix Name
if (not loop_num
or loop_num == (total_loops - 1)): # First or Last Loop
if data['stackOnly']:
if loop_num == (total_loops - 1): # Last Loop
if not (total_loops - 1): # Only 1 Loop
vocal_name = '(Vocals)'
instrumental_name = '(Instrumental)'
else:
vocal_name = '(Vocal_Final_Stacked_Output)'
instrumental_name = '(Instrumental_Final_Stacked_Output)'
elif data['useModel'] == 'instrumental':
if not loop_num: # First Loop
vocal_name = '(Vocals)'
if loop_num == (total_loops - 1): # Last Loop
if not (total_loops - 1): # Only 1 Loop
instrumental_name = '(Instrumental)'
else:
instrumental_name = '(Instrumental_Final_Stacked_Output)'
elif data['useModel'] == 'vocal':
if not loop_num: # First Loop
instrumental_name = '(Instrumental)'
if loop_num == (total_loops - 1): # Last Loop
if not (total_loops - 1): # Only 1 Loop
vocal_name = '(Vocals)'
else:
vocal_name = '(Vocals_Final_Stacked_Output)'
if data['useModel'] == 'vocal':
# Reverse names
vocal_name, instrumental_name = instrumental_name, vocal_name
elif data['saveAllStacked']:
folder_name = os.path.basename(
base_name) + ' Stacked Outputs' # nopep8
save_path = os.path.join(save_path, folder_name)
if not os.path.isdir(save_path):
os.mkdir(save_path)
if data['stackOnly']:
vocal_name = f'(Vocal_{loop_num}_Stacked_Output)'
instrumental_name = f'(Instrumental_{loop_num}_Stacked_Output)'
elif (data['useModel'] == 'vocal'
or data['useModel'] == 'instrumental'):
vocal_name = f'(Vocals_{loop_num}_Stacked_Output)'
instrumental_name = f'(Instrumental_{loop_num}_Stacked_Output)'
if data['useModel'] == 'vocal':
# Reverse names
vocal_name, instrumental_name = instrumental_name, vocal_name
# Save Temp File
# For instrumental the instrumental is the temp file
# and for vocal the instrumental is the temp file due
# to reversement
sf.write(f'temp.wav', wav_instrument.T, sr)
appendModelFolderName = modelFolderName.replace('/', '_')
# -Save files-
# Instrumental
if instrumental_name is not None:
instrumental_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name=
f'{os.path.basename(base_name)}_{instrumental_name}{appendModelFolderName}',
)
sf.write(instrumental_path, wav_instrument.T, sr)
# Vocal
if vocal_name is not None:
vocal_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name=
f'{os.path.basename(base_name)}_{vocal_name}{appendModelFolderName}',
)
sf.write(vocal_path, wav_vocals.T, sr)
def output_image():
norm_mask = np.uint8((1 - mask) * 255).transpose(1, 2, 0)
norm_mask = np.concatenate(
[np.max(norm_mask, axis=2, keepdims=True), norm_mask],
axis=2)[::-1]
_, bin_mask = cv2.imencode('.png', norm_mask)
text_widget.write(base_text +
'Saving Mask...\n') # nopep8 Write Command Text
with open(f'{base_name}_(Mask).png', mode='wb') as f:
bin_mask.tofile(f)
data.update(kwargs)
# Update default settings
global default_sr
global default_hop_length
global default_window_size
global default_n_fft
default_sr = data['sr']
default_hop_length = data['hop_length']
default_window_size = data['window_size']
default_n_fft = data['n_fft']
stime = time.perf_counter()
progress_var.set(0)
text_widget.clear()
button_widget.configure(state=tk.DISABLED) # Disable Button
models, devices = load_models()
modelFolderName = determineModelFolderName()
if modelFolderName:
folder_path = f'{data["export_path"]}{modelFolderName}'
if not os.path.isdir(folder_path):
os.mkdir(folder_path)
# Determine Loops
total_loops = data['stackPasses']
if not data['stackOnly']:
total_loops += 1
for file_num, music_file in enumerate(data['input_paths'], start=1):
try:
# Determine File Name
base_name = f'{data["export_path"]}{modelFolderName}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
for loop_num in range(total_loops):
# -Determine which model will be used-
if not loop_num:
# First Iteration
if data['stackOnly']:
if os.path.isfile(data['stackModel']):
model_name = os.path.basename(data['stackModel'])
model = models['stack']
device = devices['stack']
else:
raise ValueError(
f'Selected stack only model, however, stack model path file cannot be found\nPath: "{data["stackModel"]}"'
) # nopep8
else:
model_name = os.path.basename(
data[f'{data["useModel"]}Model'])
model = models[data['useModel']]
device = devices[data['useModel']]
else:
model_name = os.path.basename(data['stackModel'])
# Every other iteration
model = models['stack']
device = devices['stack']
# Reference new music file
music_file = 'temp.wav'
# -Get text and update progress-
base_text = get_baseText(total_files=len(data['input_paths']),
total_loops=total_loops,
file_num=file_num,
loop_num=loop_num)
progress_kwargs = {
'progress_var': progress_var,
'total_files': len(data['input_paths']),
'total_loops': total_loops,
'file_num': file_num,
'loop_num': loop_num
}
update_progress(**progress_kwargs, step=0)
update_constants(model_name)
# -Go through the different steps of seperation-
# Wave source
text_widget.write(
base_text +
'Loading wave source...\n') # nopep8 Write Command Text
X, sr = load_wave_source()
text_widget.write(base_text +
'Done!\n') # nopep8 Write Command Text
update_progress(**progress_kwargs, step=0.1)
# Stft of wave source
text_widget.write(
base_text +
'Stft of wave source...\n') # nopep8 Write Command Text
inst, vocal, phase, mask = stft_wave_source(X, model, device)
text_widget.write(base_text +
'Done!\n') # nopep8 Write Command Text
update_progress(**progress_kwargs, step=0.6)
# Inverse stft
text_widget.write(base_text +
'Inverse stft of instruments and vocals...\n'
) # nopep8 Write Command Text
wav_instrument, wav_vocals = invert_instrum_vocal(
inst, vocal, phase) # nopep8
text_widget.write(base_text +
'Done!\n') # nopep8 Write Command Text
update_progress(**progress_kwargs, step=0.7)
# Save Files
text_widget.write(
base_text +
'Saving Files...\n') # nopep8 Write Command Text
save_files(wav_instrument, wav_vocals)
text_widget.write(base_text +
'Done!\n') # nopep8 Write Command Text
update_progress(**progress_kwargs, step=0.8)
else:
# Save Output Image (Mask)
if data['output_image']:
text_widget.write(
base_text +
'Creating Mask...\n') # nopep8 Write Command Text
output_image()
text_widget.write(base_text +
'Done!\n') # nopep8 Write Command Text
text_widget.write(
base_text +
'Completed Seperation!\n\n') # nopep8 Write Command Text
except Exception as e:
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
message = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\nFile: {music_file}\nLoop: {loop_num}\nPlease contact the creator and attach a screenshot of this error with the file and settings that caused it!'
