mirror of
https://github.com/Anjok07/ultimatevocalremovergui.git
synced 2024-11-24 23:40:10 +01:00
440 lines
18 KiB
Python
440 lines
18 KiB
Python
import pprint
|
|
import argparse
|
|
import os
|
|
import importlib
|
|
|
|
import cv2
|
|
import librosa
|
|
import math
|
|
import numpy as np
|
|
import soundfile as sf
|
|
from tqdm import tqdm
|
|
|
|
from lib_v5 import dataset
|
|
from lib_v5 import spec_utils
|
|
from lib_v5.model_param_init import ModelParameters
|
|
import torch
|
|
|
|
# Command line text parsing and widget manipulation
|
|
from collections import defaultdict
|
|
import tkinter as tk
|
|
import traceback # Error Message Recent Calls
|
|
import time # Timer
|
|
|
|
|
|
|
|
|
|
class VocalRemover(object):
|
|
|
|
def __init__(self, data, text_widget: tk.Text):
|
|
self.data = data
|
|
self.text_widget = text_widget
|
|
self.models = defaultdict(lambda: None)
|
|
self.devices = defaultdict(lambda: None)
|
|
self._load_models()
|
|
# self.offset = model.offset
|
|
|
|
def _load_models(self):
|
|
self.text_widget.write('Loading models...\n') # nopep8 Write Command Text
|
|
|
|
nn_arch_sizes = [
|
|
31191, # default
|
|
33966, 123821, 123812, 537238 # custom
|
|
]
|
|
|
|
global args
|
|
global model_params_d
|
|
|
|
p = argparse.ArgumentParser()
|
|
p.add_argument('--paramone', type=str, default='lib_v5/modelparams/4band_44100.json')
|
|
p.add_argument('--paramtwo', type=str, default='lib_v5/modelparams/4band_v2.json')
|
|
p.add_argument('--paramthree', type=str, default='lib_v5/modelparams/3band_44100_msb2.json')
|
|
p.add_argument('--paramfour', type=str, default='lib_v5/modelparams/4band_v2_sn.json')
|
|
p.add_argument('--aggressiveness',type=float, default=data['agg']/100)
|
|
p.add_argument('--nn_architecture', type=str, choices= ['auto'] + list('{}KB'.format(s) for s in nn_arch_sizes), default='auto')
|
|
p.add_argument('--high_end_process', type=str, default='mirroring')
|
|
args = p.parse_args()
|
|
|
|
if 'auto' == args.nn_architecture:
|
|
model_size = math.ceil(os.stat(data['instrumentalModel']).st_size / 1024)
|
|
args.nn_architecture = '{}KB'.format(min(nn_arch_sizes, key=lambda x:abs(x-model_size)))
|
|
|
|
nets = importlib.import_module('lib_v5.nets' + f'_{args.nn_architecture}'.replace('_{}KB'.format(nn_arch_sizes[0]), ''), package=None)
|
|
|
|
ModelName=(data['instrumentalModel'])
|
|
|
|
ModelParam1="4BAND_44100"
|
|
ModelParam2="4BAND_44100_B"
|
|
ModelParam3="MSB2"
|
|
ModelParam4="4BAND_44100_SN"
|
|
|
|
if ModelParam1 in ModelName:
|
|
model_params_d=args.paramone
|
|
if ModelParam2 in ModelName:
|
|
model_params_d=args.paramtwo
|
|
if ModelParam3 in ModelName:
|
|
model_params_d=args.paramthree
|
|
if ModelParam4 in ModelName:
|
|
model_params_d=args.paramfour
|
|
|
|
print(model_params_d)
|
|
|
|
mp = ModelParameters(model_params_d)
|
|
|
|
# -Instrumental-
|
|
if os.path.isfile(data['instrumentalModel']):
|
|
device = torch.device('cpu')
|
|
model = nets.CascadedASPPNet(mp.param['bins'] * 2)
|
|
model.load_state_dict(torch.load(self.data['instrumentalModel'],
|
|
map_location=device))
|
|
if torch.cuda.is_available() and self.data['gpu'] >= 0:
|
|
device = torch.device('cuda:{}'.format(self.data['gpu']))
|
|
model.to(device)
|
|
|
|
self.models['instrumental'] = model
|
|
self.devices['instrumental'] = device
|
|
|
|
self.text_widget.write('Done!\n')
|
|
|
|
def _execute(self, X_mag_pad, roi_size, n_window, device, model, aggressiveness):
|
|
model.eval()
|
|
with torch.no_grad():
|
|
preds = []
|
|
for i in tqdm(range(n_window)):
|
|
start = i * roi_size
|
|
X_mag_window = X_mag_pad[None, :, :, start:start + self.data['window_size']]
|
|
X_mag_window = torch.from_numpy(X_mag_window).to(device)
|
|
|
|
pred = model.predict(X_mag_window, aggressiveness)
|
|
|
|
pred = pred.detach().cpu().numpy()
|
|
preds.append(pred[0])
|
|
|
|