tk.messagebox.showerror(master=window,
title='Untracked Error',
message=message)
print(traceback_text)
print(type(e).__name__, e)
print(message)
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
os.remove('temp.wav')
progress_var.set(0) # Update Progress
text_widget.write(f'Conversion(s) Completed and Saving all Files!\n'
) # nopep8 Write Command Text
text_widget.write(
f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}'
) # nopep8
button_widget.configure(state=tk.NORMAL) # Enable Button
def main(window: tk.Wm, text_widget: tk.Text, button_widget: tk.Button,
progress_var: tk.Variable, **kwargs: dict):
def save_files(wav_instrument, wav_vocals):
"""Save output music files"""
vocal_name = None
instrumental_name = None
save_path = os.path.dirname(base_name)
# Get the Suffix Name
if (not loop_num
or loop_num == (total_loops - 1)): # First or Last Loop
if data['stackOnly']:
if loop_num == (total_loops - 1): # Last Loop
if not (total_loops - 1): # Only 1 Loop
vocal_name = '(Vocals)'
instrumental_name = '(Instrumental)'
else:
vocal_name = '(Vocal_Final_Stacked_Output)'
instrumental_name = '(Instrumental_Final_Stacked_Output)'
elif data['useModel'] == 'instrumental':
if not loop_num: # First Loop
vocal_name = '(Vocals)'
if loop_num == (total_loops - 1): # Last Loop
if not (total_loops - 1): # Only 1 Loop
instrumental_name = '(Instrumental)'
else:
instrumental_name = '(Instrumental_Final_Stacked_Output)'
elif data['useModel'] == 'vocal':
if not loop_num: # First Loop
instrumental_name = '(Instrumental)'
if loop_num == (total_loops - 1): # Last Loop
if not (total_loops - 1): # Only 1 Loop
vocal_name = '(Vocals)'
else:
vocal_name = '(Vocals_Final_Stacked_Output)'
if data['useModel'] == 'vocal':
# Reverse names
vocal_name, instrumental_name = instrumental_name, vocal_name
elif data['saveAllStacked']:
folder_name = os.path.basename(
base_name) + ' Stacked Outputs' # nopep8
save_path = os.path.join(save_path, folder_name)
if not os.path.isdir(save_path):
os.mkdir(save_path)
if data['stackOnly']:
vocal_name = f'(Vocal_{loop_num}_Stacked_Output)'
instrumental_name = f'(Instrumental_{loop_num}_Stacked_Output)'
elif (data['useModel'] == 'vocal'
or data['useModel'] == 'instrumental'):
vocal_name = f'(Vocals_{loop_num}_Stacked_Output)'
instrumental_name = f'(Instrumental_{loop_num}_Stacked_Output)'
if data['useModel'] == 'vocal':
# Reverse names
vocal_name, instrumental_name = instrumental_name, vocal_name
# Save Temp File
# For instrumental the instrumental is the temp file
# and for vocal the instrumental is the temp file due
# to reversement
sf.write(f'temp.wav', wav_instrument.T, sr)
appendModelFolderName = modelFolderName.replace('/', '_')
# -Save files-
# Instrumental
if instrumental_name is not None:
instrumental_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name=
f'{os.path.basename(base_name)}_{instrumental_name}{appendModelFolderName}',
)
sf.write(instrumental_path, wav_instrument.T, sr)
# Vocal
if vocal_name is not None:
vocal_path = '{save_path}/{file_name}.wav'.format(
save_path=save_path,
file_name=
f'{os.path.basename(base_name)}_{vocal_name}{appendModelFolderName}',
)
sf.write(vocal_path, wav_vocals.T, sr)
data.update(kwargs)
# Update default settings
global default_sr
global default_hop_length
global default_window_size
global default_n_fft
default_sr = data['sr']
default_hop_length = data['hop_length']
default_window_size = data['window_size']
default_n_fft = data['n_fft']
stime = time.perf_counter()
progress_var.set(0)
text_widget.clear()
button_widget.configure(state=tk.DISABLED) # Disable Button
vocal_remover = VocalRemover(data, text_widget)
modelFolderName = determineModelFolderName()
if modelFolderName:
folder_path = f'{data["export_path"]}{modelFolderName}'
if not os.path.isdir(folder_path):
os.mkdir(folder_path)
# Determine Loops
total_loops = data['stackPasses']
if not data['stackOnly']:
total_loops += 1
for file_num, music_file in enumerate(data['input_paths'], start=1):
try:
# Determine File Name
base_name = f'{data["export_path"]}{modelFolderName}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
# --Seperate Music Files--
for loop_num in range(total_loops):
# -Determine which model will be used-
if not loop_num:
# First Iteration
if data['stackOnly']:
if os.path.isfile(data['stackModel']):
model_name = os.path.basename(data['stackModel'])
model = vocal_remover.models['stack']
device = vocal_remover.devices['stack']
else:
raise ValueError(
f'Selected stack only model, however, stack model path file cannot be found\nPath: "{data["stackModel"]}"'
) # nopep8
else:
model_name = os.path.basename(
data[f'{data["useModel"]}Model'])
model = vocal_remover.models[data['useModel']]
device = vocal_remover.devices[data['useModel']]
else:
model_name = os.path.basename(data['stackModel'])
# Every other iteration
model = vocal_remover.models['stack']
device = vocal_remover.devices['stack']
# Reference new music file
music_file = 'temp.wav'
# -Get text and update progress-
base_text = get_baseText(total_files=len(data['input_paths']),
total_loops=total_loops,
file_num=file_num,
loop_num=loop_num)
progress_kwargs = {
'progress_var': progress_var,
'total_files': len(data['input_paths']),
'total_loops': total_loops,
'file_num': file_num,
'loop_num': loop_num
}
update_progress(**progress_kwargs, step=0)
update_constants(model_name)
# -Go through the different steps of seperation-
# Wave source
text_widget.write(base_text + 'Loading wave source...\n')
X, sr = librosa.load(music_file,
data['sr'],
False,
dtype=np.float32,
res_type='kaiser_fast')
if X.ndim == 1:
X = np.asarray([X, X])
text_widget.write(base_text + 'Done!\n')
update_progress(**progress_kwargs, step=0.1)
# Stft of wave source
text_widget.write(base_text + 'Stft of wave source...\n')
X = spec_utils.wave_to_spectrogram(X, data['hop_length'],
data['n_fft'])
if data['tta']:
pred, X_mag, X_phase = vocal_remover.inference_tta(
X, device=device, model=model)
else:
pred, X_mag, X_phase = vocal_remover.inference(
X, device=device, model=model)
text_widget.write(base_text + 'Done!\n')
update_progress(**progress_kwargs, step=0.6)
# Postprocess
if data['postprocess']:
text_widget.write(base_text + 'Post processing...\n')
pred_inv = np.clip(X_mag - pred, 0, np.inf)
pred = spec_utils.mask_silence(pred, pred_inv)
text_widget.write(base_text + 'Done!\n')
update_progress(**progress_kwargs, step=0.65)
# Inverse stft
text_widget.write(
base_text +
'Inverse stft of instruments and vocals...\n') # nopep8
y_spec = pred * X_phase
wav_instrument = spec_utils.spectrogram_to_wave(
y_spec, hop_length=data['hop_length'])
v_spec = np.clip(X_mag - pred, 0, np.inf) * X_phase
wav_vocals = spec_utils.spectrogram_to_wave(
v_spec, hop_length=data['hop_length'])
text_widget.write(base_text + 'Done!\n')
update_progress(**progress_kwargs, step=0.7)
# Save output music files
text_widget.write(base_text + 'Saving Files...\n')
save_files(wav_instrument, wav_vocals)
text_widget.write(base_text + 'Done!\n')
update_progress(**progress_kwargs, step=0.8)
else:
# Save output image
if data['output_image']:
with open('{}_Instruments.jpg'.format(base_name),
mode='wb') as f:
image = spec_utils.spectrogram_to_image(y_spec)
_, bin_image = cv2.imencode('.jpg', image)
bin_image.tofile(f)
with open('{}_Vocals.jpg'.format(base_name),
mode='wb') as f:
image = spec_utils.spectrogram_to_image(v_spec)
_, bin_image = cv2.imencode('.jpg', image)
bin_image.tofile(f)
text_widget.write(base_text + 'Completed Seperation!\n\n')
except Exception as e:
traceback_text = ''.join(traceback.format_tb(e.__traceback__))
message = f'Traceback Error: "{traceback_text}"\n{type(e).__name__}: "{e}"\nFile: {music_file}\nLoop: {loop_num}\nPlease contact the creator and attach a screenshot of this error with the file and settings that caused it!'
tk.messagebox.showerror(master=window,
title='Untracked Error',
message=message)
print(traceback_text)
print(type(e).__name__, e)
print(message)
progress_var.set(0)
button_widget.configure(state=tk.NORMAL) # Enable Button
return
os.remove('temp.wav')
progress_var.set(0)
text_widget.write(f'Conversion(s) Completed and Saving all Files!\n')
text_widget.write(
f'Time Elapsed: {time.strftime("%H:%M:%S", time.gmtime(int(time.perf_counter() - stime)))}'
) # nopep8
button_widget.configure(state=tk.NORMAL) # Enable Button