pred = np.concatenate(preds, axis=2)
|
|
|
|
return pred
|
|
|
|
def preprocess(self, X_spec):
|
|
X_mag = np.abs(X_spec)
|
|
X_phase = np.angle(X_spec)
|
|
|
|
return X_mag, X_phase
|
|
|
|
def inference(self, X_spec, device, model, aggressiveness):
|
|
X_mag, X_phase = self.preprocess(X_spec)
|
|
|
|
coef = X_mag.max()
|
|
X_mag_pre = X_mag / coef
|
|
|
|
n_frame = X_mag_pre.shape[2]
|
|
pad_l, pad_r, roi_size = dataset.make_padding(n_frame,
|
|
self.data['window_size'], model.offset)
|
|
n_window = int(np.ceil(n_frame / roi_size))
|
|
|
|
X_mag_pad = np.pad(
|
|
X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
|
|
|
|
pred = self._execute(X_mag_pad, roi_size, n_window,
|
|
device, model, aggressiveness)
|
|
pred = pred[:, :, :n_frame]
|
|
|
|
return pred * coef, X_mag, np.exp(1.j * X_phase)
|
|
|
|
def inference_tta(self, X_spec, device, model, aggressiveness):
|
|
X_mag, X_phase = self.preprocess(X_spec)
|
|
|
|
coef = X_mag.max()
|
|
X_mag_pre = X_mag / coef
|
|
|
|
n_frame = X_mag_pre.shape[2]
|
|
pad_l, pad_r, roi_size = dataset.make_padding(n_frame,
|
|
self.data['window_size'], model.offset)
|
|
n_window = int(np.ceil(n_frame / roi_size))
|
|
|
|
X_mag_pad = np.pad(
|
|
X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
|
|
|
|
pred = self._execute(X_mag_pad, roi_size, n_window,
|
|
device, model, aggressiveness)
|
|
pred = pred[:, :, :n_frame]
|
|
|
|
pad_l += roi_size // 2
|
|
pad_r += roi_size // 2
|
|
n_window += 1
|
|
|
|
X_mag_pad = np.pad(
|
|
X_mag_pre, ((0, 0), (0, 0), (pad_l, pad_r)), mode='constant')
|
|
|
|
pred_tta = self._execute(X_mag_pad, roi_size, n_window,
|
|
device, model, aggressiveness)
|
|
pred_tta = pred_tta[:, :, roi_size // 2:]
|
|
pred_tta = pred_tta[:, :, :n_frame]
|
|
|
|
return (pred + pred_tta) * 0.5 * coef, X_mag, np.exp(1.j * X_phase)
|
|
|
|
|
|
data = {
|
|
# Paths
|
|
'input_paths': None,
|
|
'export_path': None,
|
|
# Processing Options
|
|
'gpu': -1,
|
|
'postprocess': True,
|
|
'tta': True,
|
|
'output_image': True,
|
|
# Models
|
|
'instrumentalModel': None,
|
|
'useModel': None,
|
|
# Constants
|
|
'window_size': 512,
|
|
'agg': 10
|
|
}
|
|
|
|
default_window_size = data['window_size']
|
|
default_agg = data['agg']
|
|
|
|
def update_progress(progress_var, total_files, file_num, step: float = 1):
|
|
"""Calculate the progress for the progress widget in the GUI"""
|
|
base = (100 / total_files)
|
|
progress = base * (file_num - 1)
|
|
progress += step
|
|
|
|
progress_var.set(progress)
|
|
|
|
|
|
def get_baseText(total_files, file_num):
|
|
"""Create the base text for the command widget"""
|
|
text = 'File {file_num}/{total_files} '.format(file_num=file_num,
|
|
total_files=total_files)
|
|
return text
|
|
|
|
|
|
def determineModelFolderName():
|
|
"""
|
|
Determine the name that is used for the folder and appended
|
|
to the back of the music files
|
|
"""
|
|
modelFolderName = ''
|
|
if not data['modelFolder']:
|
|
# Model Test Mode not selected
|
|
return modelFolderName
|
|
|
|
# -Instrumental-
|
|
if os.path.isfile(data['instrumentalModel']):
|
|
modelFolderName += os.path.splitext(os.path.basename(data['instrumentalModel']))[0]
|
|
|
|
if modelFolderName:
|
|
modelFolderName = '/' + modelFolderName
|
|
|
|
return modelFolderName
|
|
|
|
|
|
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 = '(Vocals)'
|
|
instrumental_name = '(Instrumental)'
|
|
save_path = os.path.dirname(base_name)
|
|
|
|
# Swap names if vocal model
|
|
|
|
VModel="Vocal"
|
|
|
|
if VModel in model_name:
|
|
# 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, mp.param['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, mp.param['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, mp.param['sr'])
|
|
|
|
data.update(kwargs)
|
|
|
|
# Update default settings
|
|
global default_window_size
|
|
global default_agg
|
|
default_window_size = data['window_size']
|
|
default_agg = data['agg']
|
|
|
|
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)
|
|
|
|
# Separation Preperation
|
|
try:
|
|
for file_num, music_file in enumerate(data['input_paths'], start=1):
|
|
# Determine File Name
|
|
base_name = f'{data["export_path"]}{modelFolderName}/{file_num}_{os.path.splitext(os.path.basename(music_file))[0]}'
|
|
# Start Separation
|
|
model_name = os.path.basename(data[f'{data["useModel"]}Model'])
|
|
model = vocal_remover.models[data['useModel']]
|
|
device = vocal_remover.devices[data['useModel']]
|
|
|
|
# -Get text and update progress-
|
|
base_text = get_baseText(total_files=len(data['input_paths']),
|
|
file_num=file_num)
|
|
progress_kwargs = {'progress_var': progress_var,
|
|
'total_files': len(data['input_paths']),
|
|
'file_num': file_num}
|
|
update_progress(**progress_kwargs,
|
|
step=0)
|
|
|
|
mp = ModelParameters(model_params_d)
|
|
|
|
# -Go through the different steps of seperation-
|
|
# Wave source
|
|
text_widget.write(base_text + 'Loading wave source...\n')
|
|
|
|
X_wave, y_wave, X_spec_s, y_spec_s = {}, {}, {}, {}
|
|
|
|
bands_n = len(mp.param['band'])
|
|
|
|
for d in range(bands_n, 0, -1):
|
|
bp = mp.param['band'][d]
|
|
|
|
if d == bands_n: # high-end band
|
|
X_wave[d], _ = librosa.load(
|
|
music_file, bp['sr'], False, dtype=np.float32, res_type=bp['res_type'])
|
|
|
|
if X_wave[d].ndim == 1:
|
|
X_wave[d] = np.asarray([X_wave[d], X_wave[d]])
|
|
else: # lower bands
|
|
X_wave[d] = librosa.resample(X_wave[d+1], mp.param['band'][d+1]['sr'], bp['sr'], res_type=bp['res_type'])
|
|
|
|
# Stft of wave source
|
|
|
|
X_spec_s[d] = spec_utils.wave_to_spectrogram_mt(X_wave[d], bp['hl'], bp['n_fft'], mp.param['mid_side'],
|
|
mp.param['mid_side_b2'], mp.param['reverse'])
|
|
|
|
if d == bands_n and args.high_end_process != 'none':
|
|
input_high_end_h = (bp['n_fft']//2 - bp['crop_stop']) + (mp.param['pre_filter_stop'] - mp.param['pre_filter_start'])
|
|
input_high_end = X_spec_s[d][:, bp['n_fft']//2-input_high_end_h:bp['n_fft']//2, :]
|
|
|
|
text_widget.write(base_text + 'Done!\n')
|
|
|
|
update_progress(**progress_kwargs,
|
|
step=0.1)
|
|
|
|
text_widget.write(base_text + 'Stft of wave source...\n')
|
|
X_spec_m = spec_utils.combine_spectrograms(X_spec_s, mp)
|
|
|
|
del X_wave, X_spec_s
|
|
|
|
if data['tta']:
|
|
pred, X_mag, X_phase = vocal_remover.inference_tta(X_spec_m,
|
|
device,
|
|
model, {'value': args.aggressiveness,'split_bin': mp.param['band'][1]['crop_stop']})
|
|
else:
|
|
pred, X_mag, X_phase = vocal_remover.inference(X_spec_m,
|
|
device,
|
|
model, {'value': args.aggressiveness,'split_bin': mp.param['band'][1]['crop_stop']})
|
|
|
|
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_m = pred * X_phase
|
|
v_spec_m = X_spec_m - y_spec_m
|
|
|
|
if args.high_end_process.startswith('mirroring'):
|
|
input_high_end_ = spec_utils.mirroring(args.high_end_process, y_spec_m, input_high_end, mp)
|
|
|
|
wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp, input_high_end_h, input_high_end_)
|
|
else:
|
|
wav_instrument = spec_utils.cmb_spectrogram_to_wave(y_spec_m, mp)
|
|
|
|
if args.high_end_process.startswith('mirroring'):
|
|
input_high_end_ = spec_utils.mirroring(args.high_end_process, v_spec_m, input_high_end, mp)
|
|
|
|
wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp, input_high_end_h, input_high_end_)
|
|
else:
|
|
wav_vocals = spec_utils.cmb_spectrogram_to_wave(v_spec_m, mp)
|
|
|
|
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)
|
|
|
|
# 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_m)
|
|
_, 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_m)
|
|
_, 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}\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
|
|
torch.cuda.empty_cache()
|
|
button_widget.configure(state=tk.NORMAL) # Enable Button |